Multi-omics analyses reveal the interaction between colonic microbiota and host in Min and Yorkshire pigs

Xiaoyu Huang , Huihui Li , Feng Cheng , Ligang Wang , Guoqing Cao , Lixian Wang , Lijun Shi

Animal Research and One Health ›› 2025, Vol. 3 ›› Issue (3) : 278 -296.

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Animal Research and One Health ›› 2025, Vol. 3 ›› Issue (3) : 278 -296. DOI: 10.1002/aro2.39
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Multi-omics analyses reveal the interaction between colonic microbiota and host in Min and Yorkshire pigs

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Abstract

Adoption of microbial preparations is becoming more and more prevalent in the pig breeding industry. Digestive tract microbes are recognized as crucial elements affecting physical characteristics of pigs. Until now, it is still challenging to establish connections between colonic microbiome and the host. In this study, weight gain models were created for Min and Yorkshire pigs. The colonic contents and colonic tissues were collected from two pig purebred strains (n = 8/group) with similar weights for multi-omics analysis. By difference analysis of colonic microbiota, Min pigs observed a significantly higher relative abundance of Bacteroides, Phocaeicola, Roseburia, and Parabacteroides, and Yorkshire pigs had a strongly higher relative abundance of Streptococcus, Vescimonas, Ligilactobacillus, and Lactococcus. Functional annotation showed that the colonic microbiota of Min pigs had extensive dietary polysaccharide and immunomodulatory capacity. Through the correlation analysis of colonic microbiota with metabolomics of colonic content or transcriptomics of colonic tissues, we provided direct and indirect relationships of microorganisms and hosts. By verification, the contents of Occludin, ZO-1, and pIgR in colonic tissue and sIgA, sIgG, and sIgM in colonic contents of Min pigs were significantly higher than that in Yorkshire pigs. This study revealed characteristics and functions of the colonic microbiota in Min and Yorkshire pigs and analyzed their interactions with the host. Also, we identified immune-related microorganisms. These results provided a theoretical basis for understanding the influence of colonic microbiota on phenotype shaping in pigs.

Keywords

biomarkers / colonic microorganism / Min pigs / Multi-omics / Yorkshire pigs

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Xiaoyu Huang, Huihui Li, Feng Cheng, Ligang Wang, Guoqing Cao, Lixian Wang, Lijun Shi. Multi-omics analyses reveal the interaction between colonic microbiota and host in Min and Yorkshire pigs. Animal Research and One Health, 2025, 3(3): 278-296 DOI:10.1002/aro2.39

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References

[1]

Tao, X., Kong, F.J., Liang, Y., Yang, X.M., Yang, Y.K., Zhong, Z.J., Wang, Y., Hu, Z.H., Chen, X.H., Gong, J.J., Pang, J.H., Zhu, K.P., Wang, Y., Liao, K., Lv, X.B., He, Z.P., & Gu, Y.R. (2023). Screening of candidate genes related to differences in growth and development between Chinese indigenous and Western pig breeds. Physiological Genomics, 55(3), 147-153. https://doi.org/10.1152/physiolgenomics.00157.2022

[2]

Song, B., Zheng, C., Zheng, J., Zhang, S., Zhong, Y., Guo, Q., Li, F., Long, C., Xu, K., Duan, Y., & Yin, Y. (2022). Comparisons of carcass traits, meat quality, and serum metabolome between Shaziling and Yorkshire pigs. Animal nutrition, 8(1), 125-134. https://doi.org/10.1016/j.aninu.2021.06.011

[3]

Liu, G., Wang, Y., Jiang, S., Sui, M., Wang, C., Kang, L., & Sun, Y. (2019). Suppression of lymphocyte apoptosis in spleen by CXCL13 after porcine circovirus type 2 infection and regulatory mechanism of CXCL13 expression in pigs. Veterinary Research, 50(1), 17. https://doi.org/10.1186/s13567-019-0634-2

[4]

Liu, Y., Yang, X., Jing, X., He, X., Wang, L., Liu, Y., & Liu, D. (2017). Transcriptomics analysis on excellent meat quality traits of skeletal muscles of the Chinese indigenous min pig compared with the large white breed. International Journal of Molecular Sciences, 19(1), 21. https://doi.org/10.3390/ijms19010021

[5]

Zhang, D., Ma, S., Wang, L., Ma, H., Wang, W., Xia, J., & Liu, D. (2022). Min pig skeletal muscle response to cold stress. PLoS One, 17(9), e0274184. https://doi.org/10.1371/journal.pone.0274184

[6]

Yang, L., Liu, X., Huang, X., Li, N., Zhang, L., Yan, H., Hou, X., Wang, L., & Wang, L. (2022). Integrated proteotranscriptomics reveals differences in molecular immunity between min and large white pig breeds. Biology, 11(12), 1708. https://doi.org/10.3390/biology11121708

[7]

Tu, T., Wu, W., Tang, X., Ge, Q., & Zhan, J. (2021). Screening out important substances for distinguishing Chinese indigenous pork and hybrid pork and identifying different pork muscles by analyzing the fatty acid and nucleotide contents. Food Chemistry, 350, 129219. https://doi.org/10.1016/j.foodchem.2021.129219

[8]

Teng, T., Song, X., Sun, G., Ding, H., Sun, H., Bai, G., & Shi, B. (2023). Glucose supplementation improves intestinal amino acid transport and muscle amino acid pool in pigs during chronic cold exposure. Animal nutrition, 12, 360-374. https://doi.org/10.1016/j.aninu.2022.10.009

[9]

Yang, L., Liu, X., Huang, X., Zhang, L., Yan, H., Hou, X., Wang, L., & Wang, L. (2023). Metabolite and proteomic profiling of serum reveals the differences in molecular immunity between min and large white pig breeds. International Journal of Molecular Sciences, 24(6), 5924. https://doi.org/10.3390/ijms24065924

[10]

Chen, B., Li, D., Leng, D., Kui, H., Bai, X., & Wang, T. (2022). Gut microbiota and meat quality. Frontiers in Microbiology, 13, 951726. https://doi.org/10.3389/fmicb.2022.951726

[11]

Gardiner, G.E., Metzler-Zebeli, B.U., & Lawlor, P.G. (2020). Impact of intestinal microbiota on growth and feed efficiency in pigs: A review. Microorganisms, 8(12), 1886. https://doi.org/10.3390/microorganisms8121886

[12]

Zamojska, D., Nowak, A., Nowak, I., & Macierzynska-Piotrowska, E. (2021). Probiotics and postbiotics as substitutes of antibiotics in farm animals: A review. Animals: An Open Access Journal from MDPI, 11(12), 3431. https://doi.org/10.3390/ani11123431

[13]

Choi, H., Rao, M.C., & Chang, E.B. (2021). Gut microbiota as a transducer of dietary cues to regulate host circadian rhythms and metabolism. Nature Reviews Gastroenterology & Hepatology, 18(10), 679-689. https://doi.org/10.1038/s41575-021-00452-2

[14]

Needham, B.D., Kaddurah-Daouk, R., & Mazmanian, S.K. (2020). Gut microbial molecules in behavioural and neurodegenerative conditions. Nature Reviews Neuroscience, 21(12), 717-731. https://doi.org/10.1038/s41583-020-00381-0

[15]

Pickard, J.M., Zeng, M.Y., Caruso, R., & Nunez, G. (2017). Gut microbiota: Role in pathogen colonization, immune responses, and inflammatory disease. Immunological Reviews, 279(1), 70-89. https://doi.org/10.1111/imr.12567

[16]

Nicolas, G.R., & Chang, P.V. (2019). Deciphering the chemical lexicon of host-gut microbiota interactions. Trends in Pharmacological Sciences, 40(6), 430-445. https://doi.org/10.1016/j.tips.2019.04.006

[17]

Woo, V., & Alenghat, T. (2022). Epigenetic regulation by gut microbiota. Gut Microbes, 14(1), 2022407. https://doi.org/10.1080/19490976.2021.2022407

[18]

de Vos, W.M., Tilg, H., Van Hul, M., & Cani, P.D. (2022). Gut microbiome and health: Mechanistic insights. Gut, 71(5), 1020-1032. https://doi.org/10.1136/gutjnl-2021-326789

[19]

Zhao, G., Xiang, Y., Wang, X., Dai, B., Zhang, X., Ma, L., Yang, H., & Lyu, W. (2022). Exploring the possible link between the gut microbiome and fat deposition in pigs. Oxidative Medicine and Cellular Longevity, 2022, 1098892. https://doi.org/10.1155/2022/1098892

[20]

Wang, Z., He, Y., Wang, C., Ao, H., Tan, Z., & Xing, K. (2021). Variations in microbial diversity and metabolite profiles of female landrace finishing pigs with distinct feed efficiency. Frontiers in Veterinary Science, 8, 702931. https://doi.org/10.3389/fvets.2021.702931

[21]

Shang, P., Wei, M., Duan, M., Yan, F., & Chamba, Y. (2022). Healthy gut microbiome composition enhances disease resistance and fat deposition in Tibetan pigs. Frontiers in Microbiology, 13, 965292. https://doi.org/10.3389/fmicb.2022.965292

[22]

Zhao, X., Jiang, L., Fang, X., Guo, Z., Wang, X., Shi, B., & Meng, Q. (2022). Host-microbiota interaction-mediated resistance to inflammatory bowel disease in pigs. Microbiome, 10(1), 115. https://doi.org/10.1186/s40168-022-01303-1

[23]

Chen, S., Zhou, Y., Chen, Y., & Gu, J. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34(17), i884-i890. https://doi.org/10.1093/bioinformatics/bty560

[24]

Li, H. (2013). Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint arXiv:13033997. https://doi.org/10.6084/M9.FIGSHARE.963153.V1

[25]

Wood, D.E., Lu, J., & Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. Genome Biology, 20, 1-13. https://doi.org/10.1186/s13059-019-1891-0

[26]

Li, D., Luo, R., Liu, C.-M., Leung, C.-M., Ting, H.-F., Sadakane, K., Yamashita, H., & Lam, T.W. (2016). MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods, 102, 3-11. https://doi.org/10.1016/j.ymeth.2016.02.020

[27]

Hyatt, D., LoCascio, P.F., Hauser, L.J., & Uberbacher, E.C. (2012). Gene and translation initiation site prediction in metagenomic sequences. Bioinformatics, 28(17), 2223-2230. https://doi.org/10.1093/bioinformatics/bts429

[28]

Fu, L., Niu, B., Zhu, Z., Wu, S., & LiCD-HIT, W. (2012). Accelerated for clustering the next-generation sequencing data. Bioinformatics, 28(23), 3150-3152. https://doi.org/10.1093/bioinformatics/bts565

[29]

Patro, R., Duggal, G., Love, M.I., Irizarry, R.A., & Kingsford, C. (2017). Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods, 14(4), 417-419. https://doi.org/10.1038/nmeth.4197

[30]

Drula, E., Garron, M.L., Dogan, S., Lombard, V., Henrissat, B., & Terrapon, N. (2022). The carbohydrate-active enzyme database: Functions and literature. Nucleic Acids Research, 50(D1), D571-D577. https://doi.org/10.1093/nar/gkab1045

[31]

Huerta-Cepas, J., Szklarczyk, D., Heller, D., Hernandez-Plaza, A., Forslund, S.K., Cook, H., Mende, D.R., Letunic, I., Rattei, T., Jensen, L., von Mering, C., & Bork, P. (2019). eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Research, 47(D1), D309-D314. https://doi.org/10.1093/nar/gky1085

[32]

Liu, Y.X., Qin, Y., Chen, T., Lu, M., Qian, X., Guo, X., & Bai, Y. (2021). A practical guide to amplicon and metagenomic analysis of microbiome data. Protein & Cell, 12(5), 315-330. https://doi.org/10.1007/s13238-020-00724-8

[33]

Liu, Y.X., Chen, L., Ma, T., Li, X., Zheng, M., Zhou, X., Qian, X., Xi, J., Lu, H., Cao, H., Ma, X., Bian, B., Zhang, P., Wu, J., Gan, R., Jia, B., Sun, L., Ju, Z., & Chen, T. (2023). EasyAmplicon: An easy-to-use, open-source, reproducible, and community-based pipeline for amplicon data analysis in microbiome research. iMeta, 2(1), e83. https://doi.org/10.1002/imt2.83

[34]

Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O‘Hara, R.B.,Simpson, G.L., Solymos, P., Stevens., M. H.H., Szoecs, E., & Wagner, H. (2019). VEGAN: Community ecology package. R package version 2.5-5.

[35]

Chang, F., He, S., & Dang, C. (2022). Assisted selection of biomarkers by linear discriminant analysis effect size (LEfSe) in microbiome data. Journal of Visualized Experiments, 2022(183), e61715. https://doi.org/10.3791/61715-v

[36]

Nuli, R., Azhati, J., Cai, J., Kadeer, A., Zhang, B., & Mohemaiti, P. (2019). Metagenomics and faecal metabolomics integrative analysis towards the impaired glucose regulation and type 2 diabetes in Uyghur-related omics. Journal of Diabetes Research, 2019, 1-15. https://doi.org/10.1155/2019/2893041

[37]

Bolger, A.M., Lohse, M., & Usadel, B. (2014). Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics, 30(15), 2114-2120. https://doi.org/10.1093/bioinformatics/btu170

[38]

Kim, D., Langmead, B., & Salzberg, S.L. (2015). HISAT: A fast spliced aligner with low memory requirements. Nature Methods, 12(4), 357-360. https://doi.org/10.1038/nmeth.3317

[39]

Pertea, M., Pertea, G.M., Antonescu, C.M., Chang, T.C., Mendell, J.T., & Salzberg, S.L. (2015). StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nature Biotechnology, 33(3), 290-295. https://doi.org/10.1038/nbt.3122

[40]

Thevenot, E.A. (2016). ropls: PCA, PLS (-DA) and OPLS (-DA) for multivariate analysis and feature selection of omics data (Vol. 1). R package version.

[41]

Love, M.I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. https://doi.org/10.1186/s13059-014-0550-8

[42]

Langfelder, P., & Horvath, S. (2008). WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics, 9(1), 559. https://doi.org/10.1186/1471-2105-9-559

[43]

Zar, J.H. (2014). Spearman rank correlation: Overview. Wiley StatsRef: Statistics Reference Online.

[44]

Hirmas, B., Gasaly, N., Orellana, G., Vega-Sagardía, M., Saa, P., Gotteland, M., & Garrido, D. (2022). Metabolic modeling and bidirectional culturing of two gut microbes reveal cross-feeding interactions and protective effects on intestinal cells. mSystems, 7(5), e006466-722. https://doi.org/10.1128/msystems.00646-22

[45]

Steimle, A., Neumann, M., Grant, E.T., Turner, J.D., & Desai, M.S. (2021). Concentrated raw fibers enhance the fiber-degrading capacity of a synthetic human gut microbiome. International Journal of Molecular Sciences, 22(13), 6855. https://doi.org/10.3390/ijms22136855

[46]

Lannes-Costa, P., De Oliveira, J., da Silva Santos, G., & Nagao, P. (2021). A current review of pathogenicity determinants of Streptococcus sp. Journal of Applied Microbiology, 131(4), 1600-1620. https://doi.org/10.1111/jam.15090

[47]

Pompilio, A., Di Bonaventura, G., & Gherardi, G. (2019). An overview on Streptococcus bovis/Streptococcus equinus complex isolates: Identification to the species/subspecies level and antibiotic resistance. International Journal of Molecular Sciences, 20(3), 480. https://doi.org/10.3390/ijms20030480

[48]

Le, H.H., Lee, M.-T., Besler, K.R., & Johnson, E.L. (2022). Host hepatic metabolism is modulated by gut microbiota-derived sphingolipids. Cell Host & Microbe, 30(6), 798-808.e7. https://doi.org/10.1016/j.chom.2022.05.002

[49]

Collins, S.L., Stine, J.G., Bisanz, J.E., Okafor, C.D., & Patterson, A.D. (2023). Bile acids and the gut microbiota: Metabolic interactions and impacts on disease. Nature Reviews Microbiology, 21(4), 236-247. https://doi.org/10.1038/s41579-022-00805-x

[50]

Zhang, S., Xu, Y., Guan, H., Cui, T., Liao, Y., Wei, W., Li, J., Hassan, B.H., Zhang, H., Jia, X., Ouyang, S., & Feng, Y. (2021). Biochemical and structural characterization of the BioZ enzyme engaged in bacterial biotin synthesis pathway. Nature Communications, 12(1), 2056. https://doi.org/10.1038/s41467-021-22360-4

[51]

Schwalm, N.D., 3rd, & Groisman, E.A. (2017). Navigating the gut buffet: Control of polysaccharide utilization in Bacteroides spp. Trends in Microbiology, 25(12), 1005-1015. https://doi.org/10.1016/j.tim.2017.06.009

[52]

Mohebali, N., Ekat, K., Kreikemeyer, B., & Breitruck, A. (2020). Barrier protection and recovery effects of gut commensal bacteria on differentiated intestinal epithelial cells in vitro. Nutrients, 12(8), 2251. https://doi.org/10.3390/nu12082251

[53]

Erturk-Hasdemir, D., Oh, S.F., Okan, N.A., Stefanetti, G., Gazzaniga, F.S., Seeberger, P.H., Plevy, S.E., & Kasper, D.L. (2019). Symbionts exploit complex signaling to educate the immune system. Proceedings of the National Academy of Sciences, 116(52), 26157-26166. https://doi.org/10.1073/pnas.1915978116

[54]

Cui, Y., Zhang, L., Wang, X., Yi, Y., Shan, Y., Liu, B., Zhou, Y., & Lü, X. (2022). Roles of intestinal Parabacteroides in human health and diseases. FEMS Microbiology Letters, 369(1). https://doi.org/10.1093/femsle/fnac072

[55]

Nihira, T., Suzuki, E., Kitaoka, M., Nishimoto, M., Ohtsubo, K., & Nakai, H. (2013). Discovery of beta-1,4-D-mannosyl-N-acetyl-D-glucosamine phosphorylase involved in the metabolism of N-glycans. Journal of Biological Chemistry, 288(38), 27366-27374. https://doi.org/10.1074/jbc.m113.469080

[56]

Singh, V., Lee, G., Son, H., Koh, H., Kim, E.S., Unno, T., & Shin, J.H. (2022). Butyrate producers, “The Sentinel of Gut”: Their intestinal significance with and beyond butyrate, and prospective use as microbial therapeutics. Frontiers in Microbiology, 13, 1103836. https://doi.org/10.3389/fmicb.2022.1103836

[57]

Bajer, L., Kverka, M., Kostovcik, M., Macinga, P., Dvorak, J., Stehlikova, Z., Brezina, J., Wohl, P., Spicak, J., & Drastich, P. (2017). Distinct gut microbiota profiles in patients with primary sclerosing cholangitis and ulcerative colitis. World Journal of Gastroenterology, 23(25), 4548-4558. https://doi.org/10.3748/wjg.v23.i25.4548

[58]

Li, Z., Zhang, W., Su, L., Huang, Z., Zhang, W., Ma, L., Sun, J., Guo, J., Wen, F., Mei, K., El-Ashram, S., & Zhao, Y. (2022). Difference analysis of intestinal microbiota and metabolites in piglets of different breeds exposed to porcine epidemic diarrhea virus infection. Frontiers in Microbiology, 13, 990642. https://doi.org/10.3389/fmicb.2022.990642

[59]

Jans, C., & Boleij, A. (2018). The road to infection: Host-microbe interactions defining the pathogenicity of Streptococcus bovis/Streptococcus equinus complex members. Frontiers in Microbiology, 9, 603. https://doi.org/10.3389/fmicb.2018.00603

[60]

Liu, Z., Xu, C., Tian, R., Wang, W., Ma, J., Gu, L., Liu, F., & Hou, J. (2021). Screening beneficial bacteriostatic lactic acid bacteria in the intestine and studies of bacteriostatic substances. Journal of Zhejiang University - Science B, 22(7), 533-547. https://doi.org/10.1631/jzus.b2000602

[61]

Nebbia, S., Lamberti, C., Lo Bianco, G., Cirrincione, S., Laroute, V., Cocaign-Bousquet, M., Cavallarin, L., Giuffrida, M.G., & Pessione, E. (2020). Antimicrobial potential of food lactic acid bacteria: Bioactive peptide decrypting from caseins and bacteriocin production. Microorganisms, 9(1), 65. https://doi.org/10.3390/microorganisms9010065

[62]

Alba, C., Castejon, D., Remiro, V., Rodriguez, J.M., Sobrino, O.J., de Maria, J., Fumanal, P., Fumanal, A., & Cambero, M.I. (2023). Ligilactobacillus salivarius MP100 as an alternative to metaphylactic antimicrobials in swine: The impact on production parameters and meat composition. Animals: An Open Access Journal from MDPI, 13(10), 1653. https://doi.org/10.3390/ani13101653

[63]

Tan, Z., Wang, Y., Yang, T., Ao, H., Chen, S., Xing, K., Zhang, F., Zhao, X., Liu, J., & Wang, C. (2018). Differences in gut microbiota composition in finishing Landrace pigs with low and high feed conversion ratios. Antonie Van Leeuwenhoek, 111(9), 1673-1685. https://doi.org/10.1007/s10482-018-1057-1

[64]

Elokil, A.A., Chen, W., Mahrose, K., Elattrouny, M.M., Abouelezz, K. F.M., Ahmad, H.I., Liu, H.Z., Elolimy, A.A., Mandouh, M.I., Abdelatty, A.M., & Li, S. (2022). Early life microbiota transplantation from highly feed-efficient broiler improved weight gain by reshaping the gut microbiota in laying chicken. Frontiers in Microbiology, 13, 1022783. https://doi.org/10.3389/fmicb.2022.1022783

[65]

Wu, H., Owen, C.D., & Juge, N. (2023). Structure and function of microbial alpha-L-fucosidases: A mini review. Essays in Biochemistry, 67(3), 399-414. https://doi.org/10.1042/ebc20220158

[66]

Robinson, L.S., Lewis, W.G., & Lewis, A.L. (2017). The sialate O-acetylesterase EstA from gut Bacteroidetes species enables sialidase-mediated cross-species foraging of 9-O-acetylated sialoglycans. Journal of Biological Chemistry, 292(28), 11861-11872. https://doi.org/10.1074/jbc.m116.769232

[67]

Trastoy, B., Du, J.J., Klontz, E.H., Li, C., Cifuente, J.O., Wang, L.X., Sundberg, E.J., & Guerin, M.E. (2020). Structural basis of mammalian high-mannose N-glycan processing by human gut Bacteroides. Nature Communications, 11(1), 899. https://doi.org/10.1038/s41467-020-14754-7

[68]

Singh, N., Gurav, A., Sivaprakasam, S., Brady, E., Padia, R., Shi, H., Thangaraju, M., Prasad, P., Manicassamy, S., Munn, D., Lee, J., Offermanns, S., & Ganapathy, V. (2014). Activation of Gpr109a, receptor for niacin and the commensal metabolite butyrate, suppresses colonic inflammation and carcinogenesis. Immunity, 40(1), 128-139. https://doi.org/10.1016/j.immuni.2013.12.007

[69]

Chang, H., Lei, L., Zhou, Y., Ye, F., & Zhao, G. (2018). Dietary flavonoids and the risk of colorectal cancer: An updated meta-analysis of epidemiological studies. Nutrients, 10(7), 950. https://doi.org/10.3390/nu10070950

[70]

Begley, M., Gahan, C.G., & Hill, C. (2005). The interaction between bacteria and bile. FEMS Microbiology Reviews, 29(4), 625-651. https://doi.org/10.1016/j.femsre.2004.09.003

[71]

Watanabe, M., Fukiya, S., & Yokota, A. (2017). Comprehensive evaluation of the bactericidal activities of free bile acids in the large intestine of humans and rodents. Journal of Lipid Research, 58(6), 1143-1152. https://doi.org/10.1194/jlr.m075143

[72]

Cai, J., Sun, L., & Gonzalez, F.J. (2022). Gut microbiota-derived bile acids in intestinal immunity, inflammation, and tumorigenesis. Cell Host & Microbe, 30(3), 289-300. https://doi.org/10.1016/j.chom.2022.02.004

[73]

Sannasiddappa, T.H., Lund, P.A., & Clarke, S.R. (2017). In vitro antibacterial activity of unconjugated and conjugated bile salts on Staphylococcus aureus. Frontiers in Microbiology, 8, 1581. https://doi.org/10.3389/fmicb.2017.01581

[74]

Li, X., Kang, Y., Huang, Y., Xiao, Y., Song, L., Lu, S., & Ren, Z. (2021). A strain of Bacteroides thetaiotaomicron attenuates colonization of Clostridioides difficile and affects intestinal microbiota and bile acids profile in a mouse model. Biomedicine & Pharmacotherapy = Biomedecine & Pharmacotherapie, 137, 111290. https://doi.org/10.1016/j.biopha.2021.111290

[75]

Fultz, R., Ticer, T., Ihekweazu, F.D., Horvath, T.D., Haidacher, S.J., Hoch, K.M., Bajaj, M., Spinler, J.K., Haag, A.M., Buffington, S.A., & Engevik, M.A. (2021). Unraveling the metabolic requirements of the gut commensal Bacteroides ovatus. Frontiers in Microbiology, 12, 745469. https://doi.org/10.3389/fmicb.2021.745469

[76]

Song, L., He, M., Sun, Q., Wang, Y., Zhang, J., Fang, Y., Liu, S., & Duan, L. (2021). Roseburia hominis increases intestinal melatonin level by activating p-CREB-AANAT pathway. Nutrients, 14(1), 117. https://doi.org/10.3390/nu14010117

[77]

Furukawa, C., Ishizuka, N., Hayashi, H., Fujii, N., Manabe, A., Tabuchi, Y., Matsunaga, T., Endo, S., & Ikari, A. (2017). Up-regulation of claudin-2 expression by aldosterone in colonic epithelial cells of mice fed with NaCl-depleted diets. Scientific Reports, 7(1), 12223. https://doi.org/10.1038/s41598-017-12494-1

[78]

Rajendran, V.M., & Sandle, G.I. (2018). Colonic potassium absorption and secretion in health and disease. Comprehensive Physiology, 8(4), 1513-1536. https://doi.org/10.1002/cphy.c170030

[79]

Yang, R., Jia, Q., Mehmood, S., Ma, S., & Liu, X. (2021). Genistein ameliorates inflammation and insulin resistance through mediation of gut microbiota composition in type 2 diabetic mice. European Journal of Nutrition, 60(4), 2155-2168. https://doi.org/10.1007/s00394-020-02403-0

[80]

Gan, F., Lin, Z., Tang, J., Chen, X., & Huang, K. (2023). Deoxynivalenol at No-observed adverse-effect levels aggravates DSS-induced colitis through the JAK2/STAT3 signaling pathway in mice. Journal of Agricultural and Food Chemistry, 71(9), 4144-4152. https://doi.org/10.1021/acs.jafc.3c00252

[81]

Akbari, P., Braber, S., Gremmels, H., Koelink, P.J., Verheijden, K.A., Garssen, J., & Fink-Gremmels, J. (2014). Deoxynivalenol: A trigger for intestinal integrity breakdown. The FASEB Journal: Official Publication of the Federation of American Societies for Experimental Biology, 28(6), 2414-2429. https://doi.org/10.1096/fj.13-238717

[82]

Pracht, K., Wittner, J., Kagerer, F., Jack, H.M., & Schuh, W. (2023). The intestine: A highly dynamic microenvironment for IgA plasma cells. Frontiers in Immunology, 14, 1114348. https://doi.org/10.3389/fimmu.2023.1114348

[83]

Wu, X., Sun, M., Yang, Z., Lu, C., Wang, Q., Wang, H., Deng, C., Liu, Y., & Yang, Y. (2021). The roles of CCR9/CCL25 in inflammation and inflammation-associated diseases. Frontiers in Cell and Developmental Biology, 9, 686548. https://doi.org/10.3389/fcell.2021.686548

[84]

Siri, S., Zhao, Y., Maier, F., Pierce, D.M., & Feng, B. (2020). The macro- and micro-mechanics of the colon and rectum I: Experimental evidence. Bioengineering, 7(4), 130. https://doi.org/10.3390/bioengineering7040130

[85]

Boleij, A., & Tjalsma, H. (2013). The itinerary of Streptococcus gallolyticus infection in patients with colonic malignant disease. The Lancet Infectious Diseases, 13(8), 719-724. https://doi.org/10.1016/s1473-3099(13)70107-5

[86]

Su, F., Li, X., You, K., Chen, M., Xiao, J., Zhang, Y., Ma, J., & Liu, B. (2016). Expression of VEGF-D, SMAD4, and SMAD7 and their relationship with lymphangiogenesis and prognosis in colon cancer. Journal of Gastrointestinal Surgery: Official Journal of the Society for Surgery of the Alimentary Tract, 20(12), 2074-2082. https://doi.org/10.1007/s11605-016-3294-9

[87]

Xiao, X., Liu, Z., Wang, R., Wang, J., Zhang, S., Cai, X., Wu, K., Bergan, R.C., Xu, L., & Fan, D. (2015). Genistein suppresses FLT4 and inhibits human colorectal cancer metastasis. Oncotarget, 6(5), 3225-3239. https://doi.org/10.18632/oncotarget.3064

[88]

Leonard, N.A., Reidy, E., Thompson, K., McDermott, E., Peerani, E., Tomas Bort, E., Balkwill, F.R., Loessner, D., & Ryan, A.E. (2021). Stromal cells promote matrix deposition, remodelling and an immunosuppressive tumour microenvironment in a 3D model of colon cancer. Cancers, 13(23), 5998. https://doi.org/10.3390/cancers13235998

[89]

Landy, J., Ronde, E., English, N., Clark, S.K., Hart, A.L., Knight, S.C., Ciclitira, P.J., & Al-Hassi, H.O. (2016). Tight junctions in inflammatory bowel diseases and inflammatory bowel disease associated colorectal cancer. World Journal of Gastroenterology, 22(11), 3117-3126. https://doi.org/10.3748/wjg.v22.i11.3117

[90]

Turula, H., & Wobus, C.E. (2018). The role of the polymeric immunoglobulin receptor and secretory immunoglobulins during mucosal infection and immunity. Viruses, 10(5), 237. https://doi.org/10.3390/v10050237

[91]

Pietrzak, B., Tomela, K., Olejnik-Schmidt, A., Mackiewicz, A., & Schmidt, M. (2020). Secretory IgA in intestinal mucosal secretions as an adaptive barrier against microbial cells. International Journal of Molecular Sciences, 21(23), 9254. https://doi.org/10.3390/ijms21239254

[92]

Schroeder, H.A., Newby, J., Schaefer, A., Subramani, B., Tubbs, A., Gregory Forest, M., Miao, E., & Lai, S.K. (2020). LPS-binding IgG arrests actively motile Salmonella Typhimurium in gastrointestinal mucus. Mucosal Immunology, 13(5), 814-823. https://doi.org/10.1038/s41385-020-0267-9

[93]

Michaud, E., Mastrandrea, C., Rochereau, N., & Paul, S. (2020). Human secretory IgM: An elusive player in mucosal immunity. Trends in Immunology, 41(2), 141-156. https://doi.org/10.1016/j.it.2019.12.005

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2023 The Authors. Animal Research and One Health published by John Wiley & Sons Australia, Ltd on behalf of Institute of Animal Science, Chinese Academy of Agricultural Sciences.

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