Abstract

   There is a gradual transition from water to dryland rearing of geese.
   In this study, we performed 16S rRNA sequencing (16S rRNA-seq) and
   transcriptome sequencing (RNA-seq) to reveal the effects of cage
   rearing (CR) and floor rearing (FR) systems on the microbial
   composition and transcriptome of the goose ileum. Through 16S rRNA-seq,
   Linear Discriminant Analysis Effect Size (LEfSe) analysis identified 2
   (hgcI_clade and Faecalibacterium) and 14 (Bacteroides, Proteiniphilum,
   Proteiniclasticum, etc.) differential microbiota in CR and FR,
   respectively. The rearing system influenced 4 pathways including
   biosynthesis of amino acids in ileal microbiota. Moreover, we
   identified 1,198 differentially expressed genes (DEGs) in the ileum
   mucosa, with 957 genes up-regulated in CR and 241 genes up-regulated in
   FR. In CR, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
   analysis revealed the significant enrichment (p < 0.05) of 28 KEGG
   pathways, most of which were associated with amino acid metabolism. In
   FR, up-regulated DEGs were mainly enriched in KEGG pathways associated
   with cellular processes, including apoptosis, necroptosis, and cellular
   senescence. Spearman correlation analysis of differential microbiota
   and amino acid metabolism-related DEGs in CR showed a significant
   positive correlation. Additionally, differential microbiota of FR,
   Phascolarctobacterium and Sutterella, were positively correlated with
   FGF10 (p < 0.05) and PIK3R1 (p < 0.01), respectively. In conclusion,
   there might be differences in ileal amino acid metabolism levels
   between CR and FR geese, and the observed increase in harmful bacterial
   species in FR might impact the activity of ileal cells.

   Keywords: goose, ileum, 16S rRNA sequencing, transcriptome, rearing
   system

1. Introduction

   Rearing systems constitute pivotal non-genetic factors that
   significantly impact productivity and individual health in goose
   farming ([41]1). Cage rearing (CR) and floor rearing (FR) systems, as
   prevalent dryland rearing systems, can reduce the incidence of
   intestinal disease outbreaks caused by waterborne pathogens ([42]2).
   However, knowledge on the effects of different dryland rearing systems
   on the intestines of geese is limited.

   Most intestinal microbiota studies have focused on the cecum ([43]3,
   [44]4) or the more accessible excreta ([45]5, [46]6). However, the
   small intestine microbiota also plays an important role in host
   metabolic homeostasis ([47]7, [48]8). The ileum, as a part of the small
   intestine, undertakes the final process of food digestion and
   absorption ([49]9), and its microbial community is important for normal
   avian growth, including Lactobacillus and bacteria with
   butyrate-producing activity, like Clostridium, Streptococcus and
   Enterococcus ([50]10). Moreover, compared to other intestinal segments,
   there is an abundance of immune cells in the ileum ([51]11). Thus, the
   ileal microbiota may acts in the maintenance of intestinal health by
   constituting a well-developed immune system. Numerous studies have
   demonstrated that rearing systems exert significant influence on ileal
   development and microbial composition. In chickens, the rearing system
   was shown to alter the relative weight, microbial composition, and
   expression levels of immune factors (IL-1β, TNF-α, and IFN-γ) in the
   ileum ([52]12). Ground litter broilers exhibited higher ileal
   microbiota α diversity ([53]13), meanwhile, ground rearing increased
   the abundance of litter breeding bacteria (Facklamia, Globicatella, and
   Jeotgalicoccus) and potentially pathogenic bacteria (Streptococcus and
   Staphylococcus) in the ileum ([54]14). In ducks, diverse floor rearing
   environments altered the dominant bacterial phyla in the ileum of
   Shaoxing ducks, with ducks reared on plastic mesh floor showing
   significantly higher ileal villus height and villus height/crypt depth
   ratio compared to those reared on litter floor ([55]15). Importantly,
   dryland rearing on netting floors has been noted to enhance the
   intestinal immunity and reduce the mortality rate ([56]2). Studies on
   Nonghua ducks demonstrated that floor rearing individuals had
   significantly higher relative weight, relative length and relative
   weight/relative length ratios of the ileum compared to net rearing
   individuals ([57]16). In geese, previous research has indicated that
   cage rearing geese exhibited a higher villus height/crypt depth ratio,
   suggesting the potential benefits of cage rearing in enhancing geese
   resistance against diseases and toxins ([58]17). The impact of rearing
   systems on avian intestinal microbial composition has been extensively
   studied, while there is a paucity of research focused on elucidating
   the effects of alterations in avian intestinal microbial composition
   induced by rearing systems on the intestinal transcriptome.

   In this study, we compared the ileal microbial composition and ileal
   mucosal transcriptome of cage rearing (CR) and floor rearing (FR) geese
   to deepen our understanding of how rearing systems affect intestines,
   which provides a theoretical basis for the management of intestinal
   health in geese.

2. Materials and methods

2.1. Experiment animals and sample collection

   All animal handling procedures were approved by the Institutional
   Animal Care and Use Committee (IACUC) of Sichuan Agricultural
   University (Chengdu campus, Sichuan, China, Permit No. DKY20170913).

   The same batch of male goslings, from the Sichuan Agricultural
   University Waterfowls Breeding Farm (Ya’an, Sichuan, China), were
   reared under the same rearing environment with free access to feed and
   water until 120 days. Afterwards, they were randomly divided into 2
   groups: CR and FR. At 270 days old, 8 geese were randomly selected for
   slaughter from each group. The experimental geese were euthanised by
   carbon dioxide inhalation and cervical dislocation after approximately
   12 h of fasting. The intestinal digesta and mid-ileum mucosa were
   quickly collected and rapidly frozen in liquid nitrogen. Digesta was
   used for 16S rRNA-seq and mucosa was used for RNA-seq, and both were
   stored at −80°C prior to sequencing.

2.2. DNA extraction and 16S rRNA sequencing

   In this study, microbial DNA extraction was conducted using the
   E.Z.N.A. Stool DNA Kit (Omega Bio-Tek, Norcross, GA). DNA concentration
   and purity were characterized by NanoDrop spectrophotometer (Thermo
   Fisher Scientific, Waltham, MA, United States) and electrophoresis on
   1% agarose gels. DNA with (OD260/OD280) range from 1.8 to 2.0 and
   (OD260/OD230) range from 2.0 to 2.5 could be used for subsequent
   experiments. Based on the concentration, DNA was diluted to 1ug/μL with
   sterile water. And, the V3−V4 hypervariable region was targeted for
   amplification, utilizing primers 338-F (5′-ACTCCTACGGGAGGCAGCAG-3′) and
   806-R (5′-GGACTACNNGGGTATCTAAT-3′). The PCR reaction was performed on a
   thermocycling PCR system (Bio-Rad T100, Germany) using high-fidelity
   polymerase according to the following procedure: 98°C for 60 s;
   30 cycles of 98°C for 10 s, 50°C for 30 s, and 72°C for 30 s; 72°C for
   5 min. The same volume of IX loading buffer (contained SYB green) was
   mixed with the PCR products and detected by electrophoresis on 2%
   agarose gels. The PCR products were mixed at an equidensity ratio.
   Then, the mixture PCR products were purified with Qiagen Gel Extraction
   Kit (Qiagen, Germany). Following the manufacturer’s recommendations,
   sequencing libraries were generated using TruSeq^® DNA PCR-Free Sample
   Preparation Kit (Illumina, United States) and index codes were added.
   Library quality was assessed by Qubit@2.0 Fluorometer (Thermo
   Scientific) and Agilent Bioanalyzer 2100 system (Agilent Technologies,
   CA, United States). Purified amplicons were sequenced on Illumina
   NovaSeq 6000 platform (2 × 250 paired ends) by Novogene Co., Ltd.
   (Beijing, China).

2.3. Microbial bioinformatics analysis of the ileum

   The assembly of paired-end reads, characterized by overlaps exceeding
   10 bp, was accomplished using FLASH software (version 1.2.11) ([59]18).
   Subsequently, reads of low quality, containing ambiguous characters and
   sequences shorter than 400 bp, were excluded. QIIME2 software (version
   2023.5) ([60]19) was employed for processing and assignment of these
   assembly readings. The denoise-paired method in DADA2 was applied to
   identify amplicon sequence variants (ASVs). Annotation of results
   utilized the SILVA 138 database ([61]20), providing classifications at
   the kingdom, phylum, class, order, family and genus. QIIME2 (version
   2023.5) facilitated the calculation of α and β diversity, and
   unweighted UniFrac distance metrics were used to generate principal
   coordinate analysis (PCoA). Differences in microbial composition
   between the 2 groups were assessed using Linear Discriminant Analysis
   Effect Size (LEfSe) ([62]21), employing screening criteria of LDA
   score > 3 and p < 0.05. Additionally, we then predicted ileal microbial
   metabolic pathways using the PICRUSt2 ([63]22) and used STAMP software
   (version 2.1.3) ([64]23) to compare differences in function. Welch
   t-test (two-sided) was used for intergroup comparison, and the Welch’s
   inverted confidence interval (CI) method was used for CI calculation. p
   < 0.05 indicated a significant difference.

2.4. RNA isolation and sequencing

   Total RNA extraction from the ileum was accomplished using Trizol
   (Invitrogen, Carlsbad, CA, United States), following the manufacturer’s
   instructions. RNA integrity was assessed using the Fragment Analyzer
   5400 (Agilent Technologies, CA, United States). RNA integrity values
   range from 6.8 to 8.9. The library construction utilizing the obtained
   RNA was undertaken by Novogene Co., Ltd. (Beijing, China). All Illumina
   PE libraries were constructed and 2 × 150 bp RNA-seq was completed
   using the Illumina sequencing platform (NovaSeq 6000). The datasets
   presented in this study can be found in the National Center for
   Biotechnology Information (NCBI) under BioProject ID PRJNA1054312.

2.5. Transcriptome alignment and assembly

   Standard quality control measures were implemented using Fastp software
   (version 0.23.1) ([65]24) to filter out low-quality reads, ensuring the
   retention of clean reads for subsequent analyses. The obtained clean
   reads were aligned to the goose reference genome (BioProject ID
   PRJNA801885, data not released) using HISAT2 software (version 2.2.1)
   ([66]25). The resulting SAM (sequencing alignment/mapping) files were
   subsequently converted to BAM (binary alignment/mapping) files and
   sorted using SAMtools (version 1.15.1) ([67]26). The expression levels
   of each transcript were computed using featureCounts (version 2.0.3)
   ([68]27), while gene expression was quantified using the transcripts
   per million (TPM) method.

2.6. Identification and functional analysis of differentially expressed genes

   DEseq2 ([69]28) was employed to identify differentially expressed genes
   (DEGs) between groups. Genes exhibiting |Log[2](FC)| ≥ 1 and p < 0.01
   were designated as DEGs. And the online tool KOBAS (version 3.0)
   ([70]29) was utilized for Gene Ontology (GO) and Kyoto Encyclopedia of
   Genes and Genomes (KEGG) functional analysis, accessible at
   [71]http://kobas.cbi.pku.edu.cn/kobas3/?t=1. The functional gene
   analysis was conducted using G. gallus as a reference.

2.7. Statistical analysis

   Statistical analysis was performed using SPSS 27.0 software. Spearman’s
   correlation coefficients were calculated to analyze the correlation.
   Differences were considered statistically significant at p < 0.05.

3. Results

3.1. 16S rRNA sequencing basic information and classification of amplicon
sequence variants

   Following quality control and filtering procedures, a total of 957,019
   effective reads were generated from 16 samples, averaging 59,814 reads
   per sample ([72]Supplementary Table S1). A total of 745 ASVs were
   identified through QIIME2 analysis. Of these, 321 ASVs were found to be
   common to both rearing systems, while 221 ASVs were exclusive to the CR
   and 203 ASVs were unique to the FR ([73]Figure 1). Subsequent taxonomic
   classification categorized these ASVs into 17 phyla, 34 classes, 88
   orders, 167 families, 215 genera.

Figure 1.

   [74]Figure 1
   [75]Open in a new tab

   Venn diagram of the number of ASVs.

3.2. Ileal microbial composition and differential microbiota identification

   The complexity of the ileal microbiota was estimated on the basis of
   α-diversity indices (Observed_features, Shannon, Chao1, Faith’s PD,
   Evenness). The analysis revealed that the Faith’s PD α-diversity of CR
   was significantly lower than that of FR ([76]Figure 2A). PCoA analyses
   demonstrated that the first principal coordinate (PCo1) explained
   30.02% of the variations among samples and the second principal
   coordinate (PCo2) explained 18.34% of the variations ([77]Figure 2B).
   The ANOSIM test further confirmed significant differences in ileal
   microbial communities between CR and FR (R = 0.22, p = 0.04).
   Comparison analysis of ileal microbiota at the phylum level and genus
   levels revealed broad similarities in dominant microbiota between the 2
   rearing systems. At the phylum level, the dominant phyla in CR were
   Firmicutes (72.11%), Proteobacteria (10.39%) and Fusobacteriota
   (6.27%); in FR the dominant phyla were Firmicutes (54.64%),
   Fusobacteriota (19.76%) and Bacteroidetes (10.62%) ([78]Figure 2C). At
   the genus level, in CR the top 3 genera were Romboutsia (45.03%),
   Clostridium_sensu_stricto_1 (12.26%) and Fusobacterium (6.78%); in FR,
   the top 3 genera were also Romboutsia (23.38%), Fusobacterium (21.13%)
   and Clostridium_sensu_stricto_1 (8.85%) ([79]Figure 2D).

Figure 2.

   [80]Figure 2
   [81]Open in a new tab

   Ileal microbiological composition comparison between CR and FR. (A)
   Faith’s PD α-diversity. (B) The PCoA based on the unweighted UniFrac
   distance. (C) Phylum-level microbial composition. (D) Genus-level
   microbial composition.

   At the genus level, LEfSe analyses identified 2 and 14 differential
   microbiota from CR and FR, respectively ([82]Figures 3A,[83]B). In CR,
   the abundance of hgcI_clade and Faecalibacterium was significantly
   higher than that of FR. Meanwhile, Bacteroides, Proteiniphilum,
   Proteiniclasticum, Syner_01, Phascolarctobacterium, Sutterella,
   Thermovirga, Colidextribacter,
   Allorhizobium_Neorhizobium_Pararhizobium_Rhizobium, Paraclostridium,
   Petrimonas, Succinivibrio, Desulfovibrio, and Campylobacter had higher
   abundance in FR. In addition, PICRUSt2 predictive function analyses
   indicated that 4 metabolic pathways differed between the 2 groups,
   including Alzheimer disease, biosynthesis of amino acids, nicotinate
   and nicotinamide metabolism and pantothenate and CoA biosynthesis
   ([84]Figure 3C).

Figure 3.

   [85]Figure 3
   [86]Open in a new tab

   Differential microbiota identification and function prediction. (A)
   Differential microbiota in the CR and FR ileum. (B) Cladogram of
   differential microbiota. (C) Differential metabolic pathways predicted.

3.3. Overview of transcriptome sequencing and identification of the
differentially expressed genes

   A comprehensive set of 292,451,214 raw reads were generated across the
   7 samples, and subsequent stringent filtering yielded an average of
   40,961,820 clean reads for each sample. The quality metrics, including
   Q20, Q30, GC content, and mapping rates, exhibited favorable ranges of
   97.15–97.81%, 92.82–94.08%, 44.72–52.03%, and 86.86–93.52%,
   respectively ([87]Supplementary Table S2). These results attested to
   the robust sequencing quality essential for subsequent analyses. We
   identified a total of 1,198 DEGs ([88]Figure 4A), of which 241 were
   up-regulated and 957 were down-regulated ([89]Figure 4B). The
   clustering heatmap of TPM illustrated the expression profiles of DEGs
   in the ileum of CR and FR geese in a visually informative manner
   ([90]Figure 4C).

Figure 4.

   [91]Figure 4
   [92]Open in a new tab

   Characterizations of ileal transcriptome variation between CR and FR.
   (A) The number of DEGs. (B) Volcano map. The red dots represent
   up-regulated genes and blue dots represent down-regulated genes. (C)
   Hierarchical clustering of DEGs. DEG, differentially expressed gene;
   CI, ileum of cage rearing geese; FI, ileum of floor rearing geese.

3.4. Functional enrichment analysis of differentially expressed genes

   Compared to FR, 957 genes were up-regulated in CR. These genes were
   enriched to 220 GO terms, including 129 biological processes (BP), 44
   cellular components (CC), and 47 molecular functions (MF) (p < 0.05)
   ([93]Figure 5A). The top 3 GO terms were cytosol, protein
   homodimerization activity, and lysosome. Based on KEGG pathway
   enrichment analysis, 28 pathways were identified significantly (p
   < 0.05) ([94]Figure 5B). Metabolic pathways, lysosome, and
   glycosaminoglycan degradation were included. Further analysis revealed
   that 15 of the 28 pathways were related to metabolism, 5 to genetic
   information processing, 5 to cellular processes, 2 to environmental
   information processing, and 1 to organismal systems. Metabolism was the
   most up-regulated function, with 46.67% of the pathways associated with
   amino acid metabolism. These pathways included glycosaminoglycan
   degradation, biosynthesis of amino acids, glycosphingolipid
   biosynthesis—ganglio series, glutathione metabolism, taurine and
   hypotaurine metabolism, selenocompound metabolism, and glycine, serine
   and threonine metabolism.

Figure 5.

   [95]Figure 5
   [96]Open in a new tab

   GO terms and KEGG pathways enriched by DEGs. (A) Top 30 GO terms
   enriched by DEGs up-regulated in CR. (B) The KEGG pathways
   significantly enriched by DEGs up-regulated in CR. (C) Top 30 GO terms
   enriched by DEGs up-regulated in FR. (D) The KEGG pathways
   significantly enriched by DEGs up-regulated in FR. GO, Gene Ontology;
   KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological
   processes; CC, cellular components; MF, molecular functions.

   Compared to CR, 241 genes were up-regulated in the FR and they included
   119 BP, 39 CC, and 41 MF (p < 0.05) ([97]Figure 5C). Integral component
   of membrane, cytosol, and GTPase activator activity were the GO terms
   with the highest significance. Through KEGG enrichment analysis, we
   identified 19 signaling pathways (p < 0.05), with the highest
   percentage of pathways associated with cellular processes ([98]Figure
   5D). Pathways associated with cellular processes included apoptosis,
   regulation of actin cytoskeleton, necroptosis, cellular senescence, and
   gap junction were up-regulated in FR.

3.5. Correlation analysis of differential microbiota and key differentially
expressed genes

   Combining these findings, we generated heat maps based on the results
   of Spearman correlation analysis. Illustrated in [99]Figure 6A, we
   performed Spearman correlation analysis between up-regulated DEGs
   involved in amino acid metabolism and differential microbiota in CR.
   The abundance of hgcI_clade was significant positively correlated with
   the expression of MARS, NAGLU, ACY1, IDH2, ST6GALNAC6, HYAL1, SGSH,
   SCLY, GGT7, and GRHPR, while demonstrating a significant negative
   correlation with the expression of GLB1 (p < 0.05). Simultaneously, we
   analyzed the correlation between up-regulated DEGs involved in cellular
   processes and differential microbiota in FR, and the results showed
   that FGF10 was significantly and positively correlated with
   Phascolarctobacterium (p < 0.05), and, PIK3R1 was significantly and
   positively correlated with Sutterella (p < 0.01) ([100]Figure 6B).

Figure 6.

   Figure 6
   [101]Open in a new tab

   Heat maps for Spearman correlation analysis. (A) Spearman correlation
   analysis of differential microbiota and amino acid metabolism-related
   DEGs in CR. (B) Spearman correlation analysis of differential
   microbiota and cellular processes-related DEGs in FR.

4. Discussion

   The intestinal microbiota of animals represents a complex and dynamic
   entity susceptible to environmental influences ([102]8). Several
   studies have highlighted the profound impact of rearing systems on
   avian intestinal development ([103]30) and microbial composition
   ([104]4, [105]31, [106]32), thereby influencing intestinal functions,
   digestion, and nutrient absorption ([107]33). However, the consequences
   of altered microbial composition on intestinal transcriptome have
   received limited attention.

   Consistent with previous reports ([108]34), the rearing system was
   found to impact ileal microbial composition. Comparison of the
   microbial composition in CR and FR revealed dominant phyla such as
   Firmicutes, Proteobacteria, Fusobacteriota, and Bacteroidetes,
   consistent with previous goose studies ([109]35). Firmicutes,
   Bacteroidetes, and Proteobacteria have also been identified as the
   major phyla in the intestinal tracts of chickens and turkeys,
   collectively constituting over 90% of all sequences ([110]36).
   Additionally, Romboutsia emerged as the primary genus in both rearing
   systems, a finding somewhat divergent from certain investigations into
   ileal microbial composition in chickens ([111]37, [112]38). Notably,
   Romboutsia has been identified as the predominant genus in the ileum of
   cage rearing chickens, potentially linked to body weight maintenance
   ([113]39), lipid metabolism ([114]40), and intestinal water metabolism
   ([115]41). Further analysis revealed 2 and 14 differential microbiota
   in CR and FR, respectively.

   Differential microbiota identified in CR, hgcI_clade, belongs to
   Actinobacteria ([116]42), which has been repeatedly reported to be
   present in water bodies ([117]43, [118]44). Some reports considered it
   as a potential probiotic ([119]45). The results of Spearman’s
   correlation analysis showed, hgcI_clade was significant correlated with
   MARS, NAGLU, ACY1, IDH2, ST6GALNAC6, HYAL1, SGSH, SCLY, GGT7, GRHPR,
   and GLB1. This suggested that hgcI_clade may influence amino acid
   metabolism by modulating enzyme levels. MARS, encoding the
   methionyl-tRNA synthetase, may play a role in regulating the cell cycle
   by linking methionine and cyclin-dependent kinase 4 ([120]46). The
   NAGLU encodes an enzyme involved in the catabolism of
   glycosaminoglycans through hydrolysis of the terminal
   N-acetyl-D-glucosamine residue in N-acetyl-alpha-D-glucosaminides
   ([121]47). A Study on rats demonstrated that the expression of ACY1
   (aminoacylase 1) was associated with the development of the jejunal
   crypt-villus axis and could be used as a marker for the metabolism of
   intestinal N-α-acetylated protein metabolism ([122]48). Isocitrate
   dehydrogenase-2 (IDH2) is a marker of mitochondrial function ([123]49),
   and its main function is catalyzing the oxidative decarboxylation of
   isocitrate to producing α-ketoglutarate ([124]50). The ST6GALNAC6 gene
   encodes ST6 N-acetylgalactosaminide α-2,6-sialyltransferase 6, an
   enzyme belonging to the family of sialyltransferase that may catalyze
   the addition of sialic acid to N-acetylgalactosamine via an α-2,6
   linkage ([125]51), and its expression is associated with colon health
   ([126]52). Degradation of intracellular hyaluronan acid is the main
   function of hyaluronidase 1 (HYAL1), which affects cell proliferation,
   migration and differentiation ([127]53) and participates in
   neuroimmunomodulators in the microbiota-gut axis ([128]54). SGSH, SCLY,
   GGT7, GRHPR, and GLB1, encoding N-sulfoglucosamine sulfohydrolase
   ([129]55), selenocysteine lyase ([130]56), gamma-glutamyltransferase 7
   ([131]57), glyoxylate and hydroxypyruvate reductase ([132]58), and
   galactosidase beta 1 ([133]59), are involved in various processes of
   amino acid metabolism. Another differential microbiota in CR,
   Faecalibacterium is known to exert vital effects in immune system
   regulation, intestinal barrier protection, and microbiota regulation
   ([134]60). Recent findings have shown that spermidine produced by
   Faecalibacterium could improve intestinal function in geese ([135]61)
   and chickens ([136]62). Integrating the insights from our previous
   studies ([137]17) and the current study, Faecalibacterium may be
   involved in enhancing ileal development in CR geese.

   In our study, the FR system appeared to increase the number of harmful
   genera in the goose ileum, such as Campylobacter, Sutterella,
   Paraclostridium, Succinivibrio, and Desulfovibrio. Campylobacter is a
   common cause of gastroenteritis in humans worldwide ([138]63) and
   poultry is the primary host ([139]64, [140]65). Our findings suggested
   that cage rearing is essential for reducing Campylobacter colonization
   in the ileum, thus preventing contamination of goose products
   ([141]66). Sutterella, a gram-negative microaerophilic bacterium, has
   been implicated in various human diseases such as autism ([142]67,
   [143]68), Down syndrome ([144]69) and inflammatory bowel disease
   ([145]70). And it has been found in the liver and breast of chickens,
   which could be a potential source of contamination for humans
   ([146]71). Some species of Paraclostridium have been linked to fatal
   infections in humans and animals ([147]72), but the mechanism is
   unknown ([148]73). It was certain that Paraclostridium was associated
   with poultry meat spoilage and was difficult to eradicate ([149]74).
   Succinivibrio, belonging to the family Succinivibrionaceae, ferments
   glucose and other carbohydrates to produce large amounts of acetic acid
   and succinic acid ([150]75), which might have pro-inflammatory effects
   ([151]76). Studies in pigs have implicated Desulfovibrio as a major
   contributor in the utilization of feces for H[2]S production ([152]77).
   And, H[2]S has been hypothesized to contribute to intestinal diseases
   such as inflammatory bowel disease ([153]78), particularly ulcerative
   colitis ([154]79). The RNA-seq results seemed to reflect the effect of
   harmful genera on ileal function. Because, the increase of these
   bacteria up-regulated pathways associated with apoptosis, necroptosis,
   and cellular senescence, suggesting adverse effects on ileal cells
   ([155]80, [156]81).

5. Conclusion

   In conclusion, there might be differences in ileal amino acid
   metabolism levels between CR and FR geese. Besides, the increase in
   harmful bacterial species in FR might affect the activity of ileal
   cells. This study provides new insights into the selection of
   appropriate dryland rearing systems to maintain intestinal health in
   geese. And more comprehensive molecular regulatory networks remain to
   be further investigated.

Data availability statement

   The data presented in the study are deposited in the National Center
   for Biotechnology Information (NCBI) repository, accession number
   PRJNA1054312.

Ethics statement

   The animal study was approved by the Institutional Animal Care and Use
   Committee (IACUC) of Sichuan Agricultural University (Chengdu campus,
   Sichuan, China, Permit No. DKY20170913). The study was conducted in
   accordance with the local legislation and institutional requirements.

Author contributions

   ZH: Data curation, Formal analysis, Investigation, Methodology, Writing
   – original draft. XL: Conceptualization, Funding acquisition,
   Methodology, Project administration, Resources, Supervision, Writing –
   review & editing. XZ: Data curation, Investigation, Methodology,
   Writing – review & editing. QO: Data curation, Investigation,
   Methodology, Writing – review & editing. JH: Data curation,
   Investigation, Methodology, Writing – review & editing. SH: Resources,
   Writing – review & editing. HH: Data curation, Investigation,
   Methodology, Writing – review & editing. LL: Investigation, Writing –
   review & editing. HL: Investigation, Writing – review & editing. JW:
   Funding acquisition, Writing – review & editing.

Funding Statement

   The author(s) declare that financial support was received for the
   research, authorship, and/or publication of this article. This research
   was supported by funding from the China Agriculture Research System of
   MOF and MARA (CARS-42-4), the School Cooperation Project of Ya’an
   (21SXHZ0028), and the Key Technology Support Program of Sichuan
   Province (2021YFYZ0014).

Conflict of interest

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

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:
   [157]https://www.frontiersin.org/articles/10.3389/fvets.2024.1394290/fu
   ll#supplementary-material
   [158]Table_1.XLSX^ (11.3KB, XLSX)
   [159]Table_2.XLSX^ (10.5KB, XLSX)

References