Early-Life Milk Replacer Feeding Mediate Lipid Metabolism Disorders Induced by Colonic Microbiota and Bile Acid Profiles to Reduce Body Weight in Goat model

doi:10.21203/rs.3.rs-4373811/v1

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Research Article

Early-Life Milk Replacer Feeding Mediate Lipid Metabolism Disorders Induced by Colonic Microbiota and Bile Acid Profiles to Reduce Body Weight in Goat model

https://doi.org/10.21203/rs.3.rs-4373811/v1

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Journal Publication

published 04 Sep, 2024

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Background

Dysregulation of lipid metabolism and its consequences on growth performance in young ruminants have attracted attention, especially in the context of alternative feeding strategies. This study aims to elucidate the effects of milk replacer (MR) feeding on growth, lipid metabolism, colonic epithelial gene expression, colonic microbiota composition and systemic metabolism in goat kids compared to breast milk (BM) feeding, addressing a critical knowledge gap in early life nutrition.

Methods

Ten female goat kids were divided into two groups: those fed breast milk (BM group) and those fed a milk replacer (MR group). Over a period of 28 d, body weight was monitored and blood and tissue samples were collected for biochemical, transcriptomic and metabolomic analyses. Profiling of the colonial microbiota was performed using 16S rRNA gene sequencing. Intestinal microbiota transplantation (IMT) experiments in gnotobiotic mice were performed to validate causality.

Results

MR-fed pups exhibited reduced daily body-weight gain due to impaired lipid metabolism as evidenced by lower serum and liver total cholesterol (TC) and non-esterified fatty acid (NEFA) concentrations. Transcriptomic analysis of the colonic epithelium revealed upregulated genes involved in negative regulation of lipid metabolism, concomitant with microbiota shifts characterized by a decrease in Firmicutes and an increase in Actinobacteria. Specifically, genera such as Bifidobacterium and Prevotella were enriched in the MR group, while Clostridium and Faecalibacterium were depleted. Metabolomics analyses confirmed alterations in bile acid and fatty acid metabolic pathways. IMT experiments in mice recapitulated the metabolic phenotype observed in MR-fed goats, confirming the role of the microbiota in modulating host lipid metabolism.

Conclusions

Milk replacer feeding in goat kids disrupts lipid metabolism and gut microbiota dynamics, resulting in reduced growth rates and metabolic alterations. These findings highlight the importance of early nutritional intervention on metabolic programming and suggest that modulation of the gut microbiota may be a target for improving growth and metabolic health in ruminants. This study contributes to the understanding of nutritional management strategies in livestock and their impact on animal health and productivity.

milk replacer

lipid metabolism

colon microbiota

bile acid

goat model

In mammals, breastfeeding is universally recognized as the normative and superior nutritional foundation for newborns, primarily due to its synergistic combination of nutrients and bioactive compounds that confer significant biological effects [1]. The positive influence of breastfeeding on the health, growth and development of the offspring from a nutritional, physiological and developmental point of view is well established [2]. Breast milk supports the development of the immune system by providing age-appropriate nutrients, growth factors, antimicrobial peptides and proteins that meet the needs of the offspring [3]. However, in the context of intensive livestock production, particularly in goat production systems, the use of milk replacers (MR) is widespread due to limitations such as inadequate lactation, mastitis and postpartum paresis in ewes [4]. MR is used to compensate for the lack of maternal milk, improve litter survival and shorten the reproductive cycle of ewes [5]. Although current milk replacers are formulated to mimic the nutritional profile of breast milk using non-milk protein substitutes, they are notably lacking in many of the biologically active compounds found in natural milk. This disparity highlights the urgent need for systematic research aimed at identifying and selecting functional microorganisms and metabolites that promote the growth and maturation of offspring. These targeted microbial strains and metabolites could potentially be incorporated into novel milk replacers enriched with bioactive compounds.

The complex interplay between early life nutrition, gut microbiota composition, bile acid metabolism and lipid homeostasis have profound implications for the lifelong health and weight management of ruminants such as goats and other animals [6]. During the neonatal period, ruminants undergo a number of critical physiological adaptations that are essential for survival and future productivity. The process of ingestion of maternal milk involves the establishment and development of a diverse gut microbiota that plays an essential role in digestion, absorption [7] and metabolism of nutrients, particularly lipids [8–10]. When the natural supply of goat milk is limited or unavailable, feeding milk replacers is a standard practice to ensure adequate nutrition for rapidly growing young ruminants [11]. However, the extent to which this artificial feeding strategy affects the establishment and functionality of the gastrointestinal microbiota, particularly in the colon where bacterial fermentation is pronounced, remains an under-explored area of research. Bile acids, synthesized in the liver and secreted into the small intestine, play a central role in lipid digestion and absorption, while also serving as key regulatory signals involved in various aspects of host metabolism, including glucose-lipid balance and inflammatory pathways [12, 13]. Perturbations in the gut microbiota can induce alterations in bile acid metabolism, often leading to perturbations in bile acid pools that are closely associated with lipid metabolism disorders in different species [14, 15]. In this study, we aimed to unravel the complex interrelationships by investigating how early life milk replacer feeding affects the colonic microbiota and bile acid profiles, thereby affecting lipid metabolism and body weight in goat kids. We hypothesize that currently available commercial goat milk replacer formulae may exacerbate lipid metabolic disorders in young goats, thereby affecting their growth performance.

To address these knowledge gaps, we used milk replacer-fed goat models to systematically evaluate the effects of formula feeding on growth, microbiota structure and metabolic performance. In addition, we validated the causality of microbial dysbiosis-mediated disturbances in lipid metabolism using a pseudo germ-free mouse model. The overall aim of this investigation is to provide a comprehensive understanding of the proposed mechanisms by which milk replacers affect gut metabolism and physiology. Our research aims to contribute to the development of innovative milk replacer products and to improve the scientific understanding of milk replacer feeding practices.

The experiment was approved by the Institutional Animal Care and Use Committee of the Northwest A&F University under permit number 2020-03-015.

Animals, diets, and experimental design

Prior to the start of this study, 50 female Tibetan cashmere goats were tested for estrus by allowing visual and olfactory contact with an intact male goat, and a total of 36 female goats were selected on the basis of number of litters (2 times). These 36 goats (aged 24 months; mean live weight 25.58 kg) were reared and maintained under the same conditions at the Lhasa Tibet Cashmere Goat Breeding Farm (Lhasa, China). The selected goats had no history of diarrhea or other digestive disorders before and during the study. In addition, they were not given any medications, including antibiotics and probiotics, during the study. The diets and nutrient composition fed to the goats during the study are shown in Table S1. The goats had free access to water and feed and the diet was not changed throughout the study. Thirty female goats were treated for concurrent estrus and mated according to our standard pre-production procedure [16]. Ultimately, 18 goats were successfully conceived and delivered, and a total of 10 singleton female goat kids of similar weight (mean weight 2.23 kg) were selected for subsequent experimental treatment. Ten female kids were fed breast milk ad libitum for 3 d after parturition, and on the 4th day after birth, the kids were randomly divided into 2 groups (n = 5), with no difference in initial mean body weight between the two groups. To ensure consistency in the test environment, bulk milk samples from goats made up the milk fed to the goat kids, kids in the BM group were fed four times a day (at 8:00, 12:00, 18:00 and 22:00) with 250 mL of milk each time. Goat kids in the MR group were fed milk replacer four times a day (at 8:00, 12:00, 18:00 and 22:00) by mixing 40 g of commercial milk replacer with 250 mL of warm water and bottle feeding each kid. Each group of kids was housed in separate pens (6 m × 5 m; n = 5) in the same environment. All goat kids had free access to clean water and were offered fresh dried alfalfa ad libitum every day. Goat's milk at D7 contained 9.52% protein, 7.50% fat, 14.95% solids-non-fat, 3.99% lactose, 2.58% low lactose, 1.29% galactose and 6.36% casein. Goat's milk at D14 contained 5.91% protein, 6.70% fat, 12.14% solids-non-fat, 4.16% lactose, 2.46% low lactose, 1.01% galactose and 4.31% casein. The formula of the milk replacer is shown in Table S2. All kids were weighed at 0, 14 and 28 days after birth. At 29 d, 10 female goat kids were sacrificed when they reached 29 d of age after 12 h of fasting. Jugular vein blood was collected from the goats 1 h before slaughter, preserved in a vacuum tube containing coagulant, kept at 4 ℃ for 3 h, and then centrifuged at 2000 × g at 4°C for 10 min. They were euthanized by injection with thiopental (0.125 mg/kg body weight) and potassium chloride (5 to 10 mL) (Fig. 1A). The homogenized digesta from the colon was then snap frozen in liquid nitrogen and stored at -80℃ for subsequent DNA analysis. Colonic epithelial tissue was excised from the mucosal layers using a glass slide, immediately washed in ice-cold phosphate-buffered saline (PBS) until the PBS was clear, and then transferred directly to liquid nitrogen until tissue RNA extraction.

Five goat kids (mix pool of colon content of each group) from BM and MR groups were used for colonic digesta transplantation. The colonic content (200 mg) from the donors was suspended in sterile 0.9% saline (2 mL) and centrifuged at 1000 × g at 4°C for 5 min to obtain the bacterial suspension. Thirty-two female C57/6J mice (8 weeks) were divided into 4 groups: Con, a control group (without any treatment); Ab, an antibiotic group (which received sterile saline via gavage after antibiotic treatment stopped); BM_IMT, a group that was transplanted with group BM colonic microbiota after antibiotic treatment stopped; and MR_IMT, a group that was transplanted with MR colonic microbiota after antibiotic treatment stopped. Ab, MR_IMT, and BM_IMT groups were first treated with fresh composite antibiotics (containing 1 g/L ampicillin, 1 g/L streptomycin, 1 g/L gentamicin, and 0.5 g/L vancomycin; MACKLIN, China) using water for 14 d [17]. Each recipient mouse received 200 µL of the supernatant by oral gavage once a day continuously for 21 d. To improve the colonic microbial survival rates during the cryopreservation, colonic content samples were diluted with sterile saline, homogenized and filtered. Subsequently, the resulting suspension was added to glycerol to get a final concentration of 10%. Finally, the colonic suspensions are labeled accurately, and stored in liquid nitrogen as soon as possible to ensure colonic microbial survival. When there is the need for IMT, the frozen colon suspension was thawed at 37℃ (water bath). Upon frozen colon suspension thawing, sterile saline solution can be added to obtain a required concentration (CFU = 1 × 10⁹) and the infusion of colon suspension should be implemented as soon as possible at 37℃. All colonic material preparation processes were carried out at an anaerobic incubator (LAI-3T, Longyue, China). After transplantation, they were housed individually at room temperature (23 ± 1°C) and light control (16L:8D) with free access to feed and drinking water. Body mass was measured every day. Mice were sacrificed after 7 d of IMT (at 10:00), and the serum, colon contents, and colon tissue were collected after the mice were euthanized via CO₂ inhalation then killed through cervical dislocation. All samples were stored at − 80℃ for subsequent DNA analysis. Colonic tissue was stored in 4% paraformaldehyde in a refrigerator at 4℃.

Serum and hepatic parameters

The levels of serum total cholesterol (TC), triglycerides (TG), glucose (Glu), cortisol (Cor), corticosterone (Cort), diamine oxidase (DAO) and hypoxia-inducible factor (HIF-α) were measured using the HY-50061, HY-50062, HY-50063, HY-10062, HY-10063, HY-M0046 and HY-NE021 kits (Huaying, Beijing, China) on an automatic biochemical analyzer (Hitachi7160, Tokyo, Japan). Serum immunoglobulin A (IgA), immunoglobulin G (IgG), and immunoglobulin M (IgM) were measured by the sandwich ELISA method using the IgG/A/M (HY-50094) kit (Huaying, Beijing, China). Serum levels of interleukin-6 (IL-6), interleukin-10 (IL-10), tumor necrosis factor-α (TNF-α), and interleukin 1β (IL-1β) were measured using ELISA kit in accordance with the manufacturer’s instructions (Huaying, Beijing, China). Around 0.10 g of semi-thawed liver were homogenized an equivalent amount (1/9) of homogenizing buffer (pH 7.4; 0.01 mol/L). Screw tubes filled with 0.1 mmol/liter EDTA-Na₂, 0.8% NaCl, and Tris-HCl. The tubes were then put in frozen water for 5 minutes, shaken for 5 min at 25 Hz/s with a TissueLyser, and the procedure was performed once more. Then, using assay kits from Jiancheng Bioengineering (Jiancheng, Nanjing, China), the supernatant was collected after centrifuging it for 10 min at 2,500 rpm at 4°C to test the levels of hepatic TC, TG, and non-esterified fatty acid (NEFA) (A111-1-1, A110-1-1, A042-1-1, Jiancheng, Nanjing, China). The findings from the hepatic lipid test were adjusted using the total protein concentration in accordance with the manufacturer’s instructions (Jiancheng, Nanjing, China).

16S rRNA gene profiling

A total of 32 colonic content samples were collected from the mice model. Total DNA was then extracted from the cecum lumen content using the E.Z.N.A. stool DNA Kit (Omega Bio-Tek, Norcross, GA, USA), according to the manufacturer’s instructions. The Nanodrop 2000 UV-VI spectrophotometer (Thermo Scientific, Wilmington, USA) was used to determine the DNA concentration and purity. 1% agarose gel electrophoresis was used to assess the quality of the extracted DNA. The primers 338F (5’-ACTCCTACGGGAGGCAG CAG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’) on a thermocycler PCR system (Gene Amp 9700, ABI, USA) were used to amplify the V3-V4 region of the DNA[18]. The paired-end sequencing (2 × 300 bp) on an Illumina MiSeq platform (Illumina, San Diego, USA) was conducted according to the standard Major Biobio-Pharm Technology Co. Ltd. (Shanghai, China) protocols on the pooled equimolar ratios of the purified amplicons.

Metagenomic sequencing, assembly and construction of the gene catalog

The DNA was extracted from colon contents samples of goat using the E.Z.N.A Stool DNA Kit (Omega Bio-tek, USA) according to the manufacturer’s instructions. The 1% agarose gel electrophoresis was used to evaluate the DNA quality and integrity, and DNA concentration was measured using the NanoDrop2000 UV-VI spectrophotometers(Thermo Scientific, Wilmington, USA).

The DNA was fragmented by Covaris M220 (Genetics, China) to screen fragments of about 300 bp for subsequent construction of PE libraries. Metagenomic sequencing was conducted according to the standard Major Biobio-Pharm Technology Co. Ltd (Shanghai, China) protocols. Sequence reads were treated to remove low-quality reads, trim the read sequences, and remove the host genome sequences. Specifically, reads with sequence lengths less than 50 bp and low-quality bases (quality value ≤ 20). Host genomic sequences were removed by Bowtie2 (v 2.2.9) software [19]. MEGAHIT (v 1.1.2) [20] were used to align the clean data of each sample to contigs (≥ 300 bp). MetaGene [21] (http://metagene.cb.k.u-tokyo.ac.jp/) was performed for predicting the open reading frames according to contigs (≥ 300 bp), and genes with nucleic acid lengths greater than 100 bp were selected and translated into amino acid sequences. Afterwards, CD-HIT (v 4.7) [22] was used to clustered (95% identity, 90% coverage), and the longest gene in each class was as the representative sequence to construct the non-redundant gene set. SOAPaligner (v 2.21) [23] was used to compare the high-quality reads obtained from the sequencing of single samples with the non-redundant gene set (95% identity), and to calculate the abundance data of each target gene in the corresponding samples.

After that, the constructed non-redundant (NR) gene was subjected to species and functional annotation, and the annotation process was conducted according to the standard Major Biobio-Pharm Technology Co. Ltd. (Shanghai, China) protocols [24]. BLASTP (v 2.3.0) was used to blast the non-redundant gene set to the sequences of the NR database (e-value set to 1e-5) to obtain abundance data corresponding to each species. The non-redundant gene set sequences were aligned with KEGG database using BLASTP (v 2.3.0; e-value set to 1e-5). The annotation information of KEGG Ortholog (KO) from the KEGG database was acquired based on the relative abundance profile. HMMSCAN (v 3.1b2) [25] software was used to compare the amino acid sequence to the CAZyme (v 6.0) database (e-value set to 1e-5) and to obtain an annotation of the carbohydrate-active enzyme.

RNA isolation and RNA-seq analyses

Total RNA was extracted from colon epithelial tissues of goat kids using TRIzol Reagent (Invitrogen) according the manufacturer’s instruction. The RNA quality was evaluated using the Nanodrop2000 (NanoDrop Technologies) and Bioanalyser 2100 (Agilent). The Agilent 2100 was used to determine the RNA integrity numbers (RIN) value of the sample RNA. All RNA sample had the RIN value greater than 8.0 (RIN number: 8.20, 9.30, 8.40, 9.60, 9.40 in BM group sample, respectively, 9.40, 9.50, 9.40, 9.30, 8.60 in MR group sample, respectively). The RNA-seq transcriptome library was prepared using 5 µg of total RNA, following the TruSeq stranded RNA sample preparation kit for Illumina (San Diego, CA). RNA-Seq library was sequenced with the Illumina NovaSeq 6000 platform (2 × 150 bp read length). The raw reads were trimmed and quality controlled by the SEQPREP and Sickle software. The TopHat (v 2.1.1) [26] software was used to align the clean data with the Capra hircus reference genome (GCA001704415.1) to obtain mapped data.

Colonic content and serum metabolite measurements using LC-MS/MS

The colon content was homogenized by add ice-cold methanol/ water (70%, v/v) in a ratio of 500 µl per 50 mg and then vortex for 3 min. The sample vortexed liquid was left at -20℃ for 30 min and centrifuge for 5 min at 4℃ and 1,2000 rpm. The collected supernatant was subjected to LC-MS/MS analysis (UPLC, Shim-pack UFLC SHIMADZU CBM A system; MS, QTRAP System). Serum samples were prepared by add 300 µl of methanol/ water (70%, v/v) to 50 µl of serum, and vortexed for 3 min. The liquid was centrifuge for 10 min at 1,2000 rpm at 4℃. The filtered samples were transferred to LC-MS/MS analysis.

Quantitative PCR (qPCR) analysis

Total RNA was extracted using Trizol Reagent (Cwbio, China) from colon tissues, and qPCR was performed using LightCycler 96 (Roche, NC, USA). qPCR primers are shown in Table S3. All primers were obtained from Zhongke Biotechnology (Xi'an, China). The amplification efficiencies were 90%~110%. The reaction conditions were, 95°C for 30 s, 95°C for 10 s and 60°C for 30 s for 40 cycles. Three replicates were set for each sample. The expression of the target gene relative to the internal reference gene β-actin were calculated using the ΔΔCt method[27].

Statistical analyses

GraphPad Prism (v 8.03, GraphPad, USA) was used to generate histograms. The two-group data were subjected to independent sample T tests via SPSS (v 21.0, IBM, USA) to evaluate significance. The data are expressed as mean with standard error. Beta diversity analysis used the Bray-Curtis distance algorithm, and analysis of Similarities (ANOSIM) test was used to performed significant differences between groups with 999 permutations. The Kruskal-Wallis H test was used to identify significant differences of the relative abundance at different taxonomic levels between groups. Spearman correlation network analysis used python (v.2.7) stats, and the absolute value of the correlation coefficient was set to 0.8 and P ≤ 0.05. Linear discriminant analysis effect size (LEfSe) analysis was performed to identified the microbiome with higher relative abundance in the two groups. The non-parametric factorial Kruskal-Wallis rank sum test was used, followed by linear discriminant analysis (LDA) to evaluate the impact of each taxon abundance on the differential effect. A significant increase in microbiota abundance was defined as an LDA score (log10) greater than 3.0. Metabolites with VIP ≥ 1 and P value < 0.05 were generally considered to be significantly different. R package (heatmaply; Complex Heatmap) was used to draw heatmaps of significant metabolites, and R (igraph) was used for correlation analysis in metabolite correlation network diagrams. DESeq2 software [28] based on negative binomial distribution was used to analyze raw counts, and genes with comparative expression differences between groups were obtained based on the default parameters P - adjust < 0.05 and |log2FC| ≥1 filtering conditions. Goatools was used to perform GO enrichment analysis on the genes in the gene set [29]. The method used was Fisher's exact test, when the corrected P - adjust was < 0.05.

Dysregulation of lipid metabolism appears to be associated with the reduced daily weight gain observed in goat kids fed with milk replacer feeding

Evaluating the effect of milk replacer on the growth performance of goat kids, we found that there was no significant difference in body weight between BM and MR groups on 0 d, 14 d and 28 d after delivery, however, Goats in the BM group gained weight significantly faster than those in the MR group, where kids in the BM group gained 104.64 g/d per day compared to 69.00 g/d in the MR group (P > 0.05; Fig. 1B). Compared with BM kids, stress-related indicators showed a highly significant increase in serum concentrations of Cor, Cort, HIF-α and DAO in the MR group (P < 0.001; Fig S1A), indicators related to pro-inflammation showed a highly significant increase in serum concentrations of IL-1β, IL-6 and TNF-α in the MR group, and a significant decrease in IL-10, an indicator related to anti-inflammation (P < 0.01; Fig S1B), consistent with immune-related indicators including IgA, IgG and IgM showed a similar pattern of decrease (P < 0.01; Fig S1B). The serum Glu index was also significantly decreased in the MR group (P < 0.05; Fig. 1C). To clearly clarify the effect of milk replacer feeding on lipid metabolism in kids, a TC, TG and NEFA indictor were measured in serum and liver samples and revealed that milk replacer feeding treatment decreased TC and NEFA concentrations (P < 0.05; Fig. 1E-F), and no significant effect on TG concentrations in serum and liver (P > 0.05; Fig. 1D). Taken together, these results revealed that milk replacer feeding attenuates weight gain of kids and the lipid metabolism associated with it.

Colonic epithelial lipid transport-related genes profile is influenced by milk replacer feeding

To further investigate the impact of milk replacer feeding on the expression profile of colonic epithelial lipid transport-related genes. We performed transcriptome sequencing using goat colonic epithelium samples. A total of 97.42 Gb clean data was obtained using RNA-seq of 10 colonic epithelium samples, with an average of 668,563,298 high-quality paired reads produced per sample. The alignment rate to the Capra hircus reference genome exceeded 94%. In total, 672 differentially expressed genes (DEGs) were screened, of which 403 were upregulated, and 269 were downregulated (Fig. 2A; Table S4). Among them, genes such as ABCG8, ABCG5, SCTR, SCT, CCL25, PRAP1, FABP2, RBP2, APOC3, SLC5A12, CLDN19, SLC2A2, SLC13A4, LOC102172669, LOC102181858, LOC102186942 and LOC102186759 were significantly upregulated in the MR group. In contrast, the genes AQP5, OSR2, HOXD10, MRAP2, KRT4 and KRT6A were significantly downregulated in the MR group (|log2FC| ≥ 1 and P-adjust < 0.05; Fig. 2B). Among them, this DEGs mainly enriched in cholesterol metabolism pathway (involved in APOC3, ABCG8, SOAT2 and ABCG5), steroid hormone biosynthesis pathway, bile secretion pathway (involved in SCT, ABCG8, LOC102181069, ABCG5 and SCTR), fat digestion and absorption pathway (involved in FABP2, ABCG8 and ABCG5), insulin secretion pathway (involved in CREB3L3, SLC2A2 and PDX1) and PPAR signaling pathway (involved in FABP2 and APOC3) (Fig. 2C). They were also enriched in 336 Gene Ontology (GO) categories, mainly including negative regulation of cholesterol transport, negative regulation of sterol transport, negative regulation of intestinal lipid absorption, negative regulation of intestinal cholesterol absorption, and negative regulation of intestinal phytosterol absorption (Fig. S2A). Based on the functional enrichment analysis network, the significantly enriched pathways mainly involve downstream pathways related to negative regulation of cholesterol transport (Fig. S2B). Taken together, milk replacer feeding significantly upregulates the expression of genes associated with negative regulation of lipid metabolism in the colon, leading to lipid dysfunction in goat kids.

Milk replacer feeding altered the colonic microbiota and potential function in goat kids

To further investigate the effects of milk replacer feeding treatment on gut microbial composition and function in goat kids and the correlation between the colon microbes with lipid metabolism. The variety of the bacteria in the colon was examined in the two groups. According to the Shannon and Chao index, the MR group generally had less alpha diversity at the species level than the BM group (P = 0.02, Fig. S2A). Principal coordinate analysis (PCoA) at species level showed a clear separation of microbial community structure between the two groups (ANOSIM, r = 0.996, P = 0.001, Fig. 3A). We also analyzed variations in microbial communities between the two groups at the phylum level, and we found that the abundance of Firmicutes was significantly lower in the MR groups than in the BM group. In contrast, the abundance of Actinobacteria was significantly higher in the MR groups than in the BM group (Fig. 3B). At the genus level, the abundance of Megamonas, Bifidobacterium, Prevotella, and Parabacteroides were significantly increased in the MR group (P < 0.05, Fig. S2B), however, the abundance of Clostridium, Subdoligranulum, Faecalibacterium, Eubacterium and Lachnoclostridium were significantly decreased in the MR group (P < 0.05, Fig. S2B). Using linear discriminate analysis effect size (LEfSe) analysis to identify additional key signature species, it was discovered that MR goats had significantly higher levels of Bacteroides plebeius CAG 211, Lactobacillus mucosae, Bacteroides coprocola CAG 162, Bifidobacterium longum, Prevotella sp 109, Ruminococcus gnavus CAG 126, and Megamonas funiformis CAG 377 (LDA > 3.5, Fig. 3C; Table S5), and BM goats had significantly higher levels of Bacteroides vulgatus, Subdoligranulum variabile, Faecalibacterium prausnitzii, Ruminococcaceae bacterium AM2, Flavonifractor plautii, Clostridia bacterium UC5.1-1D1, Eubacterium desmolans, Staphylococcus sp CAG 324, Lactobacillus reuteri, Pseudoflavonifractor capillosus, and Butyricimonas virosa (LDA > 3.5, Fig. 3C). Further analysis of the differences in colonic microbial interaction networks between the two groups revealed that the MR group formed an interaction network with core species including Bacteroides plebeius, Escherichia coli, Bacteroides finegoldii, Bacteroides coprophilus, Parabacteroides distasonis, Olsenella sp. DNF00959, Ruminococcaceae bacterium GD1, Sutterella wadsworthensis, and Blautia sp. KLE 1732 (Degree Centrality > 0.45; Fig. S4), while the BM group formed an interaction network with core species including Clostridia bacterium UC5.1-1D1, Eubacterium desmolans, [Ruminococcus] gnavus, Lactobacillus amylovorus, Alistipes sp. HGB5, Alistipes finegoldii, Firmicutes bacterium CAG:424, Clostridium sp. ATCC BAA-442, and Blautia sp. KLE 1732 (Degree Centrality > 0.45; Fig. S4). This further confirms the influence of milk replacer feeding on the core structure of colonic microbiota.

At the functional level, the microbiota in the MR group was mainly enriched in pathways related to ‘starch and sucrose metabolism’, ‘fructose and mannose metabolism’, ‘biosynthesis of amino acids’, ‘phenylpropanoid biosynthesis’, ‘oxidative phosphorylation’, and ‘beta-Lactam resistance’, while the microbiota in the BM group was mainly enriched in ‘ABC transporters’, ‘pyruvate metabolism’, ‘aminoacyl-tRNA biosynthesis’, and ‘amino sugar and nucleotide sugar metabolism’ (Fig. 3F). Further analysis of the differences in the abundance of CAZyme genes encoding carbohydrate enzymes in the colonic microbiota revealed that, compared to the BM group, the MR group showed significantly upregulated enzyme gene expression related to the synthesis of glucose from butyrate (Fig. 3D), as well as enzymes involved in the degradation of L-Leucine, L-Valine, and L-Isoleucine, which are precursors for Gluconeogenesis, TCA cycle, and Fatty acid synthesis (Fig. 3E). This further confirms that disturbances in the core structure of colonic microbiota caused by milk replacer feeding may potentially affect the host's absorption and utilization of energy and lipid nutrients.

The correlations between the level of serum triglyceride (TG) and cholesterol (TC) and liver triglyceride, cholesterol and free fatty acid (NEFA) and changes in microbial abundance were evaluated by Spearman’s correlation analysis. We found that liver cholesterol and liver free fatty acid concentrations positive correlated with Clostridia_bacterium_UC5.1-1D1, Eubacterium desmolans, Flavonifractor plautii, Ruminococcaceae_bacterium_AM2, Faecalibacterium prausnitzii, Lactobacillus reuteri and Subdoligranulum variabile, negatively correlated with Bacteroides_plebeius_CAG:211, Bacteroides coprocola, Bacteroides plebeius. Besides, Bacteroides fragilis abundance positively correlated with liver NEFA, [Ruminococcus] gnavus negatively correlated with liver cholesterol concentrations, and Butyricimonas virosa positively correlated with liver cholesterol concentrations (Fig. 3G). In conclusion, these results indicated that the changes in colonic microbial composition and function during milk replacer feeding may potentially impact the host's lipid metabolism capabilities.

Milk replacer feeding affect the colonic content lipid metabolism pathways in goat kids

We analysed the effect of milk replacer feeding on differential metabolites in the colonic contents of goat kids using an LC-ESI-MS/MS system. A total of 272 metabolites were significantly altered in the MR group compared to the BM group, of which 56 metabolites were significantly downregulated and 216 metabolites were significantly upregulated (Table S6). Interesting, 44 metabolites were undetectable in the BM group, while they exhibited a high abundance in the MR group, primarily including carnitine and fatty acid-like compounds (Rs-mevalonic acid, salicylaldehyde, 3-hydroxypicolinic acid, nicotinuric acid, and 3-hydroxyhippuric acid) (Fig. 4A; Table S6). Furthermore, 15 metabolites were exclusively abundant in the BM group and were not detectable in the MR group, mainly belonging to the bile acid class (lithocholic acid, hododeoxycholic acid, glycine deoxycholic acid, and deoxycholic acid) (Fig. 4B). We further calculated Spearman’s rho coefficients between the DEG and colonic luminal metabolites, and coefficients greater than 0.8 with a P-value < 0.05 were considered significant. We found that the lipid metabolism genes ABCG5、ABCG8、SCTR、SCT was positively correlated with the metabolites choline, spermidine, acetaminophen, glycocholic acid, glutathione, DL-carnitine and cyclic amp involved in the bile acid secretion pathway and negatively correlated with deoxycholic acid and lithocholic acid. LOC102172669, LOC102186942, LOC102181858 and LOC102186759 genes was positively correlated with the metabolites LTE4, 19(S)-HETE, 5,6-DiHETrE and 20-HETE involved in the arachidonic acid metabolism pathway and negatively correlated with 15-deoxy-δ-12,14-PGJ2 (Fig. 4C).

To further analyze the correlation between differential metabolites and distinct colonic microbiota, Spearman’s coefficients between differential microbial and metabolites were then calculated, and coefficients greater than 0.8 with a P-value < 0.05 were considered significant. Interestingly, we found Bacteroides coprocola abundance positively correlated with carnitine C13:0 Isomer1, glycine linoleate, 2-methylbutyroylcarnitine, DL-carnitine, Cis-9,10-epoxystearic acid, punicic acid, linoleic acid C18:2N6C, carnitine C18:2-OH and O-phosphorylethanolamine involved in the FA. Ruminococcus_gnavus_CAG:126 abundance positively correlated with carnitine. Among the metabolites of oxidized lipid, Bacteroides_plebeius_CAG:211 positively correlated with 9,10-EpOME, 12,13-EpOME, 9,10-DiHOME, 12,13-DiHOME and 6-keto-PGF1α. Lactobacillus_reuteri, Megamonas_funiformis and Bacteroides_dorei negatively correlated with 9-HOTrE, 13(R)-HODE, 20-HETE and 9-HpODE. Lactobacillus_reuter positively correlated with glycine deoxycholic acid. Faecalibacterium_prausnitzii, Pseudoflavonifractor_capillosus, Ruminococcaceae_bacterium_D16, Eubacterium_desmolans and Subdoligranulum_variabile positively correlated with β-murine, taurodeoxycholic acid, deoxycholic acid and glycine deoxycholic acid involved in the bile acids. Ruminococcus_gnavus_CAG:126 negatively correlated with hododeoxycholic acid and lithocholic acid (Fig. 4D). These findings indicated that colon microbiota reshaping induced by milk replacer feeding leads to a deficiency in colonic secretion of certain secondary bile acids.

Milk replacer feeding affect the serum metabolism pathways in goat kids

Untargeted metabolome profiles were generated on goat serum samples using an LC-ESI-MS/MS system to assess the effect of milk replacer on differential metabolites in serum. We found 39 metabolites were significantly downregulated, and 20 metabolites were significantly upregulated in MR group (Fig. 5A; Table S7). Among them, 1,7-Dimethylxanthine, 9-HpODE, Lysope 14:0, 13-oxoODE, octapentaenoic acid, 9(S)-HpOTrE, 5,6-EET and 5,6-DiHETrE were the top 10 upregulated metabolites in the MR group. Lithocholic acid, carnitine C17:0, β-murine, urocanic acid, glycyl-L-proline, 1,2-dioctanoyl PC, 23-deoxydeoxycholic acid, 5-HETrE, capric acid (C10:0) and linoleylethanolamide were the top 10 downregulated metabolites in the MR group. Interestingly, we found that some metabolites were in high relative abundance in the BM group such as urocanic acid, 1,2-dioctanoyl PC, 5-HETrE, capric acid C10:0, linoleylethanolamide and 6-hydroxynicotinic acid, while phenylpyruvic acid, 1,7-Dimethylxanthine, 5,6-EET, methanesulfonic acid, tetracosaenoic acid and hydrocinnamic acid were in high abundance in the MR group (Fig. 5B-C; Table S7).

The KEGG compound database generated the KEGG enrichment to describe the effect of milk replacer on these responsive metabolites and revealed that the ‘arachidonic acid metabolism’, ‘Biosynthesis of unsaturated fatty acids’, ‘Cholesterol Metabolism’, ‘Fatty acid biosynthesis’, ‘Fatty acid degradation’, ‘Fatty acid elongation’, ‘Fatty acid Metabolism’, ‘Linoleic acid metabolism’, and ‘alpha − Linolenic acid metabolism’ were the most significantly affected pathways (Fig. 5D). The metabolic pathways significantly downregulated were ‘fatty acid elongation’, ‘fatty acid metabolism’ and ‘epithelial cell signaling in helicobacter pylori infection’ in the MR group (Fig. 5E). This further corroborates that milk replacer feeding significantly disrupts the concentrations of metabolites involved in lipid metabolism in the serum, thereby impacting the host's lipid metabolism capacity.

Formula feeding affect the lipid metabolism profile is transferable by IMT

To validate the causal relationship between milk replacer feeding-induced colonic microbiota disruption and host lipid metabolism, we transferred the colon microbiota from BM and MR group to bacterial-restricted SPF C57/6J mice (Fig. 6A). Compared with BM_IMT mice, the weight of MR_IMT mice decreased from day 12 of transplantation, and the weight difference between the two groups was significant at day 20 of transplantation (Fig. 6B). The organ indexes of liver and spleen of BM_IMT and MR_IMT mice showed no significant difference (Fig. 6C). To clarify the causal relationship between hindgut microbes and lipid metabolism in goat, we evaluated the effects of microbiota on lipid metabolism ability of mice. The levels of TC, TG and NEFA indictor were measured in serum and liver samples and revealed that MR_IMT group significantly decreased TC concentrations in liver and NEFA concentrations in serum and liver compared with the BM_IMT group but no significant effect on TG concentration in serum and liver (Fig. 6D-G). The mRNA expression level of genes associated with lipid metabolism in the NC, Ab, BM_IMT and MR_IMT treatment groups was further investigated. There was a significant increase in the mRNA expression of ABCG5, ABCG8 and FABP2 in the MR_IMT group, while the mRNA expression of RBP2 and APOC3 showed no significant difference (Fig. 6H).

We conducted 16S rRNA gene sequencing to examine the colon bacteria composition to confirm that fecal transplantation modulates the gut microbiota. Our results indicated that α-diversity (via Chao and Pd Index) and β-diversity were not significantly different in the BM_IMT and MR_IMT group (Fig. S5A-B). At the genus level, the abundance of Bacteroides, Colidextribacter, Parabacteroides and Escherichia-Shigella were enriched in the MR_IMT group, while u_f_Eggerthellaceae and Holdemania were enriched in the BM_IMT group (Fig. S5C). The ASV-level analysis showed that the relative abundance of ASV131 (Bacteroidaceae), ASV266 (Lachnospiraceae) were detected in the BM_IMT group. Moreover, the relative abundance of ASV398 (Muribaculaceae), ASV94 (Muribaculaceae), ASV6 (Muribaculaceae), ASV233 (Clostridia_UCG-014), ASV472 (Clostridia_vadinBB60), ASV993 (Sutterellaceae), ASV383 (Sutterellaceae) were not identified in the BM_IMT group (Fig. S5D). These results causally confirm that milk replacer feeding-induced colonic microbiota disruption is associated with a phenomenon that can affect the host's lipid metabolism capacity, with potential cross-species implications.

The purpose of this study was to understand how milk replacer feeding alters the colon microbiota in goat kids and affects their lipid metabolism, emphasizing the potential modulation of the host’s lipid metabolism pathways. We demonstrated using the multiple omics analyses of metagenomic sequencing together with metabolic profiling, RNA-seq analysis and colonic digesta transplantation that milk replacer feeding significantly reduced daily weight gain and significantly decreased the concentration of cholesterol and free fatty acids in the serum and liver of goat kids. Further analysis revealed that milk replacer feeding could affect the microbial composition and metabolite characteristics, which directly influenced the differential expression of colon lipid metabolism genes in the goat kids, thereby affecting the lipid metabolism pathway (Fig. 7).

One explicit nutritional difference between milk replacer feeding and breastfeeding is the cholesterol content. The cholesterol required for an infant's physiological growth and development is obtained through endogenous synthesis in the liver and biliary secretion to meet the intestinal intake and reabsorption [30]. Clinical studies have shown that infants fed with milk replacer feeding exhibit an increased rate of endogenous cholesterol synthesis, leading to lower serum cholesterol concentrations compared to breastfed infants [31]. Our research has revealed that, in comparison to breastfeeding, milk replacer feeding does not alter the concentrations of serum and hepatic triglycerides in neonates, but it results in a reduction in cholesterol levels. This phenomenon may be attributed to the lower cholesterol content in milk replacer feeding compared to breast milk, which may downregulate the generation of hepatic hydroxymethyl glutaryl coenzyme A reductase, consequently reducing endogenous cholesterol synthesis[32]. This discovery may have implications for the choice of infant feeding methods and their associated health effects, providing valuable insights for further research.

From a gut microbiota perspective, recent research indicates that lipids synthesized de novo and bio-transformed by the gut microbiome play crucial structural and signaling roles, impacting host cells through metabolic and immune pathways [33]. The gut microbiota is capable of both transforming and synthesizing lipids as well as breaking down dietary lipids to generate secondary metabolites with host-modulatory properties. The relative abundances of Faecalibacterium and Prevotella have been associated with circulating lipids, particularly with hemolysis-derived phosphatidylethanolamine and phosphatidylglycerol [34] [35]. Furthermore, the gut microbiota has long been recognized to play a vital role in cholesterol-derived bile acid metabolism [36]. This involves the enzymatic hydrolysis, desulfation, and dehydroxylation of bile salts to produce critical secondary bile acids such as deoxycholic acid and lithocholic acid [37]. Recent work has revealed that bacteria can also directly metabolize cholesterol itself, generating unique metabolites such as cholestanone and coprostanol through the activity of the IsmA gene-encoded dehydrogenase. Humans carrying IsmA-carrying bacteria exhibit a significant reduction in circulating cholesterol [37]. Notably, members of the Bacteroides genus, particularly Bacteroides thetaiotaomicron, are known to metabolize cholesterol, and Bacteroides thetaiotaomicron is responsible for converting cholesterol into cholesterol-3-sulfate [38–40]. These studies further affirmed that the disruption of Bacteroides spp. in the colon of goats fed with milk replacer feeding is a significant contributor to the substantial changes in bile acid levels. Correlation analyses between microbiota and bile acid metabolites in this study support this viewpoint. Therefore, the addition of safely assessed Bacteroides strains in milk replacer feeding development may effectively improve the phenotypes of slow growth and disturbances in lipid metabolism in kids.

Prior evidence has demonstrated that secondary bile acids contribute to alleviating disruptions in lipid metabolism within the host [41]. Bile acid receptors, such as FXR and TGR5, help maintain lipid (cholesterol and triglycerides) and glucose homeostasis [42]. Activation of FXR has beneficial effects on cholesterol, triglycerides, and glucose levels. In the liver, FXR inhibits triglyceride synthesis by suppressing the SREBP1c pathway. Bacteria containing the BSH gene can deconjugate tauro-β-MCA [43], an FXR antagonist, thereby allowing hepatic FXR to inhibit lipogenesis and cholesterol metabolism. This leads to a reduction in weight gain, serum cholesterol, and triglyceride levels [44]. The BSH gene is widely distributed among bacterial genera, including Bacteroides, Clostridium, Lactobacillus, Enterococcus, Streptococcus, and Bifidobacterium [45]. This study revealed the absence of certain secondary bile acid synthesis in the colon and serum of goats fed with milk replacer feeding. The lack of secondary bile acids inhibited fat emulsification, reduced pancreatic lipolysis, and hindered the absorption of lipid substances in the intestine, thus impeding lamb growth and development [46]. Cholesterol can be excreted from liver and intestinal cells to bile or the intestinal lumen through ABCG5 and ABCG8 transporters [47]. These ATP-binding cassette (ABC) family genes, ABCG5 and ABCG8, participate in cholesterol reverse transport, small intestine absorption, and bile acid secretion[48]. They interact with dietary components like saturated fatty acids and cholesterol to jointly regulate blood cholesterol levels, thereby controlling cholesterol metabolic homeostasis [49]. The results of this study, conducted in both goat and mouse models, confirmed that milk replacer feeding and milk replacer feeding induced an upregulation of ABCG5 and ABCG8 expression in the colonic epithelium of the goat. In summary, alterations in the core composition of colonic microbiota induced by milk replacer feeding impact the expression profile of colonic lipid metabolism genes, subsequently affecting the host's lipid metabolic program and, in turn, normal goat growth and development. However, further elucidation of the mechanisms involving specific functional microbiota and the regulation of colonic epithelial lipid metabolism gene expression by key secondary bile acids requires additional investigation.

This study observed that milk replacer feeding in goat reduced the concentrations of cholesterol and free fatty acids in both serum and liver, resulting in the downregulation of colonic lipid absorption and cholesterol transport metabolic pathways. These changes significantly affected the normal growth and development of the kids. Milk replacer feeding had a pronounced impact on the core microbial species composition in the colon, leading to the absence of secondary bile acids. Utilizing a germ-free mouse model confirmed a causal relationship between the disrupted colonic microbiota induced by milk replacer feeding in goat and parameters such as daily weight gain, circulating cholesterol, free fatty acid levels, and the expression of colonic lipid metabolism genes. In summary, the disruption of colonic microbiota induced by milk replacer feeding led to impairments in colonic lipid metabolism. This study provides new insights into the potential mechanisms through which milk replacer feeding affects colon metabolism and physiology in ruminant. It also offers valuable information for the development of new milk replacer feeding products containing active probiotics and prebiotics, thereby targeting specific bioactive substances and potential bacterial strains.

MR: Milk replacers

BM: Breast milk

NEFA: Non-esterified fatty acid

TC: Total cholesterol

PBS: Phosphate-buffered saline

IMT: Intestinal microbiota transplantation

TG: Triglycerides

Glu: Glucose

Cor: Cortisol

Cort: Corticosterone

DAO: Diamine oxidase

HIF-α: Hypoxia-inducible factor

IgA: Immunoglobulin A

IgG: Immunoglobulin G

IgM: Immunoglobulin M

IL-6: Interleukin-6

IL-10: Interleukin-10

TNF-α: Tumor necrosis factor-α

IL-1β: Interleukin 1β

RIN: RNA integrity numbers

LDA: Linear discriminant analysis

LEfSe: Linear discriminant analysis effect size

PCoA: Principal coordinate analysis

ABC: ATP-binding cassette

Availability of data and materials

The metagenomic sequencing and RNA-seq data are available from the national center for biotechnology information (NCBI) under accessions PRJNA1029896 (https://dataview.ncbi.nlm.nih.gov/object/PRJNA1029896?reviewer=f8oisljdbf1sdgvmu69er8df15) and PRJNA1030534 (https://dataview.ncbi.nlm.nih.gov/object/PRJNA1030534?reviewer=l6t3793h9q753mangoer3cl3rm), respectively.

Acknowledgements

Not applicable.

Funding

This work was financially supported by the Science and Technology Project of Tibet (XZ202101ZD0001N), China Agriculture Research System (CARS-39-12), and Young Talent Fund of Association for Science and Technology in Shaanxi, China (2023-6-2-1) and “Double-chain” project on livestock breeding (2022GD-TSLD-46). None of the funders had any role in the design and conduct of the study, collection, management, analysis, and interpretation of the data, as well as preparation, revision, or approval of the manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

All subjects provided informed consent to participate in this study and agreed for publication of the research results.

Competing interests

The authors declare that they have no competing interests.

Contributions

Y.C., X.W., K.Z., and Y.Y. conceived, designed, and supervised the project. K.Z., T.Z., Y.W., L.S., A.C., M.G., Y.X., collected samples and performed experiments. K.Z., Y.Z., T.Z., and M.G. carried out bioinformatic analyses. K.Z., and Y.Z., drafted the paper. Y.C., D.B., X.W., and Y.Y., revised the paper. All authors read, edited, and approved the final manuscript.

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Early-Life Milk Replacer Feeding Mediate Lipid Metabolism Disorders Induced by Colonic Microbiota and Bile Acid Profiles to Reduce Body Weight in Goat model

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Journal Publication

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Abstract

Figures

Background

Materials and methods

Animals, diets, and experimental design

Serum and hepatic parameters

16S rRNA gene profiling

Metagenomic sequencing, assembly and construction of the gene catalog

RNA isolation and RNA-seq analyses

Colonic content and serum metabolite measurements using LC-MS/MS

Quantitative PCR (qPCR) analysis

Statistical analyses

Results

Colonic epithelial lipid transport-related genes profile is influenced by milk replacer feeding

Milk replacer feeding altered the colonic microbiota and potential function in goat kids

Milk replacer feeding affect the colonic content lipid metabolism pathways in goat kids

Milk replacer feeding affect the serum metabolism pathways in goat kids

Formula feeding affect the lipid metabolism profile is transferable by IMT

Discussions

Conclusions

Abbreviations

Declarations

References

Supplementary Files

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Journal Publication

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