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Article

Study of Fecal Microbiota Transplantation Ameliorates Colon Morphology and Microbiota Function in High-Fat Diet Mice

by
Xinyu Cao
1,2,
Lu Zhou
1,3,
Yuxia Ding
2,
Chaofan Ma
4,
Qian Chen
1,
Ning Li
4,
Hao Ren
2,
Ping Yan
4 and
Jianlei Jia
1,*
1
School of Life Sciences, Qilu Normal University, Jinan 250200, China
2
College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China
3
College of Life and Geographic Sciences, Key Laboratory of Biological Resources and Ecology of Pamirs Plateauin Xinjiang Uygur Autonomous Region, Kashi University, Kashi 844000, China
4
Institute of Western Agriculture, The Chinese Academy of Agricultural Sciences, Changji 831100, China
*
Author to whom correspondence should be addressed.
Vet. Sci. 2026, 13(2), 116; https://doi.org/10.3390/vetsci13020116
Submission received: 16 December 2025 / Revised: 9 January 2026 / Accepted: 13 January 2026 / Published: 25 January 2026
(This article belongs to the Special Issue The Role of Gut Microbiome in Regulating Animal Health)
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Simple Summary

Fecal microbiota transplantation can enhance the diversity of gut microbes and modulate dominant microbial populations, thereby facilitating the restoration of disrupted gut microbiota and playing an essential role in the recovery of mucosal barrier function. The biological advantages highlighted in our study include the regulation of gut microbiota composition, stimulation of intestinal motility, maintenance of microecological homeostasis, reduction in inflammatory responses, and enhancement of gastrointestinal functional capacity. Our experimental findings indicated that when fecal microbiota from A Normal Diet Group were administered concurrently with a high-fat diet, there was a notable promotion in the restoration of several core physiological parameters. These included equilibrium in body mass, activity of digestive enzymes, structure of colon histoarchitecture, and functions related to microbial metabolism. This therapeutic approach not only supported an increase in populations of beneficial endogenous probiotics but also effectively suppressed pathways associated with inflammation caused by pathogens. Moreover, our microbiome analysis revealed that fecal microbiota transplantation from a normal diet led to a significant rise in the populations of beneficial symbiotic microorganisms. This enhancement was achieved through the improvement of nutritional metabolic networks and optimization of environmental signal transduction systems, in addition to advancing maturation of the mucosal layer. Importantly, we demonstrated that fecal microbiota transplantation from a normal dietary source effectively alleviated enteric dysfunction associated with high-fat diet consumption. This intervention reinforced the integrity of the gut barrier by facilitating microbiota-mediated adjustments in epithelial tight junction complexes and mechanisms involved in mucin biosynthesis.

Abstract

This study investigates whether fecal microbiota transplantation (FMT) can alleviate gut microbiota dysbiosis induced by a high-fat diet (HFD) through modulation of fatty acid metabolism, competition for nutrients, production of short-chain fatty acids (SCFAs), and restoration of mucus layer integrity. To elucidate the mechanisms by which FMT regulates colonic microbial function and host metabolic responses, 80 male Bal b/c mice were randomly assigned to four experimental groups (n = 20 per group): Normal Diet Group (NDG), High-Fat Diet Group (HDG), Restrictive Diet Group (RDG), and HDG recipients of NDG-derived fecal microbiota (FMT group). The intervention lasted for 12 weeks, during which body weight was monitored biweekly. At the end of the experiment, tissue and fecal samples were collected to assess digestive enzyme activities, intestinal histomorphology, gene expression related to gut barrier function, and gut microbiota composition via 16S rRNA gene sequencing. Results showed that mice in the HDG exhibited significantly higher final body weight and greater weight gain compared to those in the NDG and RDG (p < 0.05). Notably, FMT treatment markedly attenuated HFD-induced weight gain (p < 0.05), reducing it to levels comparable with the NDG (p > 0.05). While HFD significantly elevated the activities of α-amylase and trypsin (p < 0.05), FMT supplementation effectively suppressed these enzymatic activities (p < 0.05). Moreover, FMT ameliorated HFD-induced intestinal architectural damage, as evidenced by significant increases in villus height and the villus height-to-crypt depth ratio (V/C) (p < 0.05). At the molecular level, FMT significantly downregulated the expression of pro-inflammatory cytokines (IL-1β, IL-1α, TNF-α) and upregulated key tight junction proteins (Occludin, Claudin-1, ZO-1) and mucin-2 (MUC2) relative to the HDG (p < 0.05). 16S rRNA analysis demonstrated that FMT substantially increased the abundance of beneficial genera such as Lactobacillus and Bifidobacterium while reducing opportunistic pathogens including Romboutsia (p < 0.05). Furthermore, alpha diversity indices (Chao1 and ACE) were significantly higher in the FMT group than in all other groups (p < 0.05), indicating enhanced microbial richness and community stability. Functional prediction using PICRUSt2 revealed that FMT-enriched metabolic pathways (particularly those associated with SCFA production) and enhanced gut barrier-related functions. Collectively, this study deepens our understanding of host–microbe interactions under HFD-induced metabolic stress and provides mechanistic insights into how FMT restores gut homeostasis, highlighting its potential as a therapeutic strategy for diet-induced dysbiosis and associated metabolic disorders.

1. Introduction

The maturation of gut health and function in animals is essential for efficient digestion and nutrient absorption. The integrity of the gut microbiota, encompassing both gut morphology and microbial functionality, serves as a critical factor influencing animal health and disease resistance [1]. Research indicates that the variety and functional abilities of gut microbiota are essential in nutrient absorption, immune system regulation, and defense against pathogens, which in turn directly influence the growth performance of animals [2]. Multiple factors can substantially influence the maintenance of intestinal health in animals, which include feeding management, nutrient intake, gut microbiota, digestive pathogens, and infections [3]. In animal nutrition, dietary fat is crucial for animal growth and development [4]. However, long-term high-fat intake can impair the intestinal mucosal barrier and compromise its selective permeability. This impairment may allow harmful substances to enter tissues, organs, and the circulatory system, thereby adversely affecting animal health. For example, although a high-fat diet may enhance livestock fattening efficiency in the short term, its negative impact on the gastrointestinal barrier can lead to a decline in beneficial microbial populations over prolonged periods, resulting in impaired selective permeability and increased inflammatory risk [5]. Therefore, investigating strategies and mechanisms to prevent and mitigate gut barrier dysfunction is of considerable theoretical and practical importance for advancing animal healthy production.
The composition of the microbiome varies among different species and individuals, exhibiting distinct spatio-temporal patterns that significantly influence host health. Animals harbor a diverse array of gut microbiota; however, approximately 99% of these microbial species remain unculturable, which hinders the elucidation of individual microbial functions within the community [6]. The efficacy of microorganisms with probiotic properties often depends on the species ratio and the physiological state of the host’s gut. In animal feces, a fully developed microbial community exists, characterized by a dynamic balance between beneficial and detrimental microbes, the presence of appropriate species, an abundance of beneficial strains, and interactions involving competition for host nutrients and mutual inhibition [7]. As a central compartment of the mammalian hindgut, the colon plays a pivotal role in regulating nutrient metabolism and systemic immune homeostasis through key physiological processes, including water reabsorption, microbial fermentation-mediated production of short-chain fatty acids (SCFAs), and maintenance of intestinal barrier integrity [8]. These integrated functions underscore its critical contribution to improving feed conversion efficiency and promoting overall animal health. Research indicates that probiotic colonization significantly improves colon health, enhances feed conversion efficiency, promotes growth performance, and strengthens resistance against pathogenic bacterial infections in animals [9]. Supplementation with a healthy and comprehensive probiotic consortium can restore intestinal microecological balance, thereby enabling the cecum to efficiently ferment dietary fiber and produce short-chain fatty acids (SCFAs). These SCFAs not only serve as an energy source for the host but also lower cecal pH to inhibit pathogens, enhance intestinal barrier function, and mediate anti-inflammatory and immunomodulatory effects through signaling molecules such as GPR41 and GPR43 [10]. In recent years, interventions targeting the intestinal microbiota, particularly microbiota transplantation (MT), have emerged as a key area of research aimed at improving animal health and enhancing production performance [11]. Accumulating research has shown that when the proportion and diversity of microbes in the host remain within a normal range, mucosal microbiota can establish colonization resistance, thereby suppressing the proliferation and establishment of pathogenic microbes. Recently, fecal microbiota transplantation (FMT) has gained recognition as a highly effective therapeutic approach [12]. This procedure involves transferring, and potentially implanting, gut microbes from a healthy donor into a recipient, with the aim of restoring a balanced gut microbial community and inhibiting the proliferation of harmful microbes, thereby aiding in the elimination of pathogenic bacteria and preventing recurrent infections. Moreover, emerging evidence suggests that FMT holds promise for the management of other health conditions, including inflammatory bowel disease, functional gastrointestinal disorders, and metabolic syndromes [13]. Therefore, FMT can enhance the diversity of gut microbes and modulate dominant microbial populations, thereby facilitating the restoration of disrupted gut microbiota and playing an essential role in the recovery of mucosal barrier function.
While promoting rapid daily weight gain in the host, HFDs drive remodeling of the gut microbiota ecosystem, thereby shifting intestinal homeostasis toward a pro-inflammatory state [14]. As such, HFDs have emerged as a critical link between dietary patterns and the pathogenesis of various modern metabolic disorders in animals [15]. Research has demonstrated that HFDs induce a significant reduction in gut microbial species diversity, a hallmark of intestinal ecological instability characterized by an elevated Firmicutes-to-Bacteroidetes ratio [16]. Concurrently, pro-inflammatory bacterial taxa are enriched, whereas taxa producing beneficial metabolites (SCFAs, Bifidobacterium, and Lactobacillus), are depleted [17]. This depletion leads to reduced production of SCFAs (butyrate, acetate, and propionate), which are key energy substrates for intestinal epithelial cells that exert anti-inflammatory effects, maintain intestinal barrier integrity, and modulate glucose metabolism and appetite regulation [18]. Notably, HFDs, particularly those rich in saturated fat, increase the relative abundance of Gram-negative bacteria, thereby elevating luminal concentrations of lipopolysaccharide (LPS). The synergistic effects of diminished SCFA levels and elevated LPS disrupt tight junctions between intestinal epithelial cells, ultimately compromising intestinal barrier function [19]. While our understanding of the gut microbiome is advancing rapidly, particularly in its role in health and disease, numerous aspects remain to be fully elucidated; species- and strain-level dynamics and their interdependencies are still in the early stages of exploration. Fecal samples collected from healthy individuals currently represent the most dependable source of therapeutic gut microbial communities [20]. In this context, the present study established an HFD-induced model using SPF-grade Balb/c mice to systematically evaluate the effects of FMT on intestinal morphology, functional gene expression, and microbial community structure. Our results demonstrate that FMT significantly alleviates HFD-induced gut dysbiosis and modulates the expression of key intestinal barrier function factors. These findings elucidate the potential mechanism by which FMT ameliorates metabolic disturbances and provide a theoretical basis for developing microbiota-targeted interventions in nutritional disease management and animal production.

2. Materials and Methods

2.1. Ethics Statement

All methods that included the use of animals received authorization from the Animal Care and Use Committee at Qilu Normal University, China (202312176-38). Additionally, approval for humane euthanasia of animals was secured in compliance with the National Administration of Experimental Animal Slaughter and Quarantine Regulations.

2.2. Animal Models and Experimental Design

Eighty male BALB/c mice (six weeks old, body weight 18–22 g) were obtained from the Shandong Pengyue Laboratory Animal Technology Co., Ltd. (Jinan, China) and acclimatized to the experimental environment under ad libitum feeding conditions, with daily feed intake recorded over a two-week period. All groups of mice were treated for 12 weeks. To control for the influence of the solvent, the same volume of a 1:105 PBS solution was also administered to the NDG, HDG, and RDG. All mice were reared in accordance with the standard experimental animal immunization protocol with ad libitum access to drinking water. Mice were housed in standard plastic cages (n = four cages/group, and five mice each cage as a replication unit) and maintained under a 12 h light–dark cycle at constant temperature and humidity [(23 ± 1) °C and (55 ± 5) %, respectively] [21]. After the acclimatization phase, the animals were randomly assigned to four distinct dietary groups (n = 20 per group). The groups were as follows (Table 1): the Normal Diet Group (NDG, 10% of calories from fat, sourced from Open Source Diets, model D12450B., 6 g per day for each mouse via preliminary experiment measurement); the High-Fat Diet Group (HDG; 60% of calories from fat, Open Source Diets, D12492, 6 g per day for each mouse); the Restrictive Diet Group (RDG, 60% of calories from fat, D12450B, 3.6 g per day for each mouse); and the HDG supplemented with NDG fecal microbiota group (FMT, 100 µL of NDG fecal dilution solution and administered through gavage, 6 g of high-fat diet per day for each mouse). Fecal pellets were collected from NDG BALB/c mice on the same day as the fecal community transplants for the experiment. Between fifteen and twenty pellets were gathered and measured, then homogenized in phosphate-buffered saline (PBS) on a weight-to-weight basis, following a standard protocol to preserve the integrity of the fecal microbiota during processing under anaerobic conditions. The fecal microbiota was carefully prepared by serially diluting it in phosphate-buffered saline (PBS) to achieve a final dilution of 1:105. This preparation involved aliquoting the diluted samples into tubes, which were subsequently used for administration through gavage to the experimental mice. Prior to the inoculation, all dilutions of fecal microbiota underwent centrifugation at a speed of 7500 rpm for a duration of 60 s to separate the supernatant used for the inoculation process. For oral inoculation, mice received 100 µL of the prepared fecal dilution via a 21-gauge oral gavage needle. The methodology employed in this experiment was adapted from a previous study conducted by Tomkovich, ensuring a reliable and standardized approach to the administration of fecal microbiota to the subject animals [22].

2.3. Sample Collection

The body weight of mice was registered at the termination of the 12-week period. Subsequently, the mice were put to death by cervical dislocation and placed on a dissecting board. The abdominal cavity was swiftly opened, and the colon tissue was carefully excised to collect digesta samples. Following a rinse with PBS, one section of the colon tissues was preserved in 4% neutral buffered formalin for histological examination, while the other tissues were promptly stored at −80 °C until they were needed for PCR analysis. A minimum of six replicates of intestinal tissue and digesta samples were gathered from each group and stored.

2.4. Sample Measurement

The dissected colon tissues were fixed in 10–13% buffered formalin for 6–48 h, as per standard protocols. Following fixation, the specimens underwent dehydration using a graded series of ethanol concentrations, were embedded in paraffin, and serial transverse sections with a thickness of 5 to 7 µm were cut and stained with HE (hematoxylin and eosin). This staining enabled the assessment of villous height, crypt depth, and mucosal thickness.
Total microbial DNA extraction from colon digesta samples, and library construction and microbiome sequencing were consigned to Shanghai Majorbio Bio-pharm Technology Co., Ltd. (Shanghai, China). Sequencing of the 16S rDNA gene was carried out utilizing the Illumina NovaSeq/Hiseq Xten platform (Illumina Inc., San Diego, CA, USA) to analyze microbial diversity and composition of communities. PCR amplification of the V3-V4 hypervariable region of the microbial 16S rDNA gene was performed with the following primers: forward (5′-CCTACGGGNGGCWGCAG) and reverse (5′-GGACTACHVGGGTATCTAAT). The cycling conditions included an initial denaturation step at 95 °C for 2 min, followed by 35 cycles of denaturation at 95 °C for 2 min and annealing at 72 °C for 30 s, concluding with a final elongation step at 72 °C for 5 min.
Total RNA was isolated from colon tissue samples using the One-step RT-PCR Kit (TaKaRa, Beijing, China), following the guidelines provided by the manufacturer. To evaluate gene expression, reverse transcription polymerase chain reaction (RT-PCR) was performed with the SYBR Green PCR Master Mix (Tiangen, Beijing, China). The expression levels of the target genes were normalized against mRNA levels of GAPDH (glyceraldehyde-3-phosphate dehydrogenase). Primers for the target genes were designed utilizing the Primer 5.0 software and synthesized by Shanghai Biological Engineering Ltd. (Shanghai, China). (see Table 2). PCR reactions took place in a Light Cycler 480 fluorescence quantitative meter (Fritz Hoffmann-La Roche Co. Limited, Basel, Switzerland). After verifying that the amplification efficiencies of the chosen genes and GAPDH were roughly equivalent, expression level differences were calculated using the 2−ΔΔCt method.

2.5. Statistical Analysis

The 16S rDNA analysis was carried out by means of R software (version 3.1.2), QllME software (version 1.9.1), and UPARSE software (version 7.1). In accordance with the UPARSE pipeline, multiplexed reads were conglomerated into operational taxonomic units (OTUs) based on 97% sequence identity. Classification of the 16S rRNA gene sequences was fulfilled using the RDP Classifier (version 2.2). The operational taxonomic units (OTUs) were systematically refined according to several metrics used in alpha diversity analysis. This process incorporated methods like OTU rank curves and rarefaction, as well as the calculation of various diversity indices, including Shannon, Chao1, Simpson, and the abundance-based coverage estimator (ACE). These metrics provided a comprehensive assessment of microbial community diversity, allowing for an in-depth evaluation of species richness and evenness within the samples. For beta diversity analysis, Principal Coordinates Analysis (PCoA) was utilized in conjunction with the unweighted pair group method with arithmetic mean (UPGMA). These analyses were performed using QIIME software and were based on weighted UniFrac distances, a method that accounts for both the presence and absence of species while also considering their relative abundances. This approach enabled researchers to visualize and interpret the relationships and differences among microbial communities in a multidimensional space. Finally, the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was employed to predict the functional capabilities of the identified microorganisms. In addition, statistical comparisons between bacterial domains, phyla, and genera were conducted using the Wilcoxon rank sum test. A false discovery rate (FDR)-adjusted p-value of less than 0.05 was established as the threshold for statistical significance, ensuring that the findings were both reliable and meaningful within the context of microbial ecology research.
All datasets were subjected to outlier removal prior to any statistical analysis. Data were visualized using plots (including box-and-whisker plots) and assessed for normality with the Shapiro–Wilk test. Results of the analysis are presented as mean ± SEM, which are key indicators for describing the central tendency and dispersion of data. For the analysis, a fully randomized experimental design was utilized, employing version 19.0 of the Statistical Analysis System (SAS) software from SAS Inc. based in Cary, NC, USA. Duncan’s post hoc test was utilized to identify meaningful differences among groups, whereas the Kruskal–Wallis test was employed otherwise. Differences were regarded as significant at p < 0.05 and as a trend at p < 0.1.

3. Results

3.1. FMT Significantly Promotes Mice Weight Gain with a High-Fat Diet

The body weight outcomes (Table 3) manifested that the final weight and weight increment in HDG mice were conspicuously higher than those of NDG and RDG mice (p < 0.05), whilst the final weight and weight increment in FMT mice were markedly lower than those of HDG mice. In comparison with the NDG, FMT treatment did not significantly increase final weight and weight gain in mice (p > 0.05). These results indicate that FMT could efficaciously abate the unjustifiable weight gain induced by a high-fat diet.

3.2. FMT Improves Colon Digestive Enzyme Activity with a High-Fat Diet

The activities of α-amylase (AMS), lipases (LPS), and trypsin (TRS) in RDG, NDG, HDG, and FMT are depicted in Table 4. The data signified that AMS and TRS activities in the HDG surpassed those in other groups (p < 0.05), and that AMS and TRS activities notably declined upon supplementation of high-fat diet in FM (p < 0.05), which manifested no significant disparity compared with the NDG (p > 0.05). Dietary restriction conspicuously diminished AMS and TRS activities (p < 0.05). There was no appreciable difference in LPS activity across all groups (p > 0.05). Fecal microbiota-supplemented diets were capable of potentiating gut digestive enzyme activity and facilitating nutrient digestion and absorption. Specifically, the activities of TRS and LPS could attain levels comparable to those under normal feeding pattern following fecal microbiota supplementation in a high-fat diet.

3.3. FMT Improvemes Gut Histomorphology with a High-Fat Diet

The characteristics of colon tissue, including villous length, crypt invagination extent, and the ratio of villous length to crypt invagination extent (V/C) among the various groups, are summarized in Table 5 and illustrated in Figure 1. The NDG exhibited significantly greater villous length and crypt invagination extent in comparison to the RDG, HDG, and FMT groups (p < 0.05), as observed in studies examining the impact of various treatments on intestinal morphology. Although diets supplemented with fecal microbiota did increase gut villous height and crypt depth to some extent, they were unable to fully restore these parameters to NDG levels. The V/C ratio in the HDG and RDG were reduced relative to that in the FMT group; however, no significant difference was observed between HDG and FMT groups (p > 0.05). We surmise that fecal microbiota supplementation is competent enough to redress the impairment of gut villus length and crypt depth occasioned by a high-fat diet, effectively enhancing villus height and V/C, thereby improving the mucous layer and alleviating deleterious barrier structures.
To investigate gut barrier function in the context of fecal microbiota supplementation, we examined the expression of genes associated with epithelial intercellular junction complexes, inflammatory mediators, and nutrient digestion and metabolism, following a range of interventions (refer to Figure 2). RT-PCR-based gene expression analysis revealed that transcription levels of genes related to pro-inflammatory cytokines (IL-1β, IL-1α, and TNF-α) in the FMT group were significantly reduced compared with those in the HDG (p < 0.05). In contrast, the expression levels of genes linked to barrier junction proteins (Occludin, Claudin-1, and ZO-1) and digestive and absorptive metabolism (MUC2) (p < 0.05) were significantly increased, which corresponds with the general understanding of the gut microbiome’s impact on host gene expression. Although the transcription levels of MUC1 in the FMT group evinced an ascending proclivity, this variance was not statistically momentous compared with those in the HDG (p > 0.05). Our findings suggest that the transcription levels of tight junctions, inflammatory factors, and digestive metabolism-related genes underwent significant changes during fecal microbiota transplantation, suggesting that addition of fecal microbiota can palliate damage engendered by a high-fat diet to gut barrier function.

3.4. FMT Improvemes Gut Microbial Community and Function with a High-Fat Diet

Paired-end sequencing was performed on an Illumina NovaSeq platform. After removing low-quality reads and chimeric sequences, a total of 96,542 high-quality reads were obtained for downstream analysis. The quantity of sequences spanning the 24 samples fluctuated within the range of 79,538 to 101,236. Good’s coverage oscillated between 99.2% and 99.8%. The leftover high-quality sequences were combined into operational taxonomic units (OTUs) at a similarity threshold of 97% with the help of UPARSE software. A sum of 3431 OTUs were discerned, with 212 being exclusive to the NDG, 411 unique to the HDG, 603 distinctive to the FMT, and 437 unique to the RDG (Figure 3). Additionally, there were 26 shared OTUs between the HDG and NDG, and 522 shared OTUs between the FMT and NDG. It was ascertained that a high-fat diet and restrictive diet augmented the number of distinctive OTUs in the gut microbiota in contrast to a normal diet. The gut microbiota composition of the fecal microbiota group exhibited relatively stability and showed a tendency towards the microbial community structure observed in the NDG. This suggests that through FMT, we successfully identified specific OTUs linked to community alterations responsible for the observed effects, indicating that a transplanted fecal community can help in preventing disturbances in the microbiota.
The top 35 relative abundances of the colon microbiota were utilized to construct a cluster heat map, as presented in Figure 4. Lactobacillus, Bacteroides, Haemophilus, Streptococcus, and Prevotella were concentrated within the NDG. In contradistinction, Faecalibacterium, Roseburia, Romboutsia, Allobaculum, and Rikenellaceae_RC9_gut_group were accumulated in the HDG. The RDG observed an increase in the abundance of Prevotellaceae_UGG_001, Prevotellaceae_UGG_003, and Alistipes, which are associated with various health implications, while Lactobacillus was also present. Dubosiella, Bifidobacterium, Porphyromonas, and Gemella were conspicuously concentrated in the FMT. LEfSe analyses were conducted to pinpoint the notable differences in taxa (relative abundance > 1%) across all groups, with LDA results from the LEfSe analysis illustrated in Figure 5 and Figure 6. The predominant phyla in the gut microbiota were Firmicutes, Bacteroidota, and Proteobacteria. We discerned that, compared with the NDG, the abundances of Lachnospiraceae, Rikenellaceae, Erysipelotrichaceae, Deferribacterales, and Desulfovibr were significantly higher in the HDG(p < 0.05). Additionally, Desulfovibrionales, Bacteroid_massiliensis, Muribaculaceae, Saccharimonadia, Clostridia, and Lachnospiraceae manifested significantly higher abundance between the HDG and FMT. Additionally, the HDG showed a significantly greater abundance of Tannerellaceae, Parabacteroides, Allobaculum, Erysipelotrichales, and Clostridia compared to the RDG. Subsequent to FMT, the relative abundances of beneficial microorganisms, such as Lactobacillus and Bifidobacterium, significantly escalated compared to the HDG (p < 0.05), whereas the abundance of opportunistic microbes, such as Romboutsia, significantly dwindled. These findings imply that the supplemented fecal microbiota can rectify the gut microbiota community under high-fat diet stress, enhancing beneficial microbes while attenuating harmful ones.
Alpha diversity was appraised utilizing the diversity indices (Shannon and Simpson) and richness estimators (Chao1 and ACE). As illustrated in Table 6, the richness estimators (ACE and Chao1) for the FMT were significantly elevated compared to those of the RNG, HDG, and NDG (p < 0.05). These results indicate that fecal microbiota supplementation can enhance the diversity of gut microbiota, which may function as a predictor of treatment success as indicated by alpha diversity metrics. Furthermore, the study assessed the differences in microbial beta diversity across distinct groups by employing the binary Jaccard index, as depicted in Figure 7. The RDG, HDG, and FMT groups exhibited an increased beta diversity in their gut microbiota, indicating a delicate balance and broad distribution of these microbial communities. It is noteworthy that the beta diversity of the FMT group was lower compared to that of the HDG, suggesting a potential restoration of microbial community structure. Recent studies suggest that fecal microbiota supplementation may exert a beneficial influence on the gut microbiota of animals consuming a high-fat diet, potentially mitigating the adverse effects of such diets. Principal Coordinates Analysis (PCoA) utilizing the weighted UniFrac similarity approach revealed that PC1 and PC2 explained 50.3% and 16.14% of the variance among the samples, respectively, suggesting distinct clusters of gut microbiota among various groups within the ordination space (Figure 8).
To explore the functional capabilities of the gut microbiota, PICRUSt was utilized to predict the composition of COG/KEGG pathways (Figure 8). The analysis revealed significant enrichment in pathways related to sulfur metabolism (including sulfate and sulfur compound respiration), core metabolic processes, genetic information processing, environmental signal transduction, cellular community and motility, as well as those associated with diseases and organismal systems (p < 0.05). Notably, there were conspicuous dissimilarities in sulfate respiration and respiration of sulfur compounds between the FMT and HDG groups, with results statistically significant with a p-value of less than 0.05, as indicated in Figure 9. The specific pathways that showed significant differences between the HDG and FMT groups are detailed in Figure 10. Furthermore, the functional prediction outcomes intimated that the supplementation of fecal microbiota could exert a affirmative impact on gut microbiota function and fortify the gut barrier.

4. Discussion

The gut microbiome plays an essential role in the decomposition of intricate dietary elements, including fats, and in managing the host’s metabolism of bile acids, lipids, and amino acids. This intricate process involves microbiota influences on bile acid metabolism, where it converts primary bile acids into secondary metabolites, and impacts lipid metabolism through affecting fatty acid absorption and transport [23]. Microbial communities achieve remarkable feats by engaging in metabolic exchanges with the host and participating in signal transduction pathways that regulate gene expression and energy balance [24]. Previous research has demonstrated that increasing dietary fat levels can lead to an increase in both body weight and performance in animals [25]. Our findings indicate that mice fed a high-dietary fat diet experienced significantly greater weight gain and a higher average daily increase compared with those on a diet with limited fat intake or a regular fat diet. Furthermore, recent studies have indicated that the FMT group exhibited a slower growth rate compared to the HDG, suggesting potential benefits in metabolic regulation. The formation mechanism of intestinal flora dysbiosis is marked by complexity arising from multiple factors. This study revealed that following pathogenic Escherichia coli infection, the relative abundance of potential pathogenic bacteria, including Enterobacteriaceae, increased significantly in the host intestinal tract, whereas the abundance of butyrate-producing bacteria, such as Roseburia, declined to 42.3% compared with the control group. This phenomenon is in line with the “pathogen colonization advantage—commensal function loss” theory proposed by Alshehri [26]. It is noteworthy that common environmental stress factors in modern intensive livestock husbandry, including animal stress caused by high-density rearing [27,28], mycotoxin contamination in feed (in this study, the residual amount of aflatoxin B1 reached 12.3 μg/kg), and long-term sub-therapeutic use of veterinary antibiotics [29], may jointly aggravate the process of flora imbalance by altering the intestinal redox state (the GSH/GSSG ratio decreased by 1.8 times) and mucin secretion (the expression level of MUC2 mRNA decreased by 67%). These outcomes imply that varying degrees of dietary fat gradients can enhance growth performance in mice, while the gut microbiome supplemented with fecal microbiota from NDG may facilitate the regulation of gut energy balance and maintenance of homeostasis by carrying out vital functions and resisting pathogens. This encompasses the efficient extraction of nutrients from the diet, which could give rise to enhanced economic benefits.
Excessive energy consumption altered the dietary digestion patterns in mice, while a high-fat diet shortened the time nutrients remained in the stomach, resulting in increased carbohydrate levels and enhanced microbial fermentation in the intestines. Key digestive enzymes within the gut, such as AMS, LPS, and TRS, play an essential role in directly impacting the efficacy of dietary fat digestion [30]. Starch functions as a principal energy source for the intestinal tract of animals, and can be broken down into glucose, maltose, and oligosaccharides through the action of AMS [31,32]. Simultaneously, TRS breaks down unprocessed starch, fats, and proteins into smaller molecules essential for nutrition and plays a crucial role in the immune response of animals. LPS accelerate the breakdown of dietary fats into usable nutrients that meet the animals’ nutritional needs [33]. In this research, AMS and TRS activities were relatively high within the high-fat group, whereas the group nourished with FMT evinced enzyme levels that were proximate to normal. Conversely, the LPS group did not showcase superior digestion or absorption in comparison with the other groups. We surmise that a high-fat diet could enhance nutrient acquisition; nevertheless, excessive fat content suppressed the activity of LPS, resulting in diminished efficiency, thereby reducing fat metabolism and impeding nutrient absorption through the intestinal epithelium, ultimately constraining energy provision to the organisms. The addition of fecal microbiota was ascertained to enhance the activity of colon digestive enzymes and facilitate nutrient digestion and absorption toward normal levels.
Alterations in the morphophysiological attributes of the gut epithelium in animals, such as the presence of single-layer columnar epithelium, reflect their ability to digest and absorb dietary nutrients effectively. These changes serve as clear indicators of the intestinal environment quality and overall gut health [34,35]. The maturation of the structure and function of gut villus is imperative for efficacious absorption of nutrients. Numerous studies have demonstrated that the interaction between gut microbiota and the host can promote the growth of gut villus and maintain functional integrity, which is crucial for the absorption of dietary nutrients [36]. Microorganisms expedite the breakdown of various nutrients into SCFAs, culminating in an increment in gut VFAs (volatile fatty acids) and a concomitant decrement in gut pH [37]. VFAs exhibit substantial lipid solubility when pH dips below a certain threshold, empowering them to permeate gut mucosal cells and induce cellular acidification. This process may compromise gut barrier and instigate inflammatory responses [38]. The study found that increased dietary fat intake was associated with a reduction in villous height, villous crypt depth, and V/C, which in turn resulted in a decreased absorptive surface area of the villi. Moreover, the capacity to distinguish environmental factors, such as dietary nutrients and potentially harmful substances, was diminished under a high-fat diet, thereby adversely affecting the selective absorption of nutrients in the mouse gut. Feeding mice with diets supplemented with fecal microbiota manifested an improvement in gut epithelial function to a certain degree, suggesting that symbiotic interactions between host microbes and NDG fecal microbiota could erect a protective barrier by adhering to the epithelial surfaces of enterocytes, thereby diminishing the likelihood of pathogenic bacterial incursion. PCR results indicated that fecal microbiota transplantation could modulate the transcription levels of genes associated with tight junctions, inflammatory factors, and digestive metabolism. These findings are consonant with previous reports that furnish evidence for the pernicious effects of a high-fat diet on the gut of mice. Recent studies have shown that high-fat diets can disrupt gut barrier function and promote inflammation, potentially leading to chronic gut conditions. However, the addition of fecal microbiota has been found to mitigate these adverse effects by promoting villus development and enhancing overall gut health in mice.
The microbiome is construed to encompass both microbiota and their genetic material. Microbiome dysfunction often becomes evident through alterations in its composition, including a scarcity of beneficial microbes, the emergence of potentially harmful microorganisms, or decreased microbial diversity [38]. As a ‘second genome’ that governs the host phenotype, the gut microbiota are intricately linked to animal nutrition, metabolism, and immunity [39]. This perspective converges with research aimed at establishing correlations between compositional and functional variances in gut microbiota. Probiotics are known to safeguard gut health in two main ways: by increasing the levels of bacteria that protect the gut barrier, like bifidobacteria, and by preventing the growth of organisms that produce endotoxins [40]. Thus, we conclude that targeted modulation of the gut microbiota composition towards a normal diet-associated profile could help mitigate dysbiosis induced by a high-fat diet. Moreover, the energy content of the diet has a significant impact on the structure of gut microbiota. At the phylum tier, this research revealed that Bacteroidota, Firmicutes, and Actinobacteriota accounted for more than 90% of the gut microbiota. The findings of this study demonstrated that the gut microbiota composition in mice administered with fecal microbiota transplantation and a high-fat diet resembled that of mice on a standard diet. Additionally, our investigation suggested that fecal microbiota can partially alleviate the gut microbiota imbalance caused by a high-fat diet. Furthermore, following fecal microbiota intervention, there was a significant reduction in the relative abundance of Firmicutes, including Prevotellaceae and Alistipes, within the gut microbiota of mice fed with a high-fat diet. In contrast, the relative abundance of Bacteroidota (which encompasses Lactobacillus, Dubosiella, Bifidobacterium, Porphyromonas, and Gemella) showed a marked increase. One potential explanation for these findings is the positive relationship identified between the Bacteroidota phylum and the processing of lipid-soluble nutrients, whereas a negative relationship was noted in the Firmicutes phylum. This suggests that the intervention involving fecal microbiota may improve the metabolism of dietary lipid-soluble nutrients. The dominant microbial community among the four treatment groups consisted of Lachnospiraceae, Rikenellaceae, Erysipelotrichaceae, Deferribacterales, Desulfovibrionales, Bacteroid_massiliensis, Muribaculaceae, Saccharimonadia, Clostridia, Tannerellaceae, Parabacteroides, and Allobaculum. Our research findings indicate that FMT significantly enhances gut microbial diversity, as evidenced by increased species richness and evenness, as measured by indices such as Chao1 and Shannon. Interestingly, despite consistency in species composition among the dominant bacterial families across various treatments, the proportional representation of these dominant microbiota exhibited variability. Consistent with our previous research, which was in consonance with findings from Shang’s results, fecal microbiota intervention appeared to alleviate microbial community perturbations caused by a high-fat diet. Functional prediction results indicated that pathways related to nutrient metabolism and environmental information processing were significantly ameliorated following fecal microbiota intervention. Additionally, we noted an improvement in the balance of gut microbiota in mice, highlighting the intervention’s antibacterial and anti-inflammatory effects and regulation of digestibility.

5. Conclusions

Our study demonstrates that fecal microbiota transplantation effectively alleviates the adverse effects induced by a high-fat diet in mice through modulation of gut microbiota composition and enhancement of intestinal barrier integrity. Specifically, FMT significantly attenuated body weight gain, restored normal digestive enzyme activities, improved intestinal morphology, and upregulated the expression of tight junction proteins, while simultaneously suppressing pro-inflammatory cytokine production. Notably, FMT increased the abundance of beneficial bacterial genera such as Lactobacillus and Bifidobacterium, promoted microbial diversity, and enriched functional pathways associated with nutrient metabolism. These findings provide direct evidence that modulating the gut microbiota via FMT represents a promising therapeutic strategy to counteract metabolic disturbances caused by high-energy diets in livestock, offering potential applications for enhancing animal health and productivity in intensive farming systems.

Author Contributions

Conceptualization, P.Y. and J.J.; methodology, X.C. and L.Z.; software, Q.C.; validation, N.L., H.R., and J.J.; formal analysis, J.J. and Q.C.; investigation, N.L., Y.D., and C.M.; resources, P.Y.; data curation, J.J.; writing—original draft preparation, X.C.; writing—review and editing, P.Y. and J.J.; visualization, N.L., C.M., and Q.C.; supervision, P.Y.; project administration, P.Y.; funding acquisition, J.J. and P.Y. All authors have read and agreed to the published version of the manuscript.

Funding

The research was supported by the Natural Science Foundation of Shandong Province (No. ZR2023MC164) and the Youth Innovation Team Project Program of Shandong Provincial Education Department (No. 2022KJ137).

Institutional Review Board Statement

All procedures involving the use of animals were approved by the Animal Care Committee at the Qilu Normal University, China (202312176-38). Furthermore, approval for the slaughtering of the animals was obtained in accordance with the National Administration of Experimental Animal Slaughtering and Quarantine Regulations.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original data presented in the study are openly available on the Genome Sequence Archive with accession number PRJNA1245063 (http://www.ncbi.nlm.nih.gov/bioproject/1245063, accessed on 2 April 2025).

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. HE-stained colon samples. Morphology of colon tissues from different treatment groups revealed by hematoxylin and eosin (H&E) staining. Representative microphotographs show colon sections from the Normal Diet Group (NDG), High-Fat Diet Group (HDG), Fecal Microbiota Transplantation group (FMT), and Restrictive Diet Group (RDG). Images were captured at 40× magnification.
Figure 1. HE-stained colon samples. Morphology of colon tissues from different treatment groups revealed by hematoxylin and eosin (H&E) staining. Representative microphotographs show colon sections from the Normal Diet Group (NDG), High-Fat Diet Group (HDG), Fecal Microbiota Transplantation group (FMT), and Restrictive Diet Group (RDG). Images were captured at 40× magnification.
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Figure 2. Fecal microbiota-supplemented diets effect on target genes expression in gut tissues of mice subjected to a high-fat intervention. (A) Tight junction protein-coding genes; (B) inflammatory factor protein-coding genes; (C) digestive metabolism protein-coding genes. Note: Data are expressed as mean ± SEM (n = six male mice per group). Statistical significance was determined by one-way ANOVA with Duncan’s multiple range test (values with the same letter are not significantly different, while those with different letters are significantly different; p < 0.05).
Figure 2. Fecal microbiota-supplemented diets effect on target genes expression in gut tissues of mice subjected to a high-fat intervention. (A) Tight junction protein-coding genes; (B) inflammatory factor protein-coding genes; (C) digestive metabolism protein-coding genes. Note: Data are expressed as mean ± SEM (n = six male mice per group). Statistical significance was determined by one-way ANOVA with Duncan’s multiple range test (values with the same letter are not significantly different, while those with different letters are significantly different; p < 0.05).
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Figure 3. Effect of fecal microbiota-supplemented diets on OTUs via Venn analysis under high-fat intervention in mice gut microbiota. Note: Data are expressed as mean ± SEM (n = six male mice per group). Ellipses of different colors denote different groups. Overlapping areas represent shared OTUs, while the non-overlapping areas represent OTUs exclusive to each group.
Figure 3. Effect of fecal microbiota-supplemented diets on OTUs via Venn analysis under high-fat intervention in mice gut microbiota. Note: Data are expressed as mean ± SEM (n = six male mice per group). Ellipses of different colors denote different groups. Overlapping areas represent shared OTUs, while the non-overlapping areas represent OTUs exclusive to each group.
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Figure 4. Effect of fecal microbiota-supplemented diets on the abundance of the top 35 terms via a cluster heat map in the gut microbiota of high-fat intervention in mice. Note: Data are expressed as mean ± SEM (n = six male mice per group). Each row in the graph represents a microbial genus, each column represents a treatment. Red represents a positive correlation, while blue represents a negative correlation.
Figure 4. Effect of fecal microbiota-supplemented diets on the abundance of the top 35 terms via a cluster heat map in the gut microbiota of high-fat intervention in mice. Note: Data are expressed as mean ± SEM (n = six male mice per group). Each row in the graph represents a microbial genus, each column represents a treatment. Red represents a positive correlation, while blue represents a negative correlation.
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Figure 5. Effect of fecal microbiota-supplemented diet on the abundance of mice gut microbiota via a species evolutionary tree under high-fat intervention.
Figure 5. Effect of fecal microbiota-supplemented diet on the abundance of mice gut microbiota via a species evolutionary tree under high-fat intervention.
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Figure 6. Effect of fecal microbiota-supplemented diet on the abundance changes in mice gut microbiota via LEfSe analysis under high-fat intervention.
Figure 6. Effect of fecal microbiota-supplemented diet on the abundance changes in mice gut microbiota via LEfSe analysis under high-fat intervention.
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Figure 7. Effect of fecal microbiota-supplemented diets on beta diversity analysis under high-fat intervention.
Figure 7. Effect of fecal microbiota-supplemented diets on beta diversity analysis under high-fat intervention.
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Figure 8. Effect of fecal microbiota-supplemented diets on Principal Coordinates Analysis (PCoA) under high-fat intervention. Note: Ellipses of different colors denote different groups.
Figure 8. Effect of fecal microbiota-supplemented diets on Principal Coordinates Analysis (PCoA) under high-fat intervention. Note: Ellipses of different colors denote different groups.
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Figure 9. Effect of fecal microbiota-supplemented diets on COG analysis under high-fat intervention.
Figure 9. Effect of fecal microbiota-supplemented diets on COG analysis under high-fat intervention.
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Figure 10. Effect of fecal microbiota-supplemented diets on significance analysis of COG analysis pathways between the HDG and FMT groups under high-fat intervention. Note: The red elongated bar is HDG and the blue elongated bar is the FMT group, * denotes significance (p < 0.05).
Figure 10. Effect of fecal microbiota-supplemented diets on significance analysis of COG analysis pathways between the HDG and FMT groups under high-fat intervention. Note: The red elongated bar is HDG and the blue elongated bar is the FMT group, * denotes significance (p < 0.05).
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Table 1. Feed nutrient composition and energy supply ratio.
Table 1. Feed nutrient composition and energy supply ratio.
Nutrient CompositionNormal Diet (D12450B)High-Fat Diet (D12450B)
Proteins (%)18.8823.25
Fat (%)5.2634.55
Carbohydrate (%)61.9527.00
Total86.0984.80
Energy contribution of protein (kcal%)12.0018.40
Energy contribution of fat (kcal%)20.6060.68
Energy contribution of carbohydrate (kcal%)67.4021.22
Total100100
Note. The sources of Normal Diet (D12450B) mainly include maize, wheat bran, soybean meal, bone meal, lysine, and salt. The sources of High-Fat Diet (D12450B) mainly include maize, wheat bran, soybean meal, bone meal, lysine, soybean oil, and salt.
Table 2. Primer sequences of target genes.
Table 2. Primer sequences of target genes.
Gene Name Primer Sequences (5′ → 3′)
ZO-1Primer FGTGGAATGATGTCGGAATA
Primer RCTACAATGCGGCGATAAA
Claudin-1Primer FTCTTCGACTCCTTGCTGAATCTGAAC
Primer RCCATCCACATCTTCTGCACCTCATC
OccludinPrimer FTGGATCTATGTACGGCTCAC
Primer RCCATCTTTCTTCGGGTTT
MUC1Primer FAGCCACCAGTCCAGACCACAG
Primer RTAGGTAGCACCGAGGAGCCATTG
MUC2Primer FGCTGACGAGTGGTTGGTGAATG
Primer RGATGAGGTGGCAGACGGAGAC
IL-1βPrimer FCCGTGGACCTTCCAGGATGA
Primer RGGGAACGTCACACACCAGCA
IL-1αPrimer FCAAACTGATGAAGCTCGTCA
Primer RTCTCCTTGAGCGCTCACGAA
TNF-αPrimer FCCCTCACACTCAGATCATCTTCT
Primer RGCTACGACGTGGGCTACAG
GAPDHPrimer FAGGTCGGTTGTGACGGATTTG
Primer RTGTAGACCATGTAGTTGAGGTCA
Table 3. Effect of FMT for mice weight gain caused by a high-fat diet.
Table 3. Effect of FMT for mice weight gain caused by a high-fat diet.
RDGNDGHDGFMT
Initial Weight (g)19.55 ± 0.3519.60 ± 0.5019.83 ± 0.1519.82 ± 0.15
Final Weight (g)26.32 ± 1.45 c34.98 ± 2.05 b37.98 ± 2.15 a36.70 ± 1.70 b
Weight Gain (g)6.82 ± 1.30 c15.32 ± 1.65 b18.08 ± 2.30 a16.88 ± 1.75 b
Note. Data are expressed as mean ± SEM (n = 20, 10 male and 10 female mice per group). Statistical significance was determined by one-way ANOVA with Duncan’s multiple range test (values with the same letter are not significantly different, while those with different letters are significantly different; p < 0.05).
Table 4. Effect of FMT for mice digestive enzyme activity caused by a high-fat diet.
Table 4. Effect of FMT for mice digestive enzyme activity caused by a high-fat diet.
GroupsAMS (U/mg)TRS (U/mg)LPS (U/mg)
RDG5.96 ± 0.07 c502.46 ± 9.73 c0.65 ± 0.04
NDG6.41 ± 0.09 b518.22 ± 11.56 b0.71 ± 0.02
HDG6.92 ± 0.11 a559.43 ± 10.46 a0.79 ± 0.03
FMT6.53 ± 0.12 b521.92 ± 11.27 b0.72 ± 0.04
Note. Data are expressed as mean ± SEM (n = six male mice per group). Statistical significance was determined by one-way ANOVA with Duncan’s multiple range test (values with the same letter are not significantly different, while those with different letters are significantly different; p < 0.05).
Table 5. Effect of FMT for mice colon tissue morphology (villus height, crypt depth, and villus height-to-crypt depth ratio [V/C]) caused by a high-fat diet.
Table 5. Effect of FMT for mice colon tissue morphology (villus height, crypt depth, and villus height-to-crypt depth ratio [V/C]) caused by a high-fat diet.
GroupsVillous Height (V)/μmCrypt Depth (C)/μmV/C
RDG321.80 ± 18.80 c163.09 ± 7.74 b1.97 ± 0.14 c
NDG488.34 ± 6.62 a190.57 ± 8.02 a2.56 ± 0.04 a
HDG429.36 ± 14.51 b125.67 ± 7.81 c2.41 ± 0.22 b
FMT508.92 ± 18.71 b214.54 ± 3.77 ab2.37 ± 0.01 bc
Note: Data are expressed as mean ± SEM (n = six male mice per group). Statistical significance was determined by one-way ANOVA with Duncan’s multiple range test (values with the same letter are not significantly different, while those with different letters are significantly different; p < 0.05).
Table 6. Alpha diversity of different treatment groups.
Table 6. Alpha diversity of different treatment groups.
ShannonSimpsonChao1ACEGoods-Coverage%
RDG6.23 ± 0.11 c0.96 ± 0.004618.87 ± 16.79 c633.77 ± 21.84 c99.80 ± 0.00
HDG9.46 ± 0.19 b0.96 ± 0.010938.09 ± 79.72 b914.70 ± 76.50 b99.53 ± 0.01
FMT17.35 ± 0.17 a0.95 ± 0.0071527.62 ± 94.15 a1547.18 ± 105.40 a99.20 ± 0.26
NDG7.25 ± 0.14 c0.96 ± 0.004624.70 ± 32.89 c613.60 ± 20.78 c99.76 ± 0.06
Note. Data are expressed as mean ± SEM (n = six male mice per group). Statistical significance was determined by one-way ANOVA with Duncan’s multiple range test (values with the same letter are not significantly different, while those with different letters are significantly different; p < 0.05).
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MDPI and ACS Style

Cao, X.; Zhou, L.; Ding, Y.; Ma, C.; Chen, Q.; Li, N.; Ren, H.; Yan, P.; Jia, J. Study of Fecal Microbiota Transplantation Ameliorates Colon Morphology and Microbiota Function in High-Fat Diet Mice. Vet. Sci. 2026, 13, 116. https://doi.org/10.3390/vetsci13020116

AMA Style

Cao X, Zhou L, Ding Y, Ma C, Chen Q, Li N, Ren H, Yan P, Jia J. Study of Fecal Microbiota Transplantation Ameliorates Colon Morphology and Microbiota Function in High-Fat Diet Mice. Veterinary Sciences. 2026; 13(2):116. https://doi.org/10.3390/vetsci13020116

Chicago/Turabian Style

Cao, Xinyu, Lu Zhou, Yuxia Ding, Chaofan Ma, Qian Chen, Ning Li, Hao Ren, Ping Yan, and Jianlei Jia. 2026. "Study of Fecal Microbiota Transplantation Ameliorates Colon Morphology and Microbiota Function in High-Fat Diet Mice" Veterinary Sciences 13, no. 2: 116. https://doi.org/10.3390/vetsci13020116

APA Style

Cao, X., Zhou, L., Ding, Y., Ma, C., Chen, Q., Li, N., Ren, H., Yan, P., & Jia, J. (2026). Study of Fecal Microbiota Transplantation Ameliorates Colon Morphology and Microbiota Function in High-Fat Diet Mice. Veterinary Sciences, 13(2), 116. https://doi.org/10.3390/vetsci13020116

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