Eothenomys miletus were collected from Jingdong County (JD) and Xianggelila County (XGLL) in winter of 2023, respectively. Experimental animals were all non-breeding healthy adult individuals. The habitat characteristics of the sampling sites were shown in Table 1.

Eothenomys miletus that were captured in two separate locations were sterilized to remove fleas, brought back to the animal breeding room at Yunnan Normal University, and put in separate rat cages (260 × 160 × 150 mm). After 4 days of acclimatization, E. miletus were divided into three groups using a two-factor (region × food restriction) experimental design: a 0 d control group, a 7 days food-restricted group, and a 14 days re-feeding group after food restriction (Figure 1). The restricted-food group fed 80% of the control group’s food intake (Yang et al., 2013), while the control group had free access to food and water. That is, JD control group (CJD, n = 7), JD restricted food group (FRJD, n = 7), JD re-feeding group (FR-ReJD, n = 7), XGLL control group (CXGLL, n = 7), XGLL restricted food group (FRXGLL, n = 7), XGLL re-feeding group (FR-ReXGLL, n = 6). Room temperature was controlled at 25 ± 1°C with a photoperiod of 12 L:12D (Light: Dark). The trial period lasted 21 days, during which the animals were fed standard rat chow (from Kunming Medical University, Kunming, China). Body mass, food intake, RMR and feces were measured simultaneously when the test was carried out on day 0, 7, and 21. Body mass was measured with an LT502 electronic balance (accurate to 0.01 g), food intake was measured with the food balance method, and RMR was measured with a portable respirometer (Gong et al., 2022). They were carried out under carbon dioxide anesthesia following the identification of the pertinent markers, and serum and rectal feces were collected.

At the end of the experiment, blood was collected, allowed to stand for 1 h in the refrigerator at 4°C, and was centrifuged at 4°C (4,000 r/min, 30 min), taking up serum in a centrifuge tube, stored in the refrigerator (−80°C), and set aside. Measurements of leptin, glucose (Glu), triglyceride (Tg), total cholesterol (Tc), SCFAs, LPS, fasting-induced adipocyte factor (FIAF), and TNF-α were measured the serum using an enzyme-linked immunosorbent assay (ELISA). After the experimental animals were executed, brown adipose tissue (BAT) was carefully removed, weighed and put into centrifuge tubes, and stored in a low-temperature refrigerator for storage. The content and activity of uncoupling protein 1 (UCP1) were determined using ELISA. The assay was conducted following the instruction manual, and the product numbers of the assay kits were leptin Assay Kit (JM-11498 M1), Glu Assay Kit(S0104F-1), Tg Assay Kit (S0104F-1) Tg Assay Kit (S0140O-1), Tc Assay Kit (S05042-1), SCFAs Assay Kit (JM-11498 M1), LBP Assay Kit (JM-12488 M1), FIAF Assay Kit (JM-12613 M1), TNF-α AssayKit(JM-02415 M1).

Organs and other tissues are separated and then weighted after draining the surface liquid with a filter, (accurate to 0.001 g). After removing the digestive tract, carefully remove the mesentery, connective tissue, and fat from the stomach, small intestine, colon, and cecum. Then, weigh and measure the length of each organ.

A centrifugal column-based soil genome extraction kit (DNeasy®PowerSoil®Kit, Germany) was used to collect the rectal feces and enrich the total DNA on the filter membrane. The concentration of purified PCR products was determined using a Nanodrop 2000 spectrophotometer, and valid samples were defined as having a nucleic acid concentration greater than 10 ng/uL and a purity (A260/A180) greater than 1.8. Purified DNA samples were mixed at equimolar concentrations and sequenced using the Illumina Miseq platform (Illumina, San Diego, CA, USA).

The 2 × 250 bp double end sequences were obtained by sequencing on the Illumina Miseq platform (Illumina, San Diego, CA, USA) and these raw data were processed and analyzed using the QIIME platform (version 1.8). The double-end sequences were first spliced using Flash software (version 1.2.111) and then matched to a unique barcode label for each sample. Low quality sequences (sequence length less than 300 or base mass fraction less than 30) were removed during the splicing process. By QIIME software, the raw data was processed, using Flash software to clear low-quality sequences, and then removing the chimeras in the sequences by Usearch 7.0 software. The OTU sequences with more than 97% recognition were clustered using the Uclust algorithm, and the representative OTU sequences were analyzed and identified based on the Ribosomal Database Project, and finally, the sequences of all samples were normalized by the “Daisychopper” script code. Finally, we standardized the sequences of all the samples by using the code “Daisychopper.”

Body mass of E. miletus was significantly affected by area and time (Region: F = 35.751, p < 0.01; Time: F = 7.895, p < 0.05). Region and time significantly affected RMR of E. miletus (Region: F = 15.928, p < 0.01; Time: F = 17.141, p < 0.01). The interaction of region and time significantly affected liver weight in E. miletus (Region: F = 32.960, p < 0.01; Time: F = 8.225, p < 0.01; Interaction: F = 12.614, p < 0.01). Region significantly affected kidney weight and lung weight in the E. miletus (Region: F = 14.679, p < 0.05; Time: F = 12.245, p < 0.05). Cecum length was significantly affected by region and time (Region: F = 21.508, p < 0.01; Time: F = 3.321, p < 0.05). Time significantly affected WAT weight (F = 4.744, p < 0.05) and the interaction of region and time significantly affected BAT weight (Region: F = 32.618, p < 0.01; Time: F = 7.154, p < 0.05; and Interaction: F = 10.955, p < 0.01) in E. miletus (Figure 2). Region significantly affected the Leptin content of the E. miletus (F = 18.688, p < 0.01) (Figure 3). Region and time did not significantly affect other indices of E. miletus in XGLL (Table 2). E. miletus in XGLL had smaller changes in leptin, larger changes in RMR, greater weight loss in main organs, and larger changes in cecum.

Data were mean ± standard deviation. Different letters indicated significant differences between treatments in the same region, xy indicates JD region and ab indicates XGLL region. *p < 0.05, **p < 0.01, JD and XGLL were compared on the same day.

Firmicutes, Bacteroidetes, and Spirochaetes were the fecal microbial dominant phylum of E. miletus (Figure 4), with mean relative abundances of 80.71, 9.22, and 8.66%, respectively. The relative abundance of Firmicutes, the predominant phylum of fecal microbes in E. miletus, did not differ significantly between the two regions of JD and XGLL, similar to the relative abundance of Bacteroidetes did not change between the regions at different treatment periods. Conversely, the dominant phylum Spirochaetes’ average relative abundance in XGLL exhibited a tendency of decline followed by an increase, while the shift was less noticeable in JD.

At the genus level, the main dominant genera of fecal microorganisms in the E. miletus were Lactobacillus, Treponema, and S24-7 (UG), with average relative abundances of 73.72, 8.63, and 6.68% in all groups (Figure 5). Lactobacillus did not exhibit a significant variation in mean relative abundance over time or place in E. miletus. In XGLL, the dominating genus Treponema’s average relative abundance first trended downward before rising.

Food restriction did not significantly affect the α-diversity (Chao1 and Shannon diversity) of E. miletus (Figure 6). The PCoA plot showed that the β-diversity of E. miletus in the various food-restricted group regions was dispersed, without any discernible tendency of aggregation (Figure. 7), which implied that the fecal microbial β-diversity of E. miletus was not significantly affected by the food-restricted treatment.

Subsequent PERMANOVA testing revealed that the overall distribution of fecal microbial β-diversity (weighted and unweighted matrices) in E. miletus was not significantly affected by treatment or location (Table 3).

There were 67 genera of fecal microorganisms in the food-restricted group of E. miletus in the JD (Figure 8). Among them, 18 genera were unique to CJD0 group, 27 genera were unique to the FRJD7 group, and 21 genera were unique to FR-ReJD21 group. The food-restricted group of E. miletus at XGLL had 60 genera of fecal bacteria. In XGLL, there were 31 genera that were particular to the CXGLL0 group, 15 genera that were specific to FRXGLL7 group, and 26 genera that were exclusive to FR-ReXGLL21 group. It showed that the common and endemic bacteria in XGLL and JD were almost identical.

There were 56 genera of fecal bacteria in various places and periods as Figure 9 demonstrated. Among them, 44 genera were exclusive to CJD0 group, 62 genera were exclusive to FRJD7 group, and 53 genera were exclusive to the FR-ReJD21 group. In XGLL, there were 50 genera were exclusive to CXGLL0 group, 32 genera were exclusive to FRXGLL7 group, and 52 genera were exclusive to FR-ReXGLL21 group.

The relative abundance of microorganisms differed in the feces of JD and XGLL E. miletus as shown in Figure 10. Denovo61049 (f_S24-7) was most enriched in JD control group (p < 0.05). The majority of microbial OTUs, including denovo17865 (g_Lactobacillus), denovo20621 (g_SMB53), denovo34950 (f_S24-7), denovo45512 (g_Jeotgalicoccus), etc., were higher enriched in the FRJD7 group (p < 0.05). Compared to the CJD0 and FRJD7 groups, denovo56247 (g_Lactobacillus) was significantly enriched in FR-ReJD21 groups (p < 0.05).

In XGLL, OTUs enriched in the CXGLL0 group included denovo8828 (g_Lactobacillus) and denovo41044 (f_Lachnospiraceae). Comparing the CXGLL0 and FRXGLL7 groups, denovo190 (f_Erysipelotrichaceae), denovo20731 (f_[Mogibacteriaceae]), and denovo24790 (f_S24-7) were significantly enriched (p < 0.05) in FR-ReXGLL21 groups. Similarly to JD, microbial OTUs were more enriched in the 7-day group, such as denovo31484 (g_AJDercreutzia), denovo34950 (f_S24-7), denovo43527 (g_AJDercreutzia), and denovo46811 (g_ Lactobacillus), etc.

The correlation between physiological indicators and the dominant genera (top ten relative abundance of all samples) in the feces of E. miletus in JD was shown in Figure 11. It was found between various physiological indicators and some of the main genera found in the feces of E. miletus in JD. For instance, there was a significant negative relationship between liver weight and AJDercreutzia abundance (p < 0.05), a significant negative correlation between BAT mass and Bacteroides and [Ruminococcus] abundance (p < 0.05), and a significant negative correlation between WAT mass and Turicibacter abundance (p < 0.05).

In JD, it illustrated the relationship between detection markers and fecal dominating genera (top ten relative abundance of all samples) of E. miletus (Figure 12). There was a substantial and positive correlation (p < 0.05) between Tc and the abundance of S24-7 (UG); conversely, there was a significant and negative correlation (p < 0.01) between Glu and Turicibacter.

Figure 13 displayed the relationship between physiological markers and fecal dominating genera (the top ten relative abundances of all samples) for E. miletus in XGLL. In comparison to the JD, XGLL had more physiological indications linked to the fecal dominating genus of E. miletus. Among them, Lactobacillus abundance was significantly negatively correlated (p < 0.05) with the majority of the physiological indicators (body mass, heart weight, liver weight, spleen weight, kidney weight, lung weight, WAT mass), whereas in contrast, Treponema abundance was significantly positively correlated (p < 0.05) with most of the physiological indicators (body mass, food intake, heart weight, liver weight, spleen weight, kidney weight, lung weight, WAT mass); BAT mass was significantly positively correlated (p < 0.05) with the abundance of Lachnospiraceae (UG), Clostridiales (UG), Bacteroides, and [Ruminococcus]. The abundance of AJDercreutzia was significantly negatively correlated (p < 0.05) with body mass, heart weight, and liver weight, and the abundance of Rikenellaceae (UG) was positively correlated (p < 0.05) with the abundance of food intake, liver weight, spleen weight, and lung weight. Furthermore, it showed a substantial positive correlation (p < 0.05) between lung weight and the majority of prominent species, including Treponema, Lachnospiraceae (UG), Clostridiales (UG), Rikenellaceae (UG), Turicibacter, and [Ruminococcus].

Figure 14 illustrated the relationship between the detection indexes and the dominating genera (top ten relative abundance of all samples) in E. miletus in XGLL. TNF-α was significantly negatively correlated (p < 0.05) with S24-7 (UG) abundance; Glu and Tc were significantly positively correlated (p < 0.05) with Treponema and Rikenellaceae (UG) abundance; Tg was significantly and positively correlated (p < 0.05) with the abundance of Lachnospiraceae (UG), Clostridiales (UG), and Bacteroides; Leptin was significantly and negatively correlated (p < 0.05) with the abundance of Lactobacillus and significantly and positively correlated with the abundance of Treponema.

Figure 15 illustrated the relationship between various regional physiological markers and the fecal dominance OTUs in E. miletus. For instance, the abundance of denovo35645 (g_Lactobacillus) was negatively linked with food intake, lung weight, and BAT mass. The denovo52794 (g_Treponema) abundance showed a positive correlation with heart weight, liver weight, kidney weight, and lung weight, while the denovo14407 (g_Lactobacillus) abundance showed a negative correlation. Lung weight and BAT mass were positively correlated with denovo33406 (g_Turicibacter) abundance.

The correlation network of the dominant OTUs found in the feces of E. miletus from various regions as shown in Figure 16. In this network, the top 200 OTUs by relative abundance were included. Gephi 0.9.2 was used to create a network with 200 nodes and 502 edges, of which 501 were positive and 1 was negative, suggesting that the microorganisms in the co-occurring network of this dominant OTU primarily interact cooperatively.

Eothenomys miletus in JD and XGLL had drastically different body mass, RMR, cecum length, BAT weight, and leptin level in the control group. XGLL had a higher elevation compared to JD, and smaller body mass of E. miletus may suggest that it has some degree of size adaption in high-altitude regions. Since body size affects practically all physiological, behavioral, and ecological feature of mammals, changes in body size are typically a crucial approach for adapting to rapidly changing environmental situations (Searing et al., 2023). Rats’ larger lung-to-body-weight ratio might help maintaining respiratory efficiency in an oxygen-sparse high-altitude environment by improving the body’s capacity to absorb, transport, and diffuse oxygen (Shao et al., 2010). Furthermore, higher RMR indicated that rats use more energy to sustain body temperature, perform vital functions, and cope with food scarcity in the high-altitude setting. For small and winter-active rats to survive in high-elevation environments, sustained aerobic thermogenesis is necessary (Storz et al., 2019). The cecum contains fiber hydrolase (Frias et al., 2023), and a longer cecum may indicate that E. miletus consume more cellulose at high elevations, which calls for longer cecums to facilitate the digestion of cellulose. The larger BAT weight suggested that E. miletus need more calories to stay warm while at high altitudes. Lower levels of the hormone leptin, which controls hunger and energy metabolism and is mostly produced by adipocytes (Perakakis et al., 2021), and lower levels of leptin may indicate E. miletus had an increased appetite at high altitude and need to consume more food to satisfy their energy requirements. After re-feeding, larger changes in body mass, RMR and liver weight were observed in JD than in XGLL, which may be related to their larger body mass. We suggesting that different physiological adaptation may be used by E. miletus living in the XGLL to cope with food restriction. These strategies could include improving energy use pathways and lowering activity levels to avoid needless energy consumption.

In this experiment, the dominant phyla in E. miletus from different regions were the Firmicutes, Bacteroidetes and Spirochaetes. The Chao1 index was used to assess the community’s richness, and the larger the value, the higher the species richness (Wang et al., 2020). A steady and nutrient-rich environment supplied by the host is also necessary for the survival and reproduction of gut microbes (Bäckhed et al., 2004). The gut microbiota of E. miletus in XGLL abundance declined following food restriction, according to α diversity analysis. This could be explained by the fact that there was less food available at higher altitudes, which led the organisms to respond by reducing the number of certain microorganisms in order to conserve energy. Higher Shannon index has been found to correlate with better bacterial community stabilization and increased disease resistance (Liu et al., 2023). The results of the food restriction treatment indicated that food reduction may result in a decrease in the stability of the bacterial community in the intestinal tract, which weakens their resistance to disease. Shannon index of E. miletus was lower in XGLL than that of in JD, that is contrary to previous studies on the intestinal microbiota of goats (Zhang et al., 2024). We speculated that this phenomenon occurs because of differences in physiology, digestive systems, and behavior between E. miletus and goats, which influence their reliance on and selection of gut microbiota. E. miletus may be more dependent on specific microbes to cope with high altitude environments, whereas goats’ digestive systems may be more inclined to promote microbial diversity. Moreover, there is a non-significant decreasing trend in Shannon’s index in E. miletus in both JD and XGLL.

The animals exhibit broader diversity of gut community as the altitude increases, which is consistent with the adaptation of rhesus monkey and plateau pika gut microbes to high-altitude environments (Li et al., 2018; Wu et al., 2020). A comparison of the JD and XGLL control groups revealed that the gut community richness of E. miletus was greater at higher altitudes. Animals that live at high altitudes have a variety of environmental obstacles, including low temperatures, reduced oxygen levels, and higher UV radiation (Liu, 2020). To adapt to these changes, their gut microbiota has undergone adaptive alterations. The animal gut microbiota is more adaptive due to the increased community diversity, and it can aid the host in surviving and thriving in the relatively harsh environment of high altitude. Important ecological traits of gut community include stability, resistance, and resilience (Lozupone et al., 2012). It showed that the β diversity of gut microbiota in E. miletus was not significantly affected by food restriction treatments or different regions, which was inconsistent with the results of previous studies of gut microbiota in wild pikas. Plateau pika (Ochotona curzoniae) is an important high-altitude model animal, the β-diversity of their gut microbiota increased with elevation (Li et al., 2019), but for E. miletus, their diet was relatively monotonous, indicating that the gut microbial community of E. miletus was relatively stable and resistant to some degree of external interference.

In the present study, there were less OTUs in XGLL than in JD. Rhesus monkeys and bumblebees both showed a decline in the OTUs of gut bacteria with an increase in altitude, reflecting that the gut community has adapted to the high-altitude environment (Wu et al., 2020; Zhang et al., 2023). We speculated that only microorganisms adapted to this more extreme environment may survive at high altitude, as a result of a natural selection pressure exerted by the comparatively harsh environment of high altitude on animal gut microbiota. In the JD and XGLL regions, there was little difference between endemic and shared microorganisms. Animal gut microbial communities are frequently greatly impacted by their host species (Wang et al., 2022). Research has demonstrated that at varying elevations, the variety and quantity of gut microbiota in striped strong-ribbed lizards are similar (Montoya-Ciriaco et al., 2020). There is minimal diversity in the gut microbial species of the same animal species since their physiological traits and metabolic processes are comparable throughout geographic regions. Furthermore, individual gut microbiota abundance is more stable over time, and the effects of environmental differences across different places may not be significant (Chen et al., 2021). Animal gut microbiota may therefore stay essentially unchanged in a variety of settings.

The genera of gut microbes enriched in E. miletus in JD and XGLL can be divided into Bacteroidetes and Firmicutes, as the enrichment analysis graph illustrated. In contrast to the Bacteroidetes in JD, the microorganisms in the control groups were richer in Firmicutes in XGLL due to its higher elevation, less plant growth, and food scarcity. Bacteriophages can break down proteins and carbohydrates, while Firmicutes can break down a greater variety of organic materials and fibers (Waite and Taylor, 2014; He et al., 2023). Because Firmicutes was physiologically well-adapted and breaks down fiber more forcefully, E. miletus may be more dependent on them for energy at higher elevations. Firmicutes, which contain genes involved in energy metabolism, showed a notable enrichment in the food restriction groups in both locations (Kaakoush, 2015), and Bacteroidetes, which breaks down polysaccharides (McKee et al., 2021). E. miletus may be altering their energy intake and metabolism mechanisms to gather energy more efficiently, as evidenced by the phenomenon’s occurrence after food limitation. Further, it can be shown that AJDercreutzia was enriched in XGLL but not in JD when comparing the genera enriched in the food restriction group in both locations. By producing isoflavones through metabolism, AJDercreutzia can have anti-inflammatory properties (Galipeau et al., 2021). Isoflavones possess anti-inflammatory, anti-oxidant, immunomodulatory, and antifibrotic qualities (Alipour and Karimi-Sales, 2020). Furthermore, it has the potential to mitigate irritable bowel syndrome and improve intestinal community (Huang et al., 2022). Thus, we speculated that in a high-altitude, food-limited environment, the gut microbiota of E. miletus experienced distinct adaptive modifications in response to potential inflammatory reactions.

Figure 11 demonstrated the significant and negative correlation between BAT mass and the presence of Bacteroides and Ruminococcus, as well as the strong and negative correlation between WAT mass and the abundance of Turicibacter. Small mammals’ BAT, which uses energy to generate heat (Ghesmati et al., 2024). Following a food restriction, BAT’s weight decreased in E. miletus. Because of its potent ability to use polysaccharides, Bacteroides can nourish other bacteria and promote the symbiotic relationship of the gut community (Rakoff-Nahoum et al., 2016). Reduced food intake brought on by an increase in altitude may result in an increase in Bacteroides abundance, which would facilitate the organism’s adaptation to a food shortfall. This will improve how well food sugars are absorbed and used. Studies have indicated that a notable proportion of rumen bacteria are called Ruminococcus, and that diets high in fiber often lead to an increase in Ruminococcus abundance (Morrison and Miron, 2000). Our results were consistent, and one possible reason was that when food intake was restricted at high altitude and the food’s fiber content was contained, Ruminococcus becomes more numerous, making better use of the limited nutritional resources. When one consumes less food, their body breaks down WAT to replenish lost energy from daily metabolism. WAT functions as an energy reserve, an endocrine organ that regulates whole-body metabolism, and a source of fatty acids for other tissues through lipolysis as needed (Qian et al., 2022). In rodent and human studies, the relative abundance of Turicibacter is usually negatively correlated with dietary fat (Lynch et al., 2023). The fact that Turicibacter abundance increased following the dietary restriction treatment is likewise in line with our findings. Thus, WAT mass showed a significant negative correlation with Turicibacter abundance.

The majority of the physiological parameters of E. miletus in XGLL corresponded with the predominant genus of gut microorganisms. A significant negative association was seen between the abundance of Lactobacillus and most physiological indices, such as body mass, liver weight, heart weight. Lactobacillus abundance was significantly increased in both animal and human obesity studies (Drissi et al., 2017), which is closely related to the physiological functions of Lactobacillus. Lactobacillus can affect the balance of the intestinal community (Walter, 2008), producing lactic acid and short chain fatty acids that lower the pH of the intestinal tract and regulating the adaptation of other bacteria (Slattery et al., 2019). These acids can aid in weight control by improving lipolysis and oxidation and reducing the formation and accumulation of fat. They can also regulate energy and fat metabolism. At low pH values and in bile, lactobacillus can survive (Lynch et al., 2023). Additionally, by promoting the release of host antimicrobial peptides, acetic acid and propionic acid, which are generated by metabolism, can function as antimicrobials (Fukuda et al., 2011), which helps to maintain intestinal health. Furthermore, by producing short-chain fatty acids, Lactobacillus can induce the body to release the tyrosyl peptide PYY and glucagon-like peptide 1 (GLP-1) (Psichas et al., 2015). PYY controls intestinal motility, improves satiety, and decreases food intake, which lowers body mass; SCFAs suppress appetite and energy intake by encouraging the synthesis and secretion of PYY and GLP-1 by intestinal epithelial gland cells; and GLP-1 increases insulin secretion and sensitivity in the body, which prevents stomach emptying and encourages intestinal peristalsis (Delzenne et al., 2005; Byrne et al., 2015; Chambers et al., 2015). In our study, it showed that there had a positive correlation between Tc and S24-7(UG) abundance. It has been demonstrated that diabetic-sensitive animals given a high-fat diet have an increase in S24-7 abundance (Serino et al., 2012). This supports our findings that the body speeds up lipid metabolism to provide energy for daily tasks when food intake is restricted. It also restricted the amount of dietary cholesterol that is available and the liver’s rate of synthesis. It has been proven that Turicibacter influences bile acids and lipids in the host, which can lower adipose tissue mass and serum cholesterol (Lynch et al., 2023). Following food limitation, blood levels of glu drop, and Turicibacter may supply energy by increasing the abundance of the bacteria that break down fat and cholesterol. Adipose tissue secretes the hormone leptin, and the amount of this hormone in the serum varies with the size of the animal’s adipose tissue (Friedman and Halaas, 1998). In adipose tissue, Lactobacillus stimulates oxidative phosphorylation, which increases energy expenditure (Yoon et al., 2020). Therefore, there was a negative correlation between leptin and Lactobacillus quantity.

It showed that Treponema and Bacteroides, the two predominant bacteria of E. miletus in XGLL, helped E. miletus adapt to the high-altitude habitat. It has been shown that Treponema is associated with cellulose degradation (Baniel et al., 2021). Cellulose, a major component of plant cell walls, was also frequently present in plant-based diets that E. miletus may eat. Therefore, when there was a shortage of food, Treponema may help the body use dietary resources more effectively for energy and nutrients by breaking down polysaccharides. It has been demonstrated that Bacteroides stimulate the bile acid-TGR5-PPAR-α axis to activate fat oxidation in adipose tissue (Yang et al., 2017). Tg and Bacteroides abundance showed a positive correlation, which may indicate that Bacteroides promoted fatty acid oxidative degradation, which increased the host body’s energy source and helps E. miletus survive and proliferate in high-altitude environments.

RAD analysis showed that the physiological parameters of E. miletus responded differently to the relative abundance of distinct dominating bacteria in its intestine. The interactions within the intestinal population of E. miletus could be the cause of the occurrence. Figure 14 demonstrated the positive and negative correlations, respectively, between the abundance of Lactobacillus and Turicibacter and BAT mass. BAT mass was an important effector tissue for adaptive thermogenesis in humans and rodents (Greenhill, 2024), and was also linked to energy metabolism and body mass regulation (Liu et al., 2020). Turicibacter can modify lipid and bile acid metabolism, which impacts host lipid metabolism (Lynch et al., 2023). Therefore, by affecting bile acid and lipid metabolism, Turicibacter may indirectly alter BAT, however, more research is required to identify the precise mechanisms and effects. In the gut, Lactobacillus is a probiotic that controls key elements of the microbial community (Shi et al., 2024; Mani-López et al., 2022). Hence, we postulated that Lactobacillus might affect host energy balance and lipid metabolism via altering the composition of the gut microbial community. Network analysis is frequently used to infer the microbiome under theories of symbiosis, parasitism, and competition (Weiss et al., 2016). The co-occurrence network analysis reveals that microbes mostly cooperate with one another to support the stability and well-being of the gut environment.