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Article

Deciphering the Mechanism by Which Carbon Dioxide Extends the Shelf Life of Raw Milk: A Microbiomics- and Metabolomics-Based Approach

by
Anran Zheng
1,2,
Chaokun Wei
1,2,*,
Jun Liu
3,
Ningxia Bu
1,2 and
Dunhua Liu
1,2,*
1
School of Animal Science and Technology, Ningxia University, Yinchuan 750021, China
2
School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
3
School of Life Sciences, Hubei Normmal University, Huangshi 435002, China
*
Authors to whom correspondence should be addressed.
Molecules 2024, 29(2), 329; https://doi.org/10.3390/molecules29020329
Submission received: 1 December 2023 / Revised: 26 December 2023 / Accepted: 3 January 2024 / Published: 9 January 2024
(This article belongs to the Special Issue Food Processing and Health: Novel Insights and Applications)
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Abstract

:
Microbial community succession in raw milk determines its quality and storage period. In this study, carbon dioxide (CO2) at 2000 ppm was used to treat raw milk to investigate the mechanism of extending the shelf life of raw milk by CO2 treatment from the viewpoint of microbial colonies and metabolites. The results showed that the shelf life of CO2-treated raw milk was extended to 16 days at 4 °C, while that of the control raw milk was only 6 days. Microbiomics analysis identified 221 amplicon sequence variants (ASVs) in raw milk, and the alpha diversity of microbial communities increased (p < 0.05) with the extension of storage time. Among them, Pseudomonas, Actinobacteria and Serratia were the major microbial genera responsible for the deterioration of raw milk, with a percentage of 85.7%. A combined metagenomics and metabolomics analysis revealed that microorganisms altered the levels of metabolites, such as pyruvic acid, glutamic acid, 5′-cmp, arginine, 2-propenoic acid and phenylalanine, in the raw milk through metabolic activities, such as ABC transporters, pyrimidine metabolism, arginine and proline metabolism and phenylalanine metabolism, and reduced the shelf life of raw milk. CO2 treatment prolonged the shelf life of raw milk by inhibiting the growth of Gram-negative aerobic bacteria, such as Acinetobacter guillouiae, Pseudomonas fluorescens, Serratia liquefaciens and Pseudomonas simiae.

1. Introduction

Raw milk is commonly produced intensively due to its collection from dispersed farms and subsequent transportation to processing plants for uniform treatment. The stages of the collection, transportation, and waiting for processing of raw milk last for a significant duration (several hours), imposing heightened demands on its quality [1,2,3]. The diversity of microbial communities in raw milk is usually considered as one of the key factors affecting milk quality, because udder surfaces, milking instruments, containers and environment are potential sources of microorganisms, which leads to microorganisms being inevitable in raw milk [4]. Given its nutrient-rich composition, raw milk provides an optimal environment and a nutrient matrix that facilitate rapid microbial reproduction [5]. Consequently, this can result in undesirable outcomes, such as off-flavors, sourness, discoloration, and a loss of the characteristic freshness associated with milk [1]. Additionally, microorganisms break down and consume essential nutrients present in milk, including fats, proteins and vitamins. This process may lead to a reduction in the nutrient content of raw milk, thereby impacting the nutritional value of the final dairy product [6]. In particular, psychrophilic and psychrotolerant bacteria (such as Pseudomonas and Enterobacteriaceae) that grow rapidly at low temperatures are considered the most abundant and harmful spoilage bacteria in raw milk [7]. The heat-resistant proteases and lipases produced during the heat treatment process before processing cannot be completely inactivated, leading to a decrease in the yield and quality of dairy products, such as high-temperature sterilized milk, cheese, yogurt, and ice cream [8]. To ensure the provision of high-quality and safe dairy products while prioritizing consumer health and satisfaction, it is imperative to implement effective control measures aimed at mitigating or eliminating microbial hazards inherent in raw milk [2,5].
For the past few years, researchers have explored the use of high pressure to inactivate microorganisms in raw milk while preserving its nutritional quality. However, due to the complexity of high-pressure equipment and limited treatment efficiency, it is not suitable for large-scale implementation [9]. Non-thermal plasma treatment offers an alternative approach to generating reactive oxygen and nitrogen species that can penetrate microorganisms and inhibit or kill them. However, these oxides can also cause the oxidation of raw milk, leading to a decline in its quality as a complex emulsion system [10]. Conventional sterilization methods may destabilize raw milk by affecting its composition and structure. To overcome these challenges, alternative technologies, such as gas conditioning or non-thermal treatments, are being investigated to minimize adverse effects on raw milk while preserving its nutritional value and sensory properties [11].
CO2 has been used for fresh milk storage and preservation because it does not affect the nutritional content or flavor compared to conventional heat treatments. Also, CO2 is a natural compound that does not leave harmful residues in the product or the environment [12,13]. CO2 has been used for food preservation. When CO2 is dissolved in milk, it forms carbonic acid, which lowers the pH of the milk. Many bacteria, including those that cause spoilage, cannot survive or grow effectively in this more acidic environment [14,15]. In addition, by replacing oxygen with CO2 in the storage container, packaging or food systems (a technique known as modified atmosphere packaging), the growth of aerobic bacteria and fungi is inhibited. These microorganisms require oxygen to grow, thus removing or reducing oxygen levels can help extend the shelf life of raw milk [16].
In view of the complexity of the raw milk system and microbial community succession, the metabolic pattern of CO2 treatment on raw milk and microorganisms was analyzed by using macro-genome and metabolome, and then the molecular mechanism by which CO2 prolongs the shelf life of raw milk was clarified. The results of the study provide a theoretical basis for the application of CO2 in raw milk.

2. Results

2.1. Physicochemical Characteristics of Raw Milk

Total bacterial counts (TBC) were utilized to assess the storage period of raw milk (Figure 1A). The control group was identified as spoilage on day 6, with the TBC greater than 6 lg (cfu/mL). In contrast, the TBC of the CO2-treated group was only 4.03 lg (cfu/mL) on day 6 and did not exceed 6 lg (cfu/mL) until day 16, indicating that the CO2 treatment prolonged the storage time of raw milk by significantly inhibiting the growth of the bacteria. It is noteworthy that the TBC of the control raw milk increased steadily during the first 3 days, and showed a logarithmic increase on day 4, suggesting that day 4 of refrigeration may be a critical time point for changes in the quality of raw milk. Therefore, this study selected samples from days 0, 4 and 6 for metagenomic and metabolomic analysis to explore the mechanism of CO2 prolonging the cold storage period of raw milk.
In addition, CO2 at 2000 ppm significantly reduced the pH of raw milk (p < 0.05) (Figure 1B). But interestingly, when they were degassed, the pH value increased to exceed that of the control group until day 16 (pH 6.61, similar to the pH value (6.64) on day 6 of the control group) (Figure 1C). The zeta potential, viscosity and color in the samples were then analyzed. As shown in Figure 1D–E, the zeta potential and viscosity of both the control and treated samples tended to increase during the refrigeration period, indicating a decrease in the stability of the emulsion system. However, the rate of decrease in both treatment groups was slower than that of the control group during the same refrigeration time, indicating that the addition of CO2 increases the stability of raw milk. The color difference (ΔE) reflected the magnitude of the color change, and when ΔE > 3.8, the raw milk showed a color difference visible to the naked eye [17]. Throughout the refrigeration period, the ΔE values between the CO2 treated group and the control group ranged from 0.05 to 0.11 (Figure 1F), indicating that CO2 treatment had almost no effect on the color of the raw milk.

2.2. Microbial Diversity of Raw Milk

Analysis of the composition of the biome in the raw milk using the Illumina sequencing platform showed that there were 221 ASVs in the raw milk. The control group contained a total of 189 ASVs, 136 of which were unique to that group. Of these, group A0 had 62 ASVs, 27 of which were unique to that group; group A4 had 67 ASVs, three of which were unique to that group; group A6 had 176 ASVs, 114 of which were unique to that group; and the CO2-treated group contained a total of 85 ASVs, five of which were unique to that group. Of these, the day 4 group had 41 ASVs, one of which was unique to that group; and the day 6 group had 48 ASVs, four of which were unique to that group (Figure 2A). These findings suggested that CO2 treatment reduced the microbial diversity compared to the control group. The community richness of raw milk microbiota (Simpson diversity index and Chao1 richness estimator) also showed that CO2 treatment reduced raw milk microbial α-diversity (Figure 2B,D). Principal component analysis (PCA) showed that the samples from day 0 clustered in the third quadrant. With the extension of refrigeration time, the control group continued to move towards the fourth quadrant along the x-axis direction, while the CO2 treated group continued to move towards the second quadrant along the y-axis direction. There was a significant separation between the two groups, confirming a significant difference in microbial β-diversity between the CO2 treated raw milk and the control group (Figure 2C).

2.3. Microbiological Composition of Raw Milk

The differences in microbial community composition between the two groups were analyzed by nonparametric Kruskal–Wallis and Wilcoxon rank-sum tests, combined with linear discriminant analysis (LDA) and effect size. A total of 100 microbial markers with LDA > 2 and p < 0.05 were identified at different levels. Notably, Figure 2A and Figure 3A showed consistent differences in α- and β-diversity indices, especially in the A6 microbial community, the number of biomarkers was much higher than the D6 group, suggesting that the CO2 treatment inhibited microbial reproduction. As shown in Figure 3B, the initial microbial communities significantly enriched in raw milk included s_Stenotrophomonas_maltophilia, g_Stenotrophomonas, f_Xanthomonadaceae, s_Lactobacillus_helveticus, g_Lactobacillus_helveticus and g_Lactobacillus. When raw milk spoiled, the microorganisms significantly enriched in group A6 were mainly k_Bacteria, p_Proteobacteria, c_Gammaproteobacteria, f_Pseudomonadaceae, o_Pseudomonas and g_Pseudomonas, suggesting that these may be the key microorganisms responsible for the deterioration of raw milk quality. The significantly enriched in the D6 group treated with CO2 were p_Firmicutes, c_Bacilli, o_Lactobacillales, f_Lactobacillaceae, g_Leuconostoc and s_Leuconostoc_mesenteroides. The sequences were annotated at the genus level for classification, and the results were shown in Figure 3C. Stenotrophomonas accounted for 70.2% of the initial flora of raw milk (A0/D0), and the abundance gradually decreased with the extension of the cold storage time, but still accounted for the highest proportion in groups A4, D4 and D6 (45.7%, 62.1% and 57.9%, respectively). CO2 treatment had a significant effect on the microbial species at the genus level, with Pseudomonas, Actinobacteria and Serratia significantly increasing to 49.2%, 20.9% and 14.6% in the control group at day 6, but only to 0.09%, 3.0% and 0.03% in the D6 group.

2.4. Metabolites

2.4.1. Data Quality Control

Microorganisms use raw milk as a substrate for reproduction and, in addition to changes in microbial numbers and species, alter the raw milk metabolite system through their own metabolic activities. The study aimed to assess how CO2 treatment affects raw milk metabolite evolution by influencing microbial succession and, consequently, raw milk quality. LC–MS-based untargeted metabolomics identified a total of 1234 metabolites in six batches of raw milk. PCA was initially used as a way to explore the structure of the data and assess its distribution and metabolite variability in PC space. Six raw milk samples and four QC samples were used as the dataset. First, according to Figure 4A,B, the distributions of the four QC samples in the PCA model overlapped almost completely, and the correlation coefficients were all greater than 0.998, suggesting that mass spectrometry detection was reliable. As shown in Figure 4A, A0 and D0 sample metabolites had good similarity, indicating that CO2 did not affect the quality of raw milk. A4, A6, D4 and D6 were distinguished from the 0-day raw milk group. However, the A4, D4 and D6 metabolites partially overlapped in the whole, indicating that it is not feasible to use PCA unsupervised modeling alone to distinguish the raw milk metabolite groups.

2.4.2. Overview of Differences between Samples

Orthogonal partial least squares data analysis (OPLS-DA) was further applied to develop discriminative models that could differentiate the metabolites of the control group and CO2-treated group with different storage times. The results of the OPLS-DA models developed showed that R2X = 32.8%, R2Y = 99.7%, and Q2 = 95.2 in the control group and R2X = 35%, R2Y = 98.3%, and Q2 = 89.7% in the CO2-treated group, and the Q2 was greater than 0.5 based on the results of 500 tests of cross-validated alignments (Figure 5) with Q2s of 0.8988 and 0.8735 both being greater than 0.5, indicating that these models are reliable, and different treatments and storage time have significant effects on raw milk metabolites.

2.4.3. Differential Metabolites (DMs)

OPLS-DA analysis was used to obtain the variable importance for the projection (VIP) of metabolites to measure the strength of the influence of metabolite expression patterns on the categorical discrimination of each group of samples and the ability to explain, so as to assist in the screening of the marker metabolites (VIP > 1.0 was used as the screening criteria). DMs were indicated by VIP > 1 and p < 0.05. As shown in Figure 6A,C, 402 and 377 DMs were identified in the control and CO2-treated groups, respectively.
The structure and function of the metabolites and the DMs in each comparison group were categorized and counted by the class and ontology substance classification method of HMDB. As shown in Figure 6B,D, the main DMs in the raw milk were carboxylic acids and derivatives (A-14.32%, D-14.75%), fatty acyls (A-12.24%, D-7.77%), benzene and substituted derivatives (A-9.11%, D-12.06%), organooxygen compounds (A-5.47%, D-6.7%), and so on. Notably, the class classification of metabolites in the control and CO2-treated groups showed significant differences, mainly in the increase of 10 benzene and substituted derivatives and the decrease of 18 fatty acyl groups in the CO2-treated group.

2.5. Combined Microbiomics and Metabolomics Analysis

Correlation analysis and elemental screening were performed to investigate the relationship between key microbial community structures and metabolite level parameters. As shown in Figure 7A,B, some microbes with metabolite changes were identified. Notably, the control microbial community showed a stronger positive correlation with metabolites, indicating that CO2 treatment was effective in reducing the metabolic activities of the raw milk system. The relationship between microbes and metabolites was shown by correlation hierarchical clustering heatmaps (Figure 7C,D). In the control group, the top 28 microorganisms (Mycoplasmopsis, Aequoribacter, Aeromonas, Carnobacterium, Citrobacter, Cupriavidus, etc.) and metabolites (delta-tridecalactone, cytidine, alpha-aminoorcein, D-glucosamine, 3-hydroxycapric acid, etc.) were correlated. Only five microorganisms, Stenotrophomonas, Anaerobutyricum, Blautia, Leuconostoc and Propionibacterium were correlated with the metabolites in the CO2-treated group compared to the control group.

2.6. Metabolic Pathway of Microbiomics and Metabolomics

Based on Illumina NovaSeq/HiSeq high-throughput sequencing platform, the whole genome shotgun (WGS) strategy was used to extract the total DNA of the macro-genome of the obtained colonies, or the cDNA double strand of the macro-transcriptome synthesized by using mRNA as a template. The coding regions of prokaryotic microbial and macrogenomic gene sequences were predicted to obtain the corresponding gene sequence files, protein sequence files for protein sequence species and functional annotation. As shown in Figure 8A and Table S1, CO2 treatment significantly reduced metabolic processes, such as metabolism (limonene and pinene degradation, energy metabolism, xenobiotics biodegradation and metabolism, C5-branched dibasic acid metabolism, β-alanine metabolism, monobactam biosynthesis, caprolactam and styrene degradation), cellular processes (bacterial chemotaxis and biofilm formation—pseudomonas aeruginosa), environmental information processing (ABC transporters), and genetic information processing (sulfur relay system).
The metabolic activities of environmental information processing (ABC transporters), metabolism (biosynthesis of cofactors, pyrimidine metabolism, arginine and proline metabolism, biosynthesis of amino acids, carbon metabolism, phenylalanine metabolism, 2-oxocarboxylic acid metabolism, and pantothenate and CoA biosynthesis), and organismal systems (protein digestion and absorption) were significantly stronger in the control group than in the CO2-treated group, and microbial metabolism and cellular processes altered raw material metabolites (Figure 8B and Table S1). As shown in Table 1 of Figure 8C, shared metabolic pathways identified by the macrogenome and metabolome mainly included cellular processes, environmental information processing, genetic information processing, metabolism and organismal systems. A total of 61 shared metabolic pathways involving 53 DMs were identified. Among them, ABC transporters, pyrimidine metabolism, arginine and proline metabolism and phenylalanine metabolism metabolic processes had the most significant effects on metabolites, involving metabolites, such as pyruvic acid (C00022), glutamic acid (C00025), 5′-CMP (C00055), arginine (C00062), 2-propenoic acid (C00074), and phenylalanine (C00079).

3. Discussion

In the present study, Stenotrophomonas maltophilia, Lactococcus lactis and Chryseobacterium sp. NEB161 were found to be the dominant strains of raw milk, with a high percentage of 81.7%. The cold storage process altered the microbial colony diversity of raw milk, and the dominant strains at 6 days were Acinetobacter guillouiae, Pseudomonas fluorescens, Serratia liquefaciens, Pseudomonas simiae and Leuconostoc mesenteroides, with a percentage of 56.2%. These results also verified that the microbial community is a key factor in the spoilage of raw milk. Therefore, it is necessary to use appropriate techniques to inhibit the growth and multiplication of microorganisms in raw milk. Research has shown that adding CO2 to food reduced the rate of food spoilage and the growth of disease-causing microorganisms [18]. It was found that CO2 treatment significantly inhibited the propagation of the dominant colonies, Acinetobacter guillouiae, Pseudomonas fluorescens, Serratia liquefaciens and Pseudomonas simiae as compared with the control.The percentage in the CO2-treated group was only 0.2%. CO2 treatment significantly improved the storage period of raw milk, indicating that CO2 improved the quality of raw milk by suppressing microbial levels and diversity.
Through microbiomics analysis, CO2 was found to prolong the storage period of raw milk by inhibiting microbial reproduction, but the exact mechanism of its antimicrobial effect is still unclear [13]. CO2 is highly soluble in water and lipids, and can form carbonic acid in aqueous solutions, which can lead to changes in pH in the suspension medium, and a decrease in pH inhibits the growth and metabolism of certain microorganisms [19]. The current study revealed the reduction in the pH of raw milk treated with CO2. Another view is that oxygen is a necessary electron acceptor in the metabolism of aerobic bacteria and is a specific electron acceptor for parthenogenetic anaerobes. Supplementation of raw milk with CO2 reduced the oxygen content of the system, which in turn inhibited bacterial metabolism and reduced growth rates [20]. CO2 treatment significantly decreased the levels of Acinetobacter guillouiae, Pseudomonas fluorescens, Serratia liquefaciens and Pseudomonas simiae, which inhibited Gram-negative aerobic bacteria, suggesting that CO2 inhibited the growth and reproduction of aerobic microorganisms and prolonged the shelf life of raw milk by reducing the oxygen content of the system.
Microorganisms in raw milk systems can utilize the nutrients in milk for growth and reproduction while breaking down substances, such as proteins, fats and lactose. For example, the pH of milk decreases when lactose is broken down to lactic acid [21,22]. Proteins are broken down, producing toxic substances, such as indole, sulfur and fecal deodorant, as well as foul odors. To be precise, the growth and reproduction of microorganisms is the metabolic process which leads to changes in the metabolites of the raw milk system through a variety of metabolic processes reducing the stability of the raw milk system and even spoilage [22,23]. It is worth noting that the microbial effects on metabolites are mainly metabolic, including pyrimidine metabolism, arginine and proline metabolism, phenylalanine metabolism, aminoacyl tRNA biosynthesis, glycine, serine and threonine metabolism, lysine degradation, pantothenate and coenzyme A biosynthesis, and pyruvate metabolism (Table 1).
Pyrimidine metabolism refers to the process of pyrimidine nucleotide synthesis and degradation in the cell and is an important component of nucleic acid synthesis. Through pyrimidine metabolism, cells are able to synthesize and maintain sufficient levels of pyrimidine nucleotides to support DNA and RNA synthesis and the stability of genetic material [24,25]. Microbial reproduction requires the replication of genetic material, and reduced levels of metabolites, such as cytidylic acid, orotate and uridine, inhibit microbial growth and reproduction. In addition, pyrimidine nucleotide degradation produces uracil, which in turn is converted to β-aminobutyric acid, which is involved in amino acid and energy metabolic pathways. The inhibition of energy production results from reduced levels of orotate [26]. The accumulation of N-carbamoyl-L-aspartate, a key compound in the metabolism of alanine, aspartic acid and glutamic acid suggested that the processes involved in the synthesis of amino acids and the metabolism of other nitrogen compounds were inhibited.
Arginine and proline metabolism can generate nitric oxide and polyamine compounds, which play important roles in physiological processes, such as cell signaling, immunomodulation and blood flow regulation [27]. In addition, proline metabolism produces polyamines and cofactors required for the post-translational modification of proteins, which play key roles in cell growth and development [28]. Phenylalanine metabolism produces tyrosine and other important bioactive substances which play important roles in neurotransmitter, hormone and pigment synthesis. Glycine, serine and threonine metabolism involves a variety of enzymes and intermediates that have multiple effects on metabolites. For example, glycine metabolism produces alanine, which is involved in glucose and fatty acid metabolic pathways. Serine metabolism produces tryptophan. Threonine metabolism produces threonine and methionine, the compounds that play important roles in methionine metabolism and thionine metabolism [29]. Amino acid metabolic processes are attenuated, but accumulation of betaine occurs in the glycerophospholipid metabolic pathway of glycine, serine, and threonine metabolism, which may be due to enhanced upstream glycerophospholipid metabolic activity. Lysine degradation generates intermediates, such as malondialdehyde, acetaldehyde and aminobutyric acid, which can be further involved in oxidation that can alter the stability of the raw milk system [30]. Reduction in glutarate and N6, N6, N6-trimethyl-L-lysine levels were found in the lysine degradation pathway. It was found that CO2 treatment significantly inhibited amino acid metabolism and reduced microbial biological activity and acid accumulation. In addition, metabolic processes, such as carbohydrate metabolism, lipid metabolism, cell growth and death, and energy metabolism were also inhibited. Overall, CO2 treatment effectively reduced the metabolism of microorganisms and thus inhibited their growth and reproduction.

4. Materials and Methods

4.1. Sample Collection

Fresh raw milk [the somatic cells (<2 × 105/mL) were counted using a flow cell fluorescence counter (Counters II FL, Thermo Fisher, Waltham, MA, USA), and the TBC (<103 CFU/mL) was counted using the spread plate method] was collected aseptically from milk tanks at Helan Mountain Dairy Farm, Ningxia, Yinchuan, China in June 2022, refrigerated at 4 °C and transported to the laboratory within one hour. Then, 0.6 g solid CO2 (dry ice, food grade, Yinchuan, China) was added to 500 mL PET bottles (Servicebio, Wuhan, China) containing 300 mL of raw milk to achieve a CO2 concentration of 2000 ppm in a sterile room immediately [31]. Raw milk without CO2 treatment was used as a control group. The control group and the treated group were divided into 17 and 7 equal portions of 100 mL in sterile sealed PET bottles, respectively, and stored at 4 °C. One aliquot was used for analysis each day. The day of sample collection was defined as day 0.

4.2. Bacterial Growth Studies

The TBC was measured by the spread plate method. Briefly, 1 mL sample was taken and serially diluted 10 times with 0.85% sterile saline. Then, the 200 μL dilutions were spread onto plate count agar (PCA) and cultured at 37 °C for 48 h. Analysis of a sample was terminated when it was spoiled, i.e., the TBC reached a threshold of 6 lg (cfu/mL) [32].

4.3. pH Measurement

Raw milk pH was determined using a pH acidimeter (PHS-3C, Shanghai Yidian Scientific Instrument Co., Ltd., Shanghai, China) with a three-point calibration standard prior to testing. CO2-treated samples were degassed with agitation to eliminate the effect of CO2 on the sample [33], and then the pH was measured again.

4.4. Colour Measurement

Briefly, 30 mL of the degassed sample were added into a transparent container, and a hand-held colorimeter (CR-400, Konica Minoita, Tokyo, Japan) was used to measure the parameters L*, a*, and b* of the milk sample. L* denoted the brightness, a* denoted the red and green values, and b* denoted the yellow and blue values. Based on the obtained values of L*, a*, and b*, the ΔE between the control group and the CO2-treated group was calculated, by the following formula: ΔE = [(ΔL*)2 + (Δa*)2 + (Δb*)2]1/2. Where ΔL*, Δa* and Δb* are the differences between the control group and the CO2-treated group for L*, a* and b*, respectively.

4.5. Viscosity Measurement

Referring to the method described by Ning et al. [34], the specific viscosity of the degassed raw milk was determined using an Ubbelohde viscometer (Shanghai Shenyi Glass Products Co., Ltd., Shanghai, China). The specific viscosity was calculated by the ratio of the outflow time of a certain amount of liquid to the outflow time of the control liquid (deionized water).

4.6. Zeta Potential Measurement

The zeta potential values of casein micelles were determined using a nanoparticle size potential analyzer (Malvern, UK). The degassed raw milk samples were diluted 2-fold with deionized water, and the dilutions were used to determine the zeta potential at room temperature. The test mode was automatic with 3 replicates for each sample.

4.7. Metagenomics Analysis of Raw Milk

4.7.1. Raw Milk DNA Extraction and Processing

After centrifugation of raw cow’s milk, all microbial DNA in raw cow’s milk was extracted by DNeasy PowerFood Microbial DNA Isolation Kit (QIAGEN, Dusseldorf, Germany). DNA integrity was determined by 1.2% agarose gel and gel Doc EZ imager (Bio-Rad, Hercules, CA, USA), and DNA concentration was measured by Qubit 4.0 Fluorescence Quantification Instrument (Invitrogen, Carlsbad, CA, USA). Qubit™ dsDNA HS Assay Kit (Invitrogen, Carlsbad, CA, USA) was used to accurately quantify the concentration of the genome and determine the total amount of DNA required for library construction. The DNA was fragmented using Covaris 220 ultrasonic crusher (Woburn, MA, USA), concentrated and recovered, and then purified and its concentration determined using Qubit 4.0.

4.7.2. Library Construction and Sequencing

Library construction was carried out according to the instructions of the library construction kit (ONT, Oxford, UK) for Hieff NGS® high-throughput sequencing platform. Briefly, the purified DNA was subjected to end modification using NEBNext FFPE DNA Repair kit (New England Biolabs, Ipswich, MA, USA), junction ligation and magnetic bead sorting to purify the ligated product, and the purified product was subjected to PCR amplification and enrichment, and the PCR amplification product was purified, and the size of library was determined by 2% agarose gel electrophoresis, and the concentration of library was measured by Qubit 4.0, and the qualified library was used. After the libraries were qualified, the Illumina NovaSeq platform (Illumina, San Diego, CA, USA) was used to analyze the libraries by end-pair sequencing.

4.7.3. Data Analysis

In order to ensure the quality of information, the raw data were filtered, and the clean data were obtained by removing the sequences with junctions and low-quality sequences. After removing the junctions in the reads, global cropping and window quality cropping, and the host contamination, the quality of the sequenced raw data were evaluated by Fastp-0.36, and the validated reads were analyzed statistically after the data filtering.

4.8. Metabolomics Analysis of Raw Milk

4.8.1. Metabolite Extraction

Raw cow’s milk was vortexed at a low speed, 100 μL of sample was pipetted into a centrifuge tube, then 400 μL of pre-cooled pure methanol solution was added, vortexed and mixed, then sonicated in an ice bath for 20 min, and then allowed to stand at −20 °C for 1 h. Frozen samples were centrifuged at 16,000× g, 4 °C for 20 min, and the supernatant was dried in a high-speed vacuum concentrator centrifuge. The supernatant was dried in a high-speed vacuum concentration centrifuge. The dried sample was raw milk metabolites, and 100 μL of methanol-water solution (1:1, v/v) were added to dissolve the metabolites for spectrometry, and then centrifuged at 20,000× g, at 4 °C for 15 min, and then the supernatant was taken as the sample for analysis.

4.8.2. LC-MS/MS Analysis

Chromatographic Separation

The samples were analyzed in an autosampler at 4 °C. The samples were separated on a SHIMADZU-LC30 ultra-high-performance liquid chromatography (UHPLC) system (Shimadzu, Kyoto, Japan) using an ACQUITY UPLC® HSS T3 column (2.1 × 100 mm, 1.8 µm) (Waters, Milford, MA, USA). The flow rate was 0.3 mL/min at 40 °C with an injection volume of 4 μL. The chromatographic mobile phases were A: 0.1% formic acid aqueous solution, and B: acetonitrile. The chromatographic gradient elution program was as follows: from 0 to 2 min, 100% A; from 2 to 6 min, A varied linearly from 100% to 52%; from 6 to 10 min, A varied linearly from 52% to 0%; from 10 to 12 min, A was maintained at 0%; from 12 to 12.1 min, A was maintained at 0%; from 12 to 12.1 min, A was maintained at 0%; and from 12 to 12.1 min, A was maintained at 0%. 0%; from 12 to 12.1 min, A varied linearly from 0% to 100%, and from 12.1 to 15 min, A was maintained at 100%.

Mass Spectrometry Acquisition

Each sample was collected by electrospray ionization (ESI) in positive (+) and negative (−) modes. The samples were separated by UPLC and analyzed by mass spectrometry using a QE Plus mass spectrometer (Thermo Scientific, Waltham, MA, USA). The ionization conditions were as follows: spray voltage: 3.8 kv (+) and 3.2 kv (−); capillary temperature: 320 (±); spray voltage: 3.8 kv (+) and 3.2 kv (−); capillary temperature: 320 (±); sheath gas: 30 (±); aux gas: 5 (±); probe heater temp: 350 (±); S-lens RF level: 50.

4.8.3. Data Pre-Processing

The raw data were subjected to peak alignment, retention time correction and peak area extraction using MSDIAL software (ver 4.18). The structural identification of metabolites was performed by exact mass number matching (mass tolerance < 10 ppm) and secondary spectra matching (mass tolerance < 0.01 Da), and the public databases, such as HMDB, MassBank, GNPS, etc., as well as the self-constructed metabolite standard library of Dubai Spectrum (BP-DB), were searched. Ion peaks with >50% missing values were removed from the extracted data and were not included in the subsequent statistical analysis.

4.9. Statistical Analysis

To identify the perturbed biological pathways, the DM data were subjected to KEGG pathway analysis using KEGG database (http://www.kegg.jp (accessed on 18 May 2023)). KEGG enrichment analyses were carried out with the Fisher’s exact test, and an FDR correction was applied to account for multiple testing. Enriched KEGG pathways were considered statistically significant at the p < 0.05 level. All physicochemical data were analyzed by one way ANOVA and t-test using SPSS 25 program (SPSS Inc., Chicago, NY, USA), and significant difference was considered at p < 0.05. Plotting was done using Origin 2023 (Origin Lab., Northampton, MA, USA).

5. Conclusions

It was found that CO2 at 2000 ppm extended the shelf life of raw milk from 6 to 16 days without adversely affecting emulsion stability and color, providing a theoretical basis for its practical application. CO2 mainly inhibited Gram-negative aerobic bacteria, such as Acinetobacter guillouiae, Pseudomonas fluorescens, Serratia liquefaciens and Pseudomonas simiae, and their growth and multiplication accelerated ABC transporters, pyrimidine metabolism, amino acid metabolism, carbohydrate and lipid metabolism, and metabolism-produced metabolites can lead to raw milk quality deterioration. Therefore, the use of CO2 is important for the storage, preservation and quality control of raw milk, and can be explored for implementation in dairy farms.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/molecules29020329/s1.

Author Contributions

Conceptualization, D.L.; Data curation, A.Z.; Formal analysis, A.Z.; Funding acquisition, C.W.; Investigation, A.Z. and N.B.; Methodology, A.Z.; Project administration, C.W. and D.L.; Resources, D.L.; Software, J.L.; Supervision, C.W.; Writing—original draft, A.Z. and N.B.; Writing—review and editing, J.L All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of Ningxia Province (2021AAC02019, 2021AAC03050), the Key research and development projects in Ningxia Province (2021BEB04049).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article and supplementary materials.

Acknowledgments

We are very grateful for the support from the Key research and development projects in Ningxia Province and National Natural Science Foundation of Ningxia Province. We thank Shanghai Bioprofile Technology Company for technical support in metagenomics and metabolomics. Meanwhile, we sincerely thank the anonymous reviewers for their critical comments and suggestions for improving the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ndahetuye, J.B.; Artursson, K.; Bage, R.; Ingabire, A.; Karege, C.; Djangwani, J.; Nyman, A.; Ongol, M.P.; Tukei, M.; Persson, Y. MILK Symposium review: Microbiological quality and safe of milk from farm to milk collection centers in Rwanda. J. Dairy Sci. 2020, 103, 9730–9739. [Google Scholar] [CrossRef] [PubMed]
  2. Mense, L.; Roessler, S.; Hanusch, R.; Rossberg, C.; Ruediger, M. Bacterial contamination of mechanically extracted breast milk. Am. J. Perinatol. 2014, 31, 293–298. [Google Scholar] [CrossRef]
  3. Pistocchini, E.; Stella, S.; Belli, P.; Cantafora, A.F.A.; Turini, J.; Zecchini, M.; Crimella, C. Dairy production in periurban area of Niamey: Milk quality and microbial contamination. Trop. Anim. Health Prod. 2009, 41, 145–147. [Google Scholar] [CrossRef] [PubMed]
  4. Apostolakos, I.; Skarlatoudi, T.; Vatavali, K.; Giannouli, A.; Bosnea, L.; Matarakas, M. Genomic and phenotypic characterization of mastitis ausing Staphylococci and probiotic lactic acid bacteria isolated from raw sheep and rsquo’s Milk. Int. J. Mol. Sci. 2023, 24, 13883. [Google Scholar] [CrossRef] [PubMed]
  5. Kuhn, E.; Meunier-Goddik, L.; Waite-Cusic, J.G. Effect of leaving milk trucks empty and idle for 6 h between raw milk loads. J. Dairy Sci. 2018, 101, 1767–1776. [Google Scholar] [CrossRef] [PubMed]
  6. Peeler, J.T.; Bunning, V.K. Hazard Assessment of Listeria monocytogenes in the Processing of Bovine Milk. J. Food Prot. 1994, 57, 689–697. [Google Scholar] [CrossRef] [PubMed]
  7. Martin, N.H.; Evanowski, R.L.; Wiedmann, M. Invited review: Redefining raw milk quality-Evaluation of raw milk microbiological parameters to ensure high-quality processed dairy products. J. Dairy Sci. 2023, 106, 1502–1517. [Google Scholar] [CrossRef]
  8. Nornberg, M.F.B.L.; Friedrich, R.S.C.; Weiss, R.D.N.; Tondo, E.C.; Brandelli, A. Proteolytic activity among psychrotrophic bacteria isolated from refrigerated raw milk. Int. J. Dairy Technol. 2010, 63, 41–46. [Google Scholar] [CrossRef]
  9. Viazis, S.; Farkas, B.E.; Allen, J.C. Effects of high-pressure processing on immunoglobulin A and lysozyme activity in human milk. J. Hum. Lact. 2007, 23, 253–261. [Google Scholar] [CrossRef]
  10. Sriraksha, M.S.; Ayenampudi, S.B.; Noor, M.; Raghavendra, S.N.; Chakka, A.K. Cold plasma technology: An insight on its disinfection efficiency of various food systems. Food Sci. Technol. Int. 2023, 29, 428–441. [Google Scholar]
  11. Masotti, F.; Cattaneo, S.; Stuknyte, M.; De Noni, I. Current insights into non-thermal preservation technologies alternative to conventional high-temperature short-time pasteurization of drinking milk. Crit. Rev. Food Sci. Nutr. 2023, 63, 5643–5660. [Google Scholar] [CrossRef] [PubMed]
  12. Li, P.; Mei, J.; Xie, J. The regulation of carbon dioxide on food microorganisms: A review. Food Res. Int. 2023, 172, 113170. [Google Scholar] [CrossRef] [PubMed]
  13. Singh, P.; Wani, A.A.; Karim, A.A.; Langowski, H. The use of carbon dioxide in the processing and packaging of milk and dairy products: A review. Int. J. Dairy Technol. 2012, 65, 161–177. [Google Scholar] [CrossRef]
  14. Dixon, N.M.; Kell, D.B. The inhibition by CO2 of the growth and metabolism of micro-organisms. J. Appl. Bacteriol. 1989, 67, 109–136. [Google Scholar] [CrossRef] [PubMed]
  15. Fang, Y.; Franke, C.; Manthei, A.; McMullen, L.; Temelli, F.; Gänzle, M.G. Effects of high-pressure carbon dioxide on microbial quality and germination of cereal grains and beans. J. Supercrit. Fluids 2021, 175, 105272. [Google Scholar] [CrossRef]
  16. Yang, J.; Yang, X.; Liang, R.; Zhu, L.; Mao, Y.; Dong, P.; Hopkins, D.L.; Luo, X.; Zhang, Y. The response of bacterial communities to carbon dioxide in high-oxygen modified atmosphere packaged beef steaks during chilled storage. Food Res. Int. 2022, 151, 110872. [Google Scholar] [CrossRef] [PubMed]
  17. Pagliarini, E.; Vernile, M.; Peri, C. Kinetic study on color changes in milk due to heat. J. Food Sci. 1990, 55, 1766–1767. [Google Scholar] [CrossRef]
  18. D’Incecco, P.; Limbo, S.; Hogenboom, J.A.; Pellegrino, L. Novel technologies for extending the shelf life of drinking milk: Concepts, research trends and current applications. LWT-Food Sci. Technol. 2021, 148, 111746. [Google Scholar] [CrossRef]
  19. Dertli, E.; Sert, D.; Akin, N. The effects of carbon dioxide addition to cheese milk on the microbiological properties of Turkish White brined cheese. Int. J. Dairy Technol. 2012, 65, 387–392. [Google Scholar] [CrossRef]
  20. Couvert, O.; Koullen, L.; Lochardet, A.; Huchet, V.; Thevenot, J.; Le Marc, Y. Effects of carbon dioxide and oxygen on the growth rate of various food spoilage bacteria. Food Microbiol. 2023, 114, 104289. [Google Scholar] [CrossRef]
  21. Lemos, Á.T.; Casal, S.; Barba, F.J.; Phimolsiripol, Y.; Delgadillo, I.; Saraiva, J.A. Preservation of high pressure pasteurised milk by hyperbaric storage at room temperature versus refrigeration on inoculated microorganisms, fatty acids, volatile compounds and lipid oxidation. Food Chem. 2022, 387, 132887. [Google Scholar] [CrossRef] [PubMed]
  22. Hotchkiss, J.H.; Werner, B.G.; Lee, E.Y.C. Addition of carbon dioxide to dairy products to improve quality: A comprehensive review. Compr. Rev. Food. Sci. Food Saf. 2006, 5, 158–168. [Google Scholar] [CrossRef]
  23. Lei, Y.; Zhang, Y.; Cheng, Y.; Huang, J.; Huang, M. Monitoring and identification of spoilage-related microorganisms in braised chicken with modified atmosphere packaging during refrigerated storage. Food Sci. Hum. Wellness 2023, 12, 28–34. [Google Scholar] [CrossRef]
  24. Wang, Z.; Sun, Y.; Wu, Y.; Chen, R.; Xu, Y.; Cai, Y.; Chu, M.; Dou, X.; Zhang, Y.; Qin, Y.; et al. Metabonomic analysis of human and 12 kinds of livestock mature milk. Food Chem. X 2023, 17, 100581. [Google Scholar] [CrossRef] [PubMed]
  25. Le, T.T.; Ziemba, A.; Urasaki, Y.; Hayes, E.; Brotman, S.; Pizzorno, G. Disruption of uridine homeostasis links liver pyrimidine metabolism to lipid accumulation. J. Lipid Res. 2013, 54, 1044–1057. [Google Scholar] [CrossRef] [PubMed]
  26. Stretton, S.; Goodman, A.E. Carbon dioxide as a regulator of gene expression in microorganisms. Antonie Van Leeuwenhoek 1998, 73, 79–85. [Google Scholar] [CrossRef]
  27. Wu, Z.; Heng, J.; Tian, M.; Song, H.; Chen, F.; Guan, W.; Zhang, S. Amino acid transportation, sensing and signal transduction in the mammary gland: Key molecular signalling pathways in the regulation of milk synthesis. Nutr. Res. Rev. 2020, 33, 287–297. [Google Scholar] [CrossRef]
  28. Peng, C.; Andersen, B.; Arshid, S.; Larsen, M.R.; Albergaria, H.; Lametsch, R.; Arneborg, N. Proteomics insights into the responses of Saccharomyces cerevisiae during mixed-culture alcoholic fermentation with Lachancea thermotolerans. FEMS Microbiol. Ecol. 2019, 95, fiz126. [Google Scholar] [CrossRef]
  29. Li, M.; Li, W.; Kong, F.; Kang, S.; Liang, X.; Han, H.; Wu, J.; Zheng, Y.; Li, Q.; Yue, X.; et al. Metabolomics methods to analyze full spectrum of amino acids in different domains of bovine colostrum and mature milk. Eur. Food Res. Technol. 2020, 246, 213–224. [Google Scholar] [CrossRef]
  30. Leandro, J.; Houten, S.M. The lysine degradation pathway: Subcellular compartmentalization and enzyme deficiencies. Mol. Genet. Metab. 2020, 131, 14–22. [Google Scholar] [CrossRef]
  31. Kosasih, L.; Bhandari, B.; Prakash, S.; Bansal, N.; Gaiani, C. Physical and functional properties of whole milk powders prepared from concentrate partially acidified with CO2 at two temperatures. Int. Dairy J. 2016, 56, 4–12. [Google Scholar] [CrossRef]
  32. Lo, R.; Turner, M.S.; Weeks, M.; Bansal, N. Culture-independent bacterial community profiling of carbon dioxide treated raw milk. Int. J. Food Microbiol. 2016, 233, 81–89. [Google Scholar] [CrossRef] [PubMed]
  33. Zheng, A.; Liu, J.; Wang, M.; Bu, N.; Liu, D.; Wei, C. Footprint analysis of CO2 in microbial community succession of raw milk and assessment of its quality. Front. Nutr. 2023, 10, 1285653. [Google Scholar] [CrossRef]
  34. Ning, Y.; Ma, M.; Zhang, Y. Antibacterial mechanism of sucrose laurate against Bacillus cereus by attacking multiple targets and its application in milk beverage. Food Res. Int. 2022, 154, 111018. [Google Scholar] [CrossRef]
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Figure 1. (A) Total bacterial counts of raw milk stored at 4 °C. (B) Effect of CO2 treatment on raw milk pH. (C) Effect of CO2 treatment after degassing on the pH of raw milk. (D) Effect of CO2 treatment after degassing on the Zeta potential of raw milk. (E) Effect of CO2 treatment after degassing on the viscosity of raw milk. (F) Effect of CO2 treatment on the color difference of raw milk. (Different lowercase letters and * indicate a significant difference, p < 0.05).
Figure 1. (A) Total bacterial counts of raw milk stored at 4 °C. (B) Effect of CO2 treatment on raw milk pH. (C) Effect of CO2 treatment after degassing on the pH of raw milk. (D) Effect of CO2 treatment after degassing on the Zeta potential of raw milk. (E) Effect of CO2 treatment after degassing on the viscosity of raw milk. (F) Effect of CO2 treatment on the color difference of raw milk. (Different lowercase letters and * indicate a significant difference, p < 0.05).
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Figure 2. Microbial diversity of raw milk from the control and CO2-treated groups. (A) Number of common and unique microbial taxa in raw milk from both groups. (B) Simpson’s index to assess the alpha diversity (genus level) of microbial communities in the two groups. (C) Principal component analysis (PCA) to assess β-diversity (genus level). (D) Chao1 index to assess the α-diversity (genus level) of the two microbial communities. (A0, sample of control group at 0 d; D0, sample of CO2-treated group at 0 d, and so on.) (Significant differences were recorded by * p < 0.05, ** p < 0.01, *** p < 0.001).
Figure 2. Microbial diversity of raw milk from the control and CO2-treated groups. (A) Number of common and unique microbial taxa in raw milk from both groups. (B) Simpson’s index to assess the alpha diversity (genus level) of microbial communities in the two groups. (C) Principal component analysis (PCA) to assess β-diversity (genus level). (D) Chao1 index to assess the α-diversity (genus level) of the two microbial communities. (A0, sample of control group at 0 d; D0, sample of CO2-treated group at 0 d, and so on.) (Significant differences were recorded by * p < 0.05, ** p < 0.01, *** p < 0.001).
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Figure 3. Microbial community structure of raw milk from the control and CO2 treated groups. (A) Linear discriminant analysis effect size (LEfSe) species branching diagram. The taxonomic branching diagram shows the taxonomic hierarchy of the major taxonomic units in the sample community from phylum to species (from inner circle to outer circle, phylum, order, order, family, genus and species), with node sizes corresponding to the average relative abundance of the taxonomic unit; hollow nodes represent taxonomic units with insignificant intergroup differences, while nodes of other colors indicate that these taxonomic units exhibit significant intergroup differences and are more abundant in the sample than in the group represented by the color. Higher abundance in the sample. Letters identify the names of taxonomic units with significant intergroup differences. (B) Histogram of LEfSe. Vertical coordinates show taxonomic units with significant differences between groups, and horizontal coordinates visualize the logarithmic scores of LDA analysis for each taxonomic unit in a bar chart. Taxonomic units are sorted by the size of the score to characterize their specificity within the sample grouping. Longer lengths indicate more significant differences for that taxonomic unit, and the color of the bar indicates the sample subgroup with the highest abundance corresponding to that taxonomic unit. (C) Taxonomic analysis of bacteria at the genus level. Horizontal coordinates are arranged according to the sample name, each bar represents one sample and is color-coded to distinguish each taxonomic unit, vertical coordinates represent the relative abundance of each taxonomic unit, the longer the bar, the higher the relative abundance of that taxonomic unit in the corresponding sample.(A_0d, sample of control group at 0 d, D_0d, sample of CO2 treated group at 0 d, and so on).
Figure 3. Microbial community structure of raw milk from the control and CO2 treated groups. (A) Linear discriminant analysis effect size (LEfSe) species branching diagram. The taxonomic branching diagram shows the taxonomic hierarchy of the major taxonomic units in the sample community from phylum to species (from inner circle to outer circle, phylum, order, order, family, genus and species), with node sizes corresponding to the average relative abundance of the taxonomic unit; hollow nodes represent taxonomic units with insignificant intergroup differences, while nodes of other colors indicate that these taxonomic units exhibit significant intergroup differences and are more abundant in the sample than in the group represented by the color. Higher abundance in the sample. Letters identify the names of taxonomic units with significant intergroup differences. (B) Histogram of LEfSe. Vertical coordinates show taxonomic units with significant differences between groups, and horizontal coordinates visualize the logarithmic scores of LDA analysis for each taxonomic unit in a bar chart. Taxonomic units are sorted by the size of the score to characterize their specificity within the sample grouping. Longer lengths indicate more significant differences for that taxonomic unit, and the color of the bar indicates the sample subgroup with the highest abundance corresponding to that taxonomic unit. (C) Taxonomic analysis of bacteria at the genus level. Horizontal coordinates are arranged according to the sample name, each bar represents one sample and is color-coded to distinguish each taxonomic unit, vertical coordinates represent the relative abundance of each taxonomic unit, the longer the bar, the higher the relative abundance of that taxonomic unit in the corresponding sample.(A_0d, sample of control group at 0 d, D_0d, sample of CO2 treated group at 0 d, and so on).
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Figure 4. (A) PCA score plots for raw milk metabolite and quality control (QC) sample datasets. (B) Correlation plot for QC samples. (*** Indicating significant correlation, p < 0.001).
Figure 4. (A) PCA score plots for raw milk metabolite and quality control (QC) sample datasets. (B) Correlation plot for QC samples. (*** Indicating significant correlation, p < 0.001).
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Figure 5. (A) OPLS-DA of the metabolite dataset of the control group. (B) OPLS-DA of the metabolite dataset of the CO2-treated group. (C) Alignment test results of the OPLS-DA of the control group. (D) Alignment test results of OPLS-DA for the CO2-treated group.
Figure 5. (A) OPLS-DA of the metabolite dataset of the control group. (B) OPLS-DA of the metabolite dataset of the CO2-treated group. (C) Alignment test results of the OPLS-DA of the control group. (D) Alignment test results of OPLS-DA for the CO2-treated group.
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Figure 6. (A) Heatmap of DMs in the control group. (B) Heat map of DMs in the CO2-treated group. (C) Classification loop diagram (Ontology) of DMs in the control group. (D) Classification loop diagram (Ontology) of DMs in the CO2-treated group.
Figure 6. (A) Heatmap of DMs in the control group. (B) Heat map of DMs in the CO2-treated group. (C) Classification loop diagram (Ontology) of DMs in the control group. (D) Classification loop diagram (Ontology) of DMs in the CO2-treated group.
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Figure 7. (A) Histogram of the distribution of microbiomics and metabolomics correlation coefficients (r) in the control group. (B) Histogram of the distribution of microbiomics and metabolomics correlation coefficients (r) in the CO2-treated group. The horizontal coordinate is the distribution of r between the two histologies, and the vertical coordinate is the density of the distribution of the corresponding r. r ≥ 0.7 or ≤−0.7 indicates that the correlation is very strong, i.e., the blue negative correlation and the yellow positive correlation part of the plot. (C) Hierarchical clustering heat map of microbial and metabolite correlations in the control group (Top 28). (D) Hierarchical clustering heatmap of microbial and metabolite correlations in the CO2-treated group. Rows indicate metabolites and columns indicate genera. *** denotes correlation test p < 0.001, ** denotes correlation test p < 0.01, * denotes correlation test p < 0.05.
Figure 7. (A) Histogram of the distribution of microbiomics and metabolomics correlation coefficients (r) in the control group. (B) Histogram of the distribution of microbiomics and metabolomics correlation coefficients (r) in the CO2-treated group. The horizontal coordinate is the distribution of r between the two histologies, and the vertical coordinate is the density of the distribution of the corresponding r. r ≥ 0.7 or ≤−0.7 indicates that the correlation is very strong, i.e., the blue negative correlation and the yellow positive correlation part of the plot. (C) Hierarchical clustering heat map of microbial and metabolite correlations in the control group (Top 28). (D) Hierarchical clustering heatmap of microbial and metabolite correlations in the CO2-treated group. Rows indicate metabolites and columns indicate genera. *** denotes correlation test p < 0.001, ** denotes correlation test p < 0.01, * denotes correlation test p < 0.05.
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Figure 8. Microbiomics and untargeted metabolomics KEGG enrichment pathway analysis. (A) Microbiomics and identification of KEGG secondary metabolic pathway composition bar graphs for each sample. The vertical coordinates represent the relative abundance of each functional taxon; the longer the bar, the higher the relative abundance of that functional taxon in the corresponding sample. (B) Comparison group A-6d and D-6d KEGG pathway category bar graphs. G is Genetic Information Processing, M is Metabolism, E is Environmental Information Processing, o is Organismal Systems, H is Human Diseases. (C) Interaction maps of KEGG-enriched pathway-regulated metabolite networks shared by microbiomics and non-targeted metabolomics.
Figure 8. Microbiomics and untargeted metabolomics KEGG enrichment pathway analysis. (A) Microbiomics and identification of KEGG secondary metabolic pathway composition bar graphs for each sample. The vertical coordinates represent the relative abundance of each functional taxon; the longer the bar, the higher the relative abundance of that functional taxon in the corresponding sample. (B) Comparison group A-6d and D-6d KEGG pathway category bar graphs. G is Genetic Information Processing, M is Metabolism, E is Environmental Information Processing, o is Organismal Systems, H is Human Diseases. (C) Interaction maps of KEGG-enriched pathway-regulated metabolite networks shared by microbiomics and non-targeted metabolomics.
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Table 1. Macrogenomics screened proteins corresponding to KO abundance and metabolomics annotated KEGG shared pathway analysis.
Table 1. Macrogenomics screened proteins corresponding to KO abundance and metabolomics annotated KEGG shared pathway analysis.
No.IDIDDescriptionCountKEGG IDTop Class
1ko02010bta02010ABC transporters10C00719/C00062/C00025/C00120/C00093/C00299/C00475/C00881/C00079/C01181Metabolism
2ko00240bta00240Pyrimidine metabolism9C00055/C00380/C00299/C00475/C00881/C00106/C00438/C02067/C00295Environmental Information Processing
3ko00330bta00330Arginine and proline metabolism7C00062/C00581/C02305/C00025/C04281/C00022/C00300Metabolism
4ko00360bta00360Phenylalanine metabolism5C00082/C01586/C00805/C00079/C00022Metabolism
5ko00770bta00770Pantothenate and CoA biosynthesis4C00864/C00522/C00106/C00022Metabolism
6ko00620bta00620Pyruvate metabolism4C02504/C00074/C00149/C00022Metabolism
7ko00260bta00260Glycine, serine and threonine metabolism4C00719/C00581/C00022/C00300Organismal Systems
8ko00310bta00310Lysine degradation4C03793/C00489/C01181/C05548Metabolism
9ko00970bta00970Aminoacyl-tRNA biosynthesis4C00062/C00025/C00082/C00079Human Diseases
10ko04141bta04964Proximal tubule bicarbonate reclamation3C00074/C00025/C00149Metabolism
11ko00020bta00020Citrate cycle (TCA cycle)3C00074/C00149/C00022Metabolism
12ko04922bta04922Glucagon signaling pathway3C00074/C00149/C00022Organismal Systems
13ko00250bta00250Alanine, aspartate and glutamate metabolism3C00025/C00438/C00022Genetic Information Processing
14ko05204bta05207Chemical carcinogenesis—receptor activation3C14240/C03690/C00410Metabolism
15ko00010bta00010Glycolysis/Gluconeogenesis3C00074/C00221/C00022Human Diseases
16ko00400bta00400Phenylalanine, tyrosine and tryptophan biosynthesis3C00074/C00082/C00079Human Diseases
17ko00030bta00030Pentose phosphate pathway3C00257/C00221/C00022Metabolism
18ko00564bta00564Glycerophospholipid metabolism3C00588/C00093/C04230Metabolism
19ko00630bta00630Glyoxylate and dicarboxylate metabolism3C00025/C00149/C00022Metabolism
20ko00380bta00380Tryptophan metabolism3C01717/C05834/C00328Human Diseases
21ko00230bta00230Purine metabolism3C00366/C01444/C05513Metabolism
22ko00460bta05030Cocaine addiction2C00025/C00082Metabolism
23ko05034bta05034Alcoholism2C00025/C00082Metabolism
24ko05014bta05014Amyotrophic lateral sclerosis2C00062/C00025Environmental Information Processing
25ko00220bta00220Arginine biosynthesis2C00062/C00025Cellular Processes
26ko00290bta00290Valine, leucine and isoleucine biosynthesis2C02504/C00022Organismal Systems
27ko00780bta00780Biotin metabolism2C00120/C01909Metabolism
28ko04216bta04216Ferroptosis2C00025/C00418Metabolism
29ko05200bta05200Pathways in cancer2C00410/C00149Cellular Processes
30ko00730bta00730Thiamine metabolism2C00082/C00022Environmental Information Processing
31ko00410bta00410beta-Alanine metabolism2C00864/C00106Human Diseases
32ko00650bta00650Butanoate metabolism2C00025/C00022Metabolism
33ko00760bta00760Nicotinate and nicotinamide metabolism2C01020/C00022Metabolism
34ko00350bta00350Tyrosine metabolism2C00082/C00022Human Diseases
35ko00520bta00520Amino sugar and nucleotide sugar metabolism2C00270/C00446Human Diseases
36ko00860bta00860Porphyrin and chlorophyll metabolism2C00025/C02800Human Diseases
37ko04150bta04150mTOR signaling pathway1C00062Metabolism
38ko04114bta04114Oocyte meiosis1C00410Metabolism
39ko04914bta04914Progesterone-mediated oocyte maturation1C00410Metabolism
40ko04068bta04068FoxO signaling pathway1C00025Human Diseases
41ko05142bta05142Chagas disease1C00062Metabolism
42ko05016bta05016Huntington disease1C00025Organismal Systems
43ko05143bta05143African trypanosomiasis1C00328Metabolism
44ko00472bta00472D-Arginine and D-ornithine metabolism1C00062Environmental Information Processing
45ko05215bta05215Prostate cancer1C00410Metabolism
46ko04721bta04721Synaptic vesicle cycle1C00025Environmental Information Processing
47ko04066bta04066HIF-1 signaling pathway1C00022Metabolism
48ko00910bta00910Nitrogen metabolism1C00025Human Diseases
49ko04152bta04152AMPK signaling pathway1C00022Metabolism
50ko05012bta05012Parkinson disease1C00082Metabolism
51ko00480bta00480Glutathione metabolism1C00025Metabolism
52ko00561bta00561Glycerolipid metabolism1C00093Metabolism
53ko00640bta00640Propanoate metabolism1C05984Metabolism
54ko00052bta00052Galactose metabolism1C00446Metabolism
55ko00340bta00340Histidine metabolism1C00025Metabolism
56ko00071bta00071Fatty acid degradation1C00489Environmental Information Processing
57ko00524bta04080Neuroactive ligand-receptor interaction1C00025Metabolism
58ko00061bta00061Fatty acid biosynthesis1C08362Metabolism
59ko00040bta00040Pentose and glucuronate interconversions1C00022Metabolism
60ko00270bta00270Cysteine and methionine metabolism1C00022Metabolism
61ko00130bta00130Ubiquinone and other terpenoid-quinone biosynthesis1C00082Metabolism
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Zheng, A.; Wei, C.; Liu, J.; Bu, N.; Liu, D. Deciphering the Mechanism by Which Carbon Dioxide Extends the Shelf Life of Raw Milk: A Microbiomics- and Metabolomics-Based Approach. Molecules 2024, 29, 329. https://doi.org/10.3390/molecules29020329

AMA Style

Zheng A, Wei C, Liu J, Bu N, Liu D. Deciphering the Mechanism by Which Carbon Dioxide Extends the Shelf Life of Raw Milk: A Microbiomics- and Metabolomics-Based Approach. Molecules. 2024; 29(2):329. https://doi.org/10.3390/molecules29020329

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Zheng, Anran, Chaokun Wei, Jun Liu, Ningxia Bu, and Dunhua Liu. 2024. "Deciphering the Mechanism by Which Carbon Dioxide Extends the Shelf Life of Raw Milk: A Microbiomics- and Metabolomics-Based Approach" Molecules 29, no. 2: 329. https://doi.org/10.3390/molecules29020329

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