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. 2026 Sep 17;39:104476. doi: 10.1016/j.fochx.2026.104476

Lactiplantibacillus plantarum CM48 modulates protein metabolism in fermented lamb sausages: A Metaproteomics-based model

Daixun Wang a,b, Xueqian Yang a,b, Feng Cheng a,b, Ye Jin a,b, Jianjun Tian a,b,⁎
PMCID: PMC13625896  PMID: 42820053

Abstract

Lactic acid bacteria (LAB) can modulate protein metabolism in fermented sausages, but the specific molecular basis remain unclear. In this study, we evaluated the apparent indicators of protein degradation in fermented lamb sausages and characterized both meat-derived and microbial-derived proteins using a metaproteomics approach. The results showed that fermentation with Lactiplantibacillus plantarum CM48 was associated with enhanced proteolysis in the sausage matrix. Based on metaproteomic data, we propose a hypothesis that increased abundance of ribosomal proteins and energy metabolism-related enzymes in L. plantarum CM48 is correlated with changes in glycolysis and oxidative stress responses. These coordinated changes may be associated with enhanced protein metabolism. This study provides a correlative model for how L. plantarum CM48 may be associated with protein metabolism in fermented lamb sausages, providing insights into the proteolytic function of LAB in fermented meat products.

Keywords: Fermented lamb sausage, Lactiplantibacillus plantarum, Proteolysis, Metaproteomics, Starter culture

1. Introduction

Meat is a vital component of the human diet, offering high-quality nutrients and essential trace elements. The Inner Mongolia region of China is rich in its high-quality lamb, characterized by tender and easily digestible meat, serving as a significant dietary protein source (F. Gao, Wang, et al., 2024). Lamb fermented sausage is a favored type of fermented meat due to its nutritional richness and distinctive flavor. Protein degradation is the core biochemical process in sausage production, which alters protein structure, impacting the colour, water retention, and gel structure of meat products. Conversely, protein degradation generates peptides, amino acids, and amines, which are crucial for developing taste and flavor in meat products. Therefore, maintaining the balance of protein degradation metabolism in sausage is particularly important to ensure the quality of sausage (Zhu et al., 2020).

Lactic acid bacteria (LAB) are prevalent in naturally fermented meat products, where their metabolic activities contribute to the distinctive flavor and quality of fermented sausages. Moreover, due to its good ability to adapt to the meat environment and the microbial structure of the leading products, LAB has been used as a special starter for meat products. Research indicates that LAB starter cultures enhance fermentation by reducing time, promoting protein degradation, managing undesirable fermentation, and improving sensory and nutritional qualities, thus ensuring consistent quality and flavor (Xiao et al., 2020; Zhao et al., 2022). Although studies have shown that LAB can enhance protein degradation, the reason is often simply attributed to the proteolytic ability of bacterial proteases produced by strains. The mechanism by which LAB affects protein degradation in sausages has not been fully elucidated (Sun et al., 2019; D. Yu et al., 2021).

Advancements in omics technologies have made proteomics a standard method for identifying protein biomarkers linked to quality traits in meat products. As identified by TMT-tagged quantitative proteomics, the high protein hydrolysis index in Saccharomyces cerevisiae fermented hams is associated with down-regulated proteins of myofibrillar and catalysis-related enzymes (T. Zhang et al., 2025). Monitoring the activity of microorganisms during sausage fermentation is essential, as they play a crucial role in protein degradation. Previous research primarily focused on genome or transcriptome analyses to study microbial compositional changes in sausages, which do not reflect protein expression. In contrast, metaproteomics, through direct measurement of expressed proteins, offers more precise functional insights. Meanwhile, mass spectrometry (MS)-based proteomics analyses, which measure proteins derived from both lamb and microbiota, can help decipher lamb-microbe interactions in complex sausage ecosystems (X. Zhang et al., 2018). For example, (Gu et al., 2024) revealed by metaproteomics that Pseudomonas aeruginosa promotes enzyme synthesis and causes spoilage of refrigerated pork by enhancing transcription and translation via ribosomal proteins. To our knowledge, no study has analyzed the mechanism by which LAB affect protein metabolism in lamb fermented sausages by metaproteomics.

This study aimed to examine the impact of Lactiplantibacillus plantarum CM48 on protein degradation in sausages and to track the taxonomic, compositional, and functional shifts in microorganisms over time in lamb fermented sausages using metaproteomics, to explore their biological significance in lamb protein metabolism. This approach facilitates a deeper investigation into the mechanisms of regulating active microorganisms to enhance protein metabolism in sausage production and storage, offering theoretical support for improving fermented sausage quality.

2. Materials and methods

2.1. Sample preparation

The preparation of sausages was carried out according to the method described by (F. Gao, Zhang, et al., 2024). Fresh lamb hind leg meat and suet were obtained from Jingxin Grassland Co., Ltd., transported to the laboratory at 4 °C, and subsequently stored at −20 °C until use. Prior to processing, the lean meat and fat were combined at a ratio of 8:2 (w/w) and minced through a 1.0 cm plate. The following ingredients were added per kilogram of meat mixture: sodium chloride (20 g), glucose (5 g), sucrose (5 g), white pepper powder (2 g), dried ginger powder (2 g), Sichuan pepper powder (1 g), ascorbic acid (0.5 g), and sodium nitrite (0.1 g). After thorough mixing, the meat batter was cured at 4 °C for 12–16 h, then stuffed into collagen casings with a diameter of 40 mm.

Two treatment groups were established: the CM48 group, inoculated with L. plantarum CM48 at a concentration of 1 × 107 CFU/g, and the natural fermentation group (ZR), which received no starter culture and served as the control. Three independent batches were prepared for each treatment group, representing three biological replicates.

The stuffed sausages were fermented and ripened in a climate-controlled chamber under the following conditions: fermentation at 25 °C with 95% relative humidity for 48 h; drying at 15 °C with 70%–75% relative humidity for 3 d; and ripening at 10 °C with 60%–65% relative humidity for 7 d. Following ripening, the sausages were stored at 4 °C for 60 d.

Samples were collected at six distinct time points: raw meat (0 d), end of fermentation (2 d), end of drying (5 d), end of maturation (12 d), 30 d of storage (storage 1), and 60 d of storage (storage 2). At each time point, three sausages were taken from each treatment group for subsequent analyses.

L.plantarum CM48 utilized in this study, was supplied by the Meat Science and Technology Team at Inner Mongolia Agricultural University and is registered in the China General Microbiological Culture Collection Center with accession number CGMCC No.35965.

2.2. Measurement of pH and aw

The pH value was measured with reference to the method of Chinese national standard GB5009.237–2016. Fermented sausages at different stages were taken to remove the casing, 2 g were weighed and mixed with 18 mL of saline, and the pH value of the mixture was determined after magnetic stirring for 30 min.

The fermented sausage was chopped and weighed 10 g into the measuring cup of HD-3 A Moisture Activity Meter (Wuxi Huake Instrumentation Co., Ltd., China) for measurement.

2.3. Measurement of proteolytic indicators

Total nitrogen (N) content was determined using the Kjeldahl method as specified in Chinese standard GB5009.5–2016. Non-protein nitrogen (N0) content was determined following (Hughes et al., 2002) and the protein hydrolysis index (PI) was calculated using the formula PI=N0/N × 100%. Total Volatile Basic Nitrogen (TVB-N) in the sausages was assessed using the Kjeldahl method as described by (Ren et al., 2021). Amino acid nitrogen (AAN) content was measured via formaldehyde titration according to (Y. Liu et al., 2022) and expressed in mg/100 g. Samples weighing 3 g were combined with 27 mL of Trichloroacetic Acid (TCA) solution, then subjected to freezing and centrifugation at 10,000g for 15 min. After centrifugation, the supernatant was taken to determine the content of TCA soluble peptides (TCA—N) by biuret method colorimetry. (Visessanguan et al., 2004).

2.4. Gel electrophoresis of MP and SP

Myoplasmic proteins (SP) and myofibrillar proteins (MP) were extracted according to (Fadda et al., 1998) and (Mauriello et al., 2002) with minor modifications. A 2 g sample of fermented sausage was combined with 20 mL of PB buffer (0.02 mol/L, pH 6.5), homogenized and centrifuged at 10,000 g for 20 min at 4 °C. The supernatant was then filtered through a 0.22 μm membrane to eliminate bacterial myoplasmic proteins. The precipitate was combined with 20 mL of PB buffer containing 0.1% TritonX-100 (0.03 mol/L, pH 7.4), homogenized, and centrifuged. This process was repeated three times. The washed precipitate was then mixed with PB buffer (0.1 mol/L, pH 6.5, containing 0.7 mol/L KI) at a 1:9 ratio, homogenized, and centrifuged at 10,000 g for 20 min at 4 °C. The supernatant was filtered through a 0.22 μm membrane to isolate the collagen fibrillar proteins. The concentrations of myoplasmic and myofibrillar proteins were adjusted using a BCA kit (Soleberg Biotechnology Co., Ltd.).

The protein was concentrated to 1 mg/mL, combined with 5× sample buffer in a 4:1 ratio, and heated at 100 °C for 5 min. A 15% separating gel was used for SP, while a 12% gel was employed for MP. The gel was stained for 30 min using 0.2% Coomassie Brilliant Blue R-250 in a solution of 45% methanol and 10% glacial acetic acid, followed by washing with distilled water and repeated destaining with a solution containing 10% methanol and 10% glacial acetic acid. Protein bands were scanned with a gel imaging scanner (BioRad, USA) to detect changes in protein bands of sausage samples at different time periods and groups.

2.5. Metaproteomics analysis

2.5.1. Protein preparation and digestion

Proteins were treated with 100 mM TEAB and 10 mM TCEP at 37 °C for 60 min for reduction, followed by alkylation using 40 mM Iodoacetamide (IAM) at room temperature for 40 min, shielded from light. The sample was mixed with pre-cooled acetone at a volume ratio of 6:1 and incubated at −20 °C for 4 h to precipitate the protein. Following centrifugation at 10,000 g for 20 min, the precipitate was redissolved in 100 μL of 10 mM TEAB.

Subsequently, trypsin (enzyme-to-protein ratio of 1:50) was introduced to enzymatically digest the proteins at 37 °C. The peptides were vacuum-evacuated, re-solubilized in 0.1% trifluoroacetic acid (TFA), desalted using HLB, and lyophilized. Peptide quantification utilized a Thermo Fisher Scientific peptide quantification kit (23275).

2.5.2. LC-MS/MS analysis

Shanghai Meiji Biomedical Technology Co. conducted label-free quantitative metaproteomics. Peptides, each at a concentration of 0.25 μg/μL, were dissolved in a mass spectrometry upload buffer (2% acetonitrile, 0.1% formic acid) for analysis. An EASY-nLC 1200 HPLC system (Thermo, USA) equipped with a 75 μm × 25 cm C18 reversed-phase column (Thermo, USA) was utilized. Peptides were separated using buffer A (2% acetonitrile, 0.1% formic acid) and buffer B (80% acetonitrile, 0.1% formic acid) as mobile phases, with a flow rate of 300. The elution gradients were configured as follows: 0–56 min at 5% B, 56–62 min at 23% B, 62–63 min at 29% B, 63–63.5 min at 38% B, 63.5–64 min at 48% B, and 64–90 min at 100% B.

Full-scan MS spectra (300–1500 m/z) were obtained on a Q_Exactive HF-X mass spectrometer (Thermo, USA) with a 3e6 AGC target, 60,000 resolution, and a maximum injection time of 20 ms. The 20 ions with the strongest signals underwent secondary fragmentation via HCD, with MS spectra initiated at 100 m/z. The parameters included a resolution of 15,000, an AGC target of 1e5, a maximum injection time of 50 s, and a dynamic exclusion time of 18 s.

2.5.3. Protein identification and bioinformatics

Raw data were identified and quantified by ProteomeDiscoverer™ Software 2.4. The search parameters were configured with a precursor mass tolerance of 10 ppm and trypsin as the protein cleavage enzyme, allowing a maximum of two missed cuts. Dynamic modifications included oxidation (M), acetylation at the protein N-terminus, methionine loss at the protein N-terminus, and methionine loss with acetylation at the protein N-terminus. Carbamidomethylation (C) was set as a static modification. Result Filtering Parameters: Significance threshold P < 0.05, Peptide FDR ≤ 0.01.

The identified proteins underwent analysis using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Differentially expressed proteins (DEPs) were determined using a t-test (P < 0.05) and a fold change (FC) threshold (FC > 1.50 or FC < 0.67) for subsequent functional enrichment analysis. The STRING database (http://string-db.org/) facilitated the protein interaction network analysis.

2.6. Statistical analysis and visualization

A randomized complete block design (RCB) was adopted in this study, with three independent batches prepared per treatment group, representing three biological replicates. All experimental results are expressed as mean ± standard error of the mean (SEM).

Data processing was performed using IBM SPSS Statistics 23.0. A mixed-effects model was constructed for all physicochemical parameters and metaproteomics data, with treatment group designated as the fixed effect and batch as the random effect. Significant differences between groups were evaluated using one-way analysis of variance (ANOVA) followed by Tukey's post hoc test, with statistical significance set at P < 0.05.

For graphical representation, line charts and histograms were generated using GraphPad Prism, while non-metric multidimensional scaling (NMDS), heatmaps, and boxplots were created using the online platforms OmicShare (http://www.omicshare.com/tools) and ChiPlot (https://www.chiplot.online/).

3. Results and discussion

3.1. Physicochemical and protein degradation trends in fermented sausage

The variation in pH and Aw values of sausage was monitored throughout storage. In both groups of sausages, the pH value was observed to decrease sharply first, then increase and then tend to be stable (Fig.1A), which reflected rapid acidification driven by L. plantarum CM48 metabolism through organic acid production, mainly lactic acid. Over time, the Aw value of both sausage groups initially decreased and then increased, showing a significant difference between days 30 and 60 (P < 0.05) (Fig.1B). The decrease in pH and Aw during fermentation can affect protein structure and water retention, as well as modulate the activities of endogenous enzymes involved in proteolysis (Xiao et al., 2020).

Fig. 1.

Fig. 1

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Two groups of sausages at different time points. (A) pH value. (B) Aw value. (C) PI value. (D) TVB-N value. (E) AAN value. (F) TCA-N value. (G) Myofibrillar protein SDS-PAGE. (H) Sarcoplasmic protein SDS-PAGE. ZR and CM48 represent the groups of fermented sausages. M (standard protein Maker), 0d (raw meat), 2d (fermentation), 5d (drying), 12d (maturation), 30d (storage 1) and 60d (storage 2). Data are presented as mean ± SEM (n = 3). Asterisks indicate significant differences between CM48 and ZR groups at the same time point (* 0.01 < P ≤ 0.05，** 0.001 < P ≤ 0.01，*** P ≤ 0.001).

PI denotes the proportion of non-protein nitrogen relative to the total nitrogen content. AAN includes water-soluble proteins, peptides and amino acids. TCA-N consists of endogenous oligopeptides and free amino acids generated during processing (Z. Li et al., 2024). Through these indicators, the degree of protein degradation in sausages can be evaluated. The changes of PI, ANN and TCA-N values showed a similar trend, which increased rapidly in 0–5 d, and then slowed down in 12–60 d (Fig.1C, E, F). Starting from day 5, the CM48 group exhibited significantly higher values than the ZR group (P < 0.05), suggesting that L.plantarum CM48 inoculation enhances protein decomposition and facilitates the conversion of protein into peptides and free amino acids. It is hypothesized that mutton sausage protein primarily decomposes during the initial fermentation phase. This aligns with (Johansson et al., 1994), who found that significant alterations in water-soluble and salt-soluble proteins in sausages occur during the initial processing stages. The results of MP and SP electrophoresis also showed that the CM48 group had stronger protein degradation than the ZR group (Fig.1G, H). In the MP electrophoresis map, myosin heavy chain (200 kDa) was significantly degraded in the CM48 group. The actin (45 kDa) band and tropomyosin (40 kDa) band were reduced at 0–30 d and disappeared at 60 d, and the 10–37 kDa band showed a decreasing trend from 12 to 60 d. In contrast, the actin (45 kDa) band and the tropomyosin (40 kDa) band in the ZR group were still present although they began to weaken from 5 d, and the 10–37 kDa band showed almost no decomposition. SP electrophoresis results indicated that the 15–24 KDa bands in the CM48 group were significantly darker than those in the ZR group, suggesting that L.plantarum CM48 facilitates the breakdown of large sarcoplasmic proteins into smaller peptides (Z. Li et al., 2024). TVB-N levels, a common indicator of meat freshness affected by spoilage bacteria and enzymes (Bekhit et al., 2021), increased at a significantly higher rate in the ZR group compared to the CM48 group over time (P < 0.05) (Fig.1D). This suggests that L.plantarum CM48 inhibits spoilage bacteria growth, thereby enhancing product freshness (D. Wang et al., 2021).

3.2. Metaproteomics quality assessment

Metaproteomics offers the distinct advantage of concurrently measuring microbiota and host proteins, enabling the evaluation of spoilage microbiome functional characteristics during sample storage (Gu et al., 2024). The physicochemical data in Fig.1 showed that the most rapid pH decline occurred between days 0 and 2, and the most rapid increases in PI and AAN occurred between days 0 and 12, while most parameters had plateaued by day 30. These trends guided the selection of four time points for metaproteomic analysis: 0 days (raw meat), 2 days (end of active fermentation), 12 days (end of maturation), and 30 days (early storage).

Twenty-one sausage samples were analyzed and a total of 1,224,973 spectra were obtained. There were 302,720 that could be successfully matched to known spectra in Proteome Discoverer. As shown in Fig.2, a total of 12,478 peptides, 14,638 proteins and 1580 protein sets were identified. Among them, 58.05% of the small molecule proteins consisted of 1–4 peptides, most of the peptides were 9–20 amino acids in length, 1158 proteins had Mw ≤ 60 KDa, while only 19 high molecular weight proteins (Mw ≥ 300 KDa) were detected, and these results suggested that low molecular weight proteins occupied a greater proportion of the fermented sausage samples. In addition, at least 22.7% of the proteins showed sequence coverage higher than 40% (Fig.2D), providing credibility to the data.

Fig. 2.

Fig. 2

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Protein identification analysis results. (A) Distribution of peptide numbers. (B) Distribution of peptide length. (C) Protein molecular weight distribution. (D) Protein coverage distribution.

3.3. Species identification analysis of fermented sausages by metaproteomics

By comparing the protein sequences obtained from the identification with the species annotations obtained from the NR database, it is clear that more than 1419 proteins in the sausage samples were related to lamb, and 161 proteins were related to the microorganisms involved in the fermentation, and these results indicated the close association between the microorganisms and the lamb proteins in the fermentation process of the sausages (Fig.3A, B).

Fig. 3.

Fig. 3

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Changes in protein components of sausages at different stages. (A) phylum level species change. (B) genus level species change. (C) identified lamb meat source protein NMDS map. (D) identified microbial source protein NMDS map. (E) identified lamb meat source protein differential protein volcano map. (F) identified microbial source protein differential protein volcano map. ZR and CM48 represent the groups of fermented sausages. RAW indicates raw meat, 2d (fermentation), 12d (maturation), 30d (storage 1). Each point in the NMDS plots represents one biological replicate (n = 3 per time point). In the volcano plots, each point represents a protein, and proteins with |Log2FC| > 1 and P < 0.05 were considered differentially expressed.

Sausages are affected by a variety of microorganisms during fermentation. At the phylum level, Firmicutes, Proteobacteria, and Actinobacteria exhibited higher combined protein abundance (Fig.3A). At the genus level, only the relative abundance of Lactobacillus and Pseudomonas was greater than 1% (Fig.3B). Over time, the relative abundance of Firmicutes and Lactobacillus increased in both sausage groups, with a more pronounced trend in the CM48 group. The relative abundance of Firmicutes reached 7.04% and 7.37% at 12 d and 30 d, respectively. The relative abundance of Lactobacillus rose to 4.91% and 5.42%, surpassing that of the ZR group at the same time. Study utilizing high-throughput sequencing have examined microbial diversity in sausage production, revealing that Firmicutes, the predominant bacterial group, dominates the fermentation process (Xiao et al., 2020). This finding aligns with protein abundance data annotated via metaproteomics.

(Gu et al., 2024) conducted NMDS analysis on proteins identified as originating from lamb meat and microbes. The results of lamb-derived protein (Fig.3C) showed that the samples of ZR group and CM48 group showed different degrees of separation at each stage, and gradually weakened with the increase of time. The CM48 group had a large degree of separation from RAW at 2 days, while the ZR group increased the degree of separation from RAW at 12 days. This may be related to the changes of two groups of microorganisms caused by the addition of L. plantarum CM48. Microbial protein analysis (Fig.3D) indicated no significant difference between the ZR and RAW groups at 2 days. However, the CM48 group exhibited significant differences compared to both RAW and ZR groups, with notable differences persisting at 12 and 30 days. This suggests that sausage meat protein metabolism primarily occurs between 0 and 12 days, stabilizing from 12 to 30 days. The variation in microbial flora structure due to the addition of strain CM48 is crucial to the observed differences in meat protein metabolites between the sausage groups.

Multiple sets of DEPs volcano maps also support this speculation. Over time, the ZR and CM48 groups exhibited an increasing trend in the number of DEPs compared to the RAW group (Fig.3E, F). Among them, 528,586 and 630 differential lamb-derived proteins were obtained in each stage of CM48 group compared with RAW, and 17,401 and 512 differential lamb-derived proteins were obtained in each stage of ZR group compared with RAW. These results suggest that lamb protein is progressively decomposed into smaller peptides over time, accompanied by significant changes in protein composition. The number of DEPs in the CM48 group and the ZR group decreased from 502 to 144 and finally to 85 in the three stages, and the number gradually decreased. The impact of strain CM48 fermentation on protein components is most pronounced during the early and middle stages, with its influence potentially diminishing over time. This may be because there was no obvious dominant flora in the ZR group at the beginning, and the relative abundance of Lactobacillus increased to 2.04% at 12 days (Fig.3B). In the CM48 group, L.plantarum CM48 quickly adapted to the fermentation environment, and rapidly propagated in 2–12 d and directly became the dominant strain in the sausage, which accelerated the decomposition of lamb protein.

3.4. Changes and functional analysis of DEPs derived from lamb meat

GO enrichment analysis was conducted on the DEPs of the two sausage groups at various stages to identify the significantly enriched biological processes, cellular components, and molecular functions involving these proteins. Additionally, the PPI network of these DEPs was examined using the STRING database.

At 2 d, the sausage was in the fermentation stage. The top five pathways with the lowest P value were myofibril, actin binding, proteasome core complex, contractile fiber and proteasome core complex, alpha-subunit complex. In addition, some pathways were enriched with more differential proteins, such as actin filament binding (25), regulation of small molecule metabolic process (19), hydrolyase activity (14) and glycolytic process (14) (Fig.4A). Analysis of the PPI network, with an enrichment p-value below 1.0e-16 and a clustering coefficient of 0.396, identified Carbon metabolism, Proteasome and Striated Muscle Contraction as the primary clusters (Fig.S1A). It was also found that the endogenous enzyme Calpain-3 related to myofibrillar degradation was significantly up-regulated in CM48_2d compared with ZR_2d (Ilian et al., 2004). The study found that incorporating L. plantarum CM48 was associated with accelerated degradation of sausage muscle structure within 2 days, accompanied by significant down-regulation of several myofibril-related proteins, including MYH1, MYH2, LOC101111980, LOC442995, JEQ12_003036, MYL1, MYL2, MYL3, MYLPF, ACTC1, ACTA1, ACTB_G1 and TUBA4A. Low molecular weight muscle proteins such as JEQ12_018195, JEQ12_013340, TNNT1, TNNT3, JEQ12_016129, TPM4, etc. were generated, aligning with the findings of (T. Zhang et al., 2025). (See Fig. 5.)

Fig. 4.

Fig. 4

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GO enrichment analysis of DEPs. (A) CM48_2d VS ZR_2d GO enrichment analysis. (B) CM48_12d VS ZR_12d GO enrichment analysis. (C) CM48_30d VS ZR_30d GO enrichment analysis. ZR and CM48 represent the groups of fermented sausages. 2d (fermentation), 12d (maturation), 30d (storage 1). The significance threshold for GO enrichment was set at P < 0.05.

Fig. 5.

Fig. 5

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Differential expression of muscle fiber-related proteins in different groups and stages. ZR and CM48 represent the groups of fermented sausages. 2d (fermentation), 12d (maturation), 30d (storage 1). Each group consisted of three biological replicates (n = 3). Significant differences between groups were analyzed by one-way ANOVA followed by Tukey's post hoc test (*P < 0.05, ***P < 0.01, ***P < 0.001).

At 12 d, the sausage was in the mature stage. Based on the biological processes related to structural protein changes such as regulation of myoblast differentiation and skeletal muscle tissue development, the DEPs were enriched into peptide metabolic process, myosin complex, peptide biosynthetic process. Positive regulation of protein-containing complex disassembly and protein activation cascade are related to the production of small molecular peptides and protein complexes in bioengineering (Fig.4B). The PPI network cluster, characterized by a PPI enrichment p-value below 1.0e-16, and a clustering coefficient of 0.42, was associated with Fibrinolysis, Serine protease inhibitors, Cysteine and methionine metabolism, Striated Muscle Contraction and Actin depolymerisation factor / cofilin-like domains (Fig.S1B). Incorporating LAB in fermented foods enhances protein fragmentation and boosts the release of free amino acids and bioactive peptides (D. Yu et al., 2021). The expression of ribosomal protein contributes to tRNA stability in muscle and serves as an indicator of meat texture characteristics (Zhao et al., 2022). In this study, the small ribosomal subunit, cytosolic small ribosomal subunit and ribosome were significantly enriched by 9, 9 and 12 DEPs, respectively. The down-regulation of these ribosome-related proteins leads us to hypothesize that ribosomal pathway activity, including the initiation of protein synthesis and amino acid degradation through the ribosome, may serve as an important contributing factor to the structural transformation of fermented sausages during maturation and following CM48 inoculation.

At 30 d, the sausage was in the storage stage, and the difference between the two groups of sausages was reduced. Significant differences were observed only in ACTA1, ACTB_G1, MYL3, and MYLPF, among myofibril-related proteins, aligning with findings that sausage protein decomposition primarily occurs early in the production process. The two GO terms with the smallest P value were intramolecular oxidoreductase activity, interconverting aldoses and ketoses (Fig.4C). The significant down-regulation of intracellular oxidoreductase activity-related proteins (PDIA6, JEQ12_005184, GSTA1, MPI, JEQ12_015016, JEQ12_002549) suggests a possible oxidative stress response in the CM48 group compared to the ZR group, which may be associated with intensified or protein degradation in the CM48 group (Q. Li et al., 2025). If confirmed by future studies, this observation could have practical implications for optimizing starter culture dosage or incorporating antioxidants to maintain product quality. The PPI network, with an enrichment p-value below 1.0e-16, and a clustering coefficient of 0.488, identified Fructose and mannose metabolism as the most significant cluster at 30 days (Fig.S1C). And the monosaccharide metabolic process (GO: 0005996) -related proteins (JEQ12_005184, GPD1, JEQ12_005527, JEQ12_012932, TIGAR, PFKM, MPI, JEQ12_015016) were significantly down-regulated. The down-regulation of monosaccharide metabolic process-related proteins suggests a possible effect of L. plantarum CM48 on carbohydrate metabolism, which may influence energy availability in the sausage matrix.

3.5. Changes and functional analysis of DEPs derived from microorganisms

To precisely monitor alterations in sausage microbial structures, we employed comprehensive metaproteomic analysis to explore the relationship between protein degradation and microbial metabolism in two sausage groups, based on species annotation (Gu et al., 2024).

Firstly, the changes of microbial composition of sausage samples with time were analyzed at the phylum and genus levels. The phyla Firmicutes, Proteobacteria, Actinobacteria and Bacteroidetes were identified at the phylum level (Fig.6A). A significant difference at the phylum level was observed between the two groups at 12 days (P < 0.05). In the ZR group, the relative abundance of Firmicutes and Proteobacteria remained stable over 30 days (P > 0.05). However, Actinobacteria decreased from 15.41% at 2 days to 12.22% at 30 days, while Bacteroidetes initially increased and then decreased. The CM48 group exhibited more pronounced changes in microbial abundance than the ZR group, particularly within the initial 0–12 days. Notably, Proteobacteria and Actinobacteria significantly decreased (P < 0.05) and continued to decline. Firmicutes became the dominant phylum in lamb sausages by day 12, with a relative abundance exceeding 55%, primarily due to a significant rise in Lactobacillus, which reached 38.67% (P < 0.05) (Fig.6B). Staphylococcus is also a common genus in sausages, which has strong lipolytic and proteolytic activities. In this study, Staphylococcus showed high abundance in CM48 group at 12 d and 30 d, which may promote the formation of specific aroma compounds in CM48 group sausages (Q. Chen et al., 2017).

Fig. 6.

Fig. 6

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Microbial composition and functional changes of fermented sausage. (A) Box plot of relative abundance changes at phylum level. (B) Heatmap of relative abundance changes at the genus level. (C) Microbial enzyme-related protein annotation. (D) Module annotation of microbial protein participation. ZR and CM48 represent the groups of fermented sausages. RAW indicates raw meat, 2d (fermentation), 12d (maturation), 30d (storage 1). Each group consisted of three biological replicates (n = 3). Significant differences between groups were analyzed by one-way ANOVA followed by Tukey's post hoc test (*P < 0.05, **P < 0.01, ***P < 0.001).

It is worth noting that Pseudomonas, as an aerobic bacterium, has a strong spoilage ability and can use iron to secrete extracellular proteases in meat, which is a common spoilage microorganism in meat (Gu et al., 2024). In the CM48 group, the relative abundance of Pseudomonas decreased from 8.4% to 5.76%, significantly lower than the ZR group (P < 0.05). In addition, although no studies have shown that Arthrobacter, Hydrogenophag, Streptomyces, Zhihengliuella are directly related to meat spoilage, these bacteria may exist in soil, water, animal intestine, and high-salt brine, and may indirectly affect product quality by polluting the processing environment (Y. Chen et al., 2010; Z. Gao et al., 2023; A. Li et al., 2021; Wagner et al., 2021). This study found that the CM48 group exhibited a significantly lower relative abundance of spoilage bacteria and TVB-N content compared to the ZR group. These results suggest that inoculation of starter culture was associated with changes in the microbial community structure, potentially inhibiting the growth of harmful microorganisms and contributing to improved flavor and safety quality of the finished product (Xiao et al., 2020; Zhao et al., 2022).

Microorganisms like Lactobacillus, Staphylococcus, and Penicillium in fermented sausages actively grow and metabolize, producing enzymes that influence the flavor and quality of the products. The primary pathways are carbohydrate and amino acid metabolism (Cruxen et al., 2019). The metabolic function of active microorganisms in fermented sausage was explored by KEGG database. Among the 161 identified microbial-derived proteins, 92 proteins were annotated as enzymes and played a role in redox, transfer, transport, hydrolysis, and cracking (Fig.6C). A total 59 enzymes were identified as participants in metabolic modules, including carbohydrate, energy, and amino acid metabolism (Fig.6D). Proteases secreted by microorganisms dominate proteolysis and accelerate the maturation of various fermented foods (Ramos et al., 2021). This study annotated six peptidases involved in peptide bond hydrolysis and ten proteins associated with amino acid metabolism pathways, including lysine, branched-chain amino acids, cysteine and methionine, and serine and threonine metabolism.

The changes in the functional enrichment of microbial-derived DEPs in the two groups of sausages at different stages were analyzed. (Fig.S2A-C). Certain differential proteins consistently fell into the orthologous groups COG G (Carbohydrate transport and metabolism), COG E (Amino acid transport and metabolism) and COG O (Posttranslational modification, protein turnover, chaperones) over the period of 2 to 30 days. The DEPs associated with E (Amino acid transport and metabolism) exhibited an overall increase, suggesting a potential role of CM48 in amino acid metabolism in sausages. The high-intensity hydrolysis of proteins in the CM48 group may provide a rich source of amino acids. Up-regulated differential proteins enriched C (Energy production and conversion) at 2 days and J (Translation, ribosomal structure and biogenesis) from 2 to 12 days. These results suggest that the strong degradation of meat protein between days 2 and 12 is closely associated with microbial activity.

3.6. Correlation analysis between protein degradation, protein component changes and microbial structure changes

To explore the potential associations between L. plantarum CM48 and sausage protein degradation and metabolism, Pearson correlation analysis was performed (Fig.7A). A significant positive correlation was observed between the relative abundance of Lactobacillus and Staphylococcus and the proteolysis index (PI) (P < 0.05), suggesting a possible association between increased protein degradation and the up-regulation of proteins expressed by these genera. To further elucidate the potential molecular basis underlying the effect of LAB on protein degradation, we analyzed the differentially expressed proteins derived from L. plantarum (Table. S1-S3).

Fig. 7.

Fig. 7

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Correlation analysis. (A) Correlation heat map between protein degradation index and relative abundance of microorganisms. (B) WGCNA analysis heat map and scatter plot between protein degradation index and meat-derived protein. Red indicates positive correlation and blue indicates negative correlation. Pearson correlation coefficients and P-values are shown. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

We first focused on microbial proteins involved in protein synthesis. In the process of microbial transcription and translation, RNA polymerase uses DNA as a template to synthesize mRNA with the help of basic transcription factors, and ribosomes use mRNA as a template to synthesize proteins using aminoacyl-tRNA substrates (Gu et al., 2024). In the two sausage groups, the 50S ribosomal protein L30 from Lactiplantibacillus paraplantarum was up-regulated at 2 days (P < 0.05), and the 50S ribosomal protein L7/L12 was up-regulated between 12 and 30 days (P < 0.05). In addition to ribosomal proteins, Elongation factor Tu / G is known to play a critical role in the elongation stage of protein synthesis (H. Yu et al., 2009). At 12 days and 30 days, Lactiplantibacillus plantarum exhibited up-regulation of Elongation factor Tu / G (P < 0.05). Based on this observation, we hypothesize that this up-regulation may facilitate strain recognition and translocation during translation elongation.

Beyond changes in the protein synthesis machinery, the regulation of carbohydrate metabolism may also represent an important mechanism by which LAB influence protein degradation. Carbohydrates have been reported to be crucial in modulating microbial activity (Zheng et al., 2019), and pyruvate is known to serve as a key intermediate in multiple metabolic pathways. It has been reported that microorganisms in meat metabolize glucose via glycolysis and the TCA cycle to generate ATP and pyruvate (Gu et al., 2024), and that amino acids such as serine, lysine, and tryptophan can degrade into pyruvate or its precursors, thereby supporting microbial growth in meat (Mazelis, 1980). In this study, Phosphocarrier protein HPr, expressed by Lactiplantibacillus paraplantarum, was significantly up-regulated between days 2 and 12 (P < 0.05), and enzymes involved in carbohydrate metabolism, including D-lactate dehydrogenase, enolase, and pyruvate kinase, were significantly up-regulated from days 12 and 30 (P < 0.05).

Notably, low-temperature conditions, as another critical factor during sausage processing, may also affect the metabolic activity of LAB. The expression of cold shock protein 1 in Lactiplantibacillus plantarum was significantly up-regulated between 2 and 30 days (P < 0.05). This occurred as the processing temperature dropped from 25 °C during fermentation to 4 °C during storage, well below the optimal growth temperature of L. plantarum. As documented in previous studies, even small temperature shifts can influence the survival and adaptation of lactic acid bacteria. As a primary adaptive response to temperature changes, stress-responsive proteins are up-regulated, with cold shock proteins in particular being induced upon temperature downshift to function as RNA chaperones that sustain protein synthesis under low-temperature conditions (Chai et al., 2026; Neffe-Skocińska et al., 2011; Papadimitriou et al., 2016).

Taken together, these correlative observations, including changes in protein synthesis machinery, carbohydrate metabolism, and cold stress responses, lead us to propose a hypothesis that L.plantarum may optimize protein and enzyme synthesis through enhanced ribosomal protein expression, thereby facilitating its adaptation to low-temperature conditions and dominance in the sausage environment. The strain may also influence carbohydrate metabolism, potentially via the phosphotransferase transport system, thereby modulating meat protein degradation.

To further explore the meat-derived protein modules associated with protein degradation, we performed weighted gene co-expression network analysis (WGCNA). The WGCNA method was employed to compute the Pearson correlation coefficient between each module and phenotypic data using the protein degradation index as the phenotype (Q. Chen et al., 2025; Gu et al., 2024). The analysis identified a significant correlation between the turquoise and brown modules and the characteristic index PI associated with protein degradation (P < 0.01). On this basis, 113 key meat-derived proteins (MM > 0.8, GS > 0.2) were screened by MMGS analysis (Fig.7B), and correlation analysis was performed with key microbial-derived proteins (Fig.8).

Fig. 8.

Fig. 8

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The correlation heat map between the key protein of lamb meat and the key protein of microbial source. Red indicates positive correlation and blue indicates negative correlation. Pearson correlation coefficients and P-values are shown. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Correlation analysis revealed that proteins expressed by L. plantarum were negatively correlated with meat-derived enzymes including ATP-dependent 6-phosphofructokinase (PFK), Triosephosphate isomerase (TPI), Glycerol-3-phosphate dehydrogenase, Malate dehydrogenase [NAD (+)], Mannose-6-phosphate isomerase and, Glucose-6-phosphate isomerase. PFK is a key rate-limiting enzyme in glycolysis. As reported by (England et al., 2014), pH decline inactivates this enzyme, and in this study, the lower pH observed in the CM48 group compared with the ZR group suggests that PFK activity may be inhibited. TPI is a sarcoplasmic protein known to play a crucial role in the early phase of glycolysis and to associate with glycolytic enzymes on fine myofilaments, and peptides derived from its degradation have been reported to significantly enhance meat tenderness (Song et al., 2024). In contrast, positive correlations were observed with fructose-bisphosphate aldolase, acylphosphatase, transaldolase, aldehyde dehydrogenase [NAD (+)], and glucosamine-6-phosphate isomerase. Fructose-bisphosphate aldolase is a glycolytic enzyme, and increased abundance and activity of this enzyme can accelerate the glycolytic rate (Bai et al., 2024). Acylphosphatase can control the flux of glycolysis and participate in cell apoptosis (Pazzagli et al., 1998). Transaldolase is a crucial enzyme in the non-oxidative segment of the pentose phosphate pathway, an alternative metabolic route for glucose. It reversibly converts glyceraldehyde-3-phosphate and fructose-6-phosphate, thereby increasing the flux from the pentose phosphate pathway to glycolysis (Fuentes-Lemus et al., 2025). Aldehyde dehydrogenase [NAD (+)] is crucial in glycolysis, gluconeogenesis, and arginine and proline metabolism. Collectively, these correlative patterns suggest that L. plantarum proteins are intimately associated with glycolytic pathway components, and we hypothesize that L. plantarum may be associated with the glycolytic rate through differential regulation of multiple enzymatic steps rather than a single control point.

Malate dehydrogenase is a crucial enzyme responsible for converting malic acid to oxaloacetic acid in the final step of the TCA cycle. In this study, it was negatively correlated with L. plantarum proteins, suggesting a potential suppression of TCA cycle activity. Based on this correlation, we hypothesize that if TCA cycle flux is reduced, ATP production may become insufficient to sustain cell viability. To offset this energy deficit, glycolysis may be enhanced as a compensatory mechanism. In addition, based on our correlative data, in which proteasome subunit was positively correlated and 40S ribosomal protein SA was negatively correlated with L. plantarum proteins, we hypothesize that L. plantarum may induce an imbalance between degradation and synthesis in meat by up-regulating proteasome subunit expression while inhibiting 40S ribosomal protein SA expression. This imbalance may be associated with enhanced ATP-dependent protein degradation and cell cycle arrest, potentially contributing to the depolymerization of myofibrils, as evidenced by the down-regulation of Tubulin-specific chaperone A, Fibrinogen alpha chain, and transgelin, and the up-regulation of Tropomyosin 2, Troponin C, and Spectrin beta chain. If glycolysis cannot fully compensate for the energy deficit caused by TCA cycle suppression, metabolic stress and ATP depletion may ensue, ultimately leading to cell death. The release of cytochrome c is a hallmark of apoptosis, and its level was elevated in the CM48 group, indicating that metabolic stress and energy depletion may trigger programmed cell death (Q. Chen et al., 2025; Siddique et al., 2025). Moreover, with the prolongation of time, apoptosis was gradually replaced by strong proteolysis of myofibrillar and cytoskeletal proteins (Setyabrata et al., 2023).

Metabolic stress and enhanced protein degradation may be accompanied by oxidative challenges. The proteins expressed by L. plantarum showed positive correlations with creatine kinase, activated RNA polymerase II transcriptional coactivator p15, glutathione S-transferase, hemopexin, superoxide dismutase [Cu—Zn], ceruloplasmin and annexin, suggesting a potential activation of the oxidative stress defense network in sausage. Creatine kinase is a prevalent sarcoplasmic protein in skeletal muscle and has been reported to serve as an enzymatic substrate of proteases and an indicator of muscle response to oxidative stress (Jia et al., 2022; R. Zhang et al., 2024). Annexin, a calcium-sensing protein, has been reported to play a key role in animal stress responses and cell death by regulating the intracellular calcium signaling pathway ([Ca2+] cyt), mediating cytoskeletal remodeling, and regulating inflammatory responses (Laohavisit & Davies, 2011). The transcriptional coactivator p15 has been reported to be crucial for RNA polymerase II-mediated transcription, DNA replication, and damage repair (A. Liu et al., 2024). In addition, antioxidant proteins such as glutathione S-transferase, superoxide dismutase [Cu—Zn], hemopexin, and ceruloplasmin have been reported to eliminate ROS, remove oxidative stress products, regulate inflammatory responses, and protect myofibrillar proteins from oxidative cross-linking damage (Aloke et al., 2024; Malheiros et al., 2019; Yin et al., 2021). Heat shock proteins, another class of stress-responsive proteins, have been reported to maintain intracellular homeostasis by serving as molecular chaperones that facilitate protein assembly, folding, unfolding, and the refolding of damaged proteins. Therefore, heat shock proteins may play a role in regulating apoptosis and protecting protein conformation and biological functions. In this study, mitochondrial HSP70 was positively correlated with the proteins expressed by L. plantarum, which may contribute to protein homeostasis (Tao et al., 2021). Based on these correlative data, we hypothesize that the positive correlations between L. plantarum proteins and oxidative stress defense-related proteins may reflect a dynamic balance between oxidative stress and stress resistance in CM48 group sausage.

These findings provide a correlative framework for understanding the interplay between microbial metabolism and protein degradation in fermented meat products, and suggest potential protein markers for future quality control strategies based on L. plantarum CM48. It should be noted that the 5 day and 60 day time points were not included in the metaproteomic analysis. The four selected time points (0, 2, 12, and 30 d) were chosen to capture the major biochemical transitions of the fermentation process, whereas the 5 day point represents a transitional phase and the 60 day point represents a late storage plateau during which most parameters had stabilized. Nevertheless, the data at the four selected time points provide a clear profile of the proteolytic events associated with L. plantarum CM48.

4. Conclusions

Inoculation with L. plantarum CM48 was associated with enhanced proteolysis and improved product freshness in fermented lamb sausages, effects that likely involve the interplay between endogenous enzymes and microbial activity. Metaproteomic analysis identified several structural proteins, including MYH1, MYH2, MYL1, MYL2, MYL3, ACTC1, ACTA1, ACTB_G1, and TUBA4A, as potential protein markers associated with protein degradation in the sausage matrix, and microbial proteins such as ribosomal proteins and glycolytic enzymes were also correlated with the metabolic activity of L. plantarum CM48. These findings provide candidate biomarkers for future quality monitoring and offer proteomic-level insights into the role of starter cultures in fermented meat products.

Based on our correlative data, we propose a hypothesis that L. plantarum CM48 may optimize protein and enzyme synthesis through increased ribosomal protein expression, which may facilitate its adaptation to low-temperature conditions and dominance in the sausage environment. The increased abundance of key glycolytic enzymes, including glyceraldehyde-3-phosphate dehydrogenase, enolase, and pyruvate kinase, is correlated with carbohydrate and energy metabolism. The enhanced energy metabolism of L. plantarum CM48 may be associated with changes in the TCA cycle, glycolysis, and stress-related protein systems. These coordinated changes may collectively be associated with enhanced proteolysis in the sausage matrix.

It should be emphasized, however, that the proposed model is primarily correlational and derived from protein abundance data. The inferred regulatory relationships require further validation through targeted approaches, including enzymatic activity assays, gene expression profiling, and genetic manipulation of L. plantarum CM48. Such studies would help to clarify the causal relationships underlying the observed proteolytic effects and to refine the framework for starter culture functionality.

CRediT authorship contribution statement

Daixun Wang: Writing – original draft, Visualization, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization. Xueqian Yang: Methodology, Formal analysis, Conceptualization. Feng Cheng: Methodology, Investigation. Ye Jin: Supervision, Resources. Jianjun Tian: Writing – review & editing, Supervision, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jianjun tian reports financial support was provided by the National Natural Science Foundation of China (32460598). Daixun wang reports financial support was provided by the Inner Mongolia Autonomous Region Graduate Student Research Innovation Project (KC2025034B). Jianjun tian reports financial support was provided by the First-Class Discipline Scientific Research Special Project of the Department of Education of Inner Mongolia Autonomous Region (YLXKZX-NND-017). Jianjun tian reports financial support was provided by the 2025 Yingcai Xingmeng Project for Local Cultivation Talent Team - Grassland Beef and Mutton Processing Technology Research and Innovation Team (2026TEL16). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research received funding from the National Natural Science Foundation of China (32460598), the Inner Mongolia Autonomous Region Graduate Student Research Innovation Project (KC2025034B), the First-Class Discipline Scientific Research Special Project of the Department of Education of Inner Mongolia Autonomous Region (YLXKZX-NND-017), and the 2025 “Yingcai Xingmeng” Project for Local Cultivation Talent Team - “Grassland Beef and Mutton Processing Technology Research and Innovation Team” (2026TEL16).

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.fochx.2026.104476.

Appendix A. Supplementary data

Supplementary material.

mmc1.docx (1.9MB, docx)

Data availability

The dataset is publicly accessible.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material.

mmc1.docx (1.9MB, docx)

Data Availability Statement

The dataset is publicly accessible.

Articles from Food Chemistry: X are provided here courtesy of Elsevier

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