Transport stress alters serum biochemistry and gut microbiota in Tibetan sheep (ovis aries): a 30-day recovery assessment

Abstract

Background

Transport stress poses a major challenge to the livestock industry. Studies have shown that transportation stress can cause depression, elevated body temperature and dysbiosis of intestinal microbiota in meat goats, and in severe cases, even lead to death of the animals, which in turn affects the economic efficiency of the farming industry. However, the changes in blood biochemical indicators and flora of Tibetan sheep throughout the receiving period after long-distance transport remain unclear.

Methods

In this study, six 7-month-old Tibetan sheep with similar body weights were selected for a 30-d transport trial, and blood and faecal samples were collected for blood biochemical indices and 16 S microbiome sequencing before transport (BT), and on day 1 (AT 1), day 16 (AT 16), and day 30 (AT 30) after the end of the transport, respectively.

Result

And a number of blood biochemical indices (creatine kinase (CK), lactate dehydrogenase (LDH), alkaline phosphatase (ALP), aspartate aminotransferase (AST), cortisol (COR), and adrenocorticotropic hormone (ACTH)) were significantly elevated (P < 0.05). Combined analysis of physiological and biochemical indices with 16 S rDNA sequencing showed that Turicibacter, Romboutsia and Clostridium_sensu_stricto_1 were positively correlated with LDH, ALP, CK, AST, and ACTH, while Prevotellaceae_UCG_004 was negatively correlated. Furthermore, the observed recovery trends in vital signs after AT 16 suggest that Tibetan sheep may be able to adapt to the new environment within one month post-transport. The present study illustrated the effects of transport without measures on the vital signs, serum biochemical indicators and faecal microorganisms of Tibetan sheep throughout the receiving period, which is of great significance in guiding the transport of Tibetan sheep and solving the damage caused by transport stress on the economic benefits of the farming industry.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-025-04464-9.

Introduction

Livestock are frequently exposed to multiple stressors, including nutritional deficiencies, disease, and environmental fluctuations(e.g., heat stress [1–3], hypoxic stress at high altitudes [4]), all of which compromise growth performance and economic returns in animal husbandry. Among these, transport stress is one of the most significant yet understudied challenges, with profound implications for animal health and production efficiency. Transport stress has a significant impact on the economic benefits of animal husbandry. Animal Husbandry occupies an important position in the global agricultural economy. Transport stress arises from a combination of environmental temperature shifts, confined spaces, prolonged fasting, and physical vibrations during transit [5]. This multi-factorial stressor triggers neuroendocrine disturbances, dysregulates energy metabolism, and overactivates the hypothalamic-pituitary-adrenal (HPA) axis, leading to elevated COR and ACTH levels [6], Such hormonal imbalances can induce tissue damage and disrupt metabolic homeostasis, further complicating health assessments in transported animals [7]. In addition, transportation stress affects the richness of the ruminant microbiota [8] and alters the composition of the intestinal flora [9]. Given the critical role of gut microbes in nutrient absorption, immune function, and metabolic regulation, these disruptions can negatively impact on body weight, metabolic indicators, and blood lipids [10]. Therefore, by detecting relevant indicators in serum, it can indirectly reflect the metabolic activity of intestinal microorganisms and the changing trend of metabolites. These findings provide an indication that serum biochemical indicators could serve as potential proxies for understanding the interaction between gut microbial community and host health, offering a new perspective for further research.

Musclel and liver damage are also common consequences of transport stress, reflected in evidenced serum biomarkers such as CK, LDH, ALP, and alanine aminotransferase (ALT). These markers provide crucial insights into tissue integrity and metabolic status, highlighting the systemic effects of transport stress [7]. However, most existing studies focus on acute stress responses immediately after transport, with limited investigation into long-term physiological recovery particularly within the critical 30-day post-transport period. A deeper understanding of these dynamics is essential for developing effective stress mitigation strategies.

Recent advances in microorganisms research have revealed that gut microbiota play a pivotal role in modulating host stress responses. For example, reducing the diversity and abundance of microbial communities, resulting in a decrease in beneficial bacteria and an increase in potentially pathogenic bacteria [11]. Further studies revealed that the dynamics of gut microbes could have an impact on host metabolism [12]. CK is an enzymatic marker of muscle activity [13]. The functional structure of the intestinal microbiome has been shown to correlate with exercise status, for instance, microbial-derived short-chain fatty acids (SCFAs) such as propionate and butyrate are linked to muscle repair and energy metabolism, as indicated by correlations between fecal SCFA levels and serum CK activity [13]. The impact of environmental changes on the intestinal microbiota cannot be ignored, as they influence hormone release from the HPA axis through the gut microbiota pathway, which in turn is linked to serum biochemical markers [6]. Furthermore, gut dysbiosis has been implicated in liver injury, with elevated serum endotoxin and ALT levels observed in individuals with microbial imbalances. Despite these findings, the mechanistic connections between microbial shifts and host physiology during transport stress remain poorly understood.

To address these gaps, this study examines the dynamic changes in fecal microbial and serum biochemical markers in Tibetan sheep before and after transport, combining 16 S rRNA sequencing with correlation analyses. Given that Tibetan sheep are indigenous to high-altitude regions and likely possess unique genetic adaptations to hypoxic stress [14], studying their response to transport stress provides a unique model to disentangle the effects of novel stressors from innate resilience mechanisms. By elucidating the interactions between gut microbes and host metabolism, this research aims to provide novel insights into the mechanisms of transport stress and potential interventions to enhance animal resilience.

Materials and methods

Ethics statement

All animal management and experimental procedures were performed according to the Guidelines for Experimental Animals established by the Ministry of Science and Technology of China. All experimental protocols were approved by the Institutional Animal Care and Use Committee (IACUC) of Anhui Agricultural University (approval number AHAUXMSQ2024175).

Test animals and experimental design

Six 7-month-old Tibetan sheep in good condition and with similar body weight (mean weight 22.25 ± 0.5 kg) were selected from 150 sheep prepared for transport to Anhui Province by Bakatai Agricultural Ranch Ltd. in Qinghai Province as test subjects. The sheep were transported to the Jianghuai Watershed Comprehensive Experimental Station of Anhui Agricultural University by road for 30 h. No food or water was allowed during the transport. The test sheep were not treated with anti-stress treatment before and after transport, and the day of arrival without food was recorded as day 0, after which heart rate, body temperature and pulse rate were measured and blood and faecal samples were collected at BT and AT 1, AT 16 and AT 30, respectively (Fig. 1A).

Fig. 1.

Effects of stress during transport (A) on heart rate, pulse rate (B) and body temperature (C) of Tibetan sheep. Data are expressed as mean ± standard error (SE), (n = 6). The horizontal coordinates of the line graphs represent the different subgroups and the y-axis represents the changes in HR, PR and RT, respectively

The experimental diet is prepared in accordance with the “Nutrient requirements of meat-type sheep and goat” (NY/T 816–2021). The composition and nutritional content are shown in Table 1. Feed once every day at 8:00 and 16:00.

Table 1.

Diet formula and nutritional components

Dietary Ingredient	Contents
Corn	32.5
Soybean meal	12.00
Wheat bran	2.50
Rice straw	22.50
Sweet sorghum silage	27.50
Salt	0.25
NaHCO3	0.75
Premix1	2.00
Nutrient composition (%)
Crude protein	13.35
Ether extract	2.56
Neutral detergent fiber	40.16
Acid detergent fiber	27.78
Calcium	0.92
Phosphorus	0.33
Metabolizable energy2, MJ/kg	13.01

¹Formulated to provide (per kilogram of premix) 50 KIU of vitamin A, 10 KIU of vitamin D3, 450 mg of vitamin E, 2100 mg of Zn, 8 mg of Se, 900 mg of I, 2100 mg of Fe, 10 mg of Co, 30 mg of Mn, and 1 350 mg of Cu

²Calculated according to Ministry of Agriculture of P.R. China, 2021

Vital sign determination

Rectal temperature (RT), respiratory rate, pulse rate (PR), and heart beat (HR) were measured at 12:00 on the 1 st, 16th, and 30th d of BT and transport to destination, respectively. Rectal temperature was measured by an electronic thermometer(manufactured by Guangzhou Berrcom Medical Device Co., Ltd.); respiratory rate was measured by eye-observation of chest and abdomen; pulse rate was measured from the root of the tail using an oximetry device(manufactured by Contec Medical Systems Co., Ltd); and heart beat was measured by pressing a stethoscope(manufactured by Jiangsu Yuyue Medical Equipment&Supply Co.,Ltd.) on the left chest of the sheep and recording the number of heartbeats in two 1-minute intervals.

Determination of serum biochemical index

Vital signs were measured at the same time, blood samples (5 mL) were collected in vacutainer tubes (without EDTA) (manufactured by Shandong Chengwu medical products factory) by puncturing the jugular vein. The samples were left to stand at room temperature for 60 min and then centrifuged(4 °C, 3000 × g, 15 min). The samples were transferred into liquid nitrogen and then stored at − 80 °C for further analysis. Serum biochemical indicators include glucose (GLU), total protein (TP), albumin (ALB), CK, LDH, ALT, AST, ALP, COR and ACTH. All kits were purchased from Shanghai Jianglai Biotechnology Co., Ltd.

Extraction and sequencing of microbial DNA

BT and AT 1, AT 16 and AT 30, use a speculum to hold the anus open, collect about 10 g of rectal stool samples with a sterile cotton swab, encapsulate it in a sterile container. The samples were transferred into liquid nitrogen and then stored at − 80 °C for further analysis. Total genomic DNA was extracted from fecal samples using the TGuide S96 Magnetic Soil/Stool DNA Kit (Tiangen Biotech (Beijing) Co., Ltd.) according to manufacturer’s instructions. The quality and quantity of the extracted DNA were examined using electrophoresis on a 1.8% agarose gel, and DNA concentration and purity were determined with Nano Drop 2000 UV-Vis spectrophotometer (Thermo Scientific, Wilmington, USA). The hypervariable region V3-V4 of the bacterial 16S rRNA gene were amplified with primer pairs 338 F : 5’- ACTCCTACGGGAGGCAGCA-3’ and 806R : 5’- GGACTACHVGGGTWTCTAAT-3’. Both the forward and reverse 16 S primers were tailed with sample-specific Illumina index sequences to allow for deep sequencing. The PCR was performed in a total reaction volume of 10µL: DNA template 5–50 ng, forward primer (10µM) 0.3µL, reverse primer (10µM) 0.3µL, KOD FX Neo Buffer 5µL, dNTP (2 mM each) 2µL, KOD FX Neo 0.2µL, and finally ddH₂O up to 20µL.After with initial denaturation at 95 °C for 5 min, followed by 20 cycles of denaturation at 95 °C for 30 s, annealing at 50 °C for 30 s, and extension at 72 °C for 40 s, and a final step at 72 °C for 7 min. The amplified products were purified with Omega DNA purification kit (Omega Inc., Norcross, GA, USA) and quantified using Qsep-400 (BiOptic, Inc., New Taipei City, Taiwan, ROC). The amplicon library was paired-end sequenced (2 × 250) on an Illumina novaseq6000 (Beijing Biomarker Technologies Co., Ltd., Beijing, China).

Bioinformatics analysis

Quality filtering, trimming, denoising, and merging of fastq files was performed using the dada2 package [15] in QIIME2 software [16]. Amplicon sequence variants (ASVs) were obtained, and ASVs with relative abundance less than 0.005% were filtered. Taxonomic annotation of feature sequences using Naïve Bayes Classifier combined with a comparison approach using Silva.138 as a reference database. Sample Alpha diversity indices were assessed using QIIME2 software, and community richness and diversity were analysed by Chao1, Simpson and Shannon indices. For Beta diversity analysis, principal coordinate analysis (PcoA) was performed using the Bray-Curtis algorithm for visualisation of complex multidimensional data. Partial least squares discriminant analysis (PLS-DA) was performed using SIMCA (version 13.0). Linear discriminant analysis (LDA) effect sizes were used to select biomarkers within different groups. Tax4Fun2 is an upgraded version of Tax4Fun (Tax4Fun2 v1.1.5). It processes 16 S high-throughput sequencing data through the QIIME or SILVAngs platform, performing species classification of features based on the SILVA database. Based on the classification results, 16 S copy numbers are standardized according to NCBI genomic annotations. Finally, it constructs a linear relationship between SILVA taxonomy and prokaryotic taxonomy in the KEGG database to predict KEGG functions for prokaryotic microbial communities. Based on KEGG database information, KO, Pathway, and EC data can be obtained, and the abundance of each functional category can be calculated based on feature abundance. The online platform BMKCloud (https://www.biocloud.net) was used to analyze the sequencing data.

Data processing and analysis

All data were preliminarily collated using Excel 2021 and analyzed by one-way ANOVA test using SPSS 20.0 (SPSS Inc, Chicago, IL, USA) software and multiple comparisons using Duncan’s method to analyze the effect of transport stress on Tibetan sheep, with P < 0.05 being significant and P < 0.01 being highly significant. All values are expressed as mean ± standard error. Graphs were prepared using Graph Pad Prism 9.0 (Graph PAD Software, San Diego, CA, USA).

Results and analysis

Effect of long-distance transportation on vital signs of Tibetan sheep

The heart rate, body temperature and pulse rate of Tibetan sheep were examined at BT and AT 1, AT 16 and AT 30 during the experimental period, and it was found that the trends of the three changes were the same during the experimental period, and that they dropped to the lowest level at AT 16 d and then recovered to the normal level (Fig. 1B, C). The difference between BT and AT 16 for HR was significant (P < 0.05), and the difference between AT 1, AT 16 and AT 30 was significant (P < 0.05), but the difference between BT and AT 30 was not significant (P > 0.05); BT of RT differed significantly from the other three groups (P < 0.05), and AT1 differed significantly from AT 16 (P < 0.05), but AT 1 did not differ significantly from AT 30 (P > 0.05); PR of BT, AT 1, AT 30 differed significantly from the AT 16 (P < 0.05) (Supplementary Table 1).

Effect of long-distance transportation on the biochemical index of Tibetan sheep serum

The blood biochemical indices of Tibetan sheep were also tested during these four periods, and it was found that the levels of CK, ACTH, COR, ALP, LDH and AST continued to increase until the end of the experiment, and the differences between the groups were significant (P < 0.05) (Fig. 2A, B,C, D,E, F); ALT levels peaked at AT 1 and then gradually decreased. Compared with the BT group, significant differences in ALT levels were observed across the other three groups (P < 0.05). Furthermore, significant differences were also noted between the remaining three groups (P < 0.05) (Fig. 2G); ALB levels were highest during the AT 1 period, with a slight subsequent decrease, and the difference between ALB levels during the AT 1, AT 16 and AT 30 periods was not significant compared to the BT, but compared to the AT 1 period, ALB levels were significantly lower at AT 16 and AT 30 (P < 0.05) (Fig. 2H); TP levels exhibited an initial rise followed by a decline, reaching their peak during the AT16 period. This difference was statistically significant when compared to the BT group (P < 0.05), whereas no significant differences were observed between the other groups (P > 0.05) (Fig. 2I); Overall, GLU levels exhibited a downward trend. Compared with the BT group, only the AT30 group showed a significant difference (P < 0.05), while differences in other groups were not significant (P > 0.05) (Fig. 2J).

Fig. 2.

Effects of stress during transport on serum biochemical indices in Tibetan sheep. Data are expressed as mean ± standard error (SE), (n = 6). * denotes P < 0.05, symbol ** denotes P < 0.01, and symbol *** denotes P < 0.001. The horizontal coordinates of the bar graphs are the different subgroups and the vertical coordinates are the concentrations of the individual serum biochemical indicators. CK: Creatine kinase (A); ACTH: Adrenocorticotropic hormone (B); COR: Cortisol (C); ALP: Alkaline Phosphatase (D); LDH: Lactatedehydrogenase (E); AST: Aspartate aminotransferase (F); ALT: Alanine aminotransferase (G); ALB: Albumin (H); TP: Total protein (I); GLU: Glucose (J). BT, before transport; AT 1, 1 st d after transport; AT 16, 16th d after transport; AT 30, 30th d after transport

Effects of long-distance transport on fecal microbial diversity

Alpha diversity analysis of faecal microorganisms from four groups, BT and AT 1, AT 16 and AT 30, showed that there was no statistically significant difference in Simpson’s diversity index between groups (P > 0.05) (Fig. 3A); Differences between groups of Chao1 diversity index indicators were highly significant (P < 0.01) (Fig. 3A); Significant (P < 0.05) difference between groups for each indicator of the Shannon diversity index (Fig. 3A).

Fig. 3.

Effects of transport stress on the composition and structure of faecal microbiota in Tibetan sheep. Simposn index (A), Chao1 index (B), Shannon index (C)) discriminant analysis based on partial least squares (D), correlations and variability among faecal microbiomes at each stage of the process Veen plots (E), phylum level (F) and genus level (G) stacked plots of abundance-heat maps of Top10 species of faecal flora. Veen plot box lines represent microbial associations between groups, bars represent the total number of faecal microorganisms monitored in each group, and bars (1) represent the sum of endemic species in each group, (2) represents the sum of endemic species between two random groups, (3), (4) and so on. Heatmap horizontal coordinates are experimental subgroups, vertical coordinates are microorganisms with species abundance Top, stacked plots horizontal coordinates are species abundance, different colours represent different subgroups, and bar lengths represent species abundance. BT, before transport; AT 1, 1 st d after transport; AT 16, 16th d after transport; AT 30, 30th d after transport

In order to characterise the similarity in biological composition between individuals, the use of abundance information and evolutionary relationships between sequences in each sample, and the ability to capture significant differences in microbial communities between samples, Bata diversity was used to calculate distances between samples. In this study, PLS-DA based on Partial Least Squares method was used to assess the Bata diversity of faecal microorganisms in Tibetan sheep. According to the PLS-DA results, the microbial isolation of Tibetan sheep under transport stress conditions was significant (Fig. 3B). β-diversity was analysed using PERMANOVA (based on Bray-Curtis distance and/or UniFrac distance), revealing significant differences between groups (P = 0.001) (Supplementary Fig. 3).

Effects of long-distance transportation on fecal microorganism abundance and community composition

After microbial 16 S rDNA sequencing, a total of 27,902 microbial species were found in faeces, with 7,171, 6,689, 7,196 and 6,846 species in the four groups of BT, AT 1 d, AT 16 and AT 30, respectively. Among them, 475 species were common to the four groups, while 5,578, 4,985, 5,265 and 5,208 species were unique to the four groups of BT, AT 1, AT 16 and AT 30, respectively, with a total of 1,469 microorganisms unique to each of the two groups, and a total of 676 microorganisms unique to each of the three groups (Fig. 3E). To further explore the structural composition and differences in faecal flora between groups, we identified a total of 10 species at the phylum level, with the dominant microbial phyla being Firmicutes (45.37%−49.56%), Bacteroidota (28.83%−35.88%), Spirochaetota (4.38%−9.04%), with a combined abundance of more than 98% in the four groups. Among them, the species abundance of Bacteroidota and Verrucomicrobiota phyla decreased gradually over time, and the species abundance of Spirochaetota increased gradually (Fig. 3F). At genus level Trepenema (4.32%−7.98%), UCG 005 (5.35%−7.31%), Rikenellaceae_Rc9_gut_group (5.49%−6.86%) were more abundant. Among them, species abundance of Treponema spp. gradually increased with time, and species abundance of Akkermansia, Alistipes spp. gradually decreased with time (Fig. 3G). The relative abundance of species of the phylum Firmicutes was the greatest across the samples, followed by the phylum Bacteroidota, with Rikenellaceae_RC9_gut_group, UCG_005, unclassified_Lachnospiraceae and unclassifed_UCG_010 have greater species abundance at the genus level and are mostly in the phylum Firmicutes (Supplementary Fig. 1).

To further explore trends in important microflora during the periods before and after transport, we used STEM software to analyse trends in the samples (Fig. 4A). The results showed that the trend of abundance of microflora was significantly categorised into two features (P < 0.05), with statistically significant enrichment of microflora in profile9 and profile3, which included Turicibacter, Succinivibrio, Cellulosilyticum in profile9, Clostridium_sensu_stricto_1, and Clostridium_sensu_stricto_6 showed a gradual increase in 14 flora over time; in profile3 including Frisingicoccus, [Eubacterium]_ruminantium_group, [Eubacterium]_ventriosum_group, and so on, the abundance of 11 bacterial groups in profile3 decreased to a minimum during the AT 1 period, and then recovered to the pre-transport state(see Supplementary Table 2).

Fig. 4.

Analysis of microbial abundance trends and functional changes. Trends in fecal microbial genus levels (A), intergroup LEfSe analysis plots (LDA > 3) (B) with microbial abundance plots (C), and enrichment in different metabolic pathways and biological processes with functional predictions (D). P-values are shown in the lower left corner, the number of colonies with that trend in the upper left corner, LDA values in the horizontal coordinate, and genera with LDA > 3 in the vertical coordinate. The colour of the ring heatmap varies from blue (representing lower abundance) to red (representing higher abundance), with values ranging from − 1.5 to 1.5, and genera with species abundance > 1% in the red box. The right side of the functional prediction heatmap indicates the KEGG profiles of the third and second levels, respectively. BT, before transport; AT 1, 1 st d after transport; AT 16, 16th d after transport; AT 30, 30th d after transport

To further analyse the significant variability of genera among the four different groups, we performed LEfSe (LDA > 3) analysis at the genus level (Fig. 4B, Supplementary Fig. 2), and found that the number of significantly enriched genera showed a trend of decreasing and then increasing with the increase of post-reception time, with the AT 16 group being the least, with only two genera significantly enriched in unclassified_Oscillospiraceae, Cellulosilyticum two genera were significantly enriched, AT 30 group had the most, with twelve genera significantly enriched such as unclassified_Erysipelatoclostridiaceae, Succinivibrio, Turicibacter and so on, BT group Prevotellaceae_UCG_004, Monoglobus, unclassified_UCG_010 and ten other genera were significantly enriched, and seven genera such as Bacteroides and Christensenellaceae_R_7_group were significantly enriched in AT 1 group.

In addition, 61 genera were screened for differences (P < 0.05) at the genus level using the rank sum test (Fig. 4C). The diversity of the flora of the system decreased with increasing post-reception time, but the abundance of some functional genera increased, and the abundance of some genera surviving under specific environmental conditions changed. For example, Faecalibacterium, Candidatus_Saccharimonas, [Eubacterium]_ventriosum_group, [Eubacterium]_nodatum_group, and Christensenellaceae_R_7_groupetc. Among them, Bacteroides, Christensenellaceae_R_7_group, Prevotellaceae_UCG_004, Monoglobus and unclassified_UCG_010 differed significantly in species abundance at each stage (P < 0.05) and had species abundance > 1%. Intestinal basidiomycetes such as Bacteroides and Prevotellaceae_UCG_004 showed significant fluctuations in species abundance (P < 0.05) during the test period, Monoglobus showed a trend of decreasing and then increasing species abundance with the advancement of the test time, and the opposite was true for Christensenellaceae_R_7_group Bacterium spp on the contrary. Subsequently, we used Tax4Fun software to predict the function of each sample colony, and analysed the stability of community function in each group, and the functional variability of microbial communities among systems (Fig. 4D), and the results showed that each group was mainly enriched in Metabolic pathways, Microbial metabolism in diverse environments and Biosynthesis of secondary metabolites, and the degree of enrichment fluctuated among groups.

Analysis of the correlation between faecal bacterial genus levels and blood biochemistry

We used Spearman’s correlation to correlate the differential microbial colonies with physiological and biochemical indices between groups. And the correlation between differential abundance microbiota (61 differential genera) and physiological and biochemical indices at the genus level of different groups was demonstrated using clustering heat map (Fig. 5A). The results showed strong correlations between these differential bacterial groups and serum physiological and biochemical indices. Among them, we found that Clostridium_sensu_stricto_1 was significantly positively correlated with ACTH (P = 0.0001660, R² = 0.4824, FDR = 0.000664), Lachnospiraceae_NK3A20_groupa was significantly positively correlated with AST (P = 0.04832, R² = 0.1658, FDR = 0.04832), while Candidatus_Saccharimonas was significantly negatively correlated with ALT (P = 0.001604, R² = 0.3703, FDR = 0.002138667), and Dielma was significantly negatively correlated with LDH (P = 0.001061, R² = 0.2617, FDR = 0.002122) (Fig. 5B). The correlation network diagram between differential microorganisms and biochemical indicators further demonstrated the strong and weak relationship and positive and negative modulation of the correlation between each physiological and biochemical indicators, differential microorganisms (r > 0.6, P < 0.05). Based on further analysis of the correlation network diagrams, this study found a significant interaction between microbial communities and blood biochemical indicators. In total, eight genera were associated with 24 positive and 10 negative correlations, forming a complex network of microbe-host interactions. Among them, the number of Turicibacter related objects was as high as eight, with Prevotellaceae_UCG_004 strongly positively correlated with ACTH and ALT, and Candidatus_Saccharimonas strongly negatively correlated with ALT and COR (Fig. 5C).

Fig. 5.

Correlation analysis of physiological and biochemical indicators with differential microorganisms (A), significant correlation of microorganisms with blood biochemical indicators (B) and network diagram of correlation between differential microorganisms and biochemical indicators (C). The horizontal coordinates of the heat map were 61 genus-level significant differential microorganisms, the vertical coordinates were physiological and biochemical indicators, and the colours varied from green (representing lower abundance) to red (representing higher abundance); in the graph, * denotes the correlation, and the symbol * denotes P < 0.05, the symbol ** denotes P < 0.01, the symbol *** denotes P < 0.001; and the correlation analysis graph, the upper-left corner of which is the Regression equation about X and Y, coefficient of determination R² and P value; In the heat map of correlation network (r > 0.6, P < 0.05), the line type/line colour represents positive and negative correlation, line thickness represents the strength of correlation relationship, and point size/colour depth represents the number of related objects

Discussion

In recent years, the expansion and intensification of modern animal husbandry have led to increased frequency and distance of animal transport. Numerous studies indicate that both long-duration and short-duration transport can adversely affect animal health and performance, including alterations in blood biochemical parameters, hormones levels, metabolites and vital signs such as body temperature, respiration and pulse rate [17–20]. These changes reflect adaptive physiological responses under stress conditions. Our study investigated the effects of transport stress on Tibetan sheep from per-transport to one month post-arrival, assessing vital signs, blood indicators (e.g., CK, COR, ACTH, AST), dynamic changes in specific bacterial genera (e.g. Turicibacter, Prevotellaceae_UCG_004, Monoglobus, Bacteroides), and their correlations (Fig. 6). These findings provide important insights into the molecular mechanism underlying transport stress and suggest potential microbial interventions to mitigate its impact on host health. Future studies integrating metabolomics and transcriptomics could further elucidate the roles of microbial metabolites and their regulatory pathways in stress responses.

Fig. 6.

Effects of transport stress on blood biochemical indices and faecal microbiology in Tibetan sheep

Vital signs in Tibetan sheep decreased initially after transport and then recovered, with heart rate, body temperature and pulse rate reaching their lowest at AT 16 d before gradual recovery. This pattern may be linked to increased oxygen partial pressure at low altitude, enhancing oxygen utilization and metabolic adaptation. Tibetan sheep adapted to high altitude exhibit elevated hemoglobin concentration and respiratory rate [21, 22], and HR is negatively correlated with hemoglobin levels [23]. The post-transport reduction in heart rate may thus reflect improved oxygen dissolution in plasma due to higher pO₂. Decreased body temperature likely results from unfamiliar environments, nervousness, and prolonged fasting during transport, which reduce metabolic rate and trigger an energy-saving mode, lowering calorie production and body temperature. Recovery requires time to reestablish normal thermoregulation and metabolism.

Serum biochemical markers indicated significant stress-induced alterations. CK and LDH, key enzymes in energy metabolism [24], increased continuously, suggesting muscle damage potentially due to prolonged standing during transport. LDH facilitates gluconeogenesis and glycolysis maintaining blood glucose stability [25]. and elevated levels indicate membrane permeability changes and cellular damage [26]. ACTH and COR, critical stress indicators, increased significantly, consistent with activation of the HPA axis and sympathetic nervous system [27–29]. COR exerts anti-inflammatory effects and helps maintain homeostasis under stress [20], a response corroborated in studies such as that on Iranian velvet goats [30]. ALP elevation may indicate an enhanced inflammatory response [31]. ALT and AST, markers of liver and muscle damage [32–34], peaked at AT 16 d and then declined, indicating transient injury followed by recovery. Total protein and ALB, synthesised primarily in the liver, reflect nutritional status and liver function [35]; decreased ALB levels may signal impaired liver function or inflammatory damage [36]. These results demonstrate that transport stress induces significant physiological disturbances and tissue damage, though the specific contributions of altitude, oxygen, temperature and humidity require further investigation. To explore mitigating mechanisms, we examined host-microbiota interactions through 16 S rDNA sequencing of faecal samples.

While many studies have addressed transport effects on gut microbiolota, few have monitored changes throughout the reception period. Our analysis revealed that transport stress significantly altered the fecal microbial community structure and diversity (Simpson, Chao1, and Shannon indices; PLS-DA), indicating disruption of microbial stability, which may affect overall functional integrity. Environmental and physical stresses can modulate the intestinal microbiota while influencing the stress response of the host [37]. Thus, the intestinal microbiota is an important mediator of host health [38]. Firmicutes and Bacteroidota were the dominant phyla, consistent with consistent with findings in lake sheep [39]. Fluctuations in Firmicutes abundance may impact cellulose degradation, fermentation, and immune regulation [40], while declining Bacteroidota levels could impair oligosaccharides and cellulose digestion, reducing stress resilience [41]. Increased abundance of Spirochaetota, involved in nutrition and immune maturation [42], suggests adaptive responses to stress. STEM analysis identified increasing trends in genera such as Turicibacter, Succinivibrio, Cellulosilyticum, Clostridium_sensu_stricto_1, Clostridium_sensu_stricto_6, Paeniclostridium, Romboutsia. Turicibacter influences lipid metabolism and energy balance [43]. Succinivibrio and Cellulosilyticum enhance fermentation and cellulose digestion [44]; pathogenic Clostridium clusters (Clostridium_sensu_stricto_1 and Clostridium_sensu_stricto_6) may pose health risks [43]; and Romboutsia is linked to pro-inflammatory responses [45]. Lysinibacillus, a beneficial bacterium, may improve intestinal immune and microbial balance [43]. The results of differential analysis at the genus level showed that the changes in the number of significantly enriched genera at different stages and the fluctuation of the abundance of each genus over time reflected that transport stress induced the redistribution of the faecal microflora of the Tibetan sheep at the genus level in order to adapt to the new environment and the stressful state. Changes in altitude- (Faecalibacterium [46], Candidatus_Saccharimonas [47], [Eubacterium]_ventriosum_group [48]), meat quality- ([Eubacterium]_nodatum_group [49]), and immune-related genera([Eubacterium]_ruminantium_group [50], Christensenellaceae_R_7_group [51]) indicate microbial selection and adaptation to new environmental conditions, potentially affecting host physiology and product quality.

The intestinal flora plays a crucial role in various aspects, including digestion and nutrient absorption, immune regulation, metabolic control, defence against pathogens, effects on the nervous system and hormonal regulation [52]. Spearman correlation analysesis complex interactions between microbes genera and blood biochemical markers. Turicibacter, Clostridium_sensu_stricto_1 and Romboutsia showed significant positive correlation with LDH, ALP, CK, AST and ACTH, indicating roles in inflammation, immune response, and metabolic adaptation [53]. Turicibacter may modulate energy homeostasis and HPA axis activity [54]; Clostridium_sensu_stricto_1 likely influences protein metabolism and inflammation via metabolites like butyrate and lactate [55, 56]; and Romboutsia may contribute to energy metabolism and inflammatory regulation [57]. In contrast, Prevotellaceae_UCG_004 and Candidatus_Saccharimonas were negatively correlated with stress markers (e.g., LDH, CK, AST, and ACTH), suggesting protective roles through short-chain acid and production and anti-inflammatory effects [58]. These findings highlight how specific gut microbes may modulate host metabolic, immune, and inflammatory pathways during transport stress.

Conclusion

Long-distance transport was associated with significantly changes in the composition and diversity of faecal microbial communities, including a decrease in the abundance of Bacteroides spp. This change might represent a dynamic adjustment of intestinal microbes to adapt to the altered environment during transport. Concurrently, transport stress was correlated with significant alterations in serum biochemical indicators, including elevated levels of CK, LDH, ALP, AST and COR. Further correlation analyses revealed significant associations between specific genera in the microbial community (e.g. Turicibacter, Romboutsia and Clostridium_sensu_stricto_1, etc.) and blood biochemical indices. These correlations suggest that alterations in intestinal microbes may be linked to host metabolic and inflammatory responses under transport stress, proposing a potential mechanism worth further investigation. These findings provide new insights into the mechanisms of transport stress on host health and provide a scientific basis for optimising livestock management strategies to mitigate transport stress damage.However, as the current trial did not include a negative control group, it is impossible to entirely rule out the influence of non-transport factors (such as temperature, humidity, and duration of daylight exposure) on indicators including hormone levels and gut microbiota. To further clarify the specific role of transport itself on these relevant metrics, subsequent research will refine the experimental design by introducing standardised negative controls. This will enable the effective identification and exclusion of variations caused by non-independent variables, thereby allowing a more accurate assessment of transport’s impact on trial outcomes.

Abbreviations

BT

Before transport

AT

After transport

CK

Creatine kinase

LDH

Lactate dehydrogenase

ALP

Alkaline phosphatase

AST

Aspartate aminotransferase

COR

Cortisol

ACTH

Adrenocorticotropic hormone

ALT

Alanine aminotransferase

ALB

Albumin

GLU

Glucose

TP

Total protein

SCFAs

Short-chain fatty acids

HAP

Hypothalamic-pituitary-adrenal

RT

Rectal temperature

PR

Pulse rate

HR

Heart rate

pO₂

Pressure of oxygen

Hb

Haemoglobin

Authors’ contributions

CR, ZZ, QW and YH: designed the study and provided the sources of all the samples, and wrote the manuscript; RW and MW: writing original draft, collation and analysis of experimental data, and plotting using software; RW, JW, PW, JO, WT and GS: collected the data and helped complete the experiments; JC: provision of test sites; ZZ and XC: funding acquisition. All authors: have read and agreed to the published version of the manuscript.

Funding

This project was funded by the National Modern Meat Sheep Industry Technology System (CARS-38), the Science and Technology Program of Qinghai Province (2022-NK-134), and the Scientific Research Project of Anhui Higher Education Institutions (2023AH050981).

Data availability

The sequencing data were deposited to the Sequence Read Archive National Center for Biotechnology Information (NCBI) with accession number PRJNA1336283.

Declarations

Ethics approval and consent to participate

The animal study protocol was approved by the Animal Care Committee, Anhui Agricultural University (AHAUXMSO2024175).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.