The gut microbiota of Zhedong white goose in different intestinal segments during development

Introduction

Intestinal microorganisms coexist with animals, and play an important role in the their growth and health. Understanding the composition and changes of intestinal microorganisms during the growth of livestock and poultry will help guide the production so as to get the best production performance. Goose is an important water fowl, especially widely reared in Asia. Recent years, more and more attention have been paid to the study of goose intestinal microbes. Previous studies mainly focused on the intestinal microbiota under the effects of various external or internal factors, such as different diets (Li et al., 2018; Liu et al., 2018; Guo et al., 2019; Yan et al., 2019; Yu et al., 2021; Zhou et al., 2025; Zhu et al., 2025; Ma et al., 2026), breeds (Wang et al., 2016, 2018; Xiang et al., 2019; Zhu et al., 2023; Ni et al., 2022), intestinal segments (Drovetski et al., 2018; Yang et al., 2018; Wen et al., 2022) and developmental stages (Liu et al., 2018; Ricaud et al., 2019). These studies confirm that the intestinal microbial community could be significantly affected by multiple factors, and is related to growth or productive performance of goose.

As for the microbiota composition of goose, generally, Firmicutes, Proteobacteria and Bacteroidetes are the dominant phyla in the intestine, and the detailed microbial community can be quite variable depending on the specific conditions. For instance, our previous study found that the intestinal microbes of Landes goose are mainly composed of Firmicutes (36.60%), Proteobacteria (27.24%) and Bacteroidetes (9.78%), and that the ratio of Firmicutes/Bacteroidetes (which was associated with obesity) was increased by overfeeding (Liu et al., 2016). Guo et al. also found that Bacteroidetes and Firmicutes were the dominant phyla of Yangzhou goose, while the ratio of Firmicutes/Bacteroidetes was remarkably reduced with ryegrass intake (Guo et al., 2019). It’s worth noting that early studies used to take fecal or cloacal sample as the representative for the analysis of gut microbiota, which was actually proved to be inadequate in goose (Drovetski et al., 2018). In addition, it was found that growth stages could significantly impact the composition of gut microbiota of zi goose (Anser cygnoides) (Liu et al., 2018) and Landes goose (Anser anser) (Ricaud et al., 2019), respectively. Thus, it’s important to have a spatiotemporal classification of the intestinal microorganisms, so as to fully understand the gut microbiota of a certain breed of goose.

ZD white goose is a unique indigenous waterfowl breed in South China with distinct genetic background, feeding regime and growth performance compared with Canada geese, Greylag geese, Landes geese and Magang geese reported in prior literature; the complete spatiotemporal gut microbiota succession dataset specific to this breed fills the breed-specific microbiota research gap. Therefore, the purpose of this study was to study the gut microbiota of ZD white goose at different stages of development, so as to provide theoretical basis for regulating their growth and development through intestinal microorganisms. In the present study, we raised a batch of 1-day-old Zhedong white goose, and slaughtered the geese and sampled the intestinal contents from different segments at week 4, 8 and 10 for 16S rDNA sequencing, so as to analyze the composition and changes of their intestinal microorganisms. The results of the study showed the general picture of gut microbiota of ZD white goose during natural development.

Materials and methods

Animal husbandry and sample collection

A total of 80 healthy 1-day-old ZD white goose (female) were raised in National Gene Bank of Waterfowl Resources (Taizhou, China) and fed with a commercial diet ad libitum up to 70 days old. The detailed nutritional composition of the experimental diet is provided in Supplementary Table S1. All animal procedures were approved by the Institutional Animal Care and Use Committee (ethical approval number: JSAHVC-2025-77) and conducted in accordance with national laboratory animal care and use guidelines, with all efforts made to minimize animal suffering and ensure animal welfare compliance.

To characterize the dynamic variations in intestinal microbial and metabolic profiles across developmental stages, five geese were randomly selected and humanely euthanized at 28, 56, and 70 days of age, respectively. All birds were fasted overnight prior to sampling to eliminate the potential interference of residual intestinal feed contents. Intestinal luminal contents were immediately harvested from four intestinal segments, namely the duodenum, jejunum, cecum, and rectum. All samples were snap-frozen in liquid nitrogen within 3 min after collection to prevent alterations in microbial community structure and metabolites, and subsequently stored at −80 °C for downstream analyses.

DNA extraction

Total genomic DNA samples were extracted using the OMEGA Soil DNA Kit (D5625-01) (Omega Bio-Tek, Norcross, GA, USA), following the manufacturer’s instructions, and stored at -20 °C prior to further analysis. The quantity and quality of extracted DNAs were measured using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis, respectively. The concentration of genomic DNA should be no less than 50 ng/μl, the OD_A260/A280 and OD _A260/A230 values should be greater than 1.8 and 2.0, respectively.

16S rDNA amplicon pyrosequencing

PCR amplification of the bacterial 16S rRNA genes V3–V4 region was performed using the forward primer 338F (5’-ACTCCTACGGGAGGCAGCA-3’) and the reverse primer 806R (5’-GGACTACHVGGGTWTCTAAT-3’). Sample-specific 7-bp barcodes were incorporated into the primers for multiplex sequencing. The PCR components contained 5 μl of buffer (5×), 0.25 μl of Fast pfu DNA Polymerase (5U/μl), 2 μl (2.5 mM) of dNTPs, 1 μl (10 uM) of each Forward and Reverse primer, 1 μl of DNA Template, and 14.75 μl of ddH₂O. Thermal cycling consisted of initial denaturation at 98 °C for 5 min, followed by 25 cycles consisting of denaturation at 98 °C for 30 s, annealing at 53 °C for 30 s, and extension at 72 °C for 45 s, with a final extension of 5 min at 72 °C. PCR amplicons were purified with Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified using the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA; Cat. No. P7589). After the individual quantification step, amplicons were pooled in equal amounts, and pair-end 2×250 bp sequencing was performed using the Illlumina MiSeq platform with MiSeq Reagent Kit v3 at Shanghai Personal Biotechnology Co., Ltd (Shanghai, China).

Sequence analysis

The Quantitative Insights Into Microbial Ecology (QIIME, v1.8.0) pipeline was employed to process the sequencing data, as previously described. Briefly, raw sequencing reads with exact matches to the barcodes were assigned to respective samples and identified as valid sequences. The low-quality sequences were filtered through following criteria: sequences that had a length of <150 bp, sequences that had average Phred scores of <20, sequences that contained ambiguous bases, and sequences that contained mononucleotide repeats of >8 bp. Paired-end reads were assembled using FLASH. After chimera detection, the remaining high-quality sequences were clustered into operational taxonomic units (OTUs) at 97% sequence identity using UCLUST with default open-reference clustering parameters. A representative sequence was selected from each OTU using default parameters. OTU taxonomic classification was conducted by BLAST searching the representative sequences set against the Greengenes Database (version 13_8) using the best hit (Altschul et al., 1997). An OTU table was further generated to record the abundance of each OTU in each sample and the taxonomy of these OTUs. OTUs containing less than 0.001% of total sequences across all samples were discarded to remove spurious low-abundance OTUs originating from sequencing noise and chimera residuals, reducing false-positive microbial features for downstream diversity and differential analysis. To minimize the difference of sequencing depth across samples, an averaged, rounded rarefied OTU table was generated by averaging 100 evenly resampled OTU subsets under the 90% of the minimum sequencing depth for further analysis.

Bioinformatics and statistical analysis

Sequence data analyses were mainly performed using QIIME and R packages (v3.2.0). OTU-level alpha diversity indices, such as Chao1 richness estimator, ACE metric (Abundance-based Coverage Estimator), Shannon diversity index, and Simpson index, were calculated using the OTU table in QIIME. The significance of differentiation of microbiota structure among groups was assessed by PERMANOVA (Permutational multivariate analysis of variance) using R package “vegan”. The taxonomy compositions and abundances were visualized using MEGAN and GraPhlAn. Taxa abundances at the phylum, class, order, family, genus and species levels were statistically compared among samples or groups by Metastats, and visualized as violin plots. PICRUSt was applied to predict the functional profiles of gut microbiota based on high-quality 16S rRNA gene sequences, obtaining the relative abundance of level-2 KEGG metabolic pathways. For each KEGG pathway, one-way analysis of variance (ANOVA) followed by Duncan’s new multiple-range test was performed for seven independent sets of comparisons: comparisons among four intestinal segments (duodenum, jejunum, cecum, rectum) within D28, D56 and D70, respectively; and comparisons among three developmental time-points (D28, D56, D70) for each individual intestinal segment (duodenum, jejunum, cecum, rectum). Relative abundance values of each pathway from five biological replicates were used for statistical calculation. Data are expressed as mean ± standard deviation (SD). P < 0.05 was considered as the threshold for statistical significance.

Statistical analysis

All values are expressed as mean ± standard deviation. SPSS 16.0 (SPSS China, Shanghai, China) was used to perform the Student’s t-test or one-way analysis of variance (Duncan’s test) for statistical significance of differences between or amongst different groups. P < 0.05 was considered statistically significant.

Results

Raw data processing and operational taxonomic units clustering

Raw data from 16S rDNA sequencing was processed with QIIME (Caporaso et al., 2010) and FLASH (Magoc and Salzberg, 2011), to obtain the valid sequences. After chimera detection, the remaining high-quality sequences were clustered into operational taxonomic units (OTUs) at 97% sequence identity by UCLUST (Edgar, 2010), and the OTUs were classified into different taxonomic levels (Table 1). As is shown in the table, the main range of average valid sequence number for all intestinal segments was around 35,000 to 50,000, with significant larger numbers in D70-Duodenum and D70-Cecum (both more than 60, 000, one-way ANOVA, P < 0.05; Table 1). The valid sequences were all efficiently classified into different taxonomic levels, as the number of unclassified OTUs for each group was less than 10. However, the numbers of unique classified OTUs at a specific level differed significantly among different groups (one-way ANOVA, P < 0.05; Table 1). For example, Duodenum and Jejunum had higher numbers of classified unique OTUs at Phylum level at day 28 and day 56, while the opposite was found for day 70 (Cecum and Rectum had higher numbers of unique classified OTUs). Remarkably, D28-Rectum had the extremely low number of unique classified OTUs, indicating its lowest level of biological diversity. In addition, the number of unique classified OTUs decreased gradually from Phylum level to Species level, especially from Family level to Genus level.

Table 1. The number of valid sequences and unique OTUs at different taxonomic levels of intestinal content from different segments.

D28, D56 and D70 represent 28-day-old, 56-day-old and 70-day-old Zhedong white geese, respectively. Data are presented as mean ± standard deviation (SD); columns with different letters indicate significant differences (P<0.05), n=5. All 5 biological replicates per group were retained without sample elimination; large standard deviation mainly originates from natural individual variation of intestinal microbial load in growing geese.

The biological diversity of goose intestinal microorganisms

OTU-level alpha diversity indices, such as Chao1 richness estimator, ACE metric (Abundance-based Coverage Estimator), Shannon diversity index, and Simpson index were calculated to compare the richness and evenness of OTUs among samples. The results were shown in Table 2. The Chao1 and ACE indices, which mainly reflect the richness of intestinal microorganisms, showed the similar trend as unique OTUs number at phylum level (Table 1). At day 28 and day 56, Rectum had lower richness that the other three segments, while at day 70, the diversity of Jejunum tended to decline and became the lowest one (one-way ANOVA, P < 0.05; Table 2). The Simpson index, which mainly reflects the evenness of OTUs, showed no significant difference among all groups, except for D28-Rectum. And the Shannon index, which combines the richness and evenness together, showed the highest value in D56-Jejunum and the lowest value in D28-Rectum. PCoA ordination plots based on Bray-Curtis distance is shown in Supplementary Figure S1.

Table 2. The OTU-level alpha diversity indices of intestinal content from different segments.

D28, D56 and D70 represent 28-day-old, 56-day-old and 70-day-old Zhedong white geese, respectively. Data are presented as mean ± standard deviation (SD); columns with different letters indicate significant differences (P<0.05), n=5.

Taxonomic composition analysis

QIIME software was used to analyze the composition and abundance distribution of OTUs from different groups. The results were presented with bar chart at Phylum (Figure 1) and Genus level (Figure 2), respectively. As was shown in Figure 1, Firmicutes, Proteobacteria and Bacteroidetes were the three main phylum in most intestinal segments of all detect stages. However, to be specific, the abundance distribution of classified OTUs differed significantly among different intestinal segments or among different stages (Metastats, P < 0.05; Figure 1). At day 28, Duodenum and Jejunum had similar microorganism composition and abundance distribution, and the abundance ranking of top eight phylum were totally the same. There were four phylum which accounted for more than 5% of the OTUs, and their abundance in Duodenum/Jejunum are: Firmicutes (37.8%/37.2%), Proteobacteria (23.5%/26.3%), Actinobacteria (9.6%/10.0%), and Bacteroidetes (6.9%/9.3%). As for Cecum and Rectum, the later part of goose intestine, the composition of microorganism is relatively simple. Firmicutes and Bacteroidetes accounted for more than 90% in these segments. It’s worth noting that Firmicutes itself made up 88.4% of the total OTUs in Rectum. For day 56, the microorganism composition and abundance distribution in Jejunum and Duodenum was similar with day 28, while the proportions of Bacteroidetes and Proteobacteria in Cecum and Rectum increased (accompanied by the decrease of Firmicutes). At day 70, with the further increase of Proteobacteria in Cecum and Rectum and dramatic decrease of Actinobacteria in Jejunum and Duodenum, the abundance distribution became relatively closed among the four intestinal segments, and Firmicutes, Proteobacteria and Bacteroidetes accounted for more than 90% of the OTUs.

In contrast to the clear law of abundance distribution at phylum level, the composition of microorganism at genus level was much more complicated, with quite a large proportion of unclassified genus (Figure 2). Briefly, Turicibacter was a relatively abundant genus in multiple intestinal segments at day 28 (especially in Rectum, 21.4%), while at day 56 and day 70, Bacteroides and Desulfovibrio were relatively highly abundant in Cecum and Rectum, and Lactobacillus made up a relatively high proportion in Jejunum and Duodenum. In addition, Prevotella accounted for about 10% in the later parts of the gut (Cecum and Rectum) at day 56, and Megamonas accounted for more than 5% at day 70. Interestingly, Acinetobacter became the highly abundant genus in day 70 Jejunum, with low abundance in all the other groups (Metastats, P < 0.05; Figure 2). The full taxonomic profiles covering phylum, class, order, family and genus levels across all developmental stages and intestinal segments are provided in Supplementary Table S2.

Differential analysis of gut microorganisms between groups

The significance of differentiation of microbiota structure among groups was assessed by sofewares mentioned in the methods section. The effects of developmental stage and intestinal segment on the significance of differentiation of microbiota structure were analysed via PERMANOVA, which confirmed a significant interactive effect on gut microbial community structure (P < 0.05; Supplementary Table S3). We further analyzed the differentially inhabited microbes (DIMs) between different intestinal segments in certain stages, at phylum and genus level. The DIMs with top six abundance at phylum level were shown in Figure 3. At day 28 and day 56, Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Firmicutes and Proteobacteria were the highest abundant DIMs (Metastats, P < 0.05; Figure 3). Duodenum and Jejunum had higher level of Actinobacteria, Acidobacteria, Chloroflexi and Proteobacteria than Cecum and Rectum, while Cecum had highest level of Bacteroidetes and Rectum had highest level of Firmicutes (Metastats, P < 0.05; Figure 3). At day 70, Acidobacteria, Bacteroidetes and Proteobacteria were still top-six highly abundant DIMs, together with Verrucomicrobia, Tenericutes and Synergistetes. To be sepecific, Jejunum had higher level of Tenericutes than other intestinal segments, Cecum had higher level of Bacteroidetes, and Cecum and Rectum had higher level of Synergistetes than Duodenum and Jejunum. In addition, Cecum had the lowest level of Proteobacteria than other segments.

For genus level (Figure 4), Barnesiella, Lactococcus, Ochrobactrum, Rikenella, Ruminococcus and Prevotella were the top six DIMs at day 28 (Metastats, P < 0.05; Figure 4). Duodenum and Jejunum had higher level of Lactococcus, Ochrobactrum and Prevotella than Cecum and Rectum, while cecum had higher level of Barnesiella, Rikenella and Prevotella than other groups. At day 56, the top six DIMs were Blautia, Lactobacillus, Pseudomonas, Rhodoplanes, Ochrobactrum and Streptococcus. Duodenum and Jejunum had relatively high level of all these DIMs, Rectum had comparable level of Blautia and Lactobacillus with Duodenum and Jejunum, and Cecum had low level for all the DIMs. As for day 70, Acinetobacter, Bacteroides, Desulfovibrio, Faecalibacterium, Megamonas and parabacteroides were the top six DIMs. Jejunum had highest level of Acinetobacter and lowest level of Parabacteroides, Cecum and Rectum had higher level of Bacteroides, Desulfovibrio and Megamonas than Duodenum and Jejunum, and cecum had highest level of Faecalibacterium (Metastats, P < 0.05; Figure 4).

Functional prediction of intestinal flora

PICRUSt was further used to quantify the relative abundance of level-2 KEGG metabolic pathways across all samples. The dominant functional pathways across ages and intestinal segments included carbohydrate metabolism and amino acid metabolism (~10% relative abundance), followed by energy metabolism (~5%), metabolism of cofactors and vitamins, nucleotide metabolism and lipid metabolism (~3%), while other pathways accounted for less than 2% of total predicted functions. To quantitatively evaluate functional differences, seven sets of one-way ANOVA with Duncan’s new multiple-range test were performed for each KEGG pathway (Figure 5; Supplementary Table S4). Within each developmental stage, substantial functional divergence was observed among four intestinal segments. At D28 and D56, foregut (duodenum and jejunum) exhibited significantly higher relative abundance of xenobiotics biodegradation and metabolism, lipid metabolism and metabolism of terpenoids and polyketides compared with the hindgut (cecum and rectum) (P < 0.05; Figure 5). By contrast, the cecum and rectum possessed significantly higher nucleotide metabolism and metabolism of cofactors and vitamins (P < 0.05). At D70, inter-segment functional differences were attenuated relative to earlier time-points, although several pathways still maintained segment-specific enrichment patterns. When comparing developmental stages within each intestinal segment, the cecum displayed the most pronounced temporal functional shifts, with significant alterations in most metabolic pathways across D28, D56 and D70 (P < 0.05; Figure 5). In comparison, the jejunum showed relatively stable predicted functions over development, with only a small number of pathways differing significantly among ages. Duodenum and rectum presented intermediate magnitudes of temporal functional variation. These results demonstrated that both intestinal location and developmental age jointly shape the metabolic functional potential of ZD white goose gut microbiota.

Discussion

Previous studies have confirmed that gut microbes are closely related to the growth and performance of geese. Different breeds, developmental stages and gut segments of geese can have a significant impact on their microbial composition. Therefore, to understand the gut microbial characteristics of a particular breed of goose, a detailed study of its spatial and temporal characteristics is needed. The ZD white goose is an important local breed that is widely reared in southern China, and no systematic studies have been conducted on its gut microbial characteristics.

In this paper, we selected three important time points and four intestinal segments during the growth and development of ZD white goose, and conducted a systematic study of its intestinal microorganisms. The results of the study analyzed the specific characteristics of the intestinal microbiota of ZD white geese during natural development, and laid the foundation for regulating the growth and production performance of this breed of geese from the perspective of intestinal microbes.

Our study found that normally reared ZD white geese showed differences in microbial diversity and microbial composition at different developmental stages and in different intestinal segments, especially between the foregut (duodenum and jejunum, with higher diversity in early stage) and hindgut (cecum and rectum, with higher diversity in the later stage). This is in line with the previous findings of Drovetski et al. who, in studying the microbiota of the esophagus, duodenum, cecum and rectum in Urban Canada Geese, found that the microbiota of the four intestinal regions differed significantly in their abundance, composition and inter-individual variability (Drovetski et al., 2018), also confirming the inappropriateness of using faces as a sample for the study of the entire intestinal microbial structure.

Analysis of the specific intestinal microbial structure of ZD white geese revealed that in the early and middle stages (day 28 and 56) the microbes in the fore part of intestinal segments (duodenum and jejunum) were mainly composed of Firmicutes (37.8%/37.2%), Proteobacteria (23.5%/26.3%), Actinobacteria (9.6%/10.0%), and Bacteroidetes (6.9%/9.3%), with Firmicutes and Bacteroidetes accounting for more than 90% in the hindgut segment (cecum and rectum). And at the later stage of development (day 70), with the increase of Firmicutes and Bacteroidetes in the foregut segment and the increase of Proteobacteria in the hindgut segment, the microbial composition of the four intestinal tracts became relatively uniform and Firmicutes, Proteobacteria and Bacteroidetes accounted for more than 90% of the total microorganisms. These results show the structural characteristics of intestinal microorganisms in the main intestinal segments of ZD white geese during development. And this characteristic varies in different goose breeds and intestinal segments. For example, in the study of Urban Canada Geese, Firmicutes, Proteobacteria and Bacteroidetes also accounted for 90% of the sequence (Drovetski et al., 2018), while the main phyla of the intestine in Greylag goose (Ricaud et al., 2019) and Bar-headed geese (Anser indicus) (Wang et al., 2018) were Firmicutes and Proteobacteria, and the main bacterial phyla in feces of Magang goose were Firmicutes, Proteobacteria, and Actinobacteria (Li et al., 2020b) or Firmicutes, Proteobacteria, and Cyanobacteria (Li et al., 2020a). Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria and Bacteria were identified as the dominant phyla of rectum in Landes, Roman and Zhedong White geese (Zhu et al., 2023).Generally, Firmicutes and Proteobacteria were found to be the main phyla in the intestinal flora of geese.

It’s worth noting that further analysis identified a number of signature flora in the gut microbiota of ZD white geese. For example, Turicibacter was a relatively abundant genus in multiple intestinal segments at day 28 (especially in Rectum, 21.4%). Turicibacter was report to participate in early intestinal immune maturation of young goslings; its extremely high abundance at the brooding stage reflects the synergistic co-development of intestinal microecology and immature mucosal immunity in neonatal geese (Lu et al., 2022). At day 56 and day 70, Bacteroides and Desulfovibrio were relatively highly abundant in Cecum and Rectum, and Lactobacillus made up a relatively high proportion in Jejunum and Duodenum. In addition, Acinetobacter became the highly abundant genus in day 70 Jejunum, with low abundance in all the other groups. This variation may be linked to the shift of fattening diet (3.3% lard added at 57–70 d; Supplementary Table S1) and foregut microenvironmental pH changes, and explained the lipid metabolism-related functional potential of Acinetobacter in small intestine (He et al., 2019). In other words, we excavated breed-specific biomarker genera: high Turicibacter in rectum at D28, dominant Acinetobacter in jejunum at D70, and segment-specific differential phyla distribution, which can serve as characteristic microbial markers for ZD white goose to distinguish it from other goose varieties.

Consistent with taxonomic composition results, PICRUSt-predicted KEGG functions also exhibited obvious spatial-temporal variation in the gut microbiota of ZD white geese (Figure 5). The foregut was enriched in pathways related to xenobiotics biodegradation, lipid and terpenoid-polyketide metabolism, whereas the hindgut showed higher capacity for nucleotide and cofactor-vitamin metabolism. Such functional separation reflects the physiological division-of-labor along the goose gastrointestinal tract: the small intestine is mainly responsible for digestion and absorption of dietary lipids and exogenous substances, while the cecum acts as a fermentation site for microbial nucleotide synthesis and cofactor production. Notably, cecal microbial function underwent the most dramatic changes from D28 to D70. The cecum is the core microbial fermentation organ in waterfowl; its microbiota and corresponding metabolic capacity continuously mature as geese grow and dietary composition changes (e.g., addition of lard in the fattening period at 57–70 d; Supplementary Table S1). In contrast, jejunal predicted functions remained relatively stable throughout development. This may suggest that jejunal core microbial functions are established early post-hatch and maintain homeostasis despite host growth and dietary modification. Although PICRUSt provides only computational functional prediction based on 16S rRNA data rather than direct metatranscriptomic or metabolomic evidence, these observed spatial-temporal functional signatures offer valuable clues for future mechanistic research targeting gut-microbe-nutrient interactions in ZD white geese.

Conclusions

This study performs the first systematic spatiotemporal gut microbiota profiling specifically for ZD white goose, an indigenous southern Chinese goose breed. Duodenum and Jejunum had relatively higher microorganism diversity at day 28 and day 56, while the opposite was found for day 70. At day 70, the abundance distribution became relatively closed among the four intestinal segments, Some differentially inhabited microbes among stages or gut segments, such as higher level of Actinobacteria, Acidobacteria, Chloroflexi and Proteobacteria in Jejunum and Duodenum, extremely high level of Firmicutes in Rectum (early stage), and high level of Bacteroidetes and low level of Proteobacteria in Cecum. The distribution of different metabolic pathways showed a general consistency in each intestinal segment at all three developmental stages examined. Except for a few pathways (e.g. enzyme families, biosynthesis of other secondary metabolites), most of the pathways were significantly different in different intestinal segments, and they were mainly in the foregut (duodenum and jejunum) and hindgut (cecum and rectum). The results can provide basic data for improving the production performance of this breed by regulating the intestinal microorganisms, and can also provide a comparison and reference for the research of other goose breeds.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fanim.2026.1852125/full#supplementary-material

Feed formula for the goose at different stages. The premix provided the following per kg of diet during the brooding period: V_A 5500 IU, V_D3–400 IU, V_E 40 IU, V_{K 3} 3.5 mg, V_B1 7.0 mg, V_B2 14.5 mg, V_B6–6 mg, V_B12 0.030 mg, Niacin 170.0 mg, pantothenic acid 42.5 mg, folic acid 1.35 mg, biotin 0.6 mg, choline 3000 mg, Fe 140 mg, Cu 55 mg, Mn 150 mg, Zn 70 mg, I 0.75 mg, Se 0.55 mg. The premix provided the following per kg of diet during the breeding and fattening period: V_A 3750 IU, V_D3 500IU, V_E 46.25 IU, V_{K 3} 3.75 mg, V_B1 5.5 mg, V_B2 12.5 mg, V_B6–5 mg, V_B12 0.025 mg, Niacin 162.5 mg, pantothenic acid 37.5 mg, folic acid 1.25 mg, biotin 0.5 mg, choline 2500 mg, Fe 212.5 mg, Cu 25 mg, Mn 200 mg, Zn 200 mg, I 1.05 mg, Se 0.75mg. Supplementary material at the source

Complete taxonomic profiles of gut microbiota at five taxonomic ranks in Zhedong white geese. This table comprises 15 worksheets. For each developmental stage (D28, D56, D70), relative abundance data are provided at five taxonomic levels: phylum, class, order, family and genus (worksheet naming format: stage-taxonomic rank, e.g., “D28-class”). Within each worksheet, the first column lists taxonomic units, and the remaining four columns present the mean relative abundance of each taxon in four intestinal segments: duodenum, jejunum, cecum and rectum. D28, D56 and D70 represent 28-day-old, 56-day-old and 70-day-old Zhedong white geese, respectively. Values are expressed as mean relative abundance of five biological replicates per group. Supplementary material at the source

The effects of developmental stage and intestinal segment on the significance of differentiation of microbiota structure. The P values of differentiation of microbiota structure between different groups were analysed via PERMANOVA. Supplementary material at the source

Complete statistical results of PICRUSt-predicted KEGG level-2 functional profiles of gut microbiota in Zhedong white geese. This table comprises nine worksheets. RAW data presents the relative abundance of 12 KEGG level-2 metabolic pathways for each individual sample (five biological replicates per group) across three developmental stages (D28, D56, D70) and four intestinal segments (Duodenum, Jejunum, Cecum, Rectum). Mean ± SD summarizes the mean and standard deviation for each of the 12 experimental groups. The remaining seven worksheets report one-way ANOVA results followed by Duncan’s new multiple-range test: ANOVA_D28 among segments, ANOVA_D56 among segments and ANOVA_D70 among segments compare four intestinal segments within each developmental stage; ANOVA_Duodenum, ANOVA_Jejunum, ANOVA_Cecum and ANOVA_Rectum compare three developmental stages within each intestinal segment. Each ANOVA worksheet lists the F-value, exact P-value, group mean, and Duncan’s significance letter for every KEGG pathway. D28, D56 and D70 represent 28-day-old, 56-day-old and 70-day-old Zhedong white geese, respectively. Within each comparison, values sharing a common lowercase letter are not significantly different (P > 0.05), whereas values with different letters differ significantly (P < 0.05). Significance thresholds: ***P < 0.001, **P < 0.01, *P < 0.05, ns = not significant. Supplementary material at the source

Principal-coordinate analysis (PCoA) of gut bacterial community structures based on Bray-Curtis distance matrix for Zhedong-white geese. (A) D28 (28 d); (B) D56 (56 d); (C) D70 (70 d). Each dot represents an individual goose sample; colors distinguish four intestinal segments (Duodenum, Jejunum, Cecum, Rectum). The percentages on axes represent the proportion of total variance explained by each principal coordinate. PERMANOVA was used to test community structural dissimilarity among groups (n = 5 for each group). Supplementary material at the source

Supplementary material Supplementary material at the source

Footnotes

Edited by: Matheus Ramalho De Lima, Federal University Rural Semi-Arid, Brazil

Reviewed by: Yizhong Huang, Nanchang Normal University, China

Wenzhou Zhang, Quanzhou Medical College, China