Effects of different feeding methods on serum biochemical indexes, metabolic indexes, immune indexes, and intestinal microorganisms of Nanjiang yellow goats Article Swipe
Yangyang Luo
,
Asma Anwar
,
Cheng Pan
,
Hengbo Shi
,
Shehr Bano Mustafa
,
Yu Chen
,
Zhenzhen Zhang
,
Jingjing Li
,
Jiangjiang Zhu
,
Wangsheng Zhao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.01.10.575136
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.01.10.575136
The intestinal microbiota significantly influences the intake, storage, and absorption of nutrients in animals, thereby greatly impacting the growth and development of the animals. Factors such as diet, animal breed, growth stage, and feeding methods may affect variations in the composition of the intestinal microbiota. However, research on the variations in the intestinal microbiota and metabolites of Nanjiang Yellow goats under different feeding methods is still unclear. We measured various serum biochemical indicators and immune biochemical indicators and found that the triglyceride (TC) content in the grazing group (the FMMF) was significantly lower than in the barn-feeding group (the SSMF) ( P <0.05). Serum levels of immunoglobulin A (IgA), immunoglobulin G (IgG), and immunoglobulin G (IgM) were higher in the FMMF group. At the phylum level, the most abundant bacteria were Firmicutes, Bacteroidota , and Verrucomicrobiota . At the genus level, the most abundant microbial groups were Christensenellaceae_R-7_group, UCG-005 , and Rikenellaceae_RC9_gut_group . Differential metabolite enrichment analysis through KEGG pathways revealed that the most remarkably enriched pathway was “Metabolic pathways,” including Steroid hormone biosynthesis and Arachidonic acid metabolism, among others. Analyzed by multi-omics association, we identified notably different microbial features correlated with immune indicators and metabolites after different feeding methods. We observed a significant negative correlation ( P <0.05) between the concentrations of serum immune factors interleukin-2 (IL-2), interleukin-4 (IL-4), and g__probable_genus_10 . The concentration of IgM in serum showed a highly significant positive correlation ( P <0.01) with the relative abundance of g__Erysipelatoclostridium in the intestine. Interestingly, most differential metabolites were significantly associated with intestinal microbiota. This experiment indicates that different feeding methods may influence the diversity and relative abundance of the intestinal microbiota in Nanjiang Yellow goats. The intestinal microbiota is correlated with immune indicators and metabolism, and regulating the diversity and relative abundance of the intestinal microbiota can be a way to adjust metabolism, thereby promoting the healthy growth of the Nanjiang Yellow goats.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.01.10.575136
- https://www.biorxiv.org/content/biorxiv/early/2024/01/11/2024.01.10.575136.full.pdf
- OA Status
- green
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390765148
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Raw OpenAlex JSON
- OpenAlex ID
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- DOI
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https://doi.org/10.1101/2024.01.10.575136Digital Object Identifier
- Title
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Effects of different feeding methods on serum biochemical indexes, metabolic indexes, immune indexes, and intestinal microorganisms of Nanjiang yellow goatsWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-01-11Full publication date if available
- Authors
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Yangyang Luo, Asma Anwar, Cheng Pan, Hengbo Shi, Shehr Bano Mustafa, Yu Chen, Zhenzhen Zhang, Jingjing Li, Jiangjiang Zhu, Wangsheng ZhaoList of authors in order
- Landing page
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https://doi.org/10.1101/2024.01.10.575136Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2024/01/11/2024.01.10.575136.full.pdfDirect link to full text PDF
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2024/01/11/2024.01.10.575136.full.pdfDirect OA link when available
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Biology, Firmicutes, Immune system, Antibody, Food science, Bacteria, Microbiology, Immunology, Genetics, 16S ribosomal RNATop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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