The Price of Survival: Yaks’ Adaptation to High Altitudes

The Price of Survival: Yaks’ Adaptation to High Altitudes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Price of Survival: Yaks’ Adaptation to High Altitudes Lang Tan, Xiaojing Liu, Yonggui Ma, Jinfen Yang, Qunying Zhang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8533339/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The yak ( Bos grunniens ) serves as an exceptional model for studying high-altitude adaptation mechanisms due to its evolutionary success in the hypoxic environment of the Qinghai-Tibet Plateau. While previous research has largely focused on genetic and physiological traits of yaks, the interactions between rumen microbiota and host physiology under hypoxic conditions remain poorly understood. As the largest digestive organ in ruminants, the rumen and its microbiota play a central role in digestion and host nutrition. In this study, a comparative analysis of digestive metabolism and rumen microbiota was carried out in yaks and cattle under varying atmospheric oxygen levels. Our findings reveal that yaks have developed unique microbial strategies to cope with energy deficits in hypoxic stress. These include a shift in rumen microbiota toward amino acid degradation and enhanced long-chain fatty acid biosynthesis, thereby improving energy acquisition despite reduced nutritional intake. However, this metabolic adaptation comes at a physiological cost - reduced microbial crude protein (MCP) synthesis leads to elevated ruminal NH 3 -N levels, and increased fatty acid metabolism and urea cycle activity contribute to hepatic stress. This study presents the first evidence of metabolic trade-offs in high-altitude adaptation, demonstrating that yaks optimize microbial-mediated energy production at the expense of liver health. These insights deepen our understanding of host-microbiome coevolution mechanisms in extreme environments and highlight biological costs associated with adaptation. High-altitude environments adaptation metabolism hypoxia yak Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background The Qinghai-Tibet Plateau is known as the "Roof of the World," comprising the highest-altitude regions on Earth, with an average elevation of 3,000 meters [ 1 ]. The most prominent feature of this area is the significantly lower partial pressure of atmospheric oxygen ranging between 19.94 to 20.78% compared to plains [ 2 ], where normal air oxygen level in the atmosphere is 21.0% [ 3 ]. Hypoxia can adversely affect health of animals causing death in severe cases [ 4 ]. In studies involving humans and animal models, it has been demonstrated that exposure to chronic hypoxia can cause abnormal bone tissue structure, reduced bone density, stunted growth and development, and cardiovascular dysfunction [ 5 , 6 ]. Epidemiological data indicate that the incidence of congenital cardiovascular diseases is much higher in high-altitude regions than in plains [ 7 , 8 ]. This phenomenon, termed high-altitude hypoxic injury, has been reported in humans and non-ruminants but not documented in ruminants [ 9 , 10 ]. Although yaks possess unique hypoxia-tolerant traits [ 11 ]; statistical analysis conducted in Qinghai Province revealed that with decreasing air oxygen levels, they are associated with significant reductions in slaughter rate and meat yield of local yaks (Supplementary Fig. S1 A, B). These findings suggest that traditional genetic adaptation theories alone may not fully explain the survival mechanisms of yaks at high altitudes The rumen, a unique digestive and metabolic chamber in ruminants, hosts a complex microbial community dominated by bacteria, which constitutes over 70% of its population. Oxygen, being essential for cellular respiration and various biochemical processes, influences microbial structure in oxygen-limited environments. Under hypoxic conditions, microbial communities exhibit altitude-dependent structural characteristics due to differences in oxygen sensitivity [ 12 , 13 ]. In some studies, microbial communities at high altitudes differ significantly from those at lower altitudes [ 14 ]. As a highly symbiotic ecosystem, the rumen microbiome forms an intricate metabolic network with the host [ 15 , 16 ], playing a critical role in nutrient acquisition, immune rregulation,and disease resistance [ 17 , 18 ]. However, systematic research exploring the microbe-host axis under extreme environments remains limited, particularly in terms of quantifying the functional plasticity and compensatory potential of microbial communities. Previous studies have shown that yaks are genetically and physiologically adapted to hypoxia [ 11 ]. To investigate it further, we used cattle as an ecological control in a two-phase experimental design (baseline: 2,200m; hypoxia exposure: 3,800m). Serum biochemical indicators, metabolomic profiles, rumen fermentation parameters, and microbial dynamics were systemically monitored and compared between yaks and cattle. We hypothesized that yaks partially rely on rumen microbes to adapt to the hypoxic environment of the Qinghai-Tibet Plateau. Comparative analyses of serum biochemistry, serum metabolism, rumen fermentation parameters, and rumen microbiota between yaks and cattle were conducted to assess health-related adaptation. These findings aim to provide new theoretical insights into the health regulation mechanisms of high-altitude resilience in ruminants under hypoxic stress. Methods 1. Environmental Factor Measurement An electronic meter for measuring temperature and humidity was suspended at a height of 1.5 meters from the ground in the center of the cowshed. Temperature reading were recorded daily at 08:00, 12:00, and 18:00, and the average of these three time points was used as experimental temperature. A portable meter for measuring oxygen was suspended adjacent to the temperature and humidity meter. The temperature, pH, and dissolved oxygen (DO) of drinking water for experimental animals were measured using a multi-parameter analyzer for water quality following the manufacturer’s instructions. 2. Meat production and slaughter rate Meat production per yak (kg) was calculated by dividing the total meat production of yaks by the number of yaks slaughtered in the same year. Slaughter rate (%) of yaks was calculated as the percentage of the number of yaks slaughtered in the year-end number of yaks from the previous year. Data on meat production [ 40 ], number of yaks slaughtered [ 41 ] and number of year-end yaks [ 42 ] in 44 counties of Qinghai Province in 2018 were provided by the Agricultural and Rural Department of Qinghai Province (2021) and collected through the National Tibetan Plateau Data Center ( http://data.tpdc.ac.cn ). Annual average county-level oxygen concentrations (%) in Qinghai Province were obtained from Hu [ 43 ]. A univariate linear regression equation was constructed to analyze the relationship between yak meat production, yak slaughter rate and annual oxygen concentration. The annual average oxygen concentration was used as an independent variable, while meat production per yak and yak slaughter rate were used as dependent variables. 3. Animal Feeding and Sampling Ten cattle (initial weight = 245.5 ± 50 kg) and ten yaks (initial weight = 211.6 ± 40 kg) were procured from Datong County (36.55°N, 101.42°E, 2400 meters above sea level: m ASL), Xining City, Qinghai Province. The experiment was divided into two periods of 28 days each. The first part of the experiment was conducted in May 2021 at Qinghai Academy of Animal Science and Veterinary Science (36.65°N, 101.77°E, 2200 m ASL). For the second part of the experiment, the same animals were transported to Xueduo Yak Breeding Base, located in Henan County in the Huangnan Autonomous Prefecture of Qinghai Province (34.73°N, 101.62°E, 3800 m ASL) in June 2021. The animals were kept in individual cages (1.5 X 3 m) in an open-sided roofed structure throughout the study. Oat hay was provided ad libitum twice daily at 08:00 and 17:00, and drinking water was available round the clock. Daily feed intake was calculated by subtracting the feed refused from the feed offered. The first 21 days at each location were considered as the adaptation period, followed by 7 days for data collection. At the end of each period, the animals were weighed and blood samples were collected from the jugular vein using vacuum tubes, before morning feeding. The blood was centrifuged at 3000 x g for 20 minutes to obtain serum. Part of the serum sample was stored at -20℃ for analysis of blood biochemical indices, while the remaining part was stored at -80℃ for determination of blood metabolites. Rumen fluid was collected using a flexible oral stomach tube with a metal strainer, which was washed with clean warm water after each collection. The first 50 mL of fluid was discarded to avoid saliva contamination. The rumen fluid was filtered through four layers of gauze, and the pH was measured immediately using a pH meter (Ecoscan pH 5, Singapore). The rumen fluid was stored immediately in liquid nitrogen for determination of fermentation parameters and microbial extraction. 4. Evaluation of apparent indicators Fecal samples were collected at 08:00 and 16:00 for four consecutive days at the end of each experimental period. After mixing, 10% of the feces was divided into two parts: one part was treated with 10 mL of 10% sulfuric acid and stored at -20℃ for determination of CP; while the other part was dried at 65℃ for 48 hours until a constant weight was achieved and saved for proximate analysis. Feed samples were collected on two consecutive days each week, mixed and stored at -20°C for subsequent determination of their nutrient composition. DM and total N (crude protein = N × 6.25, procedure 976.05) were analyzed according to AOAC [ 44 ]. The NDF and ADF were determined by the method of Van Soest et al. [ 45 ] using an ANKOM 200i fiber analyzer (ANKOM Technologies, Inc., Fairport, NY, USA). The hemicellulose was calculated by the difference between NDF and ADF [ 46 ]. The serum concentrations of aspartate aminotransferase (AST), alanine aminotransferase (ALT), total protein (TP), albumin (ALB), globulin (GLOB), blood glucose (Glu), total cholesterol (TCHO), triglyceride (TRIG), alkaline phosphatase (ALP), total bilirubin (TBIL), creatine kinase (CK), lactate dehydrogenase (LDH), UREA, uric acid (UA), Ca, and P were determined using an automatic blood biochemical analyzer (SRL, Inc., Tokyo, Japan). Immunoglobulin A (IgA) and immunoglobulin M (IgM) were measured using commercial ELISA kits (Shanghai Enzyme-linked Biotechnology Co., Ltd, Shanghai, China). 5. Measurements of rumen fermentation parameters The concentrations of SCFAs (acetate, propionate, isobutyrate, butyrate, isovalerate, valerate) were measured by gas chromatography using an Agilent 7890B system (Agilent, Santa Clara, CA, United States). The column temperature was kept at 40°C, the injection temperature at 220°C, and the TCD (Thermal Conductivity Detector) temperature at 230°C, following the method described by Li et al. [ 47 ]. Air, nitrogen carrier gas, and hydrogen were maintained at a pressure of 0.05 MPa. Crotonic acid was used as the internal standard for calculating the concentrations of the SCFAs, and standard curves were established. The concentration of NH 3 -N in the rumen fluid was measured using the colorimetric method following the methods of Shen et al. [ 48 ]. For determination of MCP in the rumen fluid, the Bradford Protein Assay Kit (Beijing Solarbio Science and Technology Company, Beijing, China) was used with Coomassie brilliant blue as the dye [ 49 ]. 6. DNA extraction, 16S rRNA gene amplicon sequencing, and high-throughput sequencing The total DNA from rumen fluid was extracted using the Cetyltrimethylammonium bromide (CTAB) method described by Dai et al. [ 50 ]. The DNA concentration was measured using a Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The total bacterial 16S rRNA gene was amplified using the primers 515-F (5"-GTGCCAGCMGCCGCGGTAA-3") and 806-R (5"-GGACTACCVGGGTATCTAAT-3") using PCR thermal cycler (Eppendorf AG 22331 Hamburg, Germany). After purification using Agencourt AMPure XP magnetic beads (Beckman Coulter, Milan, Italy), library quality was assessed on the Illumina Hiseq platform at BGI Life Tech Co., Ltd. (Beijing, China) before sequencing. Ambiguous and low-quality sequences were removed using Cutadapt v2.6 software [ 51 ], and paired reads were assembled using the sequence splicing software Flash v1.2.11 [ 52 ]. This was used to obtain tags in the hypervariable region based on the overlap relationship. Operational taxonomic units (OTUs) were clustered into different characteristic sequences (Features) using the Vsearch plug-in in QIIME2 [ 53 ] with a 99% similarity cutoff standard. Rarefaction curves were obtained using QIIME2 diversity (Supplementary Fig. S6A). The relative abundances of rumen bacteria at the phylum and genus levels were determined by annotating with the Silva 16S rRNA gene database SILVA_138. The α and β diversities were analyzed using QIIME2 software, and principal coordinate analysis (PCoA) plots were generated using ggplot2. A normalization method was employed to minimize biases arising from differences in scales of the original data and to enable subsequent comparisons between yaks and cattle for identifying differential microorganisms. Specifically, given the original data points \(\:{x}_{1},{x}_{2},\cdots\:,{x}_{n},\) the 𝑖 -th normalized data point was defined as $$\:\widehat{{x}_{\text{ı}}}=\frac{{x}_{i}}{{\sum\:}_{j=1}^{n}{x}_{j}}.$$ Here, the denominator \(\:{\sum\:}_{j=1}^{n}{x}_{j}\) represents the sum of all data values. This normalization method transformed the data into [0, 1] range, preserving the relative proportions among data points while eliminating differences in absolute values. Differential microorganisms were subjected to paired Wilcoxon test after normalization to obtain p-values. The results were considered significantly different at P 0.05 and abs (Log10FC) < 0.301. After processing the samples as described above, the purified metagenomic samples were sent to Illumina Hiseq platform at BGI Life Tech Co., Ltd. (Beijing, China) for high-throughput sequencing. For quality control, the raw sequencing data were processed using Trimmomatic (v.0.39) [ 54 ]. Low-quality bases and adapter sequences were removed from the raw reads, and the quality was evaluated using FastQC (v.0.12.1) ( https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ). The filtered reads were matched with the reference genome using BWA (v.0.7.17, r1188) [ 55 ] using default settings. Reads that matched with the host genome were removed using Samtools (v.1.21) [ 56 ] to ensure that only non-host sequences were retained for downstream analysis. The filtered, host-free sequences were assembled de novo using MEGAHIT (v.1.2.9) [ 57 ] with default settings. For functional annotation, eggNOG-mapper (v2.1.12) [ 58 ] was employed, using the eggNOG database version 5.0.2, along with Diamond (v2.1.9) [ 59 ] and MMseqs2 (v15.6f452) [ 60 ] to map functional categories. The annotation was performed with the ‘--itype metagenome’ parameter, enabling the identification of functional genes and pathways within the metagenomic dataset. Filter KEGG pathway information from Eggnog results, using Python, was collected and the statistics was summarized to obtain pathway information for each sample and the corresponding read names. Normalization was performed using the same method as employed during the amplification step. After normalization, a paired Wilcoxon test was performed to obtain differential pathway information ( P < 0.05). Based on these reads, corresponding assembled sequences were extracted to annotate the species composition of these differentially expressed pathway sequences. Kraken2 databases were constructed using a previously established MAGs library. The protozoa were used to generate NCBI taxon IDs "40635", "47888", "5986", "47895", "40637", "358016" and the database of Li et al [ 61 ]. Viruses employed the database of Yan and Yu [ 62 ]. Anaerobic fungi genomic data was incorporated from the studies of Pratt et al. [ 63 ], Haitjema et al. [ 64 ], Brown et al. [ 65 ], Mondo et al. [ 66 ], Wilken et al. [ 67 ], Youssef et al. [ 68 ] and Li et al. [ 69 ], while for bacteria and archaea we used the database of Xie et al. )[ 70 ]. Kraken2 (v2.1.3) was used at the ‘--fast-build’ option to enable rapid database construction, specifically tailored to the rumen microbiome as described by Wood et al. [ 71 ]. Species-level taxonomic annotations were performed on the assembled metagenomic reads data containing differential pathways. The obtained species information was merged, and the annotated species data based on group mean values, were visualized through pie charts and microbial interaction networks. The process flowchart is shown in Supplementary Fig. S7 . WGS and amplicon information are shown in Supplementary Table S1 , 2 7. Real-time quantitative PCR Total RNA was extracted using the RN43-EASYspin Plus Kit (Aidlab Biotech Co., Ltd. China) according to the manufacturer’s protocol, and the quality of the RNA was assessed using a Nanodrop ND-1000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). The RNA was, then, reverse-transcribed into cDNA using the TRUEscript RT MasterMix Kit (Aidlab Biotech Co., Ltd. Beijing, China). Real-time quantitative PCR (qPCR) was used to analyze the change of total bacterial copy number. The PCR premix contained 10 µ L of SYBR® Green Pro Taq HS Premix (Rox Plus) (Accurate Biotechnology Co., Ltd, Hunan, China), 0.4 µ L of forward primer (CCTACGGGAGGCAGCAG), 0.4 µ L of reverse primer (ATTACCGCGGCTGCTGG), 2.0 µ L of DNA template, and 7.2 µ L of nuclease-free water (Accurate Biotechnology Co., Ltd, Hunan, China) in a 20 µ L reaction system. The thermocycling conditions were maintained as follows: Temperature of 95°C for 30 s for denaturation and activation of Taq polymerase, followed by 40 thermal cycles of 95°C for 5 s and then 60°C for 30 s. After the amplification process, melting curve analysis was conducted with 95°C for 15 s and 60°C for 60 s for the dissociation stage. The fluorescence was detected by the QuantStudio 5 Real-time PCR Instrument. 8. Blood Metabolomics Analysis The procedures for metabolomics analysis were followed as described by Cox et al. [ 72 ]. Briefly, each sample of 100 µ L was mixed with 300 µ l methanol (-20°C) in 1.5 mL centrifuge tubes and left at -20°C for one hour. After centrifuging at 10,000 x g and 4℃ for 15 min, 5 µ L of internal standard (1 µ g/mL, DL-o-Chlorophenylalanine) was added to 200 µ L of supernatant, which was then transferred to a vial for liquid chromatography-mass spectrometry (LC-MS) analysis using the LC-MS platform (Q Exactive Thermo Fisher Scientific, Walthem, MA, USA). The chromatographic separation was carried out using an ACQUITY UPLC HSS T3 column (100 × 2.1 mm 1.8 µ m) at 40°C with a flow rate of 0.3 mL/min. The injection volume was 6 µL, and the automatic injector temperature was 4°C. Data on retention time, compound molecular weight, observations and peak intensity were collected using feature extraction, preprocessed with compound discoverer software (Thermo) and normalized. To ensure data quality, peaks from less than 50% of QC samples and 80% of biological samples were discarded. The OPLS-DA was employed to visualize the overall differences and for identifying differential metabolites. Finally, differential metabolites were selected according to the importance of variable in projection (VIP), false discovery rate (FDR), P -value and Log2 fold change (Log2FC) (VIP > 1, FDR < 0.05, P -values 0.263). 9. Statistical and Data Visualization Analysis Statistical analyses were performed in R (version 4.3.3) [ 73 ]. The Kruskal-Wallis nonparametric test was used to compare differences in rumen microbial communities. For further statistical analysis and visualizations, the following R packages were used: ggtree (version 3.16.0) [ 74 ], ggtreeExtra (version 1.18.0) [ 75 ], ggplot2 (version 3.5.2) [ 76 ], tidyverse (version 2.0.0) [ 77 ], treeio (version 1.32.0) [ 78 ], dplyr (version 1.1.4) [ 79 ], reshape2 (version1.4.4) [ 80 ], ggnewscale (version 0.5.1) [ 81 ], viridis (version 0.6.5) [ 82 ], ggpubr (version 0.6.0) [ 83 ], rjson (version 0.2.23) [ 84 ], and ggrepel (version 0.9.6) [ 85 , 86 ]. Data were presented as mean ± standard error of the mean (SEM), and significance was set at P < 0.05. In addition, Python (version 3.13.0) was employed for specific tasks, including the retrieval of KO information using the requests library. ETE4 (version 4.3.0) [ 87 ] was used for constructing phylogenetic trees, while SciPy (version 1.15.3) [ 88 ] and Pandas (version 2.2.3) [ 89 ] were used for additional data manipulation and analysis. Results 1. Environmental parameters on Qinghai-Tibetan Plateau With increasing altitude, the sunrise time (SRT) advanced significantly ( P < 0.05) and the sunset time (SST) was significantly delayed ( P 0.05, Supplementary Fig. S2A). The maximum temperature (MaxT) and daily temperature range (DRT) increased significantly ( P 0.05, Supplementary Fig. S2D). The oxygen levels (Supplementary Fig. SA), air pollution index (API), PM10, PM2.5, NO 2 , SO 2 , and CO (Supplementary Fig. S2B), were water dissolved oxygen (WDO), water temperature (WT), and water pH (WpH) (Supplementary Fig. S2E) were all decreased significantly ( P < 0.05), The while the humidity (AH) and ozone (O 3 ) showed a significant increase ( P < 0.05) with increasing altitude. The barn temperature (BT) significantly decreased ( P < 0.05), while the humidity (BH) increased significantly ( P < 0.05, Supplementary Fig. S2F) with increasing altitudes. 2. Hypoxia decline in digestibility CP Food intake (FI) of both cattle and yaks increased significantly ( P < 0.05, Supplementary Fig. S3A, B) while metabolic body weight (MBW) and respiratory rate (RR) of yaks exhibited a significant decrease ( P < 0.05, Supplementary Fig. S3A, B) with increasing altitude. Digestibility parameters, including organic matter digestibility (OMD), dry matter digestibility (DMD), lignocellulose digestibility (LD), and crude protein digestibility (CPD), were all decreased significantly in both yaks and cattle ( P < 0.05). The apparent dry matter fermentability (ADFD) decreased significantly in yaks ( P 0.05) 3. Hypoxia increases ammonia nitrogen levels in yaks Microbial crude protein (MCP) synthesis significantly decreased in both cattle and yaks ( P < 0.05, Supplementary Fig. S4A) with increasing altitude. Ammonia nitrogen (NH 3 -N) significantly increased in yaks ( P < 0.05, Supplementary Fig. S4A) but not in cattle, at higher altitudes. Total Volatile Fatty Acids (TVFA, Supplementary Fig. S4B), isobutyric acid (IBA), propionic acid (PA), and acetic acid (AA) significantly increased in cattle ( P < 0.05). The IBA also significantly increased in yaks ( P 0.05, Supplementary Fig. S4C). Fatty acid and nucleotide metabolism increased (Fig. 1A) in yaks, and the yaks exhibited a greater number of metabolite changes (Fig. 1B) with increasing altitude (Supplementary Table S3 and S4). 5. Hypoxia reduced immunity and increased the inflammatory factors. Total Bilirubin (TBIL) decreased ( P < 0.05, Supplementary Fig. S5A), and glucose (GLU) level increased significantly ( P < 0.05, Supplementary Fig. S5A) both in in cattle and yaks Alanine Aminotransferase (ALT) significantly increased in yaks ( P < 0.05, Supplementary Fig. S5A). Regarding blood immunity, interleukin (IL), malondialdehyde (MDA), oligosaccharide (OGA), and immunoglobulin M (IGM) all increased significantly both in cattle and yaks ( P 0.05, Supplementary Fig. S5B). Yaks showed fewer changes in the blood metabolome (Fig. 4A, B) than in cattle (Supplementary Table S7, S8 and S9.) 6. Hypoxia alters the functions of the yak's body and rumen microorganisms In correlation analysis of differential metabolites and differential microorganisms in the rumen of yaks, Firmicutes and Bacteroidetes phyla were positively correlated with fatty acids, nucleotides, and their derivatives (Fig. 5, Supplementary Table S3). Microbial functions in the rumen were enriched during the biosynthesis pathways of unsaturated fatty acids, primary bile acid biosynthesis, and serine and threonine metabolism (Supplementary Fig. S6B). Functional annotation of rumen microorganisms using eggnog showed that the enriched pathways were mainly enriched in Fatty acid biosynthesis, Valine, leucine and isoleucine degradation, Amino sugar and nucleotide sugar metabolism, Nitrogen metabolism, Propanoate metabolism, Butanoate metabolism and related biological processes. In the species annotation of functional reads, Ruminococcus , Oscillibacter , Selenomonas , Schwartzia and Fibrobacter as the main contributors to microbial functions (Fig. 6). Microbial interaction networks (Fig. 7) analyses across various pathways revealed that distinct pathways are associated with different sets of microbial taxa (Supplementary Table S10 and S11). Discussion Based on this comparative study of cattle and yaks, we discovered unique environmental adaptation mechanisms in the rumen of yaks, including restructuring of microbial community, specialization of metabolic pathways, and host-microbe co-regulation. Notably, a unique adaptive mechanism was observed involving long-chain fatty acid metabolism, with biosynthesis pathways predominantly mediated by the genera Ruminococcus , Oscillibacter , Selenomonas , Schwartzia , and Fibrobacter forming functional core of this process. This metabolic shift was closely associated with markers of hepatic stress, highlighting the physiological cost of high-altitude adaptation in yaks. The hypoxic, high-altitude environment induces adaptive changes in the rumen microbiota of yaks. First, under both high- and low-altitude conditions, the rumen microbiota of yaks shows significant differences compared to that of cattle. This adaptive shift in microbial structure also occurs in response to other factors, including seasonal forage changes [ 19 , 20 ], different feed types [ 21 , 22 ], and high-grain diet structures [ 23 ]. Among the changes observed in the rumen metabolome of yaks, the most striking were the elevated levels of long-chain fatty acids and nucleotide content. Increased levels of long-chain fatty acids in the rumen indicate their enhanced microbial synthesis [ 24 – 27 ], while the increased level of nucleotides, particularly cAMP, reflects adjustments in carbon metabolism [ 28 – 31 ]. These signaling molecules may originate from microbial regulation or host adaptation, highlighting the need for further research on rumen microbiota modulation. Furthermore, functional annotation of rumen microbes reveals enrichment in the degradation process of amino acids, fatty acid metabolism resulting in the metabolomic changes observed. Annotation of species associated with significantly differential genes revealed that they were primarily from the genera Ruminococcus , Oscillibacter , Selenomonas , Schwartzia , and Fibrobacter . Spearman correlation analysis also revealed a positive association between Ruminococcus and long-chain fatty acid synthesis. This is also consistent with the findings of Allison et al. [ 32 ], which reported the biosynthesis of higher branched-chain fatty acids and aldehydes by members of the phylum Firmicutes and Bacteroidetes . We hypothesize that interactions among these microbes may lead to the increased level of long-chain fatty acids in the rumen. Thus, key microorganisms such as Selenomonas and Schwartzia , which are involved in other metabolic pathways contributing to acetyl CoA production, deserve further attention. Selenomonas play a central role in multiple carbohydrate metabolic pathways, with its metabolic processes–such as acetyl-CoA synthesis, propionate production, and purine metabolism) being synergistically driven by related functional microbiota [ 33 ]. Analysis of the microbial metabolic network revealed that protozoa play certain role in signal regulation, with the fatty acid synthesis functions of protozoa and fungi showing correlations with fatty acids. However, in fatty acid degradation, fungi and protozoa are significantly different in their functions or even negatively correlated, which indirectly indicates their role in regulating the rumen adaptation of yaks. Research on Schwartzia is limited; however, we speculate that it may perform functions similar to Selenomonas or provide substrates for gluconeogenesis. Integrating functional and pathway results, we conclude that the aforementioned species, along with auxiliary microbes, contribute to long-chain fatty acid synthesis. However, due to limited data, further clarification of these microbial interactions is not possible. This necessitates additional research to elucidate the unique adaptive regulatory mechanisms of rumen microbiota of yak. Secondly, the results of blood metabolomic analysis showed significant changes in pathways of unsaturated fatty acid biosynthesis, primary bile acid biosynthesis, and serine and threonine metabolism in yaks. These results demonstrate that yaks rely on fatty acid mobilization and amino acid metabolism to adapt to environmental changes at high altitudes [ 34 , 35 ]. On the contrary, the cattle did not exhibit changes in metabolic pathways of fatty acid and bile acid. Serum immune indicators revealed that yaks had a significantly increased ALT than in cattle. Metabolic enzymes such as ALP, ALT, and AST in serum are commonly used as indicators to assess liver damage and diseases [ 36 – 38 ]. The significantly increased ALT in yak serum may indicate that yaks experience some degree of inflammatory response in the liver due to the relatively challenging process of energy mobilization in the body. But this could also be an adaptation mechanism of yaks to high-altitude environments [ 39 ]. Rumen microorganisms continues to present challenges due to their complex microbial diversity, limited comprehensive genomic data, and difficulty of culturing many species, making it hard to obtain precise biological information through metagenomic approaches. The lack of taxonomic information also hinders the discovery of many potential microorganisms. In our species annotation, approximately 50% of the microorganisms could not be identified to specific microbial taxa, leading us to suspect the potential influence of unknown species. With advancements in single-cell sequencing technology, we may be able to directly obtain the full genomes of unculturable rumen microorganisms without relying on cultivation methods. This will provide a wealth of new genetic information for bioinformatics to explore, further enhancing our understanding of this unique microbial system in the rumen. In summary, our results highlight the specialized adaptive functions of the yak rumen; however, the specific microorganisms that play major roles in the rumen microbial community still could not be precisely identified. This is due to the limited taxonomic information on rumen microorganisms. Future research is required for more cultivation and sequencing of rumen microorganisms to establish a more comprehensive rumen microbial database, facilitating in-depth studies of rumen microorganisms. Conclusion Our research highlighted the specialized adaptation of yak rumen microbiota to high-altitude environments, characterized by microbial community restructuring and a metabolic shift toward long-chain fatty acid biosynthesis. Although this adaptation mechanism helps survive the yaks under hypoxic conditions, it may come with its physiological costs such as liver damage. Due to the extremely high microbial diversity and limited taxonomic resolution, nearly 50% of species remain unclassified, leaving the specific microbial drivers of these adaptive changes unclear. Current metagenomic approaches are inadequate for fully resolving this complexity. Future studies should utilize single-cell sequencing and culture-independent techniques to obtain genomes of unculturable or hard-to-culture microbes, expand the rumen microbial database, and provide a comprehensive understanding of the mechanisms by which microbiota contribute to host adaptation. These findings enhance our understanding of high-altitude ruminant adaptation and highlight that such adaptations involve inevitable health trade-offs a principle that may apply to human populations residing in high-altitude regions. Therefore, in the adaptation of high-altitude populations, greater emphasis may need to be placed on nutritional supplementation to mitigate the physiological stress associated with survival pressures of high altitudes. Abbreviations MCP Microbial Crude Protein DO Dissolved Oxygen AST Aspartate Aminotransferase ALT Alanine Aminotransferase TP Total Protein ALB Albumin GLOB Globulin GLU Blood Glucose TCHO Total Cholesterol TRIG Triglyceride ALP Alkaline Phosphatase TBIL Total Bilirubin CK Creatine Kinase LDH Lactate Dehydrogenase UREA Urea UA Uric Acid SCFAs Short-Chain Fatty Acids (acetate, propionate, isobutyrate, butyrate, isovalerate, valerate) TCD Thermal Conductivity Detector CTAB Cetyltrimethylammonium Bromide qPCR Real-Time Quantitative Polymerase Chain Reaction VIP Variable in Projection FDR False Discovery Rate SRT Sunrise Time SST Sunset Time DLD Daylight Duration UV Ultraviolet MaxT Maximum Temperature DRT Daily Temperature Range MinT Minimum Temperature API Air Pollution Index PM10 Particulate Matter ≤ 10 µm PM2.5 Particulate Matter ≤ 2.5 µm NO2 Nitrogen Dioxide SO2 Sulfur Dioxide CO Carbon Monoxide WDO Water Dissolved Oxygen WT Water Temperature WpH Water pH AH Air Humidity O3 Ozone BT Barn Temperature BH Barn Humidity FI Food Intake MBW Metabolic Body Weight RR Respiratory Rate OMD Organic Matter Digestibility DMD Dry Matter Digestibility LD Lignocellulose Digestibility CPD Crude Protein Digestibility ADFD Apparent Dry Matter Fermentability TVFA Total Volatile Fatty Acids IL Interleukin MDA Malondialdehyde OGA Oligosaccharide IGM Immunoglobulin M TNF Tumor Necrosis Factor Declarations Funding declaration This research was funded by the Qinghai Provincial Natural Science Fund for Distinguished Young Scholars (2024-ZJ-905); Qinghai University Research Ability Enhancement Project (2025KTST04); the National Key R&D Sub-project (2022YFD1302103), Qinghai University Graduate Supervisor Innovation Team (2025, L.Z.H.), Special Topics of the Second Comprehensive Scientific Expedition of the Qinghai-Tibet Plateau (2019QZKK0606), Leading talent of "Kunlun Talents High-level Innovation and Entrepreneurial Talents" in Qinghai Province (QHKLYC-GDCXCY-2024-071, L.Z.H.). Data availability The whole‑metagenome sequencing reads generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under accession [PRJNA1296762]. The 16S rRNA gene amplicon sequencing data for yak and cattle rumen samples are available in the SRA under accession [PRJNA1302440]. Conflict of interest statement The authors declare that there are no competing interests. Credit Author Statement Lang Tan : Data curation, formal analysis, software, writing – original draft, Visualization. Xiaojing Liu : Investigation, data curation, formal analysis, software, writing – original draft, validation. Allan Degen, Yonggui Ma, Jinfen Yang, Qunying Zhang, Jianbo Zhang, Binqiang Bai, Heng Ma, Ru Meng : Investigation, resources, validation. Nik Palevich : Writing – review and editing. Peijun Shi : Resources, writing – review and editing. Lizhuang Hao : Supervision, conceptualization, funding acquisition, resources, writing – review and editing. 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Liu","email":"","orcid":"","institution":"Qinghai University, Key Laboratory of Plateau Grazing Animal Nutrition and Feed Science of Qinghai Province","correspondingAuthor":false,"prefix":"","firstName":"Xiaojing","middleName":"","lastName":"Liu","suffix":""},{"id":585617185,"identity":"b5a30a5f-9bca-46c8-9ad8-ce4677c20a8a","order_by":2,"name":"Yonggui Ma","email":"","orcid":"","institution":"Academy of Plateau Science and Sustainability, Qinghai Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yonggui","middleName":"","lastName":"Ma","suffix":""},{"id":585617186,"identity":"09cd1cb3-a4d7-488b-8ded-858373513903","order_by":3,"name":"Jinfen Yang","email":"","orcid":"","institution":"Qinghai University, Key Laboratory of Plateau Grazing Animal Nutrition and Feed Science of Qinghai Province","correspondingAuthor":false,"prefix":"","firstName":"Jinfen","middleName":"","lastName":"Yang","suffix":""},{"id":585617187,"identity":"710ce37f-3e69-4d64-8350-88e4a19eed75","order_by":4,"name":"Qunying Zhang","email":"","orcid":"","institution":"Qinghai University, Key Laboratory of Plateau Grazing Animal Nutrition and Feed Science of Qinghai Province","correspondingAuthor":false,"prefix":"","firstName":"Qunying","middleName":"","lastName":"Zhang","suffix":""},{"id":585617188,"identity":"9e054910-45f5-4c92-b93b-46bb408d9cf3","order_by":5,"name":"Jianbo Zhang","email":"","orcid":"","institution":"Qinghai University, Key Laboratory of Plateau Grazing Animal Nutrition and Feed Science of Qinghai Province","correspondingAuthor":false,"prefix":"","firstName":"Jianbo","middleName":"","lastName":"Zhang","suffix":""},{"id":585617189,"identity":"4dc5e6be-d080-4f18-a91b-1be37639158f","order_by":6,"name":"Binqiang Bai","email":"","orcid":"","institution":"Qinghai University, Key Laboratory of Plateau Grazing Animal Nutrition and Feed Science of Qinghai Province","correspondingAuthor":false,"prefix":"","firstName":"Binqiang","middleName":"","lastName":"Bai","suffix":""},{"id":585617190,"identity":"dbf506d2-26f3-4b6b-8d93-99e5524ee309","order_by":7,"name":"Heng Ma","email":"","orcid":"","institution":"National Institute of Natural Hazards, Ministry of Emergency Management of China","correspondingAuthor":false,"prefix":"","firstName":"Heng","middleName":"","lastName":"Ma","suffix":""},{"id":585617191,"identity":"f1f8a859-a8b2-447a-a782-94b853ee1d53","order_by":8,"name":"Ru Meng","email":"","orcid":"","institution":"Xining Animal Disease Prevention and Control Center","correspondingAuthor":false,"prefix":"","firstName":"Ru","middleName":"","lastName":"Meng","suffix":""},{"id":585617192,"identity":"8d333fdc-ecc7-41c1-8315-237de443e5bf","order_by":9,"name":"Allan Degen","email":"","orcid":"","institution":"Desert Animal Adaptations and Husbandry, Wyler Department of Dryland Agriculture, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Allan","middleName":"","lastName":"Degen","suffix":""},{"id":585617193,"identity":"780fd0e7-79b5-49c7-9c9f-48070169a838","order_by":10,"name":"Nikola Palevich","email":"","orcid":"","institution":"AgResearch Group, Bioeconomy Science Institute, Grasslands Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Nikola","middleName":"","lastName":"Palevich","suffix":""},{"id":585617194,"identity":"ffdbea74-cf06-46e2-88fb-1a7664d9de42","order_by":11,"name":"Peijun Shi","email":"","orcid":"","institution":"State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Key Laboratory of Environmental Change and Natural Disaster","correspondingAuthor":false,"prefix":"","firstName":"Peijun","middleName":"","lastName":"Shi","suffix":""},{"id":585617195,"identity":"ee8ba6b2-96f3-42e8-bdd7-a672ebd05fe7","order_by":12,"name":"Lizhuang Hao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABHUlEQVRIie2PMUvDQBTHrwTulqdZTyL1KzwpikNpv8qFQLJYcHRw6JQOnn4Dv0M3cfLkIC6Vrgl1UIRMWbqIQqleAqKIF1fB+8HBvcf78X+PEIfjD7LpEdp80DwljvtdNm7KA6tCvygd9TCLe6CaktsV8ql4N4+pDmWjkBaFsfIJTu4Hl0wrJaiXSCb3pi9HnPiTU2FZLOlBVkZXMhZKAB1JyOJCmsX47G5qUbIAqI5QHaISHEbXPMpyMArykUXppAGsjTKvjII8AR6mxapV8WiwkeoB5nWKQAEmZdGeQunWxbkWmJfmFiV261sW28jBdovv35a8etZDnEd6+bp+2wEm94tq1e/6k7MflQ/C8fcOtI3XDH8bcDgcjn/MO+O9YiRugpKaAAAAAElFTkSuQmCC","orcid":"","institution":"Qinghai University, Key Laboratory of Plateau Grazing Animal Nutrition and Feed Science of Qinghai Province","correspondingAuthor":true,"prefix":"","firstName":"Lizhuang","middleName":"","lastName":"Hao","suffix":""}],"badges":[],"createdAt":"2026-01-06 16:09:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8533339/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8533339/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102027014,"identity":"cec0c0ee-1831-48ca-a7fa-3ee96a2de285","added_by":"auto","created_at":"2026-02-06 10:05:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1716229,"visible":true,"origin":"","legend":"\u003cp\u003eRumen metabolomics results: (A) Comparison of differential metabolites between yaks and cattle; (B) Volcano plot comparison of differential metabolites between yaks and cattle, where the 'Omitted' group represents metabolites present in both groups or P \u0026gt; 0.05 or Log2FC \u0026lt; 0.606. Significant differences between low- and high-altitude regions are denoted as follows: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8533339/v1/aa49bb498be5be4db818a9a0.jpg"},{"id":102027015,"identity":"4d9ad94c-51ab-47c9-a290-84b7aabded5a","added_by":"auto","created_at":"2026-02-06 10:05:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3147486,"visible":true,"origin":"","legend":"\u003cp\u003eRumen microbial difference results: (A) Comparison of 16S differential microorganisms between yaks and cattle; (B) Volcano plot comparison of 16S differential microorganisms between yaks and cattle, where the 'Omitted' group represents metabolites present in both groups or P \u0026gt; 0.05 or Log2FC \u0026lt; 0.606; (C) Principal coordinate analysis (PCoA) of rumen bacterial community structure in cattle based on unweighted UniFrac dissimilarity; (D) Principal coordinate analysis (PCoA) of rumen bacterial community structure in yak based on unweighted UniFrac dissimilarity;(E) Differential rumen bacteria at the phylum level in yak; (F) Differential rumen bacteria at the order level in yak；(G) Differential rumen bacteria at the family level in yak. Differential abundance is indicated by asterisks representing statistical significance: *p \u0026lt; 0.05, **p \u0026lt; 0.01\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8533339/v1/d704fced930209b717df59a4.jpg"},{"id":102027016,"identity":"c8817398-c33d-4225-b8b2-62d2ec6b33d7","added_by":"auto","created_at":"2026-02-06 10:05:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2503916,"visible":true,"origin":"","legend":"\u003cp\u003eRumen microbiota composition in cattle. The phylogenetic tree illustrates the abundance and diversity of species, predominantly belonging to Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, Lentisphaerae, Planctomycetes, and Spirochaetae；Outer ring: Species with significantly increased or decreased abundance (p \u0026lt; 0.005) are marked with black dots, accompanied by corresponding boxplots. Boxplots show abundance values on the x-axis, with light blue indicating abundance at low altitude and light red at high altitude；Second outer ring: Log2 fold change (log2FC) analysis of species abundance. Deep red and deep blue indicate significantly increased or decreased abundance (p \u0026lt; 0.005), while light red and light blue indicate significant changes at p \u0026lt; 0.05；Inner two rings: Abundance profiles of species in samples from low- and high-altitude regions (log10-transformed abundance). The inner circle represents low-altitude samples, and the outer circle represents high-altitude samples.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8533339/v1/d1501e4f826ec8774c869326.jpg"},{"id":102295492,"identity":"1d7ea786-bc99-47c8-ac8e-1cc039f0244e","added_by":"auto","created_at":"2026-02-10 10:11:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2468140,"visible":true,"origin":"","legend":"\u003cp\u003eBlood metabolomics results: (A) Comparison of differential metabolites between yaks and cattle; (B) Volcano plot comparison of differential metabolites between yaks and cattle, where the 'Omitted' group represents metabolites present in both groups or P \u0026gt; 0.05 or Log2FC \u0026lt; 0.606. Significant differences between low- and high-altitude regions are denoted as follows: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8533339/v1/2162bdc7815d3db4d1977c92.jpg"},{"id":102027021,"identity":"d95692b4-bb99-4bff-9e92-4c309795ac11","added_by":"auto","created_at":"2026-02-06 10:05:42","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5386852,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of correlation between differential microorganisms and differential metabolites in yak rumen. The Y-axis represents differential metabolites, with colored blocks distinguishing different metabolite categories, and the X-axis represents differential microorganisms at the genus level, with colored blocks distinguishing different level classifications. The heatmap colors indicate the Spearman correlation of differential metabolites. Significant differences between low- and high-altitude regions are denoted as follows: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001,\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8533339/v1/3f0e5be1a445286a7fcd33d8.jpg"},{"id":102027019,"identity":"9344f193-58c2-42b2-b35e-ccbd4802765b","added_by":"auto","created_at":"2026-02-06 10:05:42","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3209141,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of changes in rumen microbial functional genes in KO genes and KEGG pathway modules. The left bubble chart shows the KOs and MAPs corresponding to the differential KO numbers in functional annotations of microbes from both regions, the pie chart displays the abundance information of species annotations for reads from differential KOs in the corresponding left MAP, and the right side presents detailed pathway results of the main MAP functions along with their increase or decrease status.\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8533339/v1/e7c22c1bb2dc3fef0034e0b6.jpg"},{"id":102295607,"identity":"581776e6-6d27-4177-b2aa-aa9e34eaf4ce","added_by":"auto","created_at":"2026-02-10 10:13:02","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1997488,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial associated network of the pathway map of interest. Red edges represent positive correlations, while blue edges represent negative correlations (abs(correlation) \u0026gt; 0.5).\u003c/p\u003e","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8533339/v1/1ce5fa40bdb90aeaec83d1a8.jpg"},{"id":102298779,"identity":"d2c7d1ae-3c70-4fc2-bd3c-3ba74863e8e3","added_by":"auto","created_at":"2026-02-10 11:00:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":21523697,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8533339/v1/fc112d1f-d41e-4d98-92ce-d8ef09734e0d.pdf"},{"id":102027022,"identity":"aef69516-7248-4bb9-bb19-e91fd4df9042","added_by":"auto","created_at":"2026-02-06 10:05:42","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":5128192,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable.xls","url":"https://assets-eu.researchsquare.com/files/rs-8533339/v1/edf53da0c71a0b57d90b9a42.xls"},{"id":102295308,"identity":"2367bd9a-fa06-494f-8394-a8456e521f7b","added_by":"auto","created_at":"2026-02-10 10:10:49","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":419177,"visible":true,"origin":"","legend":"","description":"","filename":"FigS1S7.docx","url":"https://assets-eu.researchsquare.com/files/rs-8533339/v1/83726992a79f903f113fedd4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Price of Survival: Yaks’ Adaptation to High Altitudes","fulltext":[{"header":"Background","content":"\u003cp\u003eThe Qinghai-Tibet Plateau is known as the \"Roof of the World,\" comprising the highest-altitude regions on Earth, with an average elevation of 3,000 meters [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The most prominent feature of this area is the significantly lower partial pressure of atmospheric oxygen ranging between 19.94 to 20.78% compared to plains [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], where normal air oxygen level in the atmosphere is 21.0% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Hypoxia can adversely affect health of animals causing death in severe cases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In studies involving humans and animal models, it has been demonstrated that exposure to chronic hypoxia can cause abnormal bone tissue structure, reduced bone density, stunted growth and development, and cardiovascular dysfunction [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Epidemiological data indicate that the incidence of congenital cardiovascular diseases is much higher in high-altitude regions than in plains [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This phenomenon, termed high-altitude hypoxic injury, has been reported in humans and non-ruminants but not documented in ruminants [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Although yaks possess unique hypoxia-tolerant traits [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]; statistical analysis conducted in Qinghai Province revealed that with decreasing air oxygen levels, they are associated with significant reductions in slaughter rate and meat yield of local yaks (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA, B). These findings suggest that traditional genetic adaptation theories alone may not fully explain the survival mechanisms of yaks at high altitudes\u003c/p\u003e \u003cp\u003eThe rumen, a unique digestive and metabolic chamber in ruminants, hosts a complex microbial community dominated by bacteria, which constitutes over 70% of its population. Oxygen, being essential for cellular respiration and various biochemical processes, influences microbial structure in oxygen-limited environments. Under hypoxic conditions, microbial communities exhibit altitude-dependent structural characteristics due to differences in oxygen sensitivity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In some studies, microbial communities at high altitudes differ significantly from those at lower altitudes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. As a highly symbiotic ecosystem, the rumen microbiome forms an intricate metabolic network with the host [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], playing a critical role in nutrient acquisition, immune rregulation,and disease resistance [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, systematic research exploring the microbe-host axis under extreme environments remains limited, particularly in terms of quantifying the functional plasticity and compensatory potential of microbial communities.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that yaks are genetically and physiologically adapted to hypoxia [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. To investigate it further, we used cattle as an ecological control in a two-phase experimental design (baseline: 2,200m; hypoxia exposure: 3,800m). Serum biochemical indicators, metabolomic profiles, rumen fermentation parameters, and microbial dynamics were systemically monitored and compared between yaks and cattle. We hypothesized that yaks partially rely on rumen microbes to adapt to the hypoxic environment of the Qinghai-Tibet Plateau. Comparative analyses of serum biochemistry, serum metabolism, rumen fermentation parameters, and rumen microbiota between yaks and cattle were conducted to assess health-related adaptation. These findings aim to provide new theoretical insights into the health regulation mechanisms of high-altitude resilience in ruminants under hypoxic stress.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \n\n\n\n\n\n\n\n\n\n\u003cp\u003e\u003c/p\u003e \n\n\n\n \n\n "},{"header":"Methods","content":"\u003cdiv\u003e\u003cstrong\u003e1. Environmental Factor Measurement\u003c/strong\u003e\u003c/div\u003e\u003cp\u003eAn electronic meter for measuring temperature and humidity was suspended at a height of 1.5 meters from the ground in the center of the cowshed. Temperature reading were recorded daily at 08:00, 12:00, and 18:00, and the average of these three time points was used as experimental temperature. A portable meter for measuring oxygen was suspended adjacent to the temperature and humidity meter. The temperature, pH, and dissolved oxygen (DO) of drinking water for experimental animals were measured using a multi-parameter analyzer for water quality following the manufacturer’s instructions.\u003c/p\u003e\u003ch3\u003e2. Meat production and slaughter rate\u003c/h3\u003e\u003cp\u003eMeat production per yak (kg) was calculated by dividing the total meat production of yaks by the number of yaks slaughtered in the same year. Slaughter rate (%) of yaks was calculated as the percentage of the number of yaks slaughtered in the year-end number of yaks from the previous year. Data on meat production [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], number of yaks slaughtered [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and number of year-end yaks [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] in 44 counties of Qinghai Province in 2018 were provided by the Agricultural and Rural Department of Qinghai Province (2021) and collected through the National Tibetan Plateau Data Center (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://data.tpdc.ac.cn\u003c/span\u003e\u003cspan address=\"http://data.tpdc.ac.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Annual average county-level oxygen concentrations (%) in Qinghai Province were obtained from Hu [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. A univariate linear regression equation was constructed to analyze the relationship between yak meat production, yak slaughter rate and annual oxygen concentration. The annual average oxygen concentration was used as an independent variable, while meat production per yak and yak slaughter rate were used as dependent variables.\u003c/p\u003e\u003ch3\u003e3. Animal Feeding and Sampling\u003c/h3\u003e\u003cp\u003eTen cattle (initial weight = 245.5 ± 50 kg) and ten yaks (initial weight = 211.6 ± 40 kg) were procured from Datong County (36.55°N, 101.42°E, 2400 meters above sea level: m ASL), Xining City, Qinghai Province. The experiment was divided into two periods of 28 days each. The first part of the experiment was conducted in May 2021 at Qinghai Academy of Animal Science and Veterinary Science (36.65°N, 101.77°E, 2200 m ASL). For the second part of the experiment, the same animals were transported to Xueduo Yak Breeding Base, located in Henan County in the Huangnan Autonomous Prefecture of Qinghai Province (34.73°N, 101.62°E, 3800 m ASL) in June 2021. The animals were kept in individual cages (1.5 X 3 m) in an open-sided roofed structure throughout the study. Oat hay was provided ad libitum twice daily at 08:00 and 17:00, and drinking water was available round the clock. Daily feed intake was calculated by subtracting the feed refused from the feed offered. The first 21 days at each location were considered as the adaptation period, followed by 7 days for data collection. At the end of each period, the animals were weighed and blood samples were collected from the jugular vein using vacuum tubes, before morning feeding. The blood was centrifuged at 3000 x g for 20 minutes to obtain serum. Part of the serum sample was stored at -20℃ for analysis of blood biochemical indices, while the remaining part was stored at -80℃ for determination of blood metabolites. Rumen fluid was collected using a flexible oral stomach tube with a metal strainer, which was washed with clean warm water after each collection. The first 50 mL of fluid was discarded to avoid saliva contamination. The rumen fluid was filtered through four layers of gauze, and the pH was measured immediately using a pH meter (Ecoscan pH 5, Singapore). The rumen fluid was stored immediately in liquid nitrogen for determination of fermentation parameters and microbial extraction.\u003c/p\u003e\u003ch3\u003e4. Evaluation of apparent indicators\u003c/h3\u003e\u003cp\u003eFecal samples were collected at 08:00 and 16:00 for four consecutive days at the end of each experimental period. After mixing, 10% of the feces was divided into two parts: one part was treated with 10 mL of 10% sulfuric acid and stored at -20℃ for determination of CP; while the other part was dried at 65℃ for 48 hours until a constant weight was achieved and saved for proximate analysis. Feed samples were collected on two consecutive days each week, mixed and stored at -20°C for subsequent determination of their nutrient composition. DM and total N (crude protein = N × 6.25, procedure 976.05) were analyzed according to AOAC [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The NDF and ADF were determined by the method of Van Soest et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] using an ANKOM 200i fiber analyzer (ANKOM Technologies, Inc., Fairport, NY, USA). The hemicellulose was calculated by the difference between NDF and ADF [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The serum concentrations of aspartate aminotransferase (AST), alanine aminotransferase (ALT), total protein (TP), albumin (ALB), globulin (GLOB), blood glucose (Glu), total cholesterol (TCHO), triglyceride (TRIG), alkaline phosphatase (ALP), total bilirubin (TBIL), creatine kinase (CK), lactate dehydrogenase (LDH), UREA, uric acid (UA), Ca, and P were determined using an automatic blood biochemical analyzer (SRL, Inc., Tokyo, Japan). Immunoglobulin A (IgA) and immunoglobulin M (IgM) were measured using commercial ELISA kits (Shanghai Enzyme-linked Biotechnology Co., Ltd, Shanghai, China).\u003c/p\u003e\u003ch3\u003e5. Measurements of rumen fermentation parameters\u003c/h3\u003e\u003cp\u003eThe concentrations of SCFAs (acetate, propionate, isobutyrate, butyrate, isovalerate, valerate) were measured by gas chromatography using an Agilent 7890B system (Agilent, Santa Clara, CA, United States). The column temperature was kept at 40°C, the injection temperature at 220°C, and the TCD (Thermal Conductivity Detector) temperature at 230°C, following the method described by Li et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Air, nitrogen carrier gas, and hydrogen were maintained at a pressure of 0.05 MPa. Crotonic acid was used as the internal standard for calculating the concentrations of the SCFAs, and standard curves were established. The concentration of NH\u003csub\u003e3\u003c/sub\u003e-N in the rumen fluid was measured using the colorimetric method following the methods of Shen et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. For determination of MCP in the rumen fluid, the Bradford Protein Assay Kit (Beijing Solarbio Science and Technology Company, Beijing, China) was used with Coomassie brilliant blue as the dye [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003ch3\u003e6. DNA extraction, 16S rRNA gene amplicon sequencing, and high-throughput sequencing\u003c/h3\u003e\u003cp\u003eThe total DNA from rumen fluid was extracted using the Cetyltrimethylammonium bromide (CTAB) method described by Dai et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The DNA concentration was measured using a Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The total bacterial 16S rRNA gene was amplified using the primers 515-F (5\"-GTGCCAGCMGCCGCGGTAA-3\") and 806-R (5\"-GGACTACCVGGGTATCTAAT-3\") using PCR thermal cycler (Eppendorf AG 22331 Hamburg, Germany). After purification using Agencourt AMPure XP magnetic beads (Beckman Coulter, Milan, Italy), library quality was assessed on the Illumina Hiseq platform at BGI Life Tech Co., Ltd. (Beijing, China) before sequencing. Ambiguous and low-quality sequences were removed using Cutadapt v2.6 software [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], and paired reads were assembled using the sequence splicing software Flash v1.2.11 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This was used to obtain tags in the hypervariable region based on the overlap relationship. Operational taxonomic units (OTUs) were clustered into different characteristic sequences (Features) using the Vsearch plug-in in QIIME2 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] with a 99% similarity cutoff standard. Rarefaction curves were obtained using QIIME2 diversity (Supplementary Fig. S6A). The relative abundances of rumen bacteria at the phylum and genus levels were determined by annotating with the Silva 16S rRNA gene database SILVA_138. The α and β diversities were analyzed using QIIME2 software, and principal coordinate analysis (PCoA) plots were generated using ggplot2. A normalization method was employed to minimize biases arising from differences in scales of the original data and to enable subsequent comparisons between yaks and cattle for identifying differential microorganisms. Specifically, given the original data points \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{1},{x}_{2},\\cdots\\:,{x}_{n},\\)\u003c/span\u003e\u003c/span\u003e the 𝑖 -th normalized data point was defined as\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\widehat{{x}_{\\text{ı}}}=\\frac{{x}_{i}}{{\\sum\\:}_{j=1}^{n}{x}_{j}}.$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eHere, the denominator \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{j=1}^{n}{x}_{j}\\)\u003c/span\u003e\u003c/span\u003e represents the sum of all data values. This normalization method transformed the data into [0, 1] range, preserving the relative proportions among data points while eliminating differences in absolute values.\u003c/p\u003e\u003cp\u003eDifferential microorganisms were subjected to paired Wilcoxon test after normalization to obtain p-values. The results were considered significantly different at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05. Based on this significance level, the Omitted group was determined by \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05 and abs (Log10FC) \u0026lt; 0.301.\u003c/p\u003e\u003cp\u003eAfter processing the samples as described above, the purified metagenomic samples were sent to Illumina Hiseq platform at BGI Life Tech Co., Ltd. (Beijing, China) for high-throughput sequencing. For quality control, the raw sequencing data were processed using Trimmomatic (v.0.39) [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Low-quality bases and adapter sequences were removed from the raw reads, and the quality was evaluated using FastQC (v.0.12.1) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The filtered reads were matched with the reference genome using BWA (v.0.7.17, r1188) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] using default settings. Reads that matched with the host genome were removed using Samtools (v.1.21) [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] to ensure that only non-host sequences were retained for downstream analysis. The filtered, host-free sequences were assembled de novo using MEGAHIT (v.1.2.9) [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] with default settings.\u003c/p\u003e\u003cp\u003eFor functional annotation, eggNOG-mapper (v2.1.12) [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] was employed, using the eggNOG database version 5.0.2, along with Diamond (v2.1.9) [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] and MMseqs2 (v15.6f452) [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] to map functional categories. The annotation was performed with the ‘--itype metagenome’ parameter, enabling the identification of functional genes and pathways within the metagenomic dataset.\u003c/p\u003e\u003cp\u003eFilter KEGG pathway information from Eggnog results, using Python, was collected and the statistics was summarized to obtain pathway information for each sample and the corresponding read names. Normalization was performed using the same method as employed during the amplification step. After normalization, a paired Wilcoxon test was performed to obtain differential pathway information (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Based on these reads, corresponding assembled sequences were extracted to annotate the species composition of these differentially expressed pathway sequences. Kraken2 databases were constructed using a previously established MAGs library. The protozoa were used to generate NCBI taxon IDs \"40635\", \"47888\", \"5986\", \"47895\", \"40637\", \"358016\" and the database of Li et al [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Viruses employed the database of Yan and Yu [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Anaerobic fungi genomic data was incorporated from the studies of Pratt et al. [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], Haitjema et al. [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], Brown et al. [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], Mondo et al. [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], Wilken et al. [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], Youssef et al. [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] and Li et al. [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], while for bacteria and archaea we used the database of Xie et al. )[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Kraken2 (v2.1.3) was used at the ‘--fast-build’ option to enable rapid database construction, specifically tailored to the rumen microbiome as described by Wood et al. [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Species-level taxonomic annotations were performed on the assembled metagenomic reads data containing differential pathways. The obtained species information was merged, and the annotated species data based on group mean values, were visualized through pie charts and microbial interaction networks. The process flowchart is shown in \u003cb\u003eSupplementary Fig. S7\u003c/b\u003e. WGS and amplicon information are shown in \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, 2\u003c/b\u003e\u003c/p\u003e\u003ch3\u003e7. Real-time quantitative PCR\u003c/h3\u003e\u003cp\u003eTotal RNA was extracted using the RN43-EASYspin Plus Kit (Aidlab Biotech Co., Ltd. China) according to the manufacturer’s protocol, and the quality of the RNA was assessed using a Nanodrop ND-1000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). The RNA was, then, reverse-transcribed into cDNA using the TRUEscript RT MasterMix Kit (Aidlab Biotech Co., Ltd. Beijing, China). Real-time quantitative PCR (qPCR) was used to analyze the change of total bacterial copy number. The PCR premix contained 10 \u003cem\u003eµ\u003c/em\u003eL of SYBR® Green Pro Taq HS Premix (Rox Plus) (Accurate Biotechnology Co., Ltd, Hunan, China), 0.4 \u003cem\u003eµ\u003c/em\u003eL of forward primer (CCTACGGGAGGCAGCAG), 0.4 \u003cem\u003eµ\u003c/em\u003eL of reverse primer (ATTACCGCGGCTGCTGG), 2.0 \u003cem\u003eµ\u003c/em\u003eL of DNA template, and 7.2 \u003cem\u003eµ\u003c/em\u003eL of nuclease-free water (Accurate Biotechnology Co., Ltd, Hunan, China) in a 20 \u003cem\u003eµ\u003c/em\u003eL reaction system. The thermocycling conditions were maintained as follows: Temperature of 95°C for 30 s for denaturation and activation of Taq polymerase, followed by 40 thermal cycles of 95°C for 5 s and then 60°C for 30 s. After the amplification process, melting curve analysis was conducted with 95°C for 15 s and 60°C for 60 s for the dissociation stage. The fluorescence was detected by the QuantStudio 5 Real-time PCR Instrument.\u003c/p\u003e\u003ch3\u003e8. Blood Metabolomics Analysis\u003c/h3\u003e\u003cp\u003eThe procedures for metabolomics analysis were followed as described by Cox et al. [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Briefly, each sample of 100 \u003cem\u003eµ\u003c/em\u003eL was mixed with 300 \u003cem\u003eµ\u003c/em\u003el methanol (-20°C) in 1.5 mL centrifuge tubes and left at -20°C for one hour. After centrifuging at 10,000 x g and 4℃ for 15 min, 5 \u003cem\u003eµ\u003c/em\u003eL of internal standard (1 \u003cem\u003eµ\u003c/em\u003eg/mL, DL-o-Chlorophenylalanine) was added to 200 \u003cem\u003eµ\u003c/em\u003eL of supernatant, which was then transferred to a vial for liquid chromatography-mass spectrometry (LC-MS) analysis using the LC-MS platform (Q Exactive Thermo Fisher Scientific, Walthem, MA, USA). The chromatographic separation was carried out using an ACQUITY UPLC HSS T3 column (100 × 2.1 mm 1.8 \u003cem\u003eµ\u003c/em\u003em) at 40°C with a flow rate of 0.3 mL/min. The injection volume was 6 µL, and the automatic injector temperature was 4°C.\u003c/p\u003e\u003cp\u003eData on retention time, compound molecular weight, observations and peak intensity were collected using feature extraction, preprocessed with compound discoverer software (Thermo) and normalized. To ensure data quality, peaks from less than 50% of QC samples and 80% of biological samples were discarded. The OPLS-DA was employed to visualize the overall differences and for identifying differential metabolites. Finally, differential metabolites were selected according to the importance of variable in projection (VIP), false discovery rate (FDR), \u003cem\u003eP\u003c/em\u003e-value and Log2 fold change (Log2FC) (VIP \u0026gt; 1, FDR \u0026lt; 0.05, \u003cem\u003eP\u003c/em\u003e-values \u0026lt; 0.05, abs (Log2FC) \u0026gt; 0.263).\u003c/p\u003e\u003ch3\u003e9. Statistical and Data Visualization Analysis\u003c/h3\u003e\u003cp\u003eStatistical analyses were performed in R (version 4.3.3) [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. The Kruskal-Wallis nonparametric test was used to compare differences in rumen microbial communities. For further statistical analysis and visualizations, the following R packages were used: ggtree (version 3.16.0) [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], ggtreeExtra (version 1.18.0) [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], ggplot2 (version 3.5.2) [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], tidyverse (version 2.0.0) [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], treeio (version 1.32.0) [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e], dplyr (version 1.1.4) [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e], reshape2 (version1.4.4) [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e], ggnewscale (version 0.5.1) [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], viridis (version 0.6.5) [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e], ggpubr (version 0.6.0) [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e], rjson (version 0.2.23) [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e], and ggrepel (version 0.9.6) [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. Data were presented as mean ± standard error of the mean (SEM), and significance was set at P \u0026lt; 0.05.\u003c/p\u003e\u003cp\u003eIn addition, Python (version 3.13.0) was employed for specific tasks, including the retrieval of KO information using the requests library. ETE4 (version 4.3.0) [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e] was used for constructing phylogenetic trees, while SciPy (version 1.15.3) [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e] and Pandas (version 2.2.3) [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e] were used for additional data manipulation and analysis.\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003e1. Environmental parameters on Qinghai-Tibetan Plateau\u003c/h3\u003e\n\u003cp\u003eWith increasing altitude, the sunrise time (SRT) advanced significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and the sunset time (SST) was significantly delayed (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), resulting in a marked increase in daylight duration (DLD). While the ultraviolet (UV) index showed no significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Supplementary Fig. S2A). The maximum temperature (MaxT) and daily temperature range (DRT) increased significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with increasing altitudes; however, the minimum temperature (MinT) was not significantly different (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Supplementary Fig. S2D). The oxygen levels (Supplementary Fig. SA), air pollution index (API), PM10, PM2.5, NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, and CO (Supplementary Fig. S2B), were water dissolved oxygen (WDO), water temperature (WT), and water pH (WpH) (Supplementary Fig. S2E) were all decreased significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), The while the humidity (AH) and ozone (O\u003csub\u003e3\u003c/sub\u003e) showed a significant increase (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with increasing altitude. The barn temperature (BT) significantly decreased (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the humidity (BH) increased significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Fig. S2F) with increasing altitudes.\u003c/p\u003e\n\u003ch3\u003e2. Hypoxia decline in digestibility CP\u003c/h3\u003e\n\u003cp\u003eFood intake (FI) of both cattle and yaks increased significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Fig. S3A, B) while metabolic body weight (MBW) and respiratory rate (RR) of yaks exhibited a significant decrease (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Fig. S3A, B) with increasing altitude. Digestibility parameters, including organic matter digestibility (OMD), dry matter digestibility (DMD), lignocellulose digestibility (LD), and crude protein digestibility (CPD), were all decreased significantly in both yaks and cattle (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The apparent dry matter fermentability (ADFD) decreased significantly in yaks (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Fig. S3C), with no significant differences for these parameters (Supplementary Fig. S3C) observed in cattle (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u003c/p\u003e\n\u003ch3\u003e3. Hypoxia increases ammonia nitrogen levels in yaks\u003c/h3\u003e\n\u003cp\u003eMicrobial crude protein (MCP) synthesis significantly decreased in both cattle and yaks (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Fig. S4A) with increasing altitude. Ammonia nitrogen (NH\u003csub\u003e3\u003c/sub\u003e-N) significantly increased in yaks (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Fig. S4A) but not in cattle, at higher altitudes. Total Volatile Fatty Acids (TVFA, Supplementary Fig. S4B), isobutyric acid (IBA), propionic acid (PA), and acetic acid (AA) significantly increased in cattle (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The IBA also significantly increased in yaks (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Fig. S4C). No significant differences were observed in butyric acid (BA) in both cattle and yaks (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Supplementary Fig. S4C). Fatty acid and nucleotide metabolism increased (Fig.\u0026nbsp;1A) in yaks, and the yaks exhibited a greater number of metabolite changes (Fig.\u0026nbsp;1B) with increasing altitude (Supplementary Table S3 and S4).\u003c/p\u003e\n\u003ch3\u003e5. Hypoxia reduced immunity and increased the inflammatory factors.\u003c/h3\u003e\n\u003cp\u003eTotal Bilirubin (TBIL) decreased (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Fig. S5A), and glucose (GLU) level increased significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Fig. S5A) both in in cattle and yaks Alanine Aminotransferase (ALT) significantly increased in yaks (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Fig. S5A).\u003c/p\u003e \u003cp\u003eRegarding blood immunity, interleukin (IL), malondialdehyde (MDA), oligosaccharide (OGA), and immunoglobulin M (IGM) all increased significantly both in cattle and yaks (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Fig. S5B). No significant differences were observed in tumor necrosis factor (TNF, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Supplementary Fig. S5B). Yaks showed fewer changes in the blood metabolome (Fig.\u0026nbsp;4A, B) than in cattle (Supplementary Table S7, S8 and S9.)\u003c/p\u003e\n\u003ch3\u003e6. Hypoxia alters the functions of the yak's body and rumen microorganisms\u003c/h3\u003e\n\u003cp\u003eIn correlation analysis of differential metabolites and differential microorganisms in the rumen of yaks, Firmicutes and Bacteroidetes phyla were positively correlated with fatty acids, nucleotides, and their derivatives (Fig.\u0026nbsp;5, Supplementary Table S3). Microbial functions in the rumen were enriched during the biosynthesis pathways of unsaturated fatty acids, primary bile acid biosynthesis, and serine and threonine metabolism (Supplementary Fig. S6B). Functional annotation of rumen microorganisms using eggnog showed that the enriched pathways were mainly enriched in Fatty acid biosynthesis, Valine, leucine and isoleucine degradation, Amino sugar and nucleotide sugar metabolism, Nitrogen metabolism, Propanoate metabolism, Butanoate metabolism and related biological processes. In the species annotation of functional reads, \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eOscillibacter\u003c/em\u003e, \u003cem\u003eSelenomonas\u003c/em\u003e, \u003cem\u003eSchwartzia\u003c/em\u003e and \u003cem\u003eFibrobacter\u003c/em\u003e as the main contributors to microbial functions (Fig.\u0026nbsp;6). Microbial interaction networks (Fig.\u0026nbsp;7) analyses across various pathways revealed that distinct pathways are associated with different sets of microbial taxa (Supplementary Table S10 and S11).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on this comparative study of cattle and yaks, we discovered unique environmental adaptation mechanisms in the rumen of yaks, including restructuring of microbial community, specialization of metabolic pathways, and host-microbe co-regulation. Notably, a unique adaptive mechanism was observed involving long-chain fatty acid metabolism, with biosynthesis pathways predominantly mediated by the genera \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eOscillibacter\u003c/em\u003e, \u003cem\u003eSelenomonas\u003c/em\u003e, \u003cem\u003eSchwartzia\u003c/em\u003e, and \u003cem\u003eFibrobacter\u003c/em\u003e forming functional core of this process. This metabolic shift was closely associated with markers of hepatic stress, highlighting the physiological cost of high-altitude adaptation in yaks.\u003c/p\u003e \u003cp\u003eThe hypoxic, high-altitude environment induces adaptive changes in the rumen microbiota of yaks. First, under both high- and low-altitude conditions, the rumen microbiota of yaks shows significant differences compared to that of cattle. This adaptive shift in microbial structure also occurs in response to other factors, including seasonal forage changes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], different feed types [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and high-grain diet structures [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Among the changes observed in the rumen metabolome of yaks, the most striking were the elevated levels of long-chain fatty acids and nucleotide content. Increased levels of long-chain fatty acids in the rumen indicate their enhanced microbial synthesis [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], while the increased level of nucleotides, particularly cAMP, reflects adjustments in carbon metabolism [\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These signaling molecules may originate from microbial regulation or host adaptation, highlighting the need for further research on rumen microbiota modulation. Furthermore, functional annotation of rumen microbes reveals enrichment in the degradation process of amino acids, fatty acid metabolism resulting in the metabolomic changes observed. Annotation of species associated with significantly differential genes revealed that they were primarily from the genera \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eOscillibacter\u003c/em\u003e, \u003cem\u003eSelenomonas\u003c/em\u003e, \u003cem\u003eSchwartzia\u003c/em\u003e, and \u003cem\u003eFibrobacter\u003c/em\u003e. Spearman correlation analysis also revealed a positive association between \u003cem\u003eRuminococcus\u003c/em\u003e and long-chain fatty acid synthesis. This is also consistent with the findings of Allison et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which reported the biosynthesis of higher branched-chain fatty acids and aldehydes by members of the phylum \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e. We hypothesize that interactions among these microbes may lead to the increased level of long-chain fatty acids in the rumen. Thus, key microorganisms such as \u003cem\u003eSelenomonas\u003c/em\u003e and \u003cem\u003eSchwartzia\u003c/em\u003e, which are involved in other metabolic pathways contributing to acetyl CoA production, deserve further attention. \u003cem\u003eSelenomonas\u003c/em\u003e play a central role in multiple carbohydrate metabolic pathways, with its metabolic processes\u0026ndash;such as acetyl-CoA synthesis, propionate production, and purine metabolism) being synergistically driven by related functional microbiota [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Analysis of the microbial metabolic network revealed that protozoa play certain role in signal regulation, with the fatty acid synthesis functions of protozoa and fungi showing correlations with fatty acids. However, in fatty acid degradation, fungi and protozoa are significantly different in their functions or even negatively correlated, which indirectly indicates their role in regulating the rumen adaptation of yaks. Research on \u003cem\u003eSchwartzia\u003c/em\u003e is limited; however, we speculate that it may perform functions similar to \u003cem\u003eSelenomonas\u003c/em\u003e or provide substrates for gluconeogenesis. Integrating functional and pathway results, we conclude that the aforementioned species, along with auxiliary microbes, contribute to long-chain fatty acid synthesis. However, due to limited data, further clarification of these microbial interactions is not possible. This necessitates additional research to elucidate the unique adaptive regulatory mechanisms of rumen microbiota of yak.\u003c/p\u003e \u003cp\u003eSecondly, the results of blood metabolomic analysis showed significant changes in pathways of unsaturated fatty acid biosynthesis, primary bile acid biosynthesis, and serine and threonine metabolism in yaks. These results demonstrate that yaks rely on fatty acid mobilization and amino acid metabolism to adapt to environmental changes at high altitudes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. On the contrary, the cattle did not exhibit changes in metabolic pathways of fatty acid and bile acid. Serum immune indicators revealed that yaks had a significantly increased ALT than in cattle. Metabolic enzymes such as ALP, ALT, and AST in serum are commonly used as indicators to assess liver damage and diseases [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The significantly increased ALT in yak serum may indicate that yaks experience some degree of inflammatory response in the liver due to the relatively challenging process of energy mobilization in the body. But this could also be an adaptation mechanism of yaks to high-altitude environments [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Rumen microorganisms continues to present challenges due to their complex microbial diversity, limited comprehensive genomic data, and difficulty of culturing many species, making it hard to obtain precise biological information through metagenomic approaches. The lack of taxonomic information also hinders the discovery of many potential microorganisms. In our species annotation, approximately 50% of the microorganisms could not be identified to specific microbial taxa, leading us to suspect the potential influence of unknown species. With advancements in single-cell sequencing technology, we may be able to directly obtain the full genomes of unculturable rumen microorganisms without relying on cultivation methods. This will provide a wealth of new genetic information for bioinformatics to explore, further enhancing our understanding of this unique microbial system in the rumen.\u003c/p\u003e \u003cp\u003eIn summary, our results highlight the specialized adaptive functions of the yak rumen; however, the specific microorganisms that play major roles in the rumen microbial community still could not be precisely identified. This is due to the limited taxonomic information on rumen microorganisms. Future research is required for more cultivation and sequencing of rumen microorganisms to establish a more comprehensive rumen microbial database, facilitating in-depth studies of rumen microorganisms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur research highlighted the specialized adaptation of yak rumen microbiota to high-altitude environments, characterized by microbial community restructuring and a metabolic shift toward long-chain fatty acid biosynthesis. Although this adaptation mechanism helps survive the yaks under hypoxic conditions, it may come with its physiological costs such as liver damage. Due to the extremely high microbial diversity and limited taxonomic resolution, nearly 50% of species remain unclassified, leaving the specific microbial drivers of these adaptive changes unclear. Current metagenomic approaches are inadequate for fully resolving this complexity. Future studies should utilize single-cell sequencing and culture-independent techniques to obtain genomes of unculturable or hard-to-culture microbes, expand the rumen microbial database, and provide a comprehensive understanding of the mechanisms by which microbiota contribute to host adaptation. These findings enhance our understanding of high-altitude ruminant adaptation and highlight that such adaptations involve inevitable health trade-offs a principle that may apply to human populations residing in high-altitude regions. Therefore, in the adaptation of high-altitude populations, greater emphasis may need to be placed on nutritional supplementation to mitigate the physiological stress associated with survival pressures of high altitudes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMCP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMicrobial Crude Protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDissolved Oxygen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAST\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAspartate Aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eALT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlanine Aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eALB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGLOB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobulin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGLU\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood Glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTCHO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTRIG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eALP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlkaline Phosphatase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTBIL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Bilirubin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCK\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCreatine Kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLDH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLactate Dehydrogenase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUREA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUrea\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUric Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSCFAs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShort-Chain Fatty Acids (acetate, propionate, isobutyrate, butyrate, isovalerate, valerate)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTCD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThermal Conductivity Detector\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCTAB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCetyltrimethylammonium Bromide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eqPCR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReal-Time Quantitative Polymerase Chain Reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eVIP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVariable in Projection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFDR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse Discovery Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSRT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSunrise Time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSST\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSunset Time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDLD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDaylight Duration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUltraviolet\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMaxT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMaximum Temperature\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDRT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDaily Temperature Range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMinT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinimum Temperature\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAPI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAir Pollution Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePM10\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParticulate Matter\u0026thinsp;\u0026le;\u0026thinsp;10 \u0026micro;m\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePM2.5\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParticulate Matter\u0026thinsp;\u0026le;\u0026thinsp;2.5 \u0026micro;m\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNO2\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNitrogen Dioxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSO2\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSulfur Dioxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCarbon Monoxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWDO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWater Dissolved Oxygen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWater Temperature\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWpH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWater pH\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAir Humidity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eO3\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOzone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBarn Temperature\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBarn Humidity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFood Intake\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMBW\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetabolic Body Weight\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRespiratory Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOMD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOrganic Matter Digestibility\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDMD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDry Matter Digestibility\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLignocellulose Digestibility\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCPD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCrude Protein Digestibility\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eADFD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eApparent Dry Matter Fermentability\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTVFA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Volatile Fatty Acids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterleukin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMDA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMalondialdehyde\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOGA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOligosaccharide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIGM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImmunoglobulin M\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTNF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor Necrosis Factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Qinghai Provincial Natural Science Fund for Distinguished Young Scholars (2024-ZJ-905); Qinghai University Research Ability Enhancement Project (2025KTST04); the National Key R\u0026amp;D Sub-project (2022YFD1302103), Qinghai University Graduate Supervisor Innovation Team (2025, L.Z.H.), Special Topics of the Second Comprehensive Scientific Expedition of the Qinghai-Tibet Plateau (2019QZKK0606), Leading talent of \u0026quot;Kunlun Talents High-level Innovation and Entrepreneurial Talents\u0026quot; in Qinghai Province (QHKLYC-GDCXCY-2024-071, L.Z.H.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe whole‑metagenome sequencing reads generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under accession [PRJNA1296762]. The 16S rRNA gene amplicon sequencing data for yak and cattle rumen samples are available in the SRA under accession [PRJNA1302440].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit Author Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLang Tan\u003c/strong\u003e: Data curation, formal analysis, software, writing \u0026ndash; original draft, Visualization. \u003cstrong\u003eXiaojing Liu\u003c/strong\u003e: Investigation, data curation, formal analysis, software, writing \u0026ndash; original draft, validation. \u003cstrong\u003eAllan Degen, Yonggui Ma, Jinfen Yang, Qunying Zhang, Jianbo Zhang, Binqiang Bai, Heng Ma, Ru Meng\u003c/strong\u003e: Investigation, resources, validation. \u003cstrong\u003eNik Palevich\u003c/strong\u003e: Writing \u0026ndash; review and editing. \u003cstrong\u003ePeijun Shi\u003c/strong\u003e: Resources, writing \u0026ndash; review and editing. \u003cstrong\u003eLizhuang Hao\u003c/strong\u003e: Supervision, conceptualization, funding acquisition, resources, writing \u0026ndash; review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal procedures followed the Guidelines for the Care and Utilization of Laboratory Animals of Qinghai Province (Qinghai Agriculture and Animal Husbandry Bureau, 2002) and were approved by the Committee of Animal use of the Academy of Science and Veterinary Medicine of Qinghai University (QHU20210113).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eQiu J, China. 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While previous research has largely focused on genetic and physiological traits of yaks, the interactions between rumen microbiota and host physiology under hypoxic conditions remain poorly understood. As the largest digestive organ in ruminants, the rumen and its microbiota play a central role in digestion and host nutrition. In this study, a comparative analysis of digestive metabolism and rumen microbiota was carried out in yaks and cattle under varying atmospheric oxygen levels. Our findings reveal that yaks have developed unique microbial strategies to cope with energy deficits in hypoxic stress. These include a shift in rumen microbiota toward amino acid degradation and enhanced long-chain fatty acid biosynthesis, thereby improving energy acquisition despite reduced nutritional intake. However, this metabolic adaptation comes at a physiological cost - reduced microbial crude protein (MCP) synthesis leads to elevated ruminal NH\u003csub\u003e3\u003c/sub\u003e-N levels, and increased fatty acid metabolism and urea cycle activity contribute to hepatic stress. This study presents the first evidence of metabolic trade-offs in high-altitude adaptation, demonstrating that yaks optimize microbial-mediated energy production at the expense of liver health. These insights deepen our understanding of host-microbiome coevolution mechanisms in extreme environments and highlight biological costs associated with adaptation.\u003c/p\u003e","manuscriptTitle":"The Price of Survival: Yaks’ Adaptation to High Altitudes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-06 10:05:37","doi":"10.21203/rs.3.rs-8533339/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-16T23:55:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T15:20:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-01T12:57:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-11T12:15:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157210231407745251088614241727972335057","date":"2026-02-09T11:51:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139985748712040758358422540194907632830","date":"2026-02-04T10:21:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89283097091752717499999096021344690144","date":"2026-02-04T09:42:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-04T09:13:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-27T15:35:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-08T10:32:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Animal Microbiome","date":"2026-01-06T15:50:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"animal-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amic","sideBox":"Learn more about [Animal Microbiome](http://animalmicrobiome.biomedcentral.com)","snPcode":"42523","submissionUrl":"https://submission.nature.com/new-submission/42523/3","title":"Animal Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b4d88806-3974-4b66-9113-222bbcfee126","owner":[],"postedDate":"February 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T10:58:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-06 10:05:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8533339","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8533339","identity":"rs-8533339","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}