Evaluation of Holstein Cows with Tongue-Rolling: Plasma Metabolomics and Milk Proteomics

Abstract

Stereotypic behaviors are common in farm animals and often signal poor welfare. Tongue-rolling is the most prevalent stereotypic behavior in cows. In this study, we compared the plasma and milk composition of 16 high-frequency tongue-rolling cows (HTR group) and 16 non-stereotypic cows (CON group). All cows were primiparous cows. Biochemical tests, plasma metabolomics, and milk proteomics revealed higher plasma triiodothyronine levels in HTR cows, and lower levels of αs1-casein, β-casein, κ-casein, and lactoferrin in their milk. Multi-omics analyses identified 103 differential metabolites and 73 differential proteins, including various GTP-binding proteins, with the Ras signaling pathway being significantly upregulated in the HTR cows. GO enrichment analysis highlighted significant changes in molecular function, particularly related to GTP/GDP-binding proteins. Additionally, HTR cows exhibited elevated cellular metabolic activity. These findings suggest that high-frequency tongue-rolling is associated with altered endocrine and metabolic profiles, disrupted milk protein synthesis, and impaired immune function potential. The reduction in key milk proteins and lactoferrin may negatively impact milk quality and immune defense. Further research is needed to clarify the causal relationship between these physiological changes and tongue-rolling, providing insights into the underlying mechanisms of stereotypic behaviors in dairy cows and their implications for animal welfare and milk production.

1. Introduction

Stereotypic behaviors are continuous, repetitive, and seemingly non-functional actions observed in various animals, including farm, zoo, companion, and wild animals [1,2,3,4]. These behaviors have long been considered indicators of poor animal welfare, often associated with suboptimal living conditions [5], restricted behavioral expression [6], and inadequate dietary nutrition [3,7]. Among cows, common stereotypic behaviors include tongue-rolling, feed-tossing, and excessive rubbing [8]. Tongue-rolling (TR), characterized by repeated circular movements of the cow’s tongue inside and outside the mouth [8], is the most prevalent stereotypic behavior in dairy farming [9] and is categorized as a form of non-nutritive oral behavior (NNOB). Recent studies have shown that the prevalence of TR in dairy cows is approximately 30% [8,10], and it tends to increase initially and then decrease with the progression of days in milk.

Researchers have proposed that stereotypic behaviors arise when animals are prevented from performing highly motivated behaviors [11,12]. For instance, cows with shorter feeding times spend more time TR [13], and feed restriction has been shown to increase the expression of TR [14,15]. Frequent expression of TR is often associated with adverse physiological changes. Sun et al. [16] reported that TR cows exhibited significantly lower ruminal pH, acetate-to-propionate ratio, and total volatile fatty acid concentrations compared with control cows. In addition, serum levels of total cholesterol, triglycerides, thyroid hormones, norepinephrine, and interleukin-6 were elevated in tongue-rolling cows. Mahmoud et al. [17] further suggested that TR cows experience anemia, reduced energy availability, and oxidative stress. Research has shown that high-concentrate diets reduce feeding and rumination time in ruminants [18], which may lead to the emergence of NNOB, such as tongue-rolling [3]. Several studies have also shown that increasing roughage in the diet can reduce NNOB in Holstein cows, including both heifers and bulls [19,20,21,22].

Although omics techniques are widely applied in livestock and poultry research, including breeding, disease diagnosis, nutrition, and welfare [23,24,25], their application to animal behavior, particularly in the context of stereotypic behaviors, remains limited. Brinker et al. [26] demonstrated that γ-aminobutyric acid is involved in abnormal behavior in laying hens through whole genome sequencing. Similarly, Sun et al. [16] used 16S rDNA sequencing to analyze rumen microbes in dairy cows and found that Firmicutes were less abundant in cows exhibiting TR behavior compared to those without stereotypic behavior. Additionally, Wilson et al. [27] identified an association between tail-biting behavior in pigs and Sus scrofa chromosome 16 using genome-wide association analyses.

In our previous study, we found that milk protein percentage, milk true protein percentage, and milk crude protein percentage were significantly lower in high-frequency TR cows than in non-stereotypic cows [8]. Building on these findings, this study aims to investigate the differences in plasma metabolites and milk proteins between high-frequency TR cows and non-stereotypic cows using metabolomics and proteomics. We hypothesize that high-frequency TR cows exhibit significant metabolic differences compared to non-stereotypic cows, particularly in relation to milk protein metabolism. Furthermore, the milk quality of TR cows may be inferior.

2. Materials and Methods

2.1. Animal Welfare Statement

The experiment was performed at the dairy farm of the Shandong Yinxiang Weiye Group Company (Cao County, Shandong, China, 115°26′ E, 34°50′ N). The farm keeps Holstein cows for milk production. All experiments were approved by the Animal Ethics Committee of the Chinese Academy of Agricultural Sciences (Beijing, China, approval number IAS2023-68).

2.2. Animal, Management, and Behavioral Observations

A total of 916 first-lactation dairy cows were housed in three large cowsheds. Each cowshed contained two pens, and each pen was equipped with 200 individual stalls. All cows were milked three times a day (8:00, 15:00, 20:00), and after each milking, they were fed total mixed ration (TMR) at 8:30, 15:30, 20:30. The TMR was sieved through a four-layer Penn State Particle Separator, with 10.30%, 34.95%, 15.32%, and 39.42% of the feed retained on the first, second, third, and fourth layers, respectively. Dietary ingredients are described in

Table A1. Ingredients and nutrient composition of experimental diet (%, DM basis).

Items	Value
Ingredients	Content, %
Alfalfa	10.39
Oat hay	2.42
Dandelion	0.48
Whole corn silage	48.33
Cottonseed	2.90
Beet pulp	2.42
Ground corn	7.49
Pressed corn	9.42
Soybean meal	8.70
Rapeseed meal	1.69
DDGS 1	0.72
Extruded soybean	1.33
Mineral and vitamin mix 2	3.70
Nutrient composition
DM, % of wet TMR	62.40
CP	17.06
EE	3.32
NDF	35.75
ADF	18.20
NEL/(MJ/kg)	6.11

¹ DDGS, Distillers Dried Grains with Solubles. ² Contained the following per kg of diets: VA 170,000 IU, VD 8000 IU, VE 9000 IU, Ca 160 g, Fe 800 mg, Cu 680 mg, Mn 3500 mg, Zn 7500 mg, Se 80 mg, I 400 mg, Co 38 mg.

. The TMR diet consisted of 61.62% roughage and 38.37% concentrate. Veterinarians conducted daily health checks and assessed body condition scores (BCS, on a scale of 1 to 5) monthly. Milk yield and days in milk (DIM) of lactating cows were automatically recorded every day by a fully automated milking system.

Three trained observers conducted behavioral scans of all 916 cows over a 10-day period (21–30 April) using the scan sampling method [28]. The recorded stereotypic behaviors included TR, pica, feed-tossing, inter-sucking, head-shaking, excessive-grooming, excessive-vocalizing, and excessive-rubbing. Prior to the formal observation period, the observers underwent a 3-day calibration phase in the same cowshed, focusing on TR. The prevalence-adjusted bias-adjusted kappa (PABAK) values for all stereotypic behaviors exceeded 0.8, indicating a high level of agreement among the observers in identifying stereotypic behaviors. During the formal observation phase, three observers were randomly assigned to different cowsheds on the first day, ensuring that each cowshed had one observer. To minimize bias, the observers rotated daily among the three cowsheds, completing a full rotation every three days, such that each observer covered all cowsheds within each cycle. For each scan sampling session, the observer slowly walked through the cowshed from one end to the other, visually inspecting all cows and recording the cow’s ID and specific types of stereotypic behavior displayed. Each session lasted for 10 min, followed by a 10-min break. Observations were carried out 10 times in the morning (08:00–11:10) and another 10 times in the afternoon (14:00–17:10), resulting in 20 observations per day across 10 consecutive days, yielding a total of 200 observations.

During the behavioral observation period, ambient temperature (Ta) and relative humidity (RH) in the barn were automatically recorded every 10 min using Kestrel 5400 Heat Stress Trackers (Nielsen-Kellerman, Boothwyn, PA, USA), and the temperature–humidity index (THI) was calculated using the following formula: THI = (1.8 × Ta + 32) − (0.55 − 0.0055 × RH) × (1.8 × Ta − 26). Throughout the observation period, the THI in the cowsheds averaged 59.60 ± 6.62.

After the behavioral observation, we ranked cows based on the number of observed TR occurrences and classified those with TR occurrences exceeding 1.5 times the first quartile as high-frequency TR cows. These cows exhibited a minimum of five TR episodes during 200 behavioral observations and did not display any other stereotypic behaviors. According to statistical calculations, a total of 55 cows were classified as high-frequency TR cows. We randomly selected 16 cows from the high-frequency TR cows as the high-frequency TR group (HTR group). To minimize the potential impact of DIM on this study, we randomly selected 16 cows from 108 non-stereotypic cows whose DIM was comparable to that of the HTR group to serve as the control group (CON group). All selected cows were confirmed to be healthy following veterinary examination. We conducted behavioral observations for an additional 3 days to ensure that HTR cows continued to exhibit TR, while CON cows remained free of stereotypic behaviors.

2.3. Sample Collection

To minimize potential variations due to feeding status and circadian rhythms, blood samples were collected before the morning feeding on the first day following the 3-day behavioral observation. Blood was collected from the caudal vertebral vein of each cow using a 20 mL syringe. Collected blood samples were centrifuged at 3000 r/min for 10 min at 4 °C. The plasma was then extracted and divided into four 2 mL cryotubes, which were initially stored in liquid nitrogen and subsequently transferred to a −80 °C freezer for storage prior to plasma biochemistry and untargeted metabolomics analyses.

On the first day after completing blood sample collection, 100 mL of milk samples were collected from each selected cow during milking at a ratio of 4:3:3 (morning: noon: evening). These samples were divided into five 20 mL cryotubes, stored in liquid nitrogen, and later transferred to a −80 °C freezer for storage before milk protein and proteomics analyses.

2.4. Biochemical Testing of Plasma and Milk Samples

We collected 32 frozen blood samples (16 per group) for plasma biochemical indicators testing. The measured indicators included stress markers such as dopamine (DA), and 5-hydroxytryptamine (5-HT), adrenaline (ADR) and norepinephrine (NOR); energy metabolism markers such as total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), glucose (GLU), free fatty acids (FFA), β-hydroxybutyric acid (β-HB), thyroxine (T₄), triiodothyronine (T₃), insulin (INS), and insulin-like growth factor-1 (IGF-1); protein metabolism markers such as albumin (ALB), total protein (TP), and urea nitrogen (UREA); and antioxidant markers such as lactate dehydrogenase (LDH), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), total antioxidant capacity (T-AOC), and catalase (CAT). The detection methods for TC, TG, HDL, LDL, GLU, FFA, β-HB, ALB, TP, UREA, LDH, SOD, GSH-Px, T-AOC, and CAT were colorimetric methods, while IGF-1, and ADR were detected using enzyme linked immunosorbent assay (ELISA), and DA, 5-HT, NOR, T₄, T₃ and INS were measured using radioimmunoassay (RIA). The test kits were provided by the Beijing Sinouk Institute of Biological Technology.

We collected 32 frozen milk samples (16 per group) for the milk biochemical indicators testing, including αs1-casein (αs1-CN), αs2-casein (αs2-CN), β-casein (β-CN), κ-casein (κ-CN), α-lactalbumin (α-LA), β-lactoglobulin (β-LG), and lactoferrin (LF). These indicators were detected using ELISA. The testing kits were provided by Shanghai Hengyuan Biological Technology Co., Ltd. (Shanghai, China), and the dairy farm provided the dairy herd improvement (DHI) data for the selected cows. The DHI testing of milk samples was performed using a FOSS MilkoScan FT+ (FOSS A/S, Hillerød, Denmark), including the determination of milk protein percentage, milk fat percentage, lactose percentage, true protein percentage, and crude protein percentage.

2.5. Untargeted Metabolomics Analysis

Due to cost considerations, we randomly selected 24 high-quality frozen plasma samples (12 per group), from which 100 µL of each sample was transferred into separate 1.5 mL centrifuge tubes for metabolomics analyses. Subsequently, 300 µL of pre-cooled acetonitrile (−20 °C; Thermo Fisher Scientific, Waltham, MA, USA) was added to each tube. The samples were vortexed for 1 min and then allowed to stand overnight at −20 °C. The plasma samples were centrifuged at 12,000 rpm for 20 min at 4 °C using a Legend Micro 17R centrifuge (Thermo Fisher Scientific, Waltham, MA, USA). From each sample, 100 µL of supernatant was collected for untargeted metabolomics analysis using liquid chromatography–mass spectrometry (LC-MS). Additionally, 50 µL of each supernatant was pooled to create quality control (QC) samples. LC-MS analysis was performed on a TripleTOF 6600+ system (Sciex, Framingham, MA, USA) equipped with a Waters HSS T3 column (50 mm × 2.1 mm, 1.8 µm; Waters Corporation, Milford, MA, USA).

2.6. Statistical Analysis

2.6.1. Data-Independent Acquisition Proteomics Analysis

Due to cost considerations, we randomly selected 24 high-quality frozen milk samples (12 from each group) for data-independent acquisition (DIA) proteomic analyses. These milk samples were obtained from the same cows from which plasma samples were collected for metabolomics analysis. Peptide fractions were prepared following the method of Wiśniewski et al. [29] and redissolved in 10 µL of 0.1% formic acid solution. The samples were then separated using a Vanquish liquid chromatography system (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a nano Viper C18 column (75 µm × 250 mm, 2 µm, Thermo Fisher Scientific, Waltham, MA, USA). The mobile phase A consisted of an aqueous solution of 0.1% formic acid (Sigma-Aldrich, St. Louis, Missouri, USA), while mobile phase B was an aqueous solution of 80% acetonitrile and 0.1% formic acid. The peptide mixtures were subsequently analyzed and identified using an Orbitrap Exploris 480 mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA).

2.6.2. Biochemical Data Analysis

The information on BCS and DIM of lactating cows on the day before the start of the 10-day behavioral observation was included as categorical and continuous covariates, respectively, in the statistical analysis of biochemical data variables. Normality tests were conducted on all plasma and milk biochemical data. Data that did not conform to normality (including TG, T₃, INS, IGF-1, ADR, 5-HT, SOD, GSH-Px, and CAT) were analyzed using Mann–Whitney U tests. All other data were analyzed using Student’s t-tests. Results from data that did not follow a normal distribution were presented as medians in figures, whereas other data were presented in tables as mean values ± SD. For all biochemical data, p-values < 0.05 were considered statistically significant.

2.6.3. Metabolomics Data Analysis

The raw files obtained from mass spectrometry were imported into Progenesis QI software (Waters Corporation, Milford, MA, USA) for data preprocessing and identification. Subsequently, QC analyses were conducted to verify the accuracy and reliability of the data. Metabolomics data were median-normalized and subsequently imported into MetaboAnalyst 6.0 (https://www.metaboanalyst.ca, Canada, 13 January 2025) for further analysis [30]. We first assessed the clustering of the metabolomics data using principal components analysis (PCA), followed by a series of t-tests for all metabolites, and orthogonal partial least squares–discriminant analysis (OPLS-DA) was employed to distinguish between the HTR cows and CON cows. The OPLS-DA model was validated with 1000 permutation tests, yielding R²Y and Q² values greater than 0.5. The p-values were adjusted for multiple testing using the Benjamini–Hochberg method. Differential metabolites were identified based on an adjusted p-value < 0.05, variable importance in the projection (VIP) score ≥ 1, and a fold change (FC) > 1.2 or FC < 0.83. The results were visualized using a volcano plot.

All differential metabolites were classified at the superclass level. The compound names were entered into MBROLE 2.0 (http://csbg.cnb.csic.es/mbrole2/, Spain, 13 January 2025) [31]. We compared the differential metabolites against the Bos taurus (cow) database and conducted Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses.

The OPLS-DA plot was generated using MetaboAnalyst 6.0, while the volcano plot and KEGG pathway enrichment plot were created using SRplot (http://www.bioinformatics.com.cn, China, 13 January 2025) [32]. The pie chart depicting superclass-level classification was produced using OmicStudio tools (https://www.omicstudio.cn, China, 13 January 2025) [33].

2.6.4. Proteomics Data Analysis

The raw files obtained from mass spectrometry were analyzed using DIA-NN 1.8.1 (UK) with the UniProt_Bos taurus (Bovine)_37503_20231123.fasta database to identify the UniProt IDs of each protein. The proteomics data were processed using the Limma package in R 4.3.2 on macOS to identify differentially expressed proteins (DEPs). The p-values were adjusted for multiple testing using the Benjamini–Hochberg method, and DEPs were defined as those with an adjusted p-value < 0.05 and an FC > 1.2 or <0.83. The results were visualized as volcano plots.

The UniProt IDs of all DEPs were then entered into DAVID v2024q4 (https://davidbioinformatics.nih.gov, USA, 13 January 2025) [34] for KEGG pathway enrichment analysis and gene ontology (GO) enrichment analysis, using the Bos taurus (cow) species as a reference. The Z-score for each significantly enriched KEGG pathway was calculated to determine whether the pathway is upregulated or downregulated. The GO enrichment analysis encompassed biological processes (BP), cellular components (CC), and molecular functions (MF).

Additionally, the UniProt IDs of DEPs were input into STRING 12.0 (https://cn.string-db.org, Switzerland, 13 January 2025) [35] to identify protein–protein interactions (PPI) with a Combined_score > 0.4.

The volcano plot and KEGG pathway enrichment plot for DEPs were generated using SRplot, the GO enrichment analysis plot was created using GraphPad Prism 10 (USA), and the PPI network was constructed using Cytoscape 3.10.2 (USA).

3. Results

3.1. Comparative Analysis of Plasma and Milk Biochemical Indicators in HTR and CON Cows

Table 1. Differences in plasma biochemical indicators that conform to normality in HTR and CON cows.

Item	HTR 1	CON 2	p-Value
DA (pg/mL)	120.07 ± 11.56	121.06 ± 11.16	0.809
NOR (pg/mL)	423.51 ± 53.99	429.28 ± 46.63	0.746
TC (mmol/L)	5.24 ± 0.97	5.40 ± 1.06	0.650
HDL (mmol/L)	2.99 ± 0.48	3.05 ± 0.52	0.745
LDL (mmol/L)	1.35 ± 0.36	1.44 ± 0.41	0.474
GLU (mmol/L)	3.10 ± 0.41	3.35 ± 0.35	0.074
FFA (mmol/L)	0.28 ± 0.06	0.24 ± 0.07	0.123
β-HB (mmol/L)	0.43 ± 0.12	0.47 ± 0.12	0.344
T4 (ng/mL)	58.34 ± 3.80	58.82 ± 5.39	0.773
ALB (g/L)	31.85 ± 4.29	30.37 ± 4.07	0.327
TP (g/L)	68.91 ± 7.74	65.98 ± 7.69	0.297
UREA (mmol/L)	4.33 ± 0.59	4.61 ± 0.81	0.277
LDH (U/L)	1141.03 ± 223.94	1071.17 ± 195.14	0.360
T-AOC (U/mL)	9.12 ± 1.67	9.66 ± 1.05	0.288

¹ HTR: The group of cows with high-frequency tongue-rolling behavior. ² CON: The group of cows without stereotypic behaviors.

and Figure 1 present the differences in plasma biochemical indicators between the HTR and CON cows. It is evident that the HTR and CON cows differed only in plasma T₃ levels, with T₃ levels being higher in the HTR cows than in CON cows (p < 0.05).

Table 2. Differences in DHI and milk biochemical indicators in HTR and CON cows.

Item	HTR 1	CON 2	p-Value
Milk yield (kg/d)	33.56 ± 9.86	31.23 ± 7.94	0.449
Milk protein percentage (%)	3.37 ± 0.15	3.71 ± 0.17	<0.001
Milk fat percentage (%)	3.80 ± 0.68	3.92 ± 0.47	0.587
Milk lactose percentage (%)	5.28 ± 0.90	5.23 ± 0.10	0.155
Milk true protein percentage (%)	3.37 ± 0.15	3.69 ± 0.17	<0.001
Milk crude protein percentage (%)	3.57 ± 0.14	3.90 ± 0.17	<0.001
αs1-CN (μg/mL)	54.91 ± 1.50	56.25 ± 1.50	0.016
αs2-CN (μg/mL)	40.54 ± 1.67	41.13 ± 2.07	0.381
β-CN (μg/mL)	99.31 ± 2.42	103.73 ± 2.71	<0.001
κ-CN (μg/mL)	23.46 ± 0.86	24.25 ± 0.91	0.020
α-LA (μg/mL)	26.09 ± 1.05	26.44 ± 0.62	0.282
β-LG (μg/mL)	334.06 ± 11.54	341.09 ± 9.22	0.070
LF (μg/mL)	789.57 ± 31.37	826.24 ± 32.68	0.002

¹ HTR: The group of cows with high-frequency tongue-rolling behavior. ² CON: The group of cows without stereotypic behaviors.

details the differences in DHI and biochemical indicators of milk samples between the HTR and CON cows. HTR cows had significantly lower milk protein percentage, crude protein percentage, and true protein percentage, as well as lower levels of αs1-CN, β-CN, κ-CN, and LF in milk compared with CON cows (p < 0.05).

3.2. Metabolomics and Proteomics Profiling of HTR and CON Cows

As depicted in Figure A1A. The PCA plot showed partial overlaps between the HTR and CON cows at the metabolomics level, suggesting limited separation. Consequently, we performed OPLS-DA on the metabolomics data (Figure A1B). The OPLS-DA plot demonstrated a complete separation between the HTR and CON cows at the metabolomics level.

The volcano plot of the metabolomics data revealed 103 differential metabolites (Figure 2A), including 73 upregulated and 30 downregulated metabolites (HTR vs. CON), such as megalomicin C2 (p = 2.41 × 10⁻⁴, FC = 4.147), leukotriene C4 (p = 3.68 × 10⁻⁴, FC = 1.546), 2′,3′-Cyclic UMP (p = 0.007, FC = 0.301). These 103 metabolites were classified into 12 classes at the superclass level (including the unknown group), with 3 metabolites remaining unnamed at this time and therefore belong to the unknown group (Figure 2B). As illustrated in Figure 2B, the “Lipids and lipid-like molecules” class accounted for the largest share at 35.92%, while “Organic acids and derivatives”, “Phenylpropanoids and polyketides”, and “Organoheterocyclic compounds” each accounted for more than 10%.

KEGG enrichment analysis of all differential metabolites revealed three significantly altered metabolic pathways: “Biosynthesis of secondary metabolites” (p = 0.049), “Fc epsilon RI signaling pathway” (p = 0.039), and “Asthma” (p = 0.018), all of which were upregulated in the HTR cows relative to the CON cows (Figure 2C).

The volcano plot of the proteomics data showed 75 DEPs (Figure 3A), including 53 upregulated DEPs and 22 downregulated DEPs (HTR vs. CON). KEGG enrichment analysis revealed that the 75 DEPs were significantly enriched in 46 KEGG pathways (Figure 3B), among which only Ferroptosis (Z-score = −1) and Mineral absorption (Z-score = −0.58) were significantly downregulated (HTR vs. CON). Figure 3B displays the top 20 KEGG pathways ranked by p-value, whereas

Table A2. The information on significantly enriched KEGG pathways.

Pathway Name	p-Value	Fold Enrichment	Z-Score
Chemokine signaling pathway	<0.001	11.487	2.714
Lipid and atherosclerosis	<0.001	8.276	1.897
Ras signaling pathway	<0.001	8.075	3.162
Kaposi sarcoma-associated herpesvirus infection	<0.001	7.220	1.414
Regulation of the actin cytoskeleton	<0.001	6.907	2.121
Autophagy–animal	<0.001	8.070	1.890
Neurotrophin signaling pathway	<0.001	9.574	2.449
Renal cell carcinoma	<0.001	13.476	2.236
Rap1 signaling pathway	<0.001	6.464	2.646
Apelin signaling pathway	<0.001	8.368	2.449
Colorectal cancer	0.001	10.840	2.236
Viral carcinogenesis	0.001	5.769	1.890
Endocytosis	0.001	5.607	2.646
Efferocytosis	0.001	7.081	2.449
Ferroptosis	0.002	16.282	−1.000
Salmonella infection	0.002	5.268	1.890
Axon guidance	0.002	6.612	2.449
Mitophagy–animal	0.002	9.066	2.236
VEGF signaling pathway	0.003	13.522	2.000
Proteoglycans in cancer	0.003	5.838	2.449
MAPK signaling pathway	0.003	4.654	2.646
Relaxin signaling pathway	0.004	7.555	2.236
Fc epsilon RI signaling pathway	0.005	11.237	2.000
Phagosome	0.009	6.007	1.342
Adherens junction	0.011	8.578	2.000
B cell receptor signaling pathway	0.011	8.487	2.000
Tight junction	0.012	5.540	2.236
Fc gamma R-mediated phagocytosis	0.012	8.141	1.000
AGE-RAGE signaling pathway in diabetic complications	0.014	7.822	2.000
Pancreatic secretion	0.014	7.746	2.000
C-type lectin receptor signaling pathway	0.015	7.526	2.000
Pathways in cancer	0.018	2.870	2.121
Cholinergic synapse	0.019	6.937	2.000
Tuberculosis	0.019	4.749	1.342
Leukocyte transendothelial migration	0.020	6.761	2.000
Serotonergic synapse	0.022	6.539	2.000
Sphingolipid signaling pathway	0.023	6.382	2.000
AMPK signaling pathway	0.024	6.332	2.000
T cell receptor signaling pathway	0.025	6.233	2.000
Alcoholism	0.027	4.280	2.236
Apoptosis	0.035	5.427	1.000
Mineral absorption	0.037	9.651	−0.577
Long-term depression	0.038	9.498	1.732
Phospholipase D signaling pathway	0.040	5.180	2.000
PI3K-Akt signaling pathway	0.042	3.053	1.633
Long-term potentiation	0.046	8.548	1.732

lists all significantly enriched KEGG pathways. GO enrichment analysis revealed that the BP, CC, and MF categories were significantly enriched in a total of 61 GO terms, with 24, 26, and 11 terms, respectively (Figure 4). For BP and CC, Figure 4 presents the top 20 GO terms ranked by p-value, while

Table A3. The p-values and Fold Enrichment values of GO terms.

GO Type	GO Terms	p-Value	Fold Enrichment
BP	Iron ion transport	<0.001	25.228
BP	Vesicle docking involved in exocytosis	0.001	51.785
BP	Vesicle-mediated transport	0.002	8.816
BP	Protein transport	0.002	6.287
BP	Positive regulation of endothelial cell migration	0.005	28.938
BP	Intracellular protein transport	0.006	6.804
BP	Substrate adhesion-dependent cell spreading	0.007	23.998
BP	Antibacterial humoral response	0.007	23.426
BP	Actin filament organization	0.008	9.506
BP	Localization within membrane	0.012	163.984
BP	Cell junction assembly	0.018	109.323
BP	Positive regulation of vasculogenesis	0.018	109.323
BP	Amyloid fibril formation	0.024	81.992
BP	Golgi organization	0.026	11.713
BP	Signal transduction	0.029	3.422
BP	Cell projection assembly	0.035	54.661
BP	Establishment of epithelial cell apical/basal polarity	0.035	54.661
BP	Nitric oxide biosynthetic process	0.035	54.661
BP	Lipid transport	0.036	9.839
BP	Small GTPase-mediated signal transduction	0.037	9.742
BP	Regulation of cell shape	0.038	9.552
BP	Rac protein signal transduction	0.044	43.729
BP	Protein localization to plasma membrane	0.048	8.482
BP	Protein stabilization	0.048	8.409
CC	Golgi apparatus	<0.001	7.169
CC	Lipid droplet	<0.001	28.476
CC	Apical plasma membrane	<0.001	10.200
CC	Autophagosome membrane	<0.001	33.338
CC	Phagocytic vesicle membrane	<0.001	23.167
CC	Ruffle membrane	<0.001	21.357
CC	Golgi membrane	<0.001	6.087
CC	Endoplasmic reticulum membrane	<0.001	4.344
CC	Dendritic spine	0.002	17.086
CC	Recycling endosome	0.002	15.358
CC	Plasma membrane	0.003	1.669
CC	Early endosome	0.003	8.458
CC	Brush border membrane	0.004	31.065
CC	Lamellipodium	0.006	11.023
CC	Phagocytic vesicle	0.006	25.004
CC	Extracellular space	0.011	2.494
CC	Recycling endosome membrane	0.013	16.806
CC	Cytoplasmic side of plasma membrane	0.015	16.018
CC	Lysosome	0.025	6.328
CC	Specific granule	0.026	75.937
CC	Extracellular exosome	0.028	11.265
CC	Cytoskeleton	0.029	4.250
CC	Late endosome	0.034	10.150
CC	Membrane raft	0.042	9.072
CC	Midbody	0.045	8.762
CC	Late endosome membrane	0.045	8.762
MF	GDP binding	<0.001	55.349
MF	GTPase activity	<0.001	15.327
MF	GTP binding	<0.001	11.889
MF	G protein activity	<0.001	62.268
MF	Protein-containing complex binding	<0.001	18.280
MF	FAD binding	0.007	23.721
MF	Pyrimidine- and adenosine-specific: sodium symporter activity	0.012	166.048
MF	Oxidoreductase activity	0.017	7.339
MF	Uridine transmembrane transporter activity	0.018	110.698
MF	Signaling receptor binding	0.022	6.576
MF	Myosin V binding	0.035	55.349

BP: biological processes; CC: cellular components; MF: molecular functions.

provides detailed p-values and Fold Enrichment values for all significantly enriched BP, CC, and MF GO terms. The PPI network is depicted in Figure 5, involving 52 proteins and 122 interactions identified. Notably, ANXA5, RHOA, CDC42, XDH, and RAB7A were key nodes in the PPI network, each having more than 10 interactions, primarily involving Ras superfamily-related proteins.

4. Discussion

Based on previous research, we selected HTR cows and initially evaluated the differences in plasma metabolites and milk proteins between HTR cows and CON cows using biochemical tests and omics analyses. As hypothesized, HTR cows showed significant differences in milk proteins compared to those in the CON cows, which will be the focus of future investigations.

4.1. Enhanced Metabolic Activity in HTR Cows: Evidence from Plasma Biochemical Indicators and Milk Proteomics

4.1.1. Variations in Plasma Biochemical Indicators

The metabolic processes within animals are intricate and influenced by various factors, including environment, disease, and diet. Stereotypic behaviors have long been recognized as indicators of poor welfare. Sun et al. [16] proposed that TR behavior might be an outward expression of elevated stress in cows, as indicated by significantly higher serum 5-HT levels in TR cows compared to non-stereotypic cows. DA and 5-HT, common stress indicators, are typically positively correlated with stress levels, reflecting the activity of the locus coeruleus–norepinephrine (LC-NE) axis. Nevertheless, in this study, there were no significant differences in plasma DA and 5-HT levels between HTR and CON cows, suggesting that the stress levels were not significantly different between the two groups.

Plasma levels of T₃ and T₄ are commonly used indicators of metabolic activity. These thyroid hormones are regulated by the hypothalamic–pituitary–thyroid (HPT) axis, and their circulating levels reflect the integrated activity of this axis [36,37,38]. In this study, plasma T₃ levels were significantly higher in HTR cows compared to CON cows, corroborating the findings of Sun et al. [16] and suggesting that TR cows may possess greater metabolic activity than non-stereotypic cows. Additionally, the upregulation of the biosynthesis of the secondary metabolites pathway in HTR cows compared to CON cows suggests that HTR cows exhibit elevated metabolic activity, consistent with plasma biochemistry results, such as higher T₃.

4.1.2. Upregulation of GTP-Binding Protein Family Members Indicates Enhanced Cellular Activity in HTR Cows

GO enrichment analysis revealed greater diversity and upregulation of molecular functions related to GDP binding, GTPase activity, and GTP binding in HTR cows, suggesting enhanced cellular metabolic activity. Many of the upregulated proteins belonged to the Ras superfamily of small GTPases, which includes Rab, Rho, Ras, Arf, and Ran families [39,40]. These proteins are essential for intracellular processes such as membrane trafficking, autophagy, cytoskeletal organization, and signal transduction.

Rab1A and Rab1B regulate membrane trafficking between the endoplasmic reticulum and Golgi apparatus [41,42], while Rab2, Rab7, and Rab8 are crucial for autophagosome maturation and autophagosome–lysosome fusion [43,44,45]. Rab10 and Rab18 contribute to ER stability and lipid droplet growth, respectively [46,47,48]. The upregulation of these Rab proteins suggests enhanced intracellular trafficking and autophagic activity in HTR cows. Although no significant differences in circulating stress indicators were observed between groups, these molecular changes may still reflect local cellular adjustments associated with the sustained performance of stereotypic behavior.

KEGG enrichment analysis confirmed upregulation of the Ras signaling pathway in HTR cows. Enriched proteins included CDC42, NRas, and KRas, which regulate proliferation, migration, survival, and cytoskeletal dynamics [49,50]. The activation of the Ras signaling pathway in HTR cows may contribute to enhanced cellular plasticity, which could help buffer physiological fluctuations associated with stereotypic behavior.

PPI network analysis further revealed a strong association between CD36 and CD47. CD36, a fatty acid transporter and metabolic sensor, regulates FA uptake and homeostasis [51,52,53], while CD47 regulates immune responses and tissue homeostasis [54,55]. The co-upregulation of CD36 and CD47 suggests that HTR cows may initiate compensatory metabolic and immunological responses to maintain systemic balance.

Taken together, the molecular profiles of HTR cows indicate coordinated upregulation of metabolic, autophagic, and signaling pathways. Although no significant differences in circulating stress indicators were detected between HTR and control cows, these molecular changes may represent local or cell-level adjustments to altered energy demands and behavioral patterns associated with stereotypic behavior. This suggests that tongue-rolling may not only be a behavioral manifestation but also be underpinned by metabolic and immunological reprogramming at the molecular level.

4.2. Reduced Milk Quality in HTR Cows: Evidence from Milk Biochemical Indicators and Proteomics

In this study, the milk produced by HTR cows had significantly lower protein percentage, crude protein percentage, and true protein percentage compared with CON cows, consistent with our previous findings [8]. Therefore, we further examined the levels of various caseins, α-LA, β-LG, and LF in the milk of HTR and CON cows.

Milk protein is a vital nutrient in cow’s milk, significantly contributing to its nutritional value. The protein content in milk is one of the primary indicators of milk quality. Casein, the predominant protein in cow’s milk, constitutes about 80% of the total milk protein [56] and includes αs1-CN, αs2-CN, β-CN, and κ-CN. The primary role of caseins is to provide nutrients and minerals for the offspring. In addition, caseins supply amino acids and confer immune protection to neonates [57,58]. Caseins are associated with each other and with calcium phosphate in the form of colloidal nanostructures called micelles. Among them, αs1-CN, αs2-CN, and β-CN are involved in calcium binding and associate through weak interactions, being predominantly located in the inner regions of the spherical micelles [59,60]. In contrast, κ-CN interacts with whey proteins and provides steric and electrostatic stabilization, thereby ensuring repulsion between micelles [61]. In our previous research, we observed that high-frequency TR cows had significantly lower percentages of total milk protein, true protein, and crude protein compared to non-stereotypic cows [8]. In this study, the levels of αs1-CN, β-CN, and κ-CN were significantly lower in the HTR cows compared to the CON cows, further suggesting that milk produced by HTR cows may be of lower quality.

LF is a non-haem iron-binding glycoprotein with immune functions [62]. It has a strong iron-binding capacity, which enhances iron absorption and utilization, maintaining iron homeostasis in the body [63,64]. Additionally, LF protects the intestinal tract, modulates anti-inflammatory responses, and promotes intestinal cell growth [65,66]. In this study, we found that the milk LF concentration in HTR cows was significantly lower than in CON cows, further suggesting that milk from HTR cows is of lower quality. Proteomic analysis also supported this observation, as evidenced by reduced levels of lactotransferrin and κ-casein in HTR cows.

Proteomics KEGG enrichment analysis showed that serotransferrin was involved in both significantly downregulated pathways. Serotransferrin, also known as transferrin, not only plays a role in iron transport, absorption, and utilization in the body but also participates in cell growth, differentiation, and antimicrobial activity [67]. Serotransferrin is widely distributed in body tissues and is also present in the bovine mammary gland [68]. In this study, the serotransferrin levels in milk from the HTR cows were significantly lower than those in the CON cows. As mentioned earlier, lactoferrin levels were also significantly lower in the HTR cows compared to the CON cows. These findings suggest that HTR cows may be at risk of iron metabolism disorders, potentially compromising the antimicrobial properties and bioavailability of iron in milk.

4.3. Metabolomics Reveals Impaired Immune Function in HTR Cows

Metabolomics techniques have become integral to animal science research, aiding in the assessment of farm animal production performance and the investigation of disease mechanisms, among other applications. However, their use in the study of stereotypic behaviors has been limited. In this study, we compared metabolomic and proteomic differences between HTR cows and CON cows.

Megalomicin C2, a compound synthesized by Micromonospora megalomicea, exhibits antiparasitic, antiviral, and antimicrobial properties similar to erythromycin [69,70]. It is involved in the biosynthesis of 12-, 14-, and 16-membered macrolides and secondary metabolites (https://www.kegg.jp, 13 January 2025). Our metabolomic analysis revealed that plasma Megalomicin C2 levels were significantly higher in the HTR cows compared to the CON cows. Cyclic nucleotide monophosphates, such as 2’,3’-Cyclic UMP (cUMP), serve as ubiquitous signaling molecules involved in pyrimidine metabolism and broader metabolic pathways. Recent research has identified cUMP as a crucial second messenger in bacterial immunity against viruses [71]. In our study, plasma 2’,3’-cUMP levels were significantly lower in HTR cows than in CON cows. These alterations may indicate perturbations in pathways associated with antiviral defense mechanisms and immune regulation, though further functional studies are required to validate their biological significance.

The upregulation of the Fc epsilon RI signaling and Asthma pathways was linked to significantly higher plasma leukotriene C4 (LTC4) levels in HTR cows compared to CON cows. Arachidonic acid metabolism produces leukotrienes, which play a pivotal role in innate immunity and inflammatory responses [72,73]. LTC4, synthesized from LTA4 via LTC4 synthase, increases vascular permeability and regulates smooth muscle tone [73]. Considering these functional roles, our findings may indicate a potential involvement of inflammatory processes in HTR cows. Nevertheless, since classical indicators of inflammation were not measured, these results should be interpreted with caution, and further studies are warranted.

4.4. Metabolomics and Proteomics Correlation Analysis

Both metabolomics and proteomics analyses revealed enrichment in the Fc epsilon RI signaling pathway (Figure A2). The Fc epsilon RI complex forms through the interaction of the Fc end of antigen-specific immunoglobulin E (IgE) with a high-affinity receptor on the cell surface [74]. Fc epsilon RI is a unique molecular target that triggers mast cell responses and plays a role in allergic reactions [75,76]. In this study, compared to the CON cows, the HTR cows showed upregulation of Tyrosine-protein kinase, GTPase KRas, GTPase NRas, and LTC4, which are enriched in the Fc epsilon RI signaling pathway, leading to increased vascular permeability, smooth muscle contraction, and stimulated mucus secretion. Thus, the enrichment observed in HTR cows may reflect a potential involvement of this pathway, but the functional consequences remain to be clarified.

5. Conclusions

In conclusion, HTR cows were associated with distinct alterations in both plasma and milk profiles. HTR cows exhibited elevated plasma T₃ levels, indicating endocrine activation, while their milk contained reduced concentrations of αs1-CN, β-CN, κ-CN, and LF, suggesting impaired protein synthesis, diminished immune defense capacity, and reduced milk quality. Multi-omics analyses further demonstrated substantial changes in metabolites and proteins, with enrichment of GTP/GDP-binding proteins and activation of the Ras signaling pathway, reflecting increased cellular metabolic activity. Together, these findings provide evidence that tongue-rolling behavior is not merely a stereotypy but may reflect systemic physiological changes that compromise milk quality and cow welfare. Future studies should investigate the causal mechanisms linking metabolic dysregulation to tongue-rolling, with the ultimate goal of developing strategies to mitigate its negative impact on dairy production.