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Article

Peptide Profiling in Dairy Cow Dry Secretions: Temporal Changes and Comparative Analysis Between Healthy and Subclinical Mastitis Cows

by
Barjam Hasanllari
1,
Benjamin P. Willing
1,
Liang Li
2,
Xian Luo
2 and
Burim N. Ametaj
1,*
1
Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB T6G 2P5, Canada
2
The Metabolomics Innovation Centre, Department of Chemistry, University of Alberta, Edmonton, AB T6G 2G2, Canada
*
Author to whom correspondence should be addressed.
Dairy 2025, 6(2), 19; https://doi.org/10.3390/dairy6020019
Submission received: 14 January 2025 / Revised: 6 April 2025 / Accepted: 9 April 2025 / Published: 15 April 2025
(This article belongs to the Section Dairy Systems Biology)
Browse Figures
Versions Notes

Abstract

:
The dairy industry relies on the health and well-being of dairy cows for the optimal production of milk and dairy products. Mastitis, a prevalent and economically burdensome disease characterized by udder inflammation, poses significant challenges to dairy farmers worldwide. In this study, we employed peptidomics to explore the peptide profiles of dry secretions collected from dairy cows at specific intervals during the dry-off period. We hypothesized that alterations in peptide composition during the dry period may influence pathogen proliferation and immune cell functioning, thereby impacting mastitis susceptibility. Our objectives were to investigate the following: (i) differences in peptide composition and alterations between healthy cows and those with subclinical mastitis, potentially serving as biomarkers for early mastitis detection and offering insights into udder bioprocesses; (ii) variations in peptide profiles between the early (day 2) and later (day 21) stages of the dry-off period during both health conditions. Dry secretions were collected from 16 udder quarters of 8 cows at two defined time points—Day 2 (D2) and Day 21 (D21)—during the dry period. Our results revealed distinct peptide patterns between healthy and subclinical mastitis cows, as well as temporal variations in peptide profiles throughout the dry-off period. A total of 1235 peptides, originating from 59 distinct proteins (primarily β-casein), were detected across the four groups: subclinical mastitis day 21 (SCM-D21), subclinical mastitis day 2 (SCM-D2), healthy day 21 (H-D21), and healthy day 2 (H-D2). Furthermore, 56 out of the 1235 peptides identified in total matched known functional peptides, with a total of 17 different functions including antimicrobial, antioxidant, and immunomodulatory, suggesting their potential roles in mastitis pathogenesis and mammary gland physiology. Comparative analyses revealed changes in the levels of these functional peptides across the four different groups, suggesting their potential roles in regulating immune responses, oxidative stress, inflammatory processes, and other biological activities during subclinical mastitis and the dry-off period. These findings provide valuable insights into mastitis detection, management strategies, and dairy cow health monitoring, offering promising avenues for enhancing milk quality and dairy industry sustainability.

1. Introduction

The dairy industry is a critical component of global agriculture, providing essential nutrients through milk and milk products [1]. Efficient dairy production relies on various factors, including genetics, nutrition, and management practices. Among these, the management of the dry period plays a crucial role in ensuring the health and productivity of dairy cows in subsequent lactations. Among the health issues affecting dairy cows, mastitis is one of the most prevalent and economically burdensome diseases [2]. Apart from impacting milk quality, mastitis poses a significant threat to the welfare of dairy cows [3].
Mastitis, characterized by udder inflammation and usually caused by bacterial or mycotic infections, is the disease that concerns dairy farmers the most [4]. Early diagnosis of mastitis is very important for applying adequate measures to prevent the disease before it develops [5]. Detection methods encompass estimating the somatic cell count (SCC), measuring biomarkers linked to disease development (N-acetyl-β-D-glucosaminidase and lactate dehydrogenase enzymes), and identifying the responsible microorganisms using culturing techniques; however, these approaches have limitations [6].
Mastitis frequently finds its origins during the dry-off period [7], a critical phase in the lactation cycle when cows cease milking. While this transition period is very important to optimize milk production in the coming lactation, the mammary gland is very vulnerable to intramammary infection (IMI) at the beginning of the dry period [8,9]. The mammary gland is filled with milk during involution, leading to a higher risk of IMI [10]. To avoid infections at the beginning of the dry period, dry-off techniques and the usage of dry-off antibiotics are crucial [10]; however, in order to prevent antibiotic resistance developed by excessive usage of antibiotics, new methods for preventing mastitis are needed.
Reinhardt and Lippolis revealed that during the first 21 days of the dry period, 109 proteins were upregulated, and 68 proteins were downregulated in total in dry secretions, where some of these proteins were related to the positive or negative growth of mastitis-causing pathogens [11]. Understanding the peptide composition of dry secretion fluid, which accumulates in the mammary gland during this period, is crucial to understanding how fluctuations in peptides may impact pathogen proliferation and influence the functioning of immune cells, particularly macrophages and neutrophils.
In recent years, peptidomics has emerged as a powerful tool for investigating the composition and dynamics of peptides within biological systems [12,13]. By simultaneously detecting and quantifying a wide range of peptides in biological fluids, peptidomics has become a transformative method for gaining valuable insights into the health of dairy cows [14,15]. To this end, this study focuses on the peptide composition within dry secretions, collected at specific intervals during the dry-off period, with an emphasis on two main aspects, health status and the day of dry-off.
First, we hypothesized that there are differences in peptide concentrations in the dry secretions between healthy (H) cows and those with subclinical mastitis (SCM). These differences can serve as potential biomarkers for the early detection of mastitis, but also to understand the bioprocesses of the udder between the two groups of cows. We also hypothesized that peptide profiles in the dry secretion fluid of dairy cows vary significantly between day 2 and day 21 of the dry period.
Therefore, our objective was to investigate how the peptide composition of dry secretions changes between the early phase (day 2) and the later stage (day 21) of the dry-off period. This temporal analysis provided an opportunity to reveal dynamic changes in peptides that might be associated with the transition from active lactation to the dry period. Additionally, our study sought to identify variations in peptides that differentiate between H and SCM conditions. Subclinical mastitis, characterized by low-level inflammation and the presence of pathogens in the udder without visible clinical signs, is important due to its prevalence and economic consequences. We aimed to identify the peptide signatures associated with SCM in dry secretions of dairy cows, shedding light on potential diagnostic markers and insights into the pathogenesis of this condition.

2. Materials and Methods

All procedures carried out in this study received approval from the University of Alberta Animal Policy and Welfare Committee for Livestock. The animals were handled and cared for in strict adherence to the guidelines outlined by the Canadian Council on Animal Care [16].

2.1. Animals and Sample Collection

Milk and dry secretion were collected from 41 Holstein dairy cows (Dairy Research and Technology Centre, University of Alberta, Canada) from each quarter. Cows had an average BCS of 3.06 (range of 2.5–3.25) and an average lactation of 2.3 (range of 1–7). Milk samples were collected 2 days before drying off. Dry secretions were collected on day 2 and day 21 of the dry-off period. Standard industry dry-off treatments, including Cefa-Dri® (cephapirin benzathine) and Orbeseal®, were administered as blanket therapy. Given that cephapirin benzathine persists in the mammary gland for up to 7–10 days post-treatment, samples collected on D2 reflect an early post-treatment environment, while those from D21 represent endogenous peptide profiles with minimal antibiotic influence. First, quarters were cleaned of dirt using soap and water, then dried using single-use paper tissues and washed with 70% alcohol. A few streaks of milk and dry secretions were discarded before sample collection. Samples were collected into sterile tubes. After collection teats were dipped in iodine solution for disinfection.

2.2. Sample Preparation

Milk samples were analyzed by Lactanet (1303-91 Street SW, Edmonton, AB, Canada) for SCC, total protein, total lipids, lactose, milk urea nitrogen (MUN), and total solids. Cows were categorized into cows with sub-clinic mastitis and healthy cows based on the SCC measurement in milk. A threshold value exceeding 200,000 SCC/mL was used as a reliable indicator of an infected udder [17,18].
Thirty-two samples of dry secretions (8 samples from each of the 4 groups: SCM-D2, SCM-D21, H-D2, and H-D21; once healthy and SCM status was determined based on SCC in the D-2 (pre-dry-off) milk samples, the same 16 udder quarters (8 healthy and 8 SCM) were consistently sampled for dry secretions on D2 and D21 (Figure 1). This approach ensured that longitudinal comparisons were made within the same quarters across all time points; the total collection involved 8 cows) were thawed at room temperature, and 1 mL underwent centrifugation at 20,000× g for 15 min at 4 °C (Centrifuge 5424 R, Eppendorf, Hamburg, Germany) to separate milk fat. Following this, 500 μL of the resulting supernatant was mixed with an equal volume of 20% trichloroacetic acid (TCA) (Fisher Scientific, Schwerte, Germany) solution (20 g/100 mL) prior to centrifugation at 3000× g for 10 min at 4 °C for the removal of large proteins. The supernatant underwent solid-phase extraction (SPE) using silica cartridges (Sep-Pak Vac 6cc (1 g) silica cartridges, Waters Corporation, Ireland) and Vacuum Manifold (Supelco Visiprep-DL, Sigma-Aldrich Co., Bellefonte, PA, USA) to eliminate contaminants, primarily oligosaccharides. First, the cartridge was washed with 100% acetonitrile (ACN) (Fisher Scientific, Waltham, MA, USA) containing 0.1% trifluoroacetic acid (TFA) (Sigma-Aldrich, Saint-Quentin-Fallavier, France) to activate the cartridge, followed by a wash with 50% ACN/50% dH2O (M, EMD Millipore Corporation, Billerica, MA, USA) containing 0.1% TFA, and finally with 100% dH2O containing 0.1% TFA for column conditioning. Each wash step involved two column volumes (CV) of solution, with the column being washed slowly and the solution directed to waste. Next, the sample was loaded onto the cartridge, allowing it to enter the cartridge slowly. The cartridge was then washed with 2 CV of dH2O containing 0.1% TFA, and the eluate was collected in a waste bin. The waste bin was then replaced with a new collection tube. Subsequently, 80% ACN/20% dH2O containing 0.1% TFA was loaded onto the cartridge to recover oligopeptides for 2 CV [19]. Following elution, the peptides were processed under nitrogen blow (Reacti-Vap Evaporators TS 18825, Thermo Fisher Scientific, Bellefonte, PA, USA) until dried, with optional heating below 50 °C, for acetonitrile removal. Following drying, 500 μL of water was added to the samples.

2.3. Measurement of Protein Concentration in Dry Secretion Samples

Protein content was assessed using the “Pierce” BCA Protein Assay Kit (Thermo Fisher Scientific, Rockford, IL, USA). For each standard and dry secretion-extracted sample, 10 μL were pipetted into duplicate wells of a clear-bottom 96-well plate (Costar, Corning Incorporated, Kennebunk, ME, USA). A buffer was prepared by combining reagent A and reagent B (50:1), and 200 μL of this mixture was added to the plate. After shaking for 30 s, the plate was incubated at 37 °C for 30 min. Subsequently, the plate was allowed to cool to room temperature before measuring absorbance at 562 nm (SpectraMax M3, Molecular Devices, Beijing, China). The standard curve, generated with bovine serum albumin ranging from 0 to 2000 μg/mL, served as a reference for interpolating protein concentrations in the samples.

2.4. LC-MS Analysis

Two microliters of extracted dry secretion samples were injected into a Liquid Chromatography-Mass Spectrometry (LC-MS) instrument for analysis. The analytical column was the Phenomenex Luna Omega Polar C18 column (1.6 μm, 50 × 2.1 mm) (Taipei, Taiwan). The LC-MS system was a Thermo Vanquish Ultra-High-Performance Liquid Chromatograph (UHPLC) linked with Thermo Orbitrap Exploris 240 (Waltham, MA, USA). Mobile phase A was 0.1% formic acid in water, and mobile phase B was 0.1% formic acid in acetonitrile. The flow rate was 0.5 mL/min. The LC column was equilibrated with a 100% mobile phase A. The separation gradient was as follows: 0 min, 0% B; 1 min, 0% B; 15 min, 98% B; 17 min, 98% B. The temperature of the column compartment was set at 40 °C. MS settings were as follows: scan mode, positive; scan range (m/z), 200–2000; spray voltage, 3800 V; vaporization temperature, 375 °C; sheath gas, 55; micro scan, 1. Data-dependent acquisition, with the selection of the top 3 ions with the highest intensity, was used for Tandem Mass Spectrometry (MS/MS) generation. Stepping collision energy was applied at 20, 25, and 30 eV. The ions were excluded for MS/MS generation if they showed up two times within 10 s.

2.5. Data Processing, Peptide Identification, and Statistical Analysis

Proteome Discoverer 3.0 (Thermo Fisher Scientific) was used for data processing and peptide identification. Raw files were searched against the Bos taurus database (last date accessed 23 May 2024) using SEQUEST HT as searching engine. The precursor mass tolerance was 10 ppm, and the fragment mass tolerance was 0.02 Dalton. “No-enzyme” was selected as the “Enzyme Name”. There were no static modifications used, and “Oxidation” and “Met loss” were selected for dynamic modifications. For peptide spectral matches, the relax target False Discovery Rate (FDR) was set as 0.05, and the strict target FDR was set as 0.01. The identified peptides with intensity information were annotated by numbers, exported as .csv files, and uploaded to MetaboAnalyst 6.0 (www.metaboanalyst.ca—last date accessed 16 July 2024) for statistical analysis. A volcano plot was generated for binary analysis. The cutoff criteria were a p-value less than 0.05 and a fold change (FC) larger than 2. Multivariate analysis, including Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA), was also performed. To assess the significance of group differences, Permutational Multivariate Analysis of Variance (PERMANOVA) was employed for both PCA and PLS-DA. Variable Importance in Projection (VIP) analysis was also used to identify important variables in the PLS-DA model.

3. Results

3.1. Milk Composition Data

Milk composition data were analyzed using SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA) with a generalized linear mixed model (PROC GLIMMIX). Least squares means were compared using Tukey’s post hoc adjustment, and statistical significance was defined at a threshold of p < 0.05.
Fifteen cows had at least 1 quarter with high SCC (SCC > 200,000 cells/mL) and 26 cows had normal SCC (SCC < 200,000 cells/mL) in each quarter. Based on the data from analyzing SCC, total protein, total lipids, lactose, milk urea nitrogen (MUN), and total solids, comparisons were made between the SCM quarters and H quarters (Figure 2).
Comparisons revealed an increased fat content in SCM quarters compared to H quarters (p < 0.05), along with an increase in protein content in SCM quarters. Additionally, a decrease in lactose was observed in SCM quarters compared to H quarters. However, regarding MUN and total solids, no differences were identified between the groups.

3.2. Dry Secretion Peptide Content

Following the estimation of proteins in the extracted dry secretion samples, a comparative analysis was undertaken to assess the peptide content variation among the different groups (H-D2, H-D21, SCM-D2, and SCM-D21 quarters) (Figure 3). The group comparisons revealed that there was no difference in peptide content between the SCM-D2 and H-D2 quarters (p > 0.05). Similarly, there was no difference in peptide content between the SCM-D21 and H-D21 quarters (p > 0.05). However, the peptide content in the SCM-D21 quarters was greater compared to the SCM-D2 quarters (p < 0.05), and at the same time, the H-D21 quarters demonstrated higher peptide content compared to the H-D2 quarters (p < 0.05). These comparisons suggest that the peptide content is influenced by the different time points (H-D2 vs. H-D21, and SCM-D2 vs. SCM-D21), contrasting with the observation that health condition (SCM-D2 vs. H-D2, and SCM-D21 vs. H-D21) do not appear to have an impact on the peptide content.

3.3. LC-MS Results

A total of 1235 peptides were identified and quantified across the four groups. Peptides were derived from 59 different proteins, with most coming from β-casein, αS1-casein, αS2-casein, k-casein, Osteopontin, Fibrinogen alpha chain, GLYCAM 1, Thymosin beta-10, Thymosin beta-4, SAA-3 protein, Actin, and Complement C3 (Figure 4).
Multivariate analysis including PCA, PLS-DA, and VIP was used to analyze LC-MS data. The score plots of PCA, PLS-DA, and VIP for the 4 groups are shown in Figure 5. The PCA showed separation between the 4 groups (SCM-D2, SCM-D21, H-D2, and H-D21 quarters), where the first principal components PC1 and PC2 explained 42.6% and 13.4% of the total variation, respectively (Figure 5a). The PCA showed that groups (quarters) with the same time point had a degree of overlap (Figure 5a); however, a better separation of the groups was observed after the PLS-DA analysis (Figure 5b). The VIP showed the top 30 peptides that contributed to the separation of the 4 groups (Figure 5c and Supplementary Table S1). The top 30 peptides had VIP scores of >2 (Figure 5c).

3.3.1. SCM-D2 vs. H-D2 Comparison

Multivariate statistical analyses, including PCA, PLS-DA, and VIP scores, were employed to investigate the differences in peptidomic profiles between the SCM-D2 and H-D2 groups, as shown in Figure 6. PCA revealed that PC1 and PC2 accounted for 27.1% and 17.5% of the total variance, respectively (Figure 6a). Although PCA indicated considerable overlap between the groups, subsequent PLS-DA analysis demonstrated significantly improved group separation (Figure 6b), highlighting distinct peptidomic signatures between SCM-D2 and H-D2.
Moreover, VIP scores identified the top 30 peptides pivotal in group differentiation, each with a VIP score exceeding 2, signifying their substantial contribution to the observed separation. (Figure 6c; Supplementary Table S2).
In addition to this multivariate analysis, univariate analysis (p < 0.05 and FC > 2) revealed differential expression of 158 peptides (58 downregulated and 100 upregulated) when comparing SCM-D2 to H-D2 (Figure 6d). The p values along with the fold change of these peptides are provided in Supplementary Table S3.

3.3.2. SCM-D21 vs. H-D21 Comparison

Multivariate statistical analyses, including PCA, PLS-DA, and VIP scores, were employed to investigate the peptidomic profile differences between the SCM-D21 and H-D21 groups, as shown in Figure 7. The PCA revealed that the first two principal components (PC1 and PC2) accounted for 31.6% and 20.7% of the total variance, respectively (Figure 7a). Although PCA indicated a substantial overlap between the two groups, suggesting similar peptidomic profiles under initial examination, subsequent analysis through PLS-DA demonstrated a markedly improved group separation (Figure 7b). This enhanced differentiation highlights the presence of distinct peptidomic signatures between SCM-D21 and H-D21.
Furthermore, the VIP scores identified the top 30 peptides instrumental in differentiating between the groups, with each peptide exhibiting a VIP score exceeding 2, suggesting their significant contribution to the observed separation (Figure 7c; Supplementary Table S4).
Complementary to these multivariate approaches, univariate analysis (p < 0.05 and FC > 2) underscored the differential expression of 138 peptides (70 downregulated and 68 upregulated) when comparing SCM-D21 to H-D21 (Figure 7d). Details of the p-values and fold changes of these peptides are given in Supplementary Table S5.

3.3.3. SCM-D2 vs. SCM-D21 Comparison

Multivariate statistical analyses, comprising PCA, PLS-DA, and VIP scores, were utilized to discern differences in peptidomic profiles between the SCM-D2 and SCM-D21 groups, as depicted in Figure 8. The PCA showed that PC1 and PC2 explained 44.3% and 19.6% of the total variation, respectively (Figure 8a). Notably, both PCA and PLS-DA analyses demonstrated complete separation of the groups (Figure 8a,b). This differentiation underscores the existence of unique peptidomic signatures between SCM-D21 and H-D21.
Furthermore, VIP analysis identified the top 30 peptides contributing to group differentiation, each exhibiting VIP scores exceeding 2 (Figure 8c; Supplementary Table S6), implying their substantial role in the observed differentiation.
Subsequent univariate analysis (p < 0.05 and FC > 2) unveiled alterations in a total of 635 peptides (316 downregulated and 319 upregulated) when comparing SCM-D21 to SCM-D2 (Figure 8d), with detailed p-values and fold changes provided in Supplementary Table S7.

3.3.4. H-D2 vs. H-D21 Comparison

Multivariate statistical analyses, including PCA, PLS-DA, and VIP scores, were employed to discern the differences in peptidomic profiles between the H-D2 and H-D21 groups, as depicted in Figure 9. PCA revealed that PC1 and PC2 explained 51% and 19.9% of the total variance, respectively (Figure 9a). Both PCA and PLS-DA analyses exhibited a complete separation of the groups (Figure 9a,b). This distinction emphasizes the presence of distinct peptidomic profiles between H-D2 and H-D21.
The VIP analysis highlighted the top 30 peptides contributing to the separation of the two groups, with each peptide having VIP scores exceeding 2 (Figure 9c; Supplementary Table S8), indicating their important role in the observed differentiation.
Additionally, univariate analysis (p < 0.05 and FC > 2) indicated alterations in 651 peptides (244 downregulated and 407 upregulated) when comparing H-D21 with H-D2 (Figure 9d). Detailed information regarding the p-values and fold changes of these peptides are found in Supplementary Table S9.

3.4. Peptide Functions

Identified peptides in our study were compared against the functional peptides database “MBPDB” (The Milk Bioactive Peptide Database, Bos taurus) as described by Nielsen et al. (2017) [20]. Out of the 1235 peptides detected, 56 peptides showed a 100% match to known functional peptides derived from β-casein, αS1-casein, αS2-casein, β-lactoglobulin, and κ-casein (Table 1).
These matched peptides collectively exhibited 17 different biological functions, with several peptides demonstrating multiple functional roles. Specifically, 19 peptides were identified with known antimicrobial activity, and 7 others with immunomodulatory activity. The functions of the remaining peptides are not yet fully understood, necessitating further research to elucidate their roles.
In total, 6 functional peptides were found to be significantly altered between the SCM-D2 and H-D2 groups, while 11 functional peptides differentiated the SCM-D21 from the H-D21 group. Furthermore, 33 functional peptides distinguished the SCM-D2 group from the SCM-D21 group, and 32 functional peptides were identified as differentiating between the H-D2 and H-D21 groups. A comprehensive summary of the number of each category of functional peptides exhibiting significant alterations across the four group comparisons is presented in Table 2. Most of the alterations involve ACE-inhibitory, antioxidant, immunomodulatory, and antimicrobial peptides.
For each group comparison, peptides among the significantly altered peptides that matched (100%) known functional peptides (Bos taurus) are detailed in Table 3, Table 4, Table 5 and Table 6. Each peptide entry includes information about its originating protein, the interval of detection, the direction of change, and its associated function. Table 3 details peptides from the SCM-D2/H-D2 comparison, while Table 4 focuses on peptides identified in the SCM-D21/H-D21 comparison. Similarly, Table 5 presents peptides from SCM-D21/SCM-D2 comparison, and Table 6 summarizes those from the H-D21/H-D2 comparison. Together, these findings provide insights into peptide-level alterations and their potential biological relevance in different experimental groups.

4. Discussion

We hypothesized that the peptide composition of dry-off secretions might vary between healthy cows and those with SCM and change over time after dry-off. Our goal was to explore the variations in peptide levels in the dry-off secretions of dairy cows on the 2nd and 21st days of the dry off period, under both healthy conditions and SCM. This investigation aimed to deepen our understanding of the physiological changes occurring during the dry period and to identify potential markers for SCM in dairy cows.
The findings of this study revealed alterations in milk components two days before dry-off between SCM and healthy quarters. We observed an increase in protein content in SCM quarters compared to healthy quarters, consistent with previous research [21], which reported increased total protein in milk from quarters with SCM. This increase in total protein levels could be attributed to the release of various antimicrobial and immune defense proteins, such as lactoferrin, acute phase proteins, cathelicidins, chemokines, cytokines, and growth factors, during udder inflammation [22]. Additionally, we found a decrease in lactose in SCM quarters compared to healthy quarters, consistent with previous literature in cows with clinical mastitis [23]. The utilization of milk as a growth medium by mastitis pathogens, as well as injury to secretory epithelial cells (and translocation of lactose to systemic circulation) from inflammation and infection, can both result in alteration of lactose content [24].
Regarding fat content, our study revealed an increase in SCM quarters, which contrasts with previous findings [25]. It is possible that the effect of mastitis on milk fat content may vary depending on the severity and type of mastitis, the causative pathogen, and the stage of lactation. However, when it comes to (MUN) and total solids, no differences were identified between the groups, indicating that SCM did not affect these variables. The lack of difference in MUN between SCM and healthy quarters suggests that systemic nitrogen metabolism remained stable. MUN is influenced more by dietary protein intake and ruminal nitrogen balance than by localized mammary inflammation [26], which may explain its unaffected levels in this study. Similarly, total solids showed a numerical decrease in healthy quarters compared to SCM quarters, with a tendency towards significance (p = 0.14), though it did not reach statistical significance. This trend suggests that while total solids may be affected by SCM, the variability in individual milk components likely influences the overall statistical outcome.
We found a wide range of peptides being generated during the dry period, a critical period associated with an increase in proteinase activity [27,28,29,30,31]. The milk proteinases responsible for producing peptides in dry secretions include several categories: serine (plasmin, elastase, cathepsin G), cysteine (cathepsin B), aspartic (cathepsin D), and metallo-(gelatinases A and B) proteinases. These enzymes exhibit a broad pH preference, ranging from slightly acidic (cathepsins B and D) and neutral (cathepsin G and gelatinase B), to slightly alkaline (plasmin, elastase, and gelatinase A [27] The majority of milk proteinases, including elastase, members of the cathepsin family, and gelatinase B, primarily originate from somatic cells. However, some are of humoral origin (blood), such as plasmin and gelatinase A [27].
Additionally, bacteria that invade the mammary gland during mastitis commonly produce external enzymes that are released in the milk, such as elastase [32]. Pathogenic bacteria release these exogenous enzymes to enhance their invasion and colonization [33]. For example, S. dysgalactiae exhibits an ability to cleave the FC region from casein [32], which contains identified antimicrobial peptides within its sequence (f184-210, f193-207, and f193-209) [34]. Fleminger et al. (2011) [32] suggested that by releasing FC, which is relatively resistant to further degradation, S. dysgalactiae defends itself by encapsulating antimicrobial peptides within FC, to which it is particularly sensitive. Another example is the production of antimicrobial peptides by Bacillus cereus which are effective against several species of Bacillus and Listeria monocytogenes when cultured in the presence of casein, demonstrating antagonistic competition resulting in colonization [35]. Overall, the elevated presence of these bacterial proteases can further exacerbate milk protein degradation, leading to the generation of distinct peptides.
Earlier attempts to characterize peptides found in the secretions of the bovine mammary gland during involution [27,36] have not provided a comprehensive understanding of this field. Our study identified and quantified 1235 peptides derived from a total of 59 proteins, mostly coming from caseins (the majority from β-casein). These results agree with earlier work [27], where β-casein was found to be the originating protein for the majority of peptides identified in dry secretions. However, in this study, the abundance of identified peptides was significantly more limited, and the collection of dry secretions was only up to one week after the dry-off. Another investigation [37] revealed that peptides generated from milk proteins on days 7, 14, and 21 of involution derived from α-casein, β-casein, κ-casein, and lactoferrin (Lf). Additionally, Ho et al. [27] identified a total of five known functional peptides deriving from β-casein in dry secretions.
The alterations of identified functional peptides in our study and their biological relevance at various time points during the dry-off period in healthy cows and subclinical mastitis conditions are discussed below. These peptides play roles in immune defense, inflammation regulation, oxidative stress mitigation, and other critical physiological processes, highlighting their potential as biomarkers for subclinical mastitis and targets for therapeutic interventions.
Overall, the comparative analysis of identified functional peptide profiles provided valuable insights into physiological adaptations and immune responses during different stages of subclinical mastitis and healthy conditions. Understanding these peptide alterations can aid in developing targeted strategies to enhance udder health, improve milk quality, and reduce the incidence of mastitis in dairy cows. By focusing on the dynamic changes in peptide profiles, this study underscores the importance of tailored interventions during the dry-off period. Future research should aim to identify specific peptides with potential therapeutic applications, explore their mechanisms of action, and evaluate their efficacy in vivo. This approach could lead to the development of novel peptide-based treatments that enhance immune function, mitigate inflammation, and support overall mammary gland health, ultimately contributing to more sustainable dairy farming practices.

4.1. Health Condition-Related Peptide Alterations During Dry Period

The comparative analyses of peptide expression profiles between subclinical mastitis and healthy conditions at different stages following the dry-off, provide valuable insights into the peptide alterations occurring within the mammary gland environment during these conditions.
Significant changes in the identified functional peptides were observed in comparing the two health conditions at two different time points (Table 3 and Table 4). Notably, there is a lower level of ACE-inhibitory peptides such as αS2-CN f(204-212) from H-D2 to SCM-D2, and β-CN f(74-83, 75-82, 75-83, 124-133, 206-224, 210-221), and αS2-CN f(204-212) from H-D21 to SCM-D21. The decrease in ACE inhibitors has been previously shown to increase leukocyte recruitment and infiltration, increase proinflammatory cytokine production, injury prevention to the vascular walls, enhance bactericidal and oxidative responses of neutrophils, increase ROS production and antigen presentation, enhancing the overall immune response in an attempt for clearing the infection [38], at this case, during the occurrence of subclinical mastitis.
Table 3. Identified peptides among significantly changed peptides (SCM-D2/H-D2) that match (100%) known functional peptides (Bos taurus). Each peptide is accompanied by information regarding its originating protein, the interval, the direction of change, and its associated function.
Table 3. Identified peptides among significantly changed peptides (SCM-D2/H-D2) that match (100%) known functional peptides (Bos taurus). Each peptide is accompanied by information regarding its originating protein, the interval, the direction of change, and its associated function.
Identified Peptide/Known PeptideD. Protein * P.P *SCM-D2/H-D2FunctionReference
APSFSDIPNPIGSENSEαS1-casein191-207upAntioxidant[39,40]
SDIPNPIGSENSEKαS1-casein195-208upAntimicrobial[41]
AMKPWIQPKαS2-casein204-212downACE-inhibitory[42]
LIVTQTMKβ-lactoglobulin17-24downCytotoxic[43]
VKEAMAPKβ-casein113-120downAntimicrobial[44]
Antioxidant[45]
LLYQEPVLGPVRGPFPIIVβ-casein206-224upACE-inhibitory[46]
D. protein *—Deriving protein. P.P *—Peptide position.
Table 4. Identified peptides among significantly changed peptides (SCM-D21/H-D21) that match (100%) known functional peptides (Bos taurus). Each peptide is accompanied by information regarding its originating protein, the interval, the direction of change, and its associated function.
Table 4. Identified peptides among significantly changed peptides (SCM-D21/H-D21) that match (100%) known functional peptides (Bos taurus). Each peptide is accompanied by information regarding its originating protein, the interval, the direction of change, and its associated function.
Identified Peptide/Known PeptideD. Protein *P.P *SCM-D21/H-D21FunctionReference
YPFPGPIPβ-casein75-82downACE-inhibitory[47]
Antioxidant[47]
YPFPGPIPNβ-casein75-83downACE-inhibitory[47,48]
Antioxidant[47]
DPP-IV Inhibitory[49]
IKHQGLPQEαS1-casein21-29upAntimicrobial[41,50,51]
LLYQEPVLGPVRGPFPIIVβ-casein206-224downACE-inhibitory[46]
RPKHPIKαS1-casein16-22downAntimicrobial[52]
EPVLGPVRGPFPβ-casein210-221downACE-inhibitory[53]
SKVLPVPQβ-casein183-190upACE-inhibitory[46]
AMKPWIQPKαS2-casein204-212downACE-inhibitory[42]
YPVEPFTEβ-casein129-136downBradykinin-Potentiating[54]
VYPFPGPIPNβ-casein74-83downACE-inhibitory[55]
Antioxidant[39,55]
MPFPKYPVEPβ-casein124-133downACE-inhibitory[53]
D. protein *—Deriving protein. P.P *—Peptide position.
The lower levels of the antimicrobial peptide β-CN f(113-120) from H-D2 to SCM-D2, and αS1-CN f(16-22) from H-D21 to SCM-D21, may indicate a compromised immune response, contributing to the onset of the occurring subclinical mastitis. On the other hand, the increase in some antimicrobial peptides, such as αS1-CN f(195-208) from H-D2 to SCM-D2, and αS1-CN f(21-29) from H-D21 to SCM-D21, may indicate an attempt to enhance the immune response and fight the occurring subclinical mastitis. Therefore, the levels of antimicrobial peptides may impact the susceptibility and resistance to mastitis during this critical period.
We found a higher level of the antioxidant peptide αS1-CN f(191-207) from H-D2 to SCM-D2. During oxidative stress, a common feature of inflammatory processes associated with mastitis [56], antioxidants counteract the damaging effects of ROS [57,58]. The elevation of this antioxidant peptide may indicate a protective mechanism to mitigate oxidative damage and maintain cellular homeostasis during subclinical mastitis. On the other hand, the reduction in antioxidant peptides such as β-CN f(113-120) from H-D2 to SCM-D2, and β-CN f(74-83, 75-82, 75-83) from H-D21 to SCM-D21, may contribute to the reduced antioxidant defenses shown during subclinical mastitis [59]. This may lead to increased oxidative stress, causing tissue damage [57] during subclinical mastitis onset.

4.2. Time-Related Peptide Alterations During Dry Period

The comparison of identified functional peptide profiles between subclinical mastitis groups (SCM-D2 and SCM-D21) and healthy groups (H-D2 and H-D21) after the dry-off (Table 5 and Table 6) sheds light on the peptide alterations associated with the progression of subclinical mastitis from day 2 to day 21 of the dry period, and the physiological changes occurring from day 2 to day 21 in the mammary gland environment during the dry-off period in healthy conditions. Several peptides exhibited significant changes in expression levels, indicating potential disruptions in physiological processes during the transition from early to later stages of subclinical mastitis. These changes also reflect dynamic adaptations to environmental and metabolic demands during the transition to the non-lactating state in healthy cows.
Table 5. Identified peptides among significantly changed peptides (SCM-D21/SCM-D2) that match (100%) known functional peptides (Bos taurus). Each peptide is accompanied by information regarding its originating protein, the interval, the direction of change, and its associated function.
Table 5. Identified peptides among significantly changed peptides (SCM-D21/SCM-D2) that match (100%) known functional peptides (Bos taurus). Each peptide is accompanied by information regarding its originating protein, the interval, the direction of change, and its associated function.
Identified Peptide/Known PeptideD. Protein * P.P *SCM-D21/SCM-D2FunctionReference
FFVAPFPEVFGKαS1-casein38-49downACE-inhibitory[60]
VKEAMAPKβ-casein113-120downAntimicrobial, Gram-negative[44]
Antioxidant[45]
FVAPFPEVFGαS1-casein39-48downACE-inhibitory[61]
FALPQYLKαS2-casein189-196downACE-inhibitory[62]
Antioxidant[62]
TKVIPYVRYLαS2-casein213-222downAntimicrobial[63]
TTMPLWαS1-casein209-214downACE-inhibitory[64]
Antimicrobial, E. coli, S. aureus, M. luteus, C. albicans[65]
MPFPKYPVEPβ-casein124-133downACE-inhibitory[53]
LIVTQTMKβ-lactoglobulin17-24downCytotoxic[43]
KVLPVPQKβ-casein184-191upAntioxidant[40,45,66]
Immunomodulatory, Anti-inflammatory[67]
VAPFPEαS1-casein40-45downCholesterol regulation, Inhibition of cholesterol solubility[68]
EMPFPKβ-casein123-128downACE-inhibitory[64]
Antimicrobial[44]
Bradykinin-Potentiating[54]
Increase mucin secretion[69]
HKEMPFPKβ-casein121-128downAntimicrobial[44]
YPVEPFTEβ-casein129-136downBradykinin-Potentiating[54]
EPVLGPVRGPFPβ-casein210-221downACE-inhibitory[53]
SWMHQPHQPLPPTβ-casein157-169upAntioxidant[39]
YQEPVLGPVRβ-casein208-217upACE-inhibitory[70]
Antioxidant[71]
Antithrombotic[72]
Immunomodulatory[71,73]
LLYQEPVLGPVRGPFPIIVβ-casein206-224downACE-inhibitory[46]
IVLNPWDQVKαS2-casein119-128downAntimicrobial, B. subtilis—1363, E. coli NEB 5α—681, E. coli ATCC 25,922—1363[74]
LYQEPVLGPVRβ-casein207-217upACE-inhibitory[75]
Immunomodulatory, Anti-inflammatory[75]
AMKPWIQPKαS2-casein204-212downACE-inhibitory[42]
YKVPQLEIVPNSAEERαS1-casein119-134downIncrease calcium uptake[76]
ELNVPGEIVESβ-casein20-30upAntimicrobial[77]
EPVLGPVRGPβ-casein210-219downCytomodulatory[78]
LYQEPVLGPVRGPFPIIVβ-casein207-224downImmunomodulatory, Stimulated lymph node cell proliferation[79]
IKHQGLPQEVαS1-casein21-30upAntimicrobial, E. coli, B. subtilis[80]
YYQQKPVAκ-casein63-70upAntimicrobial, E. coli, S. carnosus[81]
RPKHPIKαS1-casein16-22downAntimicrobial[52]
YPFPGPIPβ-casein75-82downACE-inhibitory[47]
Antioxidant[47]
IKHQGLPQEαS1-casein21-29upAntimicrobial, E. coli, C. sakazakii, L. innocua, L. bulgaricus, S. mutans, C. muytjensii[41,50,51]
YLEQLLRαS1-casein109-115downAntimicrobial, B. subtilis—53.6, E. coli NEB 5α—241, E. coli ATCC 25,922—40.2[74]
SQSKVLPVPQβ-casein181-190upACE-inhibitory[53]
IHPFAQTQβ-casein64-71downProlyl endopeptidase-inhibitory[82,83]
HQPHQPLPPTβ-casein160-169upACE-inhibitory[75]
D. protein *—Deriving protein; P.P *—Peptide position.
Table 6. Identified peptides among significantly changed peptides (H-D21/H-D2) that match (100%) known functional peptides (Bos taurus). Each peptide is accompanied by information regarding its originating protein, the interval, the direction of change, and its associated function.
Table 6. Identified peptides among significantly changed peptides (H-D21/H-D2) that match (100%) known functional peptides (Bos taurus). Each peptide is accompanied by information regarding its originating protein, the interval, the direction of change, and its associated function.
Identified Peptide/Known PeptideD. Protein* P.P *H-D21/H-D2FunctionReference
APSFSDIPNPIGSENSEαS1-casein191-207upAntioxidant[39]
KVLPVPQKβ-casein184-191upAntioxidant[40,45,66]
Immunomodulatory, Anti-inflammatory[67]
TKVIPYVRYLαS2-casein213-222downAntimicrobial, C. sakazakii, L. monocytogenes[63]
LYQEPVLGPVRβ-casein207-217upACE-inhibitory[75]
Immunomodulatory, Anti-inflammatory[75]
TTMPLWαS1-casein209-214downACE-inhibitory[64,84,85]
Antimicrobial, E. coli, S. aureus, M. luteus, C. albicans[65]
VKEAMAPKβ-casein113-120downAntimicrobial[44]
Antioxidant[45]
FFVAPFPEVFGKαS1-casein38-49downACE-inhibitory[60,84,86]
AMKPWIQPKαS2-casein204-212downACE-inhibitory[42]
FALPQYLKαS2-casein189-196downACE-inhibitory[62,84]
Antioxidant[62]
SQSKVLPVPQβ-casein181-190upACE-inhibitory[53]
HKEMPFPKβ-casein121-128downAntimicrobial[44]
SWMHQPHQPLPPTβ-casein157-169upAntioxidant[39]
LPQNIPPLTβ-casein85-93upDPP-IV Inhibitory[87]
EPVLGPVRGPβ-casein210-219downCytomodulatory[78]
FVAPFPEVFGαS1-casein39-48downACE-inhibitory[61]
EMPFPKβ-casein123-128downACE-inhibitory[64]
Antimicrobial[44]
Bradykinin-Potentiating[54]
Increase mucin secretion[69]
LIVTQTMKβ-lactoglobulin17-24downCytotoxic[43]
EPVLGPVRGPFPβ-casein210-221downACE-inhibitory[53]
YKVPQLEIVPNSAEERαS1-casein119-134downIncrease calcium uptake[76]
YPFPGPIPNβ-casein75-83upACE-inhibitory[47,48]
Antioxidant[47]
DPP-IV Inhibitory[49]
HQPHQPLPPTβ-casein160-169upACE-inhibitory[75]
YQEPVLGPVRβ-casein208-217upACE-inhibitory[70]
Antioxidant[71]
Antithrombotic[72]
Immunomodulatory[71,73]
YLEQLLRαS1-casein109-115downAntimicrobial, B. subtilis—53.6, E. coli NEB 5α—241, E. coli ATCC 25,922—40.2[74]
YQEPVLGPVRGPFPIIVβ-casein208-224upACE-inhibitory[46]
Anticancer[88]
Antimicrobial[34]
Antithrombotic[89]
Immunomodulatory[90]
VLGPVRGPFPβ-casein212-221downACE-inhibitory[91,92]
VAPFPEαS1-casein40-45downCholesterol regulation, Inhibition of cholesterol solubility[68]
TQTPVVVPPFLQPEβ-casein93-106upAntioxidant[93]
IVLNPWDQVKαS2-casein119-128downAntimicrobial, B. subtilis—1363, E. coli NEB 5α—681, E. coli ATCC 25,922—1363[74]
MPFPKYPVEPβ-casein124-133downACE-inhibitory[53]
FQSEEQQQTEDELQDKβ-casein48-63upIncrease calcium uptake[76]
VLPVPQKβ-casein185-191upACE-inhibitory[94]
Antiapoptotic effect[95]
Antimicrobial[44]
Antioxidant[96,97]
Osteoanabolic[98,99]
Wound healing[100]
VLNENLLRαS1-casein30-37downAntimicrobial, E. coli, C. sakazakii, L. innocua, L. bulgaricus, S. mutans, C. muytjensii[41,50,51]
D. protein *—Deriving protein. P.P *—Peptide position.
We found a lower level of ACE-inhibitory peptides from SCM-D2 to SCM-D21, such as αS1-CN f(38-49, 39-48, 209-214), and αS2-CN f(189-196, 204-212). Since lower levels of ACE inhibitors triggers the immune response [38], this finding suggests a continuous increase in inflammatory and immune responses as subclinical mastitis progresses from day 2 to day 21. Conversely, several ACE-inhibitory peptides such as β-CN f(181-190, 207-217) were found to be upregulated from H-D2 to H-D21. Given that the upregulation of ACE inhibitors suppresses the inflammatory response [38], these peptides may contribute to the regulation and prevention of inflammation in healthy cows.
The decrease in antimicrobial peptides, such as β-CN f(113-120, 121-128, 123-128) from day 2 to day 21 regardless of health condition, may contribute to a compromised immune defense against microbial pathogens, explaining the increased susceptibility to mammary gland infections during the dry-off period [10]. This reduction highlights the critical need for monitoring and possibly of supplementing antimicrobial agents to maintain udder health during this vulnerable phase.
The higher levels of peptides, such as β-CN f(157-196, 184-191) from SCM-D2 to SCM-D21, and β-CN f(184-191) and αS1-CN f(191-207) from H-D2 to H-D21, associated with antioxidant functions, suggests a counteraction to oxidative stress and tissue damage induced by inflammatory processes [57,58] during subclinical mastitis progression. At the same time, these peptides mitigate the oxidative stress related to milk cessation [101,102] and tissue remodeling [101] during the dry-off period. This dual role emphasizes the importance of antioxidant peptides in maintaining tissue integrity and function during different physiological states.
Differences in immunomodulatory peptide levels, such as β-CN f(207-224) between SCM-D2 and SCM-D21, or β-CN f(207-217) between H-D2 and H-D21, may further impact the susceptibility and development of mastitis by affecting the overall immune response. Wan et al. (2022) [103] demonstrated that β-CN f(207-224) possesses direct antimicrobial activity against H. pylori in vitro. This finding suggests that certain peptides might play dual roles in both antimicrobial defense and immune modulation, thereby influencing the health of the mammary gland.
Moreover, the identification of peptides associated with functions such as calcium and cholesterol regulation provide insights into additional physiological roles of peptides during mammary gland involution. Levels of peptides like αS1-CN f(119-134) and β-CN f(48-63), known for promoting calcium absorption, and αS1-CN f(40-45), involved in cholesterol regulation, may play crucial roles in maintaining mammary gland health and function during the transition period. During lactation, the mammary gland draws substantial amounts of calcium from plasma to fulfill the needs of the developing neonate ([104]). The observed decrease in the αS1-CN f(119-134) peptide that promotes calcium uptake from day 2 to day 21 of the dry-off period regardless of the health condition might be due to the cessation of milk production during this period.
Additionally, the increase in the anti-inflammatory peptide β-CN f(207-217), from day 2 to day 21 during both health conditions, suggests a potential regulatory role of this peptide in modulating inflammatory responses within the mammary gland microenvironment. Anti-inflammatory mediators may exert anti-inflammatory effects by blocking/inhibiting particular inflammatory pathways, which can cause tissue damage [105], thereby contributing to the maintenance of tissue homeostasis and udder health.
Mansor et al. (2013) [14] conducted a study regarding peptides present in the milk of dairy cows with clinical mastitis and compared them to peptides found in the milk of healthy cows, aiming to identify potential biomarkers for mastitis. Out of 154 peptides included in the model to differentiate healthy versus infected samples, they identified a total of 33 peptides. Of these, 19 peptides matched those identified in our samples (Table 7). Furthermore, seven of these peptides were significantly altered in the SCM-D2/H-D2 comparison, two peptides were significantly altered in the SCM-D21/H-D21 comparison, ten peptides were significantly altered in the SCM-D21/SCM-D2 comparison, and eight peptides were significantly altered in the H-D21/H-D2 comparison. Two of these peptides, β-lactoglobulin f(17-24) (altered in SCM-D2/H-D2, SCM-D21/SCM-D2, and H-D21/H-D2 comparisons) and β-CN f(210-219) (altered in SCM-D21/SCM-D2, and H-D21/H-D2 comparisons), are known functional peptides exhibiting cytotoxic and cytomodulatory functions, respectively.
Overall, the comparative analysis of identified functional peptide profiles provided valuable insights into physiological adaptations and immune responses during different stages of subclinical mastitis and healthy conditions. Understanding these peptide alterations can aid in developing targeted strategies to enhance udder health, improve milk quality, and reduce the incidence of mastitis in dairy cows.
By focusing on the dynamic changes in peptide profiles, this study emphasizes the importance of tailored interventions during the dry-off period. Future research should aim to identify specific peptides with potential therapeutic applications, explore their mechanisms of action, and evaluate their efficacy in vivo. This approach could lead to the development of novel peptide-based treatments that enhance immune function, mitigate inflammation, and support overall mammary gland health, ultimately contributing to more sustainable dairy farming practices.

5. Conclusions

The analysis of functional peptide profiles in the dry-off secretions of dairy cows revealed significant physiological and biochemical changes during the dry period and the progression of SCM. The peptide composition varied between healthy cows and those with SCM, changing over time during the dry-off period, indicating distinct physiological states and adaptive mechanisms.
Cows with SCM exhibited increased protein content and decreased lactose levels in their milk compared to healthy cows before dry-off. The increase in protein content may be due to the upregulation of antimicrobial and immune defense proteins during udder inflammation, while the decrease in lactose levels can be attributed to mastitis pathogens and damage to secretory epithelial cells.
The decrease in antimicrobial peptides during the dry-off period, regardless of health condition, suggests compromised immune defense mechanisms, increasing susceptibility to infections. Conversely, specific peptides upregulated in healthy cows appear to regulate and prevent inflammation.
The increase in levels of antioxidant peptides in both healthy cows and those with SCM indicates a response to oxidative stress and tissue damage, playing critical roles in mitigating oxidative stress and tissue remodeling during the dry-off period.
A potential increase in proteinase activity during the dry period may lead to the generation of various peptides from milk proteins, originating from both somatic cells and bacterial sources. Pathogenic bacteria produce external enzymes that degrade milk proteins and generate specific peptides, which can aid in bacterial colonization or trigger host defense mechanisms.
Several peptides identified align with known functional peptides exhibiting cytotoxic, cytomodulatory, and antimicrobial properties. These peptides have potential as biomarkers for SCM and targets for therapeutic interventions.
The dynamic changes in peptide profiles during the dry-off period emphasize the necessity for tailored interventions. Future research should focus on identifying peptides with therapeutic potential, understanding their mechanisms of action, and evaluating their efficacy in vivo. This approach could lead to novel peptide-based treatments, enhancing immune function and supporting mammary gland health, contributing to more sustainable dairy farming practices.
Overall, this study advances our understanding of the peptide-mediated physiological adaptations in dairy cows during the dry-off period and highlights the potential of peptide profiling for improving udder health management and mastitis prevention.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/dairy6020019/s1: Table S1: PLS-DA VIP top 30 scores for peptides. The table shows the top 30 peptides that contribute to the separation between the 4 groups (SCM-D2, SCM-D21, H-D2, H-D21), their deriving proteins, their position in the deriving protein, and variations between the groups; Table S2: PLS-DA VIP top 30 scores for peptides. The table shows the top 30 peptides that contribute the most to the separation between SCM-D2 and H-D2 groups, their deriving proteins, their position in the deriving protein, and variations between the groups; Table S3: Comparison of the ‘SCM-D2’ group vs. ‘H-D2’ group (p < 0.05). The table shows peptides that have changed significantly in the comparison between the two groups, displaying the fold change and the corresponding p-value; Table S4: PLS-DA VIP top 30 for scores peptides. The table shows the top 30 peptides that contribute the most to the separation between SCM-D21 and H-D21 groups, their deriving proteins, their position in the deriving protein, and variations between the groups. Table S5: Comparison of the ‘SCM-D21’ group vs. ‘H-D21’ group (p < 0.05). The table shows peptides that have changed significantly in the comparison between the two groups, displaying the fold change and the corresponding p-value. Table S6: PLS-DA VIP top 30 scores for peptides. The table shows the top 30 peptides that contribute the most to the separation between SCM-D2 and SCM-D21 groups, their deriving proteins, their position in the deriving protein, and variations between the groups. Table S7: Comparison of the ‘SCM-D21’ group vs. ‘SCM-D2’ group (p < 0.05). The table shows peptides that have changed significantly in the comparison between the two groups, displaying the fold change and the corresponding p-value. Table S8: PLS-DA VIP top 30 scores for peptides. The table shows the top 30 peptides that contribute the most to the separation between H-D2 and H-D21 groups, their deriving proteins, their position in the deriving protein, and variations between the groups. Table S9: Comparison of the ‘H-D21’ group vs. ‘H-D2’ group (p < 0.05). The table shows peptides that have changed significantly in the comparison between the two groups, displaying the fold change and the corresponding p-value. Table S10: Comprehensive peptide annotation table. This table lists peptide numbers (P.N.s), amino acid sequences, and their corresponding source protein names. It serves as a reference for identifying the parent proteins of peptides reported in the main figures and tables throughout the manuscript.

Author Contributions

Conceptualization, B.N.A. and B.P.W.; methodology, B.N.A. and B.P.W.; validation, B.H., B.N.A. and B.P.W.; formal analysis, B.H., L.L. and X.L.; investigation, B.H., B.N.A. and B.P.W., resources, B.N.A. and B.P.W.; data curation, B.H., writing—original draft preparation, B.H.; writing—review and editing, B.N.A., B.P.W. and L.L.; visualization, B.H., B.N.A., B.P.W., X.L. and L.L.; supervision, B.N.A. and B.P.W.; project administration, B.N.A. and B.P.W.; funding acquisition, B.N.A. and B.P.W. All authors have read and agreed to the published version of the manuscript.

Funding

The project was supported by funds from the Results Driven Agriculture Research (RDAR) and Alberta Milk, Funding No. 2021F090R.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data generated in this study are not publicly available due to copyright and confidentiality restrictions. The prototype associated with this research is still under development and undergoing further investigation as part of the ongoing project. As such, the raw data are protected under a non-disclosure agreement to safeguard intellectual property and proprietary information. Requests for data can be directed to the corresponding author, subject to the terms of the agreement.

Acknowledgments

The authors would like to thank Jianping Wu’s lab and Hongbing Fan, for their assistance with the peptide extraction methodology.

Conflicts of Interest

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

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Figure 1. Experimental design—sample collection timeline. Milk samples were collected from all four udder quarters of eight cows two days prior to dry-off and analyzed for somatic cell count, total protein, total lipids, lactose, milk urea nitrogen, and total solids. Based on SCC thresholds, 16 udder quarters were selected for longitudinal analysis—8 classified as healthy and 8 as subclinical mastitis. These same quarters were subsequently sampled at two additional time points: Day 2 and Day 21 of the dry period, allowing for consistent within-quarter comparisons over time.
Figure 1. Experimental design—sample collection timeline. Milk samples were collected from all four udder quarters of eight cows two days prior to dry-off and analyzed for somatic cell count, total protein, total lipids, lactose, milk urea nitrogen, and total solids. Based on SCC thresholds, 16 udder quarters were selected for longitudinal analysis—8 classified as healthy and 8 as subclinical mastitis. These same quarters were subsequently sampled at two additional time points: Day 2 and Day 21 of the dry period, allowing for consistent within-quarter comparisons over time.
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Figure 2. Milk composition differs between the subclinical mastitis quarters and healthy quarters. Error bars indicate the standard error of the Least Squares Means (LSM).
Figure 2. Milk composition differs between the subclinical mastitis quarters and healthy quarters. Error bars indicate the standard error of the Least Squares Means (LSM).
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Figure 3. Peptide content of dry secretions from healthy and subclinical mastitis quarters at 2 and 21 days after dry off. (A) Comparison between SCM Day 2 (blue bar) vs. Healthy Day 2 (red bar). (B) Comparison between SCM Day 21 (blue bar) vs. Healthy Day 21 (red bar). (C) Comparison between SCM Day 2 (blue bar) vs. SCM Day 21 (red bar). (D) Comparison between Healthy Day 2 (blue bar) vs. Healthy Day 21 (red bar). Error bars represent the standard error of the Least Squares Means (LSM).
Figure 3. Peptide content of dry secretions from healthy and subclinical mastitis quarters at 2 and 21 days after dry off. (A) Comparison between SCM Day 2 (blue bar) vs. Healthy Day 2 (red bar). (B) Comparison between SCM Day 21 (blue bar) vs. Healthy Day 21 (red bar). (C) Comparison between SCM Day 2 (blue bar) vs. SCM Day 21 (red bar). (D) Comparison between Healthy Day 2 (blue bar) vs. Healthy Day 21 (red bar). Error bars represent the standard error of the Least Squares Means (LSM).
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Figure 4. Distribution of proteins from which peptides were derived.
Figure 4. Distribution of proteins from which peptides were derived.
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Figure 5. (a) Principal component analysis (PCA) of peptide composition (b) Partial least squares discriminant analysis (PLS-DA) scores plot of the 4 groups (H-D2, H-D21, SCM-D2, and SCM-D21) (p < 0.05); (c) VIP plot of top 30 peptides that contribute to the separation between the groups (first column from the left—Day 2 Healthy; second column from the left—Day 2 SCM; third column from the left—Day 21 Healthy; fourth column from the left—Day 21 SCM).
Figure 5. (a) Principal component analysis (PCA) of peptide composition (b) Partial least squares discriminant analysis (PLS-DA) scores plot of the 4 groups (H-D2, H-D21, SCM-D2, and SCM-D21) (p < 0.05); (c) VIP plot of top 30 peptides that contribute to the separation between the groups (first column from the left—Day 2 Healthy; second column from the left—Day 2 SCM; third column from the left—Day 21 Healthy; fourth column from the left—Day 21 SCM).
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Figure 6. (a) Principal component analysis (PCA) 2D scores plot and (b) Partial least squares discriminant analysis (PLS-DA) scores plot of the 2 groups (H-D2 and SCM-D2) (p < 0.05); (c) VIP plot of top 30 peptides that contribute to the separation between the groups (first column from the left—Day 2 Healthy; second column from the left—Day 2 SCM) and (d) Volcano Plot—Comparison of ‘SCM-D2’ group vs. ‘H-D2’ group (p < 0.05); peptides significantly decreased are highlighted in blue, whereas those significantly increased are marked in red in the comparison between the two groups. Peptides that do not show statistical significance are presented in black.
Figure 6. (a) Principal component analysis (PCA) 2D scores plot and (b) Partial least squares discriminant analysis (PLS-DA) scores plot of the 2 groups (H-D2 and SCM-D2) (p < 0.05); (c) VIP plot of top 30 peptides that contribute to the separation between the groups (first column from the left—Day 2 Healthy; second column from the left—Day 2 SCM) and (d) Volcano Plot—Comparison of ‘SCM-D2’ group vs. ‘H-D2’ group (p < 0.05); peptides significantly decreased are highlighted in blue, whereas those significantly increased are marked in red in the comparison between the two groups. Peptides that do not show statistical significance are presented in black.
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Figure 7. (a) Principal component analysis (PCA) 2D scores plot and (b) Partial least squares discriminant analysis (PLS-DA) scores plot of the 2 groups (H-D21 and SCM-D21) (p < 0.05); (c) VIP plot of top 30 peptides that contribute to the separation between the groups (first column from the left—Day 21 Healthy; second column from the left—Day 21 SCM) and (d) Volcano Plot-Comparison of ‘SCM-D21’ group vs. ‘H-D21’ group (p < 0.05); peptides significantly decreased are highlighted in blue, whereas those significantly increased are marked in red in the comparison between the two groups. Peptides that do not show statistical significance are presented in black.
Figure 7. (a) Principal component analysis (PCA) 2D scores plot and (b) Partial least squares discriminant analysis (PLS-DA) scores plot of the 2 groups (H-D21 and SCM-D21) (p < 0.05); (c) VIP plot of top 30 peptides that contribute to the separation between the groups (first column from the left—Day 21 Healthy; second column from the left—Day 21 SCM) and (d) Volcano Plot-Comparison of ‘SCM-D21’ group vs. ‘H-D21’ group (p < 0.05); peptides significantly decreased are highlighted in blue, whereas those significantly increased are marked in red in the comparison between the two groups. Peptides that do not show statistical significance are presented in black.
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Figure 8. (a) Principal component analysis (PCA) 2D scores plot and (b) Partial least squares discriminant analysis (PLS-DA) scores plot of the 2 groups (SCM-D2 and SCM-D21) (p < 0.05); (c) VIP plot of top 30 peptides that contribute to the separation between the groups (first column from the left—Day 2 SCM; second column from the left—Day 21 SCM) and (d) Comparison of ‘SCM-D21’ group vs. ‘SCM-D2’ group (p < 0.05); peptides significantly decreased are highlighted in blue, whereas those significantly increased are marked in red in the comparison between the two groups. Peptides that do not show statistical significance are presented in black.
Figure 8. (a) Principal component analysis (PCA) 2D scores plot and (b) Partial least squares discriminant analysis (PLS-DA) scores plot of the 2 groups (SCM-D2 and SCM-D21) (p < 0.05); (c) VIP plot of top 30 peptides that contribute to the separation between the groups (first column from the left—Day 2 SCM; second column from the left—Day 21 SCM) and (d) Comparison of ‘SCM-D21’ group vs. ‘SCM-D2’ group (p < 0.05); peptides significantly decreased are highlighted in blue, whereas those significantly increased are marked in red in the comparison between the two groups. Peptides that do not show statistical significance are presented in black.
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Figure 9. (a) Principal component analysis (PCA) 2D scores plot and (b) Partial least squares discriminant analysis (PLS-DA) scores plot of the 2 groups (H-D2 and H-D21) (p < 0.05); (c) VIP plot of top 30 peptides that contribute to the separation between the groups (first column from the left—Day 2 Healthy; second column from the left—Day 21 Healthy) and (d) Comparison of ‘H-D21’ group vs. ‘H-D2’ group (p < 0.05); peptides significantly decreased are highlighted in blue, whereas those significantly increased are marked in red in the comparison between the two groups. Peptides that do not show statistical significance are presented in black.
Figure 9. (a) Principal component analysis (PCA) 2D scores plot and (b) Partial least squares discriminant analysis (PLS-DA) scores plot of the 2 groups (H-D2 and H-D21) (p < 0.05); (c) VIP plot of top 30 peptides that contribute to the separation between the groups (first column from the left—Day 2 Healthy; second column from the left—Day 21 Healthy) and (d) Comparison of ‘H-D21’ group vs. ‘H-D2’ group (p < 0.05); peptides significantly decreased are highlighted in blue, whereas those significantly increased are marked in red in the comparison between the two groups. Peptides that do not show statistical significance are presented in black.
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Table 1. Result summary of the identified peptides that matched 100% known functional peptides in MBPD database (Bos taurus). A total of 56 peptides matched 100% known functional peptides, derived from 5 different proteins, and exhibiting 17 different functions.
Table 1. Result summary of the identified peptides that matched 100% known functional peptides in MBPD database (Bos taurus). A total of 56 peptides matched 100% known functional peptides, derived from 5 different proteins, and exhibiting 17 different functions.
PeptidesSpeciesFunctionsProtein IDs
Grouped Results: Bos taurus: 81Immunomodulatory: 7β-casein: 57
Increase calcium uptake: 4αS1-casein: 17
ACE-inhibitory: 23αS2-casein: 5
Antimicrobial: 19β-lactoglobulin: 1
Bradykinin-Potentiating: 2κ-casein: 1
Increase mucin secretion: 1
Antioxidant: 13
Prolyl endopeptidase-inhibitory: 1
Cytomodulatory: 1
Cytotoxic: 1
Antithrombotic: 2
DPP-IV Inhibitory: 2
Antiapoptotic effect: 1
Osteoanabolic: 1
Wound healing: 1
Cholesterol regulation: 1
Anticancer: 1
Total Counts:561175
Table 2. Summary of the quantity of each category of functional peptides exhibiting significant alterations across the four group comparisons.
Table 2. Summary of the quantity of each category of functional peptides exhibiting significant alterations across the four group comparisons.
Number of Functional Peptides
FunctionSCM-D2/H-D2SCM-D21/H-D21SCM-D21/SCM-D2H-D21/H-D2
Immunomodulatory 44
Increase calcium uptake 12
ACE-inhibitory281416
Antimicrobial221210
Bradykinin-Potentiating 121
Increase mucin secretion 11
Antioxidant2369
Prolyl endopeptidase-inhibitory 1
Cytomodulatory 11
Cytotoxic1 11
Antithrombotic 12
DPP-IV Inhibitory 1 2
Antiapoptotic effect 1
Osteoanabolic 1
Wound healing 1
Cholesterol regulation 11
Anticancer 1
Table 7. Comparison of our data with Mansor et al. (2013) [14] study. Mansor et al. (2013) [14] identified 33 peptides out of 154 included in the model developed to differentiate healthy versus infected milk samples (potential biomarkers for mastitis). 19 peptides matched identified peptides in our samples.
Table 7. Comparison of our data with Mansor et al. (2013) [14] study. Mansor et al. (2013) [14] identified 33 peptides out of 154 included in the model developed to differentiate healthy versus infected milk samples (potential biomarkers for mastitis). 19 peptides matched identified peptides in our samples.
Mansor et al. [14]Our Study
Peptides MatchedDeriving ProteinInfected/HealthySCM-D2/H-D2SCM-D21/H-D21SCM-D21/SCM-D2H-D21/H-D2
LIVTQTMKβ-lactoglobulindownDownNSCdowndown
EPVLGPVRGPβ-caseindownNSC *NSCdowndown
PFPEVFGKEKVαS1-caseinupNSCNSCNSCNSC
EMPFPKYPVEPβ-caseinOPI *NSCNSCNSCNSC
VAPFPEVFGKEKαS1-caseinupNSCNSCNSCNSC
SKVKEAMAPKHKβ-caseinupNSCNSCNSCNSC
VAPFPEVFGKEKVαS1-caseinOPINSCNSCdownNSC
EPVLGPVRGPFPIIVβ-caseindownNSCdownNSCNSC
FVAPFPEVFGKEKVαS1-caseinOPINSCNSCdownNSC
LYQEPVLGPVRGPFPβ-caseinOPINSCNSCNSCNSC
IPNPIGSENSEKTTMPαS1-caseinupNSCNSCdowndown
LYQEPVLGPVRGPFPIIβ-caseinupupNSCNSCup
GSKASADESLALGKPGKEPRFibroblast growth factor-binding proteindownNSCNSCdowndown
RGSKASADESLALGKPGKEPRFibroblast growth factor-binding proteindowndownNSCdowndown
HKEMPFPKYPVEPFTESQβ-casein NSCNSCNSCNSC
SDIPNPIGSENSEKTTMPLWαS1-caseinNSCupNSCdownNSC
SSRQPQSQNPKLPLSILKEKGLYCAM1downdownNSCdowndown
HKEMPFPKYPVEPFTESQSLβ-caseinOPIupNSCdownNSC
SSRQPQSQNPKLPLSILKEKHLGLYCAM1downdowndownNSCdown
NSC *—not significantly changed. OPI *—only present in infected samples.
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Hasanllari, B.; Willing, B.P.; Li, L.; Luo, X.; Ametaj, B.N. Peptide Profiling in Dairy Cow Dry Secretions: Temporal Changes and Comparative Analysis Between Healthy and Subclinical Mastitis Cows. Dairy 2025, 6, 19. https://doi.org/10.3390/dairy6020019

AMA Style

Hasanllari B, Willing BP, Li L, Luo X, Ametaj BN. Peptide Profiling in Dairy Cow Dry Secretions: Temporal Changes and Comparative Analysis Between Healthy and Subclinical Mastitis Cows. Dairy. 2025; 6(2):19. https://doi.org/10.3390/dairy6020019

Chicago/Turabian Style

Hasanllari, Barjam, Benjamin P. Willing, Liang Li, Xian Luo, and Burim N. Ametaj. 2025. "Peptide Profiling in Dairy Cow Dry Secretions: Temporal Changes and Comparative Analysis Between Healthy and Subclinical Mastitis Cows" Dairy 6, no. 2: 19. https://doi.org/10.3390/dairy6020019

APA Style

Hasanllari, B., Willing, B. P., Li, L., Luo, X., & Ametaj, B. N. (2025). Peptide Profiling in Dairy Cow Dry Secretions: Temporal Changes and Comparative Analysis Between Healthy and Subclinical Mastitis Cows. Dairy, 6(2), 19. https://doi.org/10.3390/dairy6020019

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