Fecal microbiota transplantation modulates gut microbiota and lipid metabolism to reduce diarrhea in low-birth-weight neonatal calves

This study aimed to elucidate the mechanism by which colonic fecal microbiota transplantation (FMT) improves lipid metabolism in low-birth-weight neonatal calves via modulation of the gut microbial ecosystem. In a completely randomized design, seventy newborn Holstein calves (1 day old, 32.63 ± 3.13 kg BW) were assigned to two groups (35 calves/treatment; 30 females and 5 males per group). A healthy calf was used as a fecal donor. A 10% fecal bacterial suspension was prepared and administered colonically (50 mL, twice daily for 5 days). All calves were fed the same milk replacer and weaned using a step-down protocol at 60 days of age. Blood samples were collected from the jugular vein on days 1, 7, and 14. Six calves near the group average body weight were selected from each treatment group for serum, fecal microbial, and serum metabolomic analyses. Fecal samples collected on day 7 were subjected to 16S rRNA sequencing (V3–V4 regions) for microbiota profiling. Serum metabolites were analyzed using LC–MS/MS with Progenesis QI 2.3. The results showed that FMT improved growth performance, reduced the incidence and duration of diarrhea, and significantly decreased serum total cholesterol. These changes were associated with a reduction in the abundance of Ruminococcus_gnavus_group, a genus positively correlated with serum total cholesterol. Metabolic pathway analysis revealed that two lipid-related pathways—glycerophospholipid metabolism and ether lipid metabolism—were altered in the FMT group compared to the control. In conclusion, FMT reduces diarrhea and enhances host health by modulating the gut microbial ecosystem to improve lipid metabolism in neonatal calves.

1 Introduction

Low birth weight (LBW)—defined as impaired prenatal growth of the fetus or its organs (Wu et al., 2006)—predisposes newborns to digestive complications such as necrotizing enterocolitis. Evidence from piglet models indicates that LBW exacerbates inflammatory responses and weakens gut barrier function (Huang et al., 2020; Tao et al., 2019), and has been correlated with intestinal injury and prolonged lipid metabolic dysregulation (Yan et al., 2017). Furthermore, LBW is associated with gut microbiota alterations that contribute to inflammatory signaling and metabolic imbalance (Huang et al., 2020). Together, these observations collectively suggest that low birth weight remodels the gut microbial ecosystem, thereby promoting disturbances in host lipid metabolism.

According to Hooks and O’Malley (2017), a shift in the gut microbial balance toward pathogenic bacteria, termed dysbiosis, can initiate intestinal disease. In young ruminants, gastrointestinal microorganisms exhibit greater plasticity than in adults, and early microbial colonization can be enhanced through technological interventions to improve performance, gastrointestinal development, and disease prevention. Fecal microbiota transplantation (FMT), recognized as a strategy for “whole gut microbiome replacement,” has been incorporated into clinical guidelines for managing recurrent Clostridioides difficile infection in humans (Yadegar et al., 2024). This procedure involves transferring carefully screened and processed gut microorganisms from a healthy donor to a diseased recipient (Cheng et al., 2022), thereby restoring a healthy gut microbial composition (Ma et al., 2021; Rosa et al., 2021; Zeng et al., 2023). In calves, FMT has proven effective in mitigating diarrhea, reducing inflammatory responses, enhancing antioxidant capacity at weaning, and modifying gut microbiota structure (Islam et al., 2022; Kim et al., 2021; Rosa et al., 2021). Similarly, in monogastric animals, FMT has been shown to alleviate diarrhea, promote growth, and improve intestinal function (Hu et al., 2017; Ma et al., 2020; Zhou et al., 2021). We employed colonic administration to bypass the potentially hostile acidic environment of the abomasum in newborn calves, ensuring direct delivery of viable microbiota to the lower gastrointestinal tract. We hypothesized that neonatal colonic FMT would (i) improve growth performance and reduce diarrhea incidence, (ii) modulate systemic immunity and lipid metabolism, and (iii) alter the gut microbial ecosystem and associated serum metabolomic profile in LBW calves.

2 Materials and methods

2.1 Calves and experimental design

The objective of this experiment was to investigate the effectiveness of colonic FMT applied during the neonatal period and its influence on microbial colonization in the gut of low-birth-weight calves. A total of seventy newborn Holstein calves (mean age: 1 day; mean body weight: 32.63 ± 3.13 kg) meeting the criterion of birth weight ≤ 36 kg were enrolled. These calves were randomly distributed into two treatment groups in a completely randomized design, with each group containing 35 calves (30 females and 5 males).

2.2 Donor selection and fecal preparation

Donor calves were selected according to established criteria from previous studies (Kim et al., 2021; Rosa et al., 2021). Sixteen healthy calves aged 30–40 days were chosen based on physical condition, nutritional status, and a fecal consistency score ≤ 2. Physical health was assessed by visual inspection of the nose, mouth, ears, rump, and hair coat; nutritional status was evaluated via body condition scoring. Fecal consistency was monitored four times daily using a 4-point scale: 1 = normal, 2 = soft, 3 = runny, and 4 = watery (Wu et al., 2022). Calves with a history of antimicrobial treatment (including antibiotics, antifungals, or antivirals) or diagnosed infectious diseases (e.g., contagious bovine pleuropneumonia, anthrax, or brucellosis), as verified from farm records, were excluded to ensure donor health and suitability. Fecal samples were collected by rectal stimulation and immediately placed on ice for transport to the laboratory. Upon arrival, samples were screened for common enteric pathogens—including Cryptosporidium, Coronavirus, Rotavirus, E. coli K99, and Giardia—using highly sensitive and specific PCR and ELISA assays (Supplementary Material). Any calf testing positive for one or more of these pathogens was excluded from the donor pool. For fecal slurry preparation, samples were mixed with 0.9% sterile saline at a 1:10 (w/v) ratio, allowed to settle for 5 min, and then filtered through medical gauze to remove large particulate matter. The resulting filtrate was aliquoted into 50 mL centrifuge tubes, stored at –20°C (Lee et al., 2016), and thawed in a 37 °C water bath immediately prior to use. This standardized protocol ensured consistency across preparations and minimized pathogen contamination. Importantly, each recipient calf received a fecal suspension derived from a single donor; fecal samples from different donors were not pooled.

2.3 Experiment

The feeding regimen was as follows: Within the first hour of life, each calf received a volume of pasteurized colostrum (IgG concentration > 50.0 g/L, determined using a colostrum densimeter) equivalent to 10% of its birth weight, followed by a second feeding of 2 L of pasteurized colostrum 6 hours later. Thereafter, pasteurized whole milk was provided until weaning. The daily milk allowance was 4.4 L from days 2 to 10 and 8.8 L from days 11 to 50, offered in two equal feedings (05:00 and 17:00) and maintained at 36–37°C to mimic fresh milk temperature, thereby promoting palatability and supporting digestion. A 10-day step-down weaning procedure was applied, with full weaning on day 60. During the preweaning phase, a commercial pelleted starter feed and water were available ad libitum from separate containers to encourage independent feeding behavior. Hay was deliberately omitted from the dietary regimen to streamline nutrient delivery and concentrate on core nutritional components. This was done to standardize nutrient intake, minimize dietary variability, and encourage consumption of the pelleted starter feed, which was the focus of the pre-weaning nutritional strategy.

The experimental treatment commenced with the first feeding of pasteurized whole milk. Calves in the control (CON) group were administered 50 mL of sterile 0.9% saline, whereas those in the FMT group received 50 mL of the fecal bacterial suspension. The infusion was performed twice daily (08:00 and 16:00) over five consecutive days. To facilitate the colonic FMT without the confounding effects of sedation, calves were manually restrained in a standing position within a chute. A fiberoptic endoscope was introduced via the rectum and advanced 20–25 cm to the posterior third of the colon for infusion of the solution. No lubricants, sedatives, or analgesics were used during the procedure. Continuous monitoring during and after the procedure revealed no adverse events (e.g., bleeding, infection, or behavioral distress), confirming the method’s safety and tolerability. All calves remained in the study until its conclusion. Post-treatment, both groups were subjected to identical feeding management.

2.4 Sample collection

2.4.1 Growth performance and diarrhea check

The following data were collected to evaluate calf performance and health: Body weight was recorded on days 1 (initial), 7, 14, and 60 (final) for the computation of average daily gain (ADG). Fecal consistency was evaluated four times per day as described above. To ensure consistency, all fecal scoring was performed by one independent, trained observer based on visual inspection of fecal deposits on the ground and soiling of the tail and hindquarters. A diarrheic day was defined as a day with a fecal score > 2, and the occurrence rate was derived from these records. A 5-point ordinal disease scoring system was used to grade overall health: 1 = no disease; 2 = one disease event during preweaning that was successfully treated; 3 = two or more treated disease events; 4 = persistent uncured disease resulting in culling; 5 = mortality. Dry matter intake (DMI) of the starter feed was determined daily by weighing the residual feed at 19:00.

2.4.2 Serum sampling and analysis

Blood samples were collected from the jugular vein before the morning feeding on days 1, 7, and 14. From each treatment group, six female calves were selected for sampling. These calves were randomly selected from the larger cohort, with the constraint that they were female and had a body weight close to the treatment group mean, to control for sex and size variability in the intensive omics analyses. At each time point, duplicate 10 mL samples were drawn into additive-free vacuum tubes. Serum was separated by centrifugation at 3000 × g for 15 min at 4°C and stored at –20°C until analysis.

Serum concentrations of immunoglobulin G (IgG), immunoglobulin A (IgA), interleukin-1β (IL-1β), and interleukin-6 (IL-6) were quantified using commercial bovine-specific ELISA kits (Angle Gene, China), according to the manufacturer’s instructions. The respective catalog numbers were #ANG-E61025B (IgG), #ANG-E61022B (IgA), #ANG-E61176B (IL-1β), and #ANG-E61008B (IL-6). All kits had an interassay CV of 9% and an intraassay CV of 15%. The measurable ranges were 11.25–675 μg/mL for IgG, 1.875–112.5 μg/mL for IgA, 7.5–450 pg/mL for IL-1β, and 18.75–1125 pg/mL for IL-6. Additionally, a suite of biochemical and antioxidant parameters—including alkaline phosphatase (ALP, U/L), alanine aminotransferase (ALT, U/L), aspartate transaminase (AST, U/L), glucose (GLU, mmol/L), high-density lipoprotein (HDL, μmol/L), low-density lipoprotein (LDL, μmol/L), total cholesterol (TC, mmol/L), triglycerides (TG, mmol/L), malondialdehyde (MDA, mmol/mg), total antioxidant capacity (T-AOC, mmol/L), glutathione peroxidase (GSH-Px, U/mL), and superoxide dismutase (SOD, U/mg). These analyses were conducted automatically through spectrophotometry according to the manufacturer’s instructions (Shimadzu 2100, Shimadzu Co., Kyoto, Japan).

2.5 Collection of feces from the rectum

Fecal samples were collected from 7-day-old calves for microbial analysis. Twelve calves were randomly and equally divided into the CON (n=6) and FMT (n=6) groups. Fecal samples for microbial analysis were collected from the same twelve 7-day-old calves (n = 6 per group) that were selected for serum metabolomic analysis. Using sterile disposable gloves, feces were obtained aseptically via rectal stimulation. Immediately upon collection, samples were snap-frozen in liquid nitrogen to preserve microbial integrity and subsequently stored at −80 °C until DNA extraction and sequencing. Blood samples for serum metabolomic analysis were collected from the same twelve 7-day-old calves (n = 6 per group).

2.5.1 DNA extraction, PCR amplification, and 16S rRNA sequencing

Microbial genomic DNA was extracted from fecal samples using the E.Z.N.A.^® Stool DNA Kit (Omega Bio-tek, Norcross, GA, USA), following the manufacturer’s instructions. The quality and concentration of the extracted DNA were assessed by 1.0% agarose gel electrophoresis and a NanoDrop2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA), respectively. DNA samples were stored at –80°C until further processing. Subsequent steps, including PCR amplification and 16S rRNA gene sequencing, were performed as previously described (Wu et al., 2023).

The V3–V4 hypervariable regions of the microbial 16S rRNA genes were amplified and then sequenced with the Illumina MiSeq platform (Majorbio BioPham Technology, Shanghai, China) using the primers 338F (50 -ACTCCTACGGGAGGCAGCA-30) and 806R (50 -GGACTACHVGGGTWTCTAAT-30) (Caporaso et al., 2011; Liu et al., 2016). Amplification reactions were conducted on a T100 Thermal Cycler PCR machine (BIO-RAD, USA) with a mixture comprising 4 μL of 5 × Fast Pfu buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer (5 μM), 0.4 μL of Fast Pfu polymerase, 10 ng of template DNA, and ddH2O to attain a final volume of 20 μL. The thermal cycling conditions were set as follows: an initial denaturation step at 95°C for 3 minutes, followed by 27 cycles of denaturation at 95°C for 30 seconds, annealing at 55°C for 30 seconds, and extension at 72°C for 45 seconds. A final extension step at 72°C for 10 minutes was included, followed by a hold at 4°C. The PCR products were then purified using a PCR Clean-Up Kit (YuHua, Shanghai, China) as per the manufacturer’s instructions, after extraction from 2% agarose gels. The purified products were quantified using a Qubit 4.0 Fluorometer (Thermo Fisher Scientific, USA).

Bioinformatic analysis of the gut microbiota was conducted utilizing the Majorbio Cloud platform (https://cloud.majorbio.com). Rarefaction curves and alpha diversity indices, including observed OTUs, Chao1 richness, Shannon index, and Good’s coverage, were calculated using Mothur v1.30.1 based on the OTU information. OTUs were clustered at a 97% sequence similarity threshold using the UPARSE pipeline. Principal coordinate analysis (PCoA) based on Bray-curtis dissimilarity was performed using the Vegan v2.5–3 package to determine the similarity among microbial communities in different samples. Permutational multivariate ANOVA (PERMANOVA) with 999 permutations was performed using the adonis2 function to test for group differences in β-diversity. Homogeneity of group dispersions was confirmed using the betadisper function. Linear discriminant analysis (LDA) effect size (LEfSe) (Segata et al., 2011) (http://huttenhower.sph.harvard.edu/LEfSe) was utilized to identify significantly abundant bacterial taxa (ranging from phylum to genera) among the different groups, with an LDA score threshold of >2 and a statistical significance threshold of P < 0.05.

2.5.2 Serum metabolome analysis

For the serum metabolome analysis, a precise extraction protocol was followed (Kim et al., 2021). As a part of the system conditioning and quality control process, a pooled quality control sample (QC) was prepared by mixing equal volumes of all samples. The QC samples were processed and tested in the same manner as the analytic samples. The QC sample was injected at regular intervals (every 5–15 samples) in order to monitor the stability of the analysis. For detailed steps refer to previous studies (Kim et al., 2021). The LC-MS/MS analysis of the sample was performed using a Thermo UHPLC-Q Exactive system, equipped with an ACQUITY HSS T3 column (100 mm × 2.1 mm internal diameter, 1.8 μm; Waters, USA) at Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The analysis was performed in both positive and negative electrospray ionization (ESI) modes. The total chromatographic runtime was 20 minutes per sample.

The pretreatment of LC/MS raw data was performed by Progenesis QI (Waters Corporation, Milford, USA) software, and a three-dimensional data matrix in CSV format was exported. The information in this three-dimensional matrix included: sample information, metabolite name and mass spectral response intensity. Internal standard peaks, as well as any known false positive peaks (including noise, column bleed, and derivatized reagent peaks), were removed from the data matrix. At the same time, the metabolites were identified by searching database, and the main databases were the HMDB (http://www.hmdb.ca/), Metlin (https://metlin.scripps.edu/) and Majorbio Database.

The data were analyzed utilizing the freely accessible online platform of Majorbio Cloud (cloud.majorbio.com). During the analysis, metabolic features that were detected in at least 80% of any sample set were retained. Post-filtering, for samples where metabolite levels fell below the lower limit of quantitation, minimum metabolite values were assigned. Normalization of metabolic features was achieved by summing the values, ensuring that the response intensity of the sample mass spectrum peaks was adjusted to account for variations stemming from sample preparation and instrument instability. This was done using the sum normalization method, resulting in the generation of a normalized data matrix. Additionally, variables exhibiting a relative standard deviation (RSD) exceeding 30% in quality control (QC) samples were excluded. Log10 transformation was then applied to the remaining data to produce the final data matrix, which was utilized for subsequent analytical procedures.

Comparisons of data between the CON and FMT groups were conducted using orthogonal partial least squares discriminant analysis (OPLS-DA) in conjunction with Student’s t-test. The screening criteria employed to identify metabolites with significant differential abundance between the two groups were as follows: variable importance in the projection (VIP) values greater than 1.0 and P-values less than 0.05. The differential metabolites identified were then mapped onto their corresponding biochemical pathways through metabolic enrichment and pathway analysis, leveraging the KEGG database (http://www.genome.jp/kegg/). These metabolites were classified based on the pathways they participated in or the functions they served. Enrichment analysis was employed to determine whether metabolites associated with a specific pathway were over-represented. This analysis extended the annotation of individual metabolites to a group-level annotation. The Python package “scipy.stats” (https://docs.scipy.org/doc/scipy/) was utilized to perform the enrichment analysis, identifying the most pertinent biological pathways associated with the experimental treatments. Significantly enriched KEGG metabolic pathways were determined using a P-value threshold of less than 0.05.

2.6 Statistical analysis

Based on previous studies (Górka et al., 2011), the PROC POWER and the GLMPOWER procedure in SAS were used to yield a sample size with a power of 0.8 under P < 0.05. According to previous similar studies, the effect size f that can make a difference in BW and diarrhea frequency are 0.68 (Brunton et al., 2014) and 1.30 (Ma et al., 2024), respectively. Based on the results of the power analysis, a total of at least 22 calves (11 calves per treatment) were needed to study growth performance and 12 calves (6 calves per treatment) to study health and intestinal microbiota to meet the power requirements when the treatment groups were 2.

Statistical analyses were performed using calf as the experimental unit. The homogeneity of variances and normality of the data were tested first using the UNIVARIATE procedure of SAS (version 9.4, SAS Institute Inc., Cary, NC, USA). Data related to BW, ADG, DMI and blood parameters were analyzed by a mixed model (PROC MIXED). The model included the fixed effects of treatment, time, their interaction (treatment × time), and calf as a random effect. Degrees of freedom were calculated using the Kenward-Roger approximation option of the MIXED procedure. The covariance structures [simple covariance structure, compound symmetry, heterogeneous first-order autoregressive structure, first-order ante-dependence structure, completely general (unstructured)] of the repeated measures within-subject were chosen based on the Akaike information criterion.

The diarrhea frequency were analyzed on a per 10-day (60-day) basis using the GLIMMIX procedure in SAS (PROC GLIMMIX, version 9.2, SAS Institute Inc., Cary, NC, USA) with fixed effects of treatment, day and their interactions (treatment × day), and the random effect of calf within treatment. Least squares means and standard error of the mean were reported, and effects were declared significant at P ≤ 0.05. GraphPad Prism (version 8.0.2; GraphPad Software, Inc., San Diego, CA, USA) was used to prepare the figures. Differences in α diversity metrics were assessed using the Kruskall-Wallis test, while β diversity differences were evaluated with permutational multivariate ANOVA (Permanova) with 999 permutations. The co-occurrence probability between microorganisms and serum indicators was tested using Networkx (Shanghai Marjorie Biomedical Technology Co., Ltd.) applying Spearman correlation with criteria of r > 0.5 and P < 0.05 to establish significance.

3 Results

3.1 Growth performance and health

This study demonstrated that neonatal FMT significantly enhanced both growth and health outcomes in calves. FMT increased body weight at weaning (P = 0.002) and average daily gain (P = 0.005), independent of changes in starter DMI (Figures 1A–C). The intervention also conferred substantial health benefits, markedly reducing the incidence (CON: 4.84, FMT: 3.31), frequency, and duration of diarrhea (Longest days of diarrhea: CON: 6.39, FMT: 4.35; Average days of diarrhea: CON: 3.75, FMT: 2.96) (P < 0.05), and lowering overall disease scores (CON: 3, FMT: 2) (P = 0.006; Figures 1D–H). The clinical relevance of these findings is underscored by the mortality data: three deaths occurred in the control group (due to diarrhea and pneumonia) compared to one in the FMT group (from encephalitis).

Figure 1

Figure 1. Effect of colonic FMT on growth, fecal parameters and health of low birth weight calves. (A, B) Effect of colonic FMT on calf growth performance; (C–G) Effect of colonic FMT on fecal parameters in calves; (H) Effect of colonic FMT on calf disease scores. n = 35. FMT = fecal microbiota transplantation.

3.2 Blood parameters

At 7 days of age, FMT calves had higher concentrations of IgG (P = 0.022; Figure 2A) and IgA (P = 0.031; Figure 2B). At 14 days of age, blood IgG concentrations remained elevated in the FMT group (P = 0.030; Figure 2A). Additionally, at 7 days of age, the FMT group exhibited decreased IL-6 in the blood (P = 0.020; Figure 2D), and at 14 days of age, there were lower concentrations of IL-1β (P = 0.010; Figure 2C) and IL-6 (P = 0.040; Figure 2D) in the FMT group. The experiment found no significant effect of FMT on blood oxidative stress indicators (P > 0.05, Supplementary Figure S1). FMT reduced serum concentrations of TC (P = 0.001; Figure 2M), HDL (P = 0.003; Figure 2I), ALP (P = 0.033; Figure 2E), and GLU (P = 0.009; Figure 2H) in 7-day-old calves; FMT had not produced any effects by day 14.

Figure 2

Figure 2. Effect of colonic FMT on blood parameters of low birth weight calves. (A) immunoglobulin G (IgG). (B) immunoglobulin A (IgA). (C) interleukin-1β (IL-1β). (D) interleukin-6 (IL-6). (E) alkaline phosphatase (ALP). (F) alanine aminotransferase (ALT). (G) aspartate transaminase (AST). (H) glucose (GLU). (I) high-density lipoprotein (HDL-C). (L) low-density lipoprotein (LDL-C). (M) total cholesterol (TC). (N) triglycerides (TG). n = 6. FMT = fecal microbiota transplantation. Graphs in panel are shown as the mean ± SEM.

3.3 FMT alters the composition of the intestinal flora

The analysis yielded 1,444,381 clean reads from the fecal samples (Supplementary Table S1). Good’s coverage indices exceeded 99.9%, indicating that the sequencing efforts captured the vast majority of bacterial species present in the samples, providing a comprehensive dataset for analyzing the microbial community.

Microbial diversity analysis revealed no effect of FMT on overall microbial diversity (P > 0.05; Supplementary Figures S2, S3). Species abundance was ranked at the phylum, family, and genus levels (Figures 3A-C). Taxonomic analyses using LEfSe and linear discriminant analysis identified one bacterial order, one family, and three genera as differentiators (Figure 3D). The genera g_Ruminococcus_gnavus_group, g_Clostridium_innocuum_group, and g_Novosphingobium were highlighted as major indicators between the control (CON) and FMT groups. The relative abundance of g_Ruminococcus_gnavus_group was 1.52% in CON vs. 0.85% in FMT calves (P = 0.020), and g_Clostridium_innocuum_group was 0.31% in CON vs. 0.18% in FMT calves (P = 0.036). FMT reduced the abundance of g_Ruminococcus_gnavus_group (P = 0.020, Figure 3E), which showed significant positive correlations with TC (P = 0.042, r = 0.650, Figure 3G) and GLU (P = 0.022, r = 0.594, Figure 3G); however, these did not survive strict FDR adjustment for all genus-level correlations. Given the strong effect and biological plausibility linking this taxon to lipid metabolism, we present these associations as promising, hypothesis-generating relationships worthy of further investigation in larger studies. These genera serve as biomarkers of the intestinal microbiota. On the other hand, FMT reduced the abundance of g_Clostridium_innocuum_group in the intestine (P = 0.036, Figure 3F).

Figure 3

Figure 3. Effects of FMT on the intestinal microbiological composition in low birth weight calves. (A–C) Community bar diagram at the phylum (A), family (B), and genus (C) levels showing taxa with relative abundance ≥ 0.1%. (D) Identification of abundant microbiota with Linear Discriminant Analysis (LDA) scores (LDA > 1, P < 0.05). (E, F) Relative abundance of different microorganisms (E, F). (G) Correlation analysis of gut microbes and blood indicators. n = 6 for each group. FMT = fecal microbiota transplantation. *P < 0.05; **P < 0.01.

3.4 FMT alters the serum metabolomic profile

Using LC-MS and GC-MS, a total of 936 metabolites were detected and identified in calf serum. Principal component analysis (PCA) revealed separation between the two groups, with 23.0% and 20.9% of variation explained by principal components PC1 and PC2, respectively (Figure 4A). Projection latent structure discriminant analysis (PLS-DA) confirmed significant separation of the clusters. The PLS-DA score plot showed visual separation between the groups (R²Y(cum) = 0.621); however, the negative Q²(cum) value (-0.521) suggests limited predictive reliability and potential overfitting, likely attributable to the small sample size (n=6 per group) characteristic of exploratory metabolomics. Therefore, primary emphasis for group separation is placed on the unsupervised Principal Component Analysis (PCA). The differential metabolites discussed subsequently were identified based on univariate statistical criteria (VIP > 1.0, P < 0.05) applied for exploratory purposes, as noted in the Methods.

Figure 4

Figure 4. Effects of FMT on serum metabolomic results in low birth weight calves. (A) Beta diversity analysis using principal component analysis (n = 6). (B) Projections to latent structures-discriminate analysis score plots (n = 6). (C) Permutation validation plots comparing FMT and CON groups (n = 6). (D) Volcano plot for differential metabolites screening between FMT and CON groups (n = 6). (E) KEGG enrichment pathway map for all serum metabolites (n = 6). (F) Heatmap displaying relative abundance of key metabolites (P < 0.05, Variable Importance in Projection (VIP) > 1). (G) Differential abundance scores for all serum metabolites (n =6). (H) Topology analysis of metabolic pathways identified between FMT and CON groups. The X-axis represents pathway impact, and the y-axis represents pathway enrichment; larger sizes and darker colors indicate greater enrichment and impact. (I) Effects of colonic FMT on the serum metabolite Glycerylphosphorylcholine (n = 6). *P < 0.05.

Volcano plot analysis estimated P-values and multiplicity of differences for all metabolites, identifying significantly upregulated (red points) and downregulated (blue points) metabolites. One metabolite (Gibberellin A53) was significantly increased, and five (1-Naphthyl sulfate, Petasitin, Astromicin, Asparagine-betaxanthin, and Glycerylphosphorylcholine) were significantly decreased in the CON group compared to the FMT group (Figure 4D). Clustering heat map analysis of these differential metabolites showed a distinct separation between the two groups (Figure 3E).

Pathway analysis indicated that three metabolic pathways were significantly altered in the FMT group compared to the CON group: choline metabolism in cancer, ether lipid metabolism, and glycerophospholipid metabolism. The ether lipid metabolism pathway had the highest absolute differential abundance score (DA score) of -0.5, suggesting that FMT had the most significant effect on this pathway (Figures 4F, G). Pathway topology analysis, performed using MetaboAnalyst 3.0 with the Kyoto Encyclopedia of Genes and Genomes (KEGG) as the knowledge base, identified two metabolic pathways with impact values higher than the relevance cut-off. These pathways were ether lipid metabolism and glycerophospholipid metabolism (Figure 4H). The primary change in these pathways was attributed to a decrease in the abundance of the metabolite glycerylphosphorylcholine following FMT (Figure 4I).

4 Discussion

FMT is increasingly recognized as a strategy to restore a healthy gut microbial composition and has shown efficacy in treating various gastrointestinal and metabolic disorders in both humans and animals. In ruminants, early microbial interventions such as FMT hold promise for enhancing performance and health during critical developmental windows. The primary aim of this study was to investigate how neonatal colonic FMT improves lipid metabolism in LBW calves by modulating the gut microbial ecosystem.

Our results demonstrate that colonic FMT induced specific alterations in the gut microbiota, which were subsequently linked to changes in host serum metabolites and metabolic pathways. Although FMT did not significantly alter overall microbial α-diversity—a finding consistent with studies in the immediate postnatal period (Rosa et al., 2021)—it led to a marked reduction in the abundance of g_Ruminococcus_gnavus_group. This genus was identified as a key discriminant between groups and showed a significant positive correlation with serum TC. This aligns with previous observations linking Ruminococcus_gnavus_group to host lipid metabolism (Lahti et al., 2013). To identify metabolites potentially associated with FMT for exploratory biological interpretation, we first applied a screening threshold of VIP > 1.0 and p < 0.05 without FDR correction. To bridge this microbial shift to host physiology, our serum metabolomic analysis revealed a corresponding decrease in glycerylphosphorylcholine, a metabolite integral to glycerophospholipid and ether lipid metabolism. These two lipid-related pathways were significantly enriched in the FMT group. Phosphatidylcholine, a major constituent of cell membranes derived from these pathways (Mountford and Wright, 1988), underscores the biological relevance of these findings. The concurrent reduction in a lipid-associated microbe (R. gnavus_group) and key lipid pathway metabolites strongly suggests that FMT modulates host lipid metabolism through directed changes in the gut ecosystem, a principle supported by research in other models (Tian et al., 2022).

These interconnected microbial and metabolic changes provide a mechanistic basis for the systemic and clinical improvements observed in LBW calves. At the systemic level, FMT improved lipid homeostasis, evidenced by reduced serum TC and HDL (Kessler et al., 2014), and enhanced immune competence, indicated by increased immunoglobulins (IgG, IgA) and modulated cytokine profiles. The latter finding is consistent with the established role of gut microbiota in educating the immune system (Ekmekciu et al., 2017; Round and Mazmanian, 2009) and with studies showing FMT’s anti-inflammatory effects in calves and other species (Rosa et al., 2021; Wu et al., 2021; Xiang et al., 2020). We propose that the improved metabolic and immune status contributed to the primary clinical benefits: enhanced growth performance (increased weaning weight and ADG) and a significant reduction in the incidence and severity of diarrhea. The growth promotion aligns with previous work in calves and pigs (Kim et al., 2021; Rosa et al., 2021; Zhou et al., 2021), while the reduction in diarrhea may be facilitated through multiple, non-exclusive mechanisms. These include the potential for FMT to support intestinal barrier function—as suggested by the concurrent decrease in C. innocuum_group and supported by literature linking similar microbes to epithelial integrity (Huang et al., 2022; Rauhavirta et al., 2014)—and the promotion of beneficial microbial metabolites like butyrate, known to strengthen the gut barrier and reduce diarrhea (Feng et al., 2018; Ma et al., 2021; Xu et al., 2021). Notably, the regulation of host lipid metabolism itself has been linked to alleviating diarrhea (Hua et al., 2020), offering another pathway through which FMT’s metabolic effects could directly improve gut health.

In summary, the clinical benefits of FMT—improved growth, reduced diarrhea and mortality—are supported by parallel improvements in immune status and lipid metabolism. These systemic changes are likely driven by FMT-induced restructuring of the gut microbial ecosystem, underscoring its potential as a multifaceted intervention for improving health in low-birth-weight calves.

5 Conclusion

In conclusion, our findings establish neonatal colonic FMT as a practical and safe microbiota-directed intervention that enhances lipid metabolism and intestinal health in low-birth-weight calves. As the first dedicated study in this cohort, it not only delineates a potential mechanism of action but also offers a scientific rationale for developing novel management strategies to enhance welfare and productivity in dairy operations. We acknowledge limitations including the single early time point for microbiome analysis and the small subset used for omics profiling. Future studies with longitudinal sampling and larger cohorts are warranted to confirm the persistence of these effects and further elucidate the mechanisms involved.

Data availability statement

The raw sequences of the 16S rRNA genes obtained from the fecal have been deposited in the European Nucleotide Archive, and are available under the accession number PRJNA1172902. The mass spectral raw data obtained from the fecal samples have been deposited in the MetaboLights Workbench, and are available under the accession number MTBLS11381.

Ethics statement

The animal studies were approved by Institutional Animal Care and Use Committee of Inner Mongolia Agricultural University. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent was obtained from the owners for the participation of their animals in this study.

Author contributions

ZZ: Writing – original draft, Methodology, Data curation, Conceptualization. DY: Software, Formal Analysis, Writing – original draft, Visualization. DW: Validation, Writing – original draft. XL: Formal Analysis, Validation, Writing – original draft. YJ: Writing – original draft, Formal Analysis. DH: Writing – review & editing. MX: Funding acquisition, Project administration, Writing – review & editing, Supervision.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This study was supported by the Project of Cross-disciplinary Research Fund for Colleges and University directly under the Inner Mongolia Autonomous Region (BR221506).

Acknowledgments

We thank the farm owners, veterinarians, and technical staff for helping with sample collection. We extend our thanks to the laboratory personnel for their invaluable support and contribution to the execution of this research.

Conflict of interest

The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author DH declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Generative AI statement

The author(s) declared that generative AI was used in the creation of this manuscript. The authors declare that Generative AI was used in the creation of this manuscript. ChatGPT (OpenAI) was used exclusively to improve the English grammar and style; no content generation or data analysis was performed.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fanim.2025.1742253/full#supplementary-material

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