Use 10 SPF male adult C57BL/6 mice at 6 weeks old, 10 SPF male adult high-fat diet-fed C57BL/6 mice at 6 weeks old, and 6 SPF male adult Wistar rats at 10 weeks old (Hans Biotechnology Co., Ltd., Hangzhou, China) as donors, male DBA mice (bred in SPF-grade conditions at Hans Biotechnology Co., Ltd., Hangzhou, China) at 7 days old as accepters. All DBA mice were housed with their mothers before weaning, and after weaning, they were housed in separate cages, and after 1 week of acclimatization feeding, mice of similar body weight were selected for the experiment. All animals were housed under controlled conditions, including a constant temperature range of 21–24°C, humidity maintained at 50–60%, and exposure to natural lighting. Water was provided ad libitum to the animals throughout the study. Donors in the T1 and T3 groups were provided with a normal diet, and donors in the T2 group were provided with a high-fat diet.

All experimental procedures were conducted in strict adherence to the guidelines for animal care and use. The research protocols were approved by the Committee for Animal Research at Zhejiang University, Hangzhou, China (approval number ZJU20220438).

After a one-week acclimation period, the DBA mice were randomly divided into four groups, each consisting of 12 mice: Control group (CK); C57BL/6 mice fecal microbiota transplantation (FMT) group (T1); High-fat C57BL/6 FMT group (T2); Wistar rats FMT group (T3). Fresh fecal samples were collected daily from C57BL/6 mice, high-fat C57BL/6 mice, and Wistar rats to serve as donors. The collected fecal material was placed in sterile tubes and homogenized using sterile normal saline (Sinopharm Chemical Reagent Co. Ltd., China). This mixture was then subjected to centrifugation at 6,000 × g for 10 min, followed by filtration through a 70-μm filter (Nest, China). The resultant material was quantified to achieve an optical density (OD) at 600 nm of 1.0, corresponding to an estimated bacterial concentration of 10⁹ colony-forming units (CFU) per milliliter. Subsequently, the fecal bacterial slurry was diluted to a final concentration of 5.0 × 10⁹ CFU/mL using a 30% sterile medical glycerin phosphate-buffered saline (PBS) (Sinopharm Chemical Reagent Co. Ltd., China). Any remaining fecal fluid was stored at −80°C for future use. The mice in the CK group were administered sterile normal saline via gavage once a day for a duration of 28 days using disposable sterile syringes (1 mL, DKBT, China) with sterile gavage needles (45 mm, Shenzhen Longreen Lighting Co., Ltd., China) (Zhang et al., 2020; Elangovan et al., 2022). In contrast, the mice in the T1 group received fecal fluid from normal C57BL/6 mice, the T2 group received fecal fluid from high-fat C57BL/6 mice, and the T3 group received fecal fluid from Wistar rats through gavage over the same 28-day period.

At the end of the experiment, the finishing mice were fasted for 24 h (6-h daytime and 16-h overnight) (Zhang et al., 2016) and at which time, blood samples were collected at baseline into a vacuum tube. Mice were anesthetized by intraperitoneal injection of 80 mg/kg sodium pentobarbital and were euthanized by intraperitoneal injection of 200 mg/kg sodium pentobarbital under deep anesthesia. The colon and liver tissues were collected immediately from all mice. After euthanasia of the mice, the same intestinal segments from the posterior half of the colon were taken and their contents squeezed into cryopreservation tubes, which were immediately stored at −80°C for subsequent sequencing. A part of the liver and colon was fixed using 4% paraformaldehyde solution, the rest of liver and colon tissue was saved at −80°C.

The mice and food intakes were meticulously weighed at the end of each week, specifically on the 1st, 2nd, 3rd, 4th, and 5th weeks. This data was then used to calculate both the daily weight gain and the daily feed intake of the mice.

The level of serum biochemical indicators were measured by Albumin (ALB) assay kit, Total Cholesterol (TC) assay kit, Triglyceride (TG) assay kit and Superoxide Dismutase (SOD) assay kit (Nanjing Jiancheng Institute of Bioengineering, China), as well as the inflammatory markers of the mice including Immunoglobulin A (IgA), Interleukin-1β (IL-1β), Interleukin-6 (IL-6), Interleukin-10 (IL-10), Interleukin-22 (IL-22), Tumor Necrosis Factor-α (TNF-α), Chemokine (C-X-C motif) ligand 1 (CXCL1) and Chemokine (C-X-C motif) ligand 2 (CXCL2) (Jiangsu Meimian Industrial Co., Ltd., China) were measured with assay kits according to the manufacturer’s instructions with UV–VIS Spectrophotometer (UV1100, MAPADA, Shanghai, China) following the manufacturer’s instructions.

Colon RNA samples from 20 different male mice were used for mRNA sequencing.

All data except for the 16S RNA results were analyzed by one-way analysis of variance (ANOVA) using SPSS statistical software (Ver. 20.0 for windows, SPSS, Inc., Chicago, IL, United States). Differences among treatments were examined using the Tukey–Kramer’s multiple range tests, which were considered significant when the p-value was less than 0.05. Results are presented as means alongside their pooled standard errors of means (SEM).

For 16S RNA analysis, based on the ASVs information, α diversity indices (including the Sobs index, Chao richness estimator, Shannon diversity index, and Simpson index) between the mice of three groups were calculated to evaluate microbial species richness and evenness (Schloss et al., 2009). The similarity among the microbial communities in different samples was determined by principal coordinate analysis (PCoA) based on Bray–curtis dissimilarity using Vegan v2.5-3 package. Use Wilcox rank-sum test analysis to analyze which bacteria differ in the intestinal flora of different groups of mice at the phylum level and genus level, respectively. Using the two-sample t-test, we analyzed the community abundance data and rigorously determined the significance of differences in species abundance between the two groups of sampled microbial communities. The hypothesis of species between the groups was tested using R-3.3.1 (stat) software and enabled identification of the species that exhibited significant differences between the groups (Mojorbio, Shanghai, China). The raw sequencing reads were deposited into the NCBI Sequence Read Archive (SRA) database (Accession Number: PRJNA1050037).

Initial body weights of DBA mice were measured at 4 weeks of age, following 28 days of FMT gavage, the final body weight (FBW) and average daily gain (ADG) of mice that received FMT from C57BL/6 mice, high-fat C57BL/6 mice, and Wistar rats exhibited substantial increased compared to the CK group, and the overall growth curve was better than that of the mice in CK group (Figure 1). These results suggested that FMT indeed enhances the growth performance of DBA mice. Moreover, it is worth noting that the growth performance improvement observed in DBA mice that received FMT from high-fat C57BL/6 mice was significantly greater than that in DBA mice receiving FMT from normal C57BL/6 mice and Wistar rats. This observation implied that the high-fat model also induces notable alterations in the intestinal flora of mice.

In order to evaluate the effect of FMT on the immune function of DBA mice, we performed a series of serum ELISA assays, including CXCL1, CXCL2, TNF-α, IL-1β, IL-6, IL-22, IL-10, and IgA (Supplementary Table S2). The results demonstrated a significant reduction in the levels of CXCL2 and IL-22 in the T1 group compared to the CK group, a significant decrease in TNF-α levels in the T2 group compared to the CK group, a significant decrease in the levels of IL-22 in the T3 group compared to the CK group, and a significant increase in IgA levels (Figure 2).

To investigate the effects of fecal microbiota transplantation on lipids, liver function and antioxidant capacity in DBA mice, serum TG, TC, TP, ALB, and SOD were measured in four groups of mice (Supplementary Table S3). Notably, serum triglyceride (TG) levels exhibited a significant increase in both the T1 and T2 groups compared to the CK group (Figure 3).

Information on the richness, diversity and coverage of species in the community was obtained through α diversity index analysis, and the test of difference between groups was applied to detect whether the values of Α diversity index were significantly different between every two groups and above. The results showed a significant increase in α-diversity indices, including ace, chao and sobs indices of the gut microbiota of T1 group than T3 and CK group (Figures 4A–C), which suggested that FMT from normal C57BL/6 mice increased bacterial richness of DBA mice (as determined by rising ace, chao and sobs indices). Then, we analyzed the β-diversity with the Bray-Curtis principal coordinate analysis (PCoA) on ASV level, to identify potential principal components that influence differences in the composition of the sample communities through dimensionality reduction (Figure 4D). PCoA exposed significant difference in gut microbiota between T1 group and CK group (R = 0.622, p = 0.003). Also, T2 group and CK group showed significant difference in gut microbiota (R = 0.744, p = 0.004), same was between T3 group and CK group (R = 0.507, p = 0.004). These results indicated that FMT modulates gut microbiota composition of DBA mice.

In order to identify high-dimensional biomarkers and reveal genomic features, we performed Linear discriminant analysis Effect Size (LFfSe) analysis of the flora in the mouse gut from the phylum level to the genus level. Figures 4E–F shows the results for all LDA values greater than 2. After FMT, the four groups of mice with important roles in the gut of microbial taxa differed significantly. Compared with the control group, mice in group T1 were significantly enriched in RF39 at the Order level (LDA = 3.014, p = 0.022), at the Family level Ruminococcaceae (LDA = 3.375, p = 0.047), Rikenellaceae (LDA = 3.980, p = 0.21) and norank_o_RF39 (LDA = 3.014, p = 0.022) were significantly enriched, at the Genus level unclassified_f_Ruminococcaceae (LDA = 2.943, p = 0.011), Negativibacillus (LDA = 2.865, p = 0.020), Blautia (LDA = 2.981, p = 0.036), Alistipes (LDA = 3.817, p = 0.047), g_norank_f_norank_o_RF39 (LDA = 3.025, p = 0.022) and g_Rikenellaceae_RC9_gut_group (LDA = 3.345, p = 0.024) were significantly enriched. T2 group only enriched at Genus level, which were Roseburia (LDA = 3.364, p = 0.009), norank_f_Ruminococcaceae (LDA = 3.094, p = 0.026), Eubacterium_xylanophilum_group (LDA = 2.950, p = 0.017), Eubacterium_oxidoreducens_group (LDA = 3.045, p = 0.027), NK4A214_group (LDA = 2.936, p = 0.049) and Anaerotruncus (LDA = 2.942, p = 0.033). T3 group only enriched in Genus A2 (LDA = 2.871, p = 0.044).

Thereafter, we assessed the relative abundance of bacteria on phylum and genus levels. Firmicutes and Bacteroidota were the most abundant, accounting for over 80% of all microorganisms in all groups at Phylum level (Figure 4G). At genus level, Lactobacillus and Bacillus were the most abundant, accounting for over 50% of all microorganisms in T1, T2, and CK groups, meanwhile Lactobacillus and Staphylococcus were the most abundant accounting for over 50% of all microorganisms in T3 group (Figure 4H).

In order to assess the level of significance of the differences in species abundance and to obtain information on the species that were significantly different between the two groups, we utilized the between-groups test of variance methodology, applying rigorous statistical methods based on the community abundance data obtained to test hypotheses about the species between the two groups of sampled microbial communities. The results showed that only T2 group and CK group showed significant difference at Phylum level, which was a significant rise in Verrucomicrobiota (p = 0.045, Figure 4I). At genus level, the abundance values of Alistipes, Rikenellaceae_RC9_gut_group, unclassified_f_Ruminococcaceae, Roseburia, and Family_XIII_AD3011_group in group T1 were all significantly increased relative to CK group (Figure 4J). The abundance of Bacillus, unclassified_f_Lachnospiraceae, Lachnospiraceae_NK4A136_group, Rikenellaceae_RC9_gut_group, Roseburia, norank_f_Ruminococcaceae, Eubacterium_xylanophilum_group, Anaerotruncus, unclassified_f_Ruminococcaceae, norank_f_Erysipelotrichaceae, norank_f_Peptococcaceae, Negativibacillus and Akkermansia of T2 group all showed a significant increase in abundance values relative to the CK group, while Aerococcus and Staphylococcus showed a decrease in abundance values relative to the CK group (Figure 4K). As is shown in Figure 4L, T3 group significantly decreased the abundance of norank_f_norank_o_RF39 and increased the abundance of Christensenellaceae_R-7_group. Thus, FMT altered the gut flora composition of DBA mice.

Since this experiment involved FMT across strains and breeds, in order to investigate whether changes in intestinal flora would alter gene expression in the gut, we took colon tissues from four groups of mice for transcriptome sequencing. By quantitatively analyzing the genes sequenced in the four groups of mice and obtaining their reads, the samples were analyzed for differential expression of genes between groups, identifying differentially expressed genes between groups, and then studying the functions of the differential genes (Figures 5A,B). Mice in group T1 had a total of 356 differential genes compared to CK group, with 216 up-regulated and 140 down-regulated genes. Mice in group T2 had a total of 222 differential genes compared to CK group, with 176 up-regulated and 46 down-regulated genes. Mice in group T3 had a total of 416 differential genes compared to CK group, with 367 up-regulated and 43 down-regulated genes.

Based on the information about the expression number of genes in different samples, the distance between genes and samples is calculated, and then an iterative method was used to classify the genes and samples, and the differentially expressed genes between each experimental group and the CK group were plotted as a heatmaps (Figures 5C–E). Acceptance of FMT from normal C57BL/6 mice up-regulated cholesterol metabolism related genes (Apoh, Apob, Apoc3, Apoa1, Apoa2), PPAR signaling pathway related genes (Apoa2, Apoa5, Apoc3, Apoa1, Fabp1) and complement and coagulation cascades related genes (Fga, C8b, Fgb, Fgg, C8a, Plg) in DBA mice. Acceptance of FMT from high-fat C57BL/6 mice similarly upregulated genes that increase the cancer related genes (Mycn, C4bp) (Figure 5D). The Pla2g3b gene was down-regulated by fecal microbiota transplants from Wistar rats (Figure 5E).

GO functional and KEGG pathway enrichment analysis were performed with the DEGs among the four groups. Enrichment results of top20 were shown at padjust <0.05, where T1 group versus CK group had enrichment results in both GO and KEGG pathway, T2 group versus CK group had only GO enrichment results, and T3 group versus CK group did not produce enrichment results in both GO and KEGG pathway. The results showed that the DEGs between T1 group and CK group were categorized into three groups [molecular function (MF), biological process (BP), and cellular component (CC)] based on GO enrichment analysis, and many of them were associated with lipid metabolic process, such as positive regulation of lipid metabolic process, regulation of lipid metabolic process, lipid metabolic process, regulation of lipid biosynthetic process, regulation of lipid biosynthetic process in BP. There were also many related to hemostasis and coagulation, such as negative regulation of blood coagulation, negative regulation of hemostasis, regulation of blood coagulation, negative regulation of coagulation, regulation of hemostasis, regulation of coagulation in BP, and heme binding in CC. Other high enriched GOs were chylomicron, collagen-containing extracellular matrix, haptoglobin-hemoglobin complex, extracellular region and extracellular space in MF, regulation of hormone secretion, negative regulation of wound healing, protein activation cascade and regulation of hormone levels in BP (Figure 5F). KEGG pathway enrichment showed that the DEGs between T1 group and CK group were categorized into three groups [metabolism (M), human disease (HD), organism system (OS)]. And many of them were associated to fat and sterol such as Cholesterol metabolism, Fat digestion and absorption in OS, and Steroid hormone biosynthesis of HD. Other high enriched KEGG pathways were African trypanosomiasis of M and Complement and coagulation cascades, PPAR signaling pathway in OS, together with Retinol metabolism in HD (Figure 5G). The DEGs between T2 group and CK group were categorized into biological process (BP), and most of them are related to the regulation of cellular activity (positive regulation of secretion by cell, positive regulation of cell differentiation, positive regulation of multicellular organismal process, regulation of secretion by cell, regulation of multicellular organismal development, regulation of multicellular organismal process, positive regulation of cell communication, regulation of cell development, regulation of cell differentiation). Others were related to neuron-related regulation, secretory regulation and the immune system (Figure 5H).

Pearson correlation was used to infer the relationship between the composition of the microbial communities on the genus level and DEGs. The top candidate genes with genus-level relationships with the bacterial community are listed in Supplementary Tables S4, S5. Correlation analysis of the 357 differential genes in the T1 and CK groups with the five genus-level differential flora yielded 111 genes with significant correlations, most of which had KEGG functional annotations related to fatty acid metabolism (Apoa5, Angptl4), fat digestion and absorption (Dgat2), cholesterol metabolism (Apoa2, Kng1), inflammation regulation (Cyp2c29) and coagulation (C8b, Fgb, C8a, Plg) (Figure 6). Correlation analysis of 221 differential genes from the T2 and CK groups with 15 genus-level differential flora yielded 111 genes with significant correlations, most of which had KEGG functional annotations related to fatty acid metabolism (Cyp8b1), cholesterol metabolism (Angptl8), and coagulation (C4bp) (Figure 7). Correlation analysis of 416 differential genes from the T3 and CK groups with 2 genus-level differential flora yielded 7 genes with significant correlations, most of which had KEGG functional annotations related to cancer and cell apoptosis (Prima1, Pidd1, Pmel, Dner, Rnf208, Igfn1) (Sun et al., 2009; Izetti et al., 2014; Weiler et al., 2022) and Nervous-System Function (Susd4, Elfn2) (Figure 8).

Normal steady-state intestinal flora is closely related to host health and forms a reciprocal relationship with the host through complex interactions. Its unique community structure and metabolites are essential for the regulation of host metabolism, growth and development, pathogen resistance, immunomodulation, adaptation, and evolution (Bäckhed et al., 2005; Engel et al., 2012; Ezenwa et al., 2012; Zhang et al., 2016). To investigate whether the effect of fecal transplantation on the growth performance of DBA mice is related to the intestinal flora, mice from three experimental groups and CK group were cultured in similar environments during the reproductive period to explore the differences in the structure of their intestinal bacterial communities. The PCoA analysis of the bacterial flora revealed that the three experimental groups and CK group were clearly distinguishable. The results showed that the T1 group significantly enhanced five dominant bacteria genera including the genera Alistipes, Rikenellaceae_RC9_gut_group, unclassified_f_Ruminococcaceae, Roseburia, and Family_XIII_AD3011_group. Alistipes and Rikenellaceae_RC9_gut_group belong to Bacteroidetes. It has been shown that Alistipes may be protective against certain diseases, including liver fibrosis, cancer immunotherapy and cardiovascular disease. In addition to this, Alistipes spp. have been directly linked to cardiovascular disease (CVD) and may also be considered as a potential producer of short-chain fatty acids (SCFA) (Jie et al., 2017; Kim et al., 2018; Parker et al., 2020). Genus Rikenellaceae_RC9_gut_group was proved to promotes lipid metabolism (Zhou et al., 2018). Unclassified_f_Ruminococcaceae (Ruminococcus belongs to this family) and Alistipes are associated with immune regulation and healthy homeostasis and are regarded as potentially beneficial bacteria (Kong et al., 2016; Shang et al., 2016; Wang et al., 2018), Which might relate to the decrease in pro-inflammatory factors in T1 group. Alistipes have been shown to produce sulfolipids (SL) as a marker of a high-fat diet (Walker et al., 2017). This may explain why the growth performance of mice in the T2 group was better than that of mice in other three groups, and why the serum TG content of the mice in the T2 group was significantly higher than that of the mice in CK group. Roseburia belongs to the phylum Firmicutes, class Clostridia, order Clostridiales, and family Lachnospiraceae. The five well-characterized species of Roseburia (Roseburia intestinalis, Roseburia hominis, Roseburia inulinivorans, Roseburia faecis, and Roseburia cecicola) all produce SCFA, such as acetate, propionate, and butyrate (Nie et al., 2021). Roseburia has been shown to prevent intestinal inflammation and maintain energy homeostasis through the production of metabolites (Kumari, 2013; Rajilić-Stojanović et al., 2013). Similarly, the Family_XIII_AD3011_ group has probiotic properties and improves intestinal health in weaned piglets after inoculation (Yu et al., 2020). The main species that significantly increased at the genus level in the T2 group compared to the CK group were Bacillus, unclassified_f_Lachnospiraceae and Lachnospiraceae_NK4A136_group, and the main species that significantly decreased were Staphylococcus and Aerococcus. The genus Bacillus is a diverse group of gram-positive, spore-forming aerobic bacilli (Weber and Rutala, 2016). Scientists at the National Institutes of Health (NIH) found that probiotic Bacillus can comprehensively eradicate intestinal as well as nasal S. aureus colonization (Piewngam et al., 2018), this might be the reason why Staphylococcus significantly decreased in T2 group compared to CK group. Lachnospiraceae_NK4A136_group can produce SCFAs through fermentation of dietary polysaccharides and has been negatively associated with several metabolic diseases and chronic inflammation (Truax et al., 2018; Hu et al., 2019; Ma et al., 2020). Staphylococcus aureus is a gram-positive bacterium with a diameter of 1 μm that can cause many forms of infection (Cheung et al., 2021). Aerococcus are granulocyte-positive bacteria that grow in clusters, and studies have shown that Aerococcus are human pathogens that can cause invasive human infections, such as urogenital focal sepsis and infective endocarditis (Williams et al., 1953; Senneby et al., 2012; Senneby et al., 2014; Senneby et al., 2015; Opota et al., 2016). The improved growth performance of mice in the T3 group may be associated with a significant increase in Christensenellaceae_R-7_group bacteria spp. Christensenellaceae_R-7_group is a member of the Christensenellaceae family. The Christensenellaceae family is a relatively new family of bacteria previously associated with host health (Waters and Ley, 2019). Christensenellaceae have been shown to be associated with the proteolytic metabolism of god is animal proteins and intestinal metabolites are positively correlated and are strongly associated with butyrate production (Beaumont et al., 2017; Haas and Blanchard, 2017; Manor et al., 2018).

The productive performance of the host can be modulated by regulating the structure of the gut microflora, which in turn is largely influenced by genetics and feed. Gut microbes are also able to influence host gene expression and methylation levels (Levy et al., 2015). Anhê et al. (2019) found that altering the intestinal flora of mice can change gene expression in the gut and can prevent obesity by increasing the energy trumpet in diet-induced obese mice. Similarly, in the current study, we found significant differences in intestinal gene expression in several experimental groups of mice compared to CK group. In the T1 group, the up-regulated genes mainly included lipid metabolism-related genes such as Apoh, Apoa2, Apob, Apoa5, Apoc3, Apoa1, and Fabp1. We found these genes in the National Center for Biotechnology Information (NCBI) database to be homologous to both human and mouse. The Apoh gene was found to inhibit fatty acid oxidation and promote lipid synthesis (Pan et al., 2022). Zaki et al. (2013) found that Apoa2 is strongly associated with obesity and lipid metabolism, and that it has a potential role in regulating appetite in humans; the Apob gene is also involved in fat digestion and absorption (Honda et al., 2022). Apoa5 is a major gene involved in triglyceride metabolism (Tai and Ordovas, 2008). Apoc3 is expressed predominantly in the intestine, where it contributes to postprandial coeliac formation, and in the liver, where it regulates central lipid metabolism and turnover (Kohan, 2016). The in vivo association of Apoc3, Apoa1, and Apoa5 with hyperlipidemia, particularly TG levels, has been demonstrated in genome-wide association studies (GWAS) (Mar et al., 2004; Lai et al., 2005; Qi et al., 2007; Pouwer et al., 2020). This may account for the significant increase in serum TG levels in DBA mice that received fecal microbiome transplants from normal C57BL/6 mice. However, DBA mice that received fecal colony transplants from high-fat C57BL/6 mice upregulated cancer-related genes containing Mycn and C4bp (Kopylov et al., 2020; Massó-Vallés et al., 2020). This may be due to the fact that a high-fat diet enhances cancer incidence by modulating gut microbes and metabolites (Yang et al., 2022).

Generally, intestinal bacteria interact with the expression of certain genes in intestinal epithelial cells (Larsson et al., 2012; Broderick et al., 2014; Richards et al., 2019). When human colonic epithelial cells (HCoEpiC) were treated with live gut microbiota extracted from five healthy humans, the most intense response (3,240 genes in any of the five microbiota samples) occurred at 2 h; 588 pairs of taxon-by-taxon unit transcripts corresponded to 121 host genes, and changes in expression correlated with the abundance of 46 taxa (Richards et al., 2019). Larsson et al. (2012) and Broderick et al. (2014) found that MyD88 is essential for microbiota-induced colonic expression of the antimicrobial genes Reg3β and Reg3γ in the epithelium. Lack of MyD88 resulted in altered bacterial diversity and a greater proportion of segmented filamentous bacteria in the small intestine. Su et al. found that the Aeromonas and Roseomonas percentage, as well as differential expression of IL12, were related to anti-disease ability (Su et al., 2021), and that the percentage of thick-walled bacteria was associated with growth performance of a new strain of Yellow River carp. Alenghant et al. found that a mouse model with tissue-specific deletion of histone deacetylase 3 (HDAC3) resulted in dysregulation of microbiome-dependent gene regulation in intestinal epithelial cells (Alenghat et al., 2013). Specifically, an intestinal epithelial cell-specific HDAC3 knockout mouse line (HDAC3 ΔIEC) was bred to study IBD progression. Conventionalized HDAC3 ΔIEC mice exhibit dysregulated host gene expression and disrupted homeostasis in vivo. In contrast, no such dysregulation was observed in the germ-free HDAC3ΔIEC mouse model, suggesting that these effects are mediated by the microbiome (Alenghat et al., 2013). From a molecular perspective, transcription factors are host proteins that bind to DNA and regulate gene transcription, and elements of the microbiome bind directly to transcription factors (Krautkramer et al., 2016; Davison et al., 2017), which may be the molecular mechanism that facilitates the linkage between microbiome and host gene expression. In this study, we analyzed the selection and CK groups in conjunction with the structure of gut bacterial communities and DEGs, and identified the Apoa2 and Apoa5 genes that were significantly associated with the Rikenellaceae_RC9_gut_group bacteria, which have been found to promote lipid digestion and absorption, and Apoa2 and Apoa5, on the other hand, are important genes in lipid metabolism. Thus, FMT from C57BL/6 mice might enhance the lipid metabolism of DBA mice by increasing the Rikenellaceae_RC9_gut_group and finally upregulated the expression of Apoa2 and Apoa5 genes in the colon.

These results suggest that gut bacteria can influence gene expression in the gut, where dominant species or bacterial structure may reflect the genetic characteristics of the host, C57BL/6 mice. Gene profiling of gut bacteria in the gut contributes to host health and performance. In this study, we used multisource FMT experimental design, in which fecal bacterial fluids derived from C57BL/6 mice, high-fat C57BL/6 mice, and Wistar rats were transplanted into DBA mice. This multisource FMT design facilitated a more comprehensive comparison of the effects of different microbial compositions on host growth and physiological functions. In addition, the effects of FMT on DBA mice were investigated by combining multilevel analyses such as 16 s rRNA sequencing and colonic gene expression sequencing. This combined multi-omics analysis helps to deeply explore the microbial regulatory mechanisms on the host, providing more precise targets for future intervention and treatment of related diseases or growth promotion. However, the mechanism of the interaction between gut flora and intestinal gene expression, as well as the mechanism of the differences between normal C57BL/6 mice and high-fat C57BL/6 mice produced as donors still need to be validated by further studies.