Integrative multi-omics analysis of dietary fibre-induced modulations in the composition and function of chicken caecal microbiota

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Integrative multi-omics analysis of dietary fibre-induced modulations in the composition and function of chicken caecal microbiota

References

  1. Govoni, C. et al. Global assessment of natural resources for chicken production. Adv. Water Resour. 154, 103987 (2021).

    Google Scholar 

  2. Farris, J., Morgan, S. & Beckman, J. Evaluating the Effects of Nontariff Measures on Poultry Trade. https://doi.org/10.32747/2024.8453400.ers.(2024).

  3. OECD/FAO. OECD-FAO Agricultural Outlook 2024-2033. https://doi.org/10.1787/4c5d2cfb-en.(2024).

  4. OECD/FAO. OECD-FAO Agricultural Outlook 2018-2027. (OECD, https://doi.org/10.1787/agr_outlook-2018-en. (2018).

  5. FAO. Gateway to poultry production and products. Food and Agriculture Organization of the United Nations. https://www.fao.org/poultry-production-products/production/en/ (2023).

  6. Bist, R. B. et al. Sustainable poultry farming practices: a critical review of current strategies and future prospects. Poult. Sci. 103, 104295 (2024).

    Google Scholar 

  7. Nguyen, H. T., Bedford, M. R., Wu, S.-B. & Morgan, N. K. Dietary soluble non-starch polysaccharide level influences performance, nutrient utilisation and disappearance of non-starch polysaccharides in broiler chickens. Animals 12, 547 (2022).

    Google Scholar 

  8. Pérez-Jiménez, J. Dietary fiber: Still alive. Food Chem. 439, 138076 (2024).

    Google Scholar 

  9. Nguyen, H. T., Bedford, M. R. & Morgan, N. K. Importance of considering non-starch polysaccharide content of poultry diets. Worlds Poult. Sci. J. 77, 619–637 (2021).

    Google Scholar 

  10. Marc, R. A. et al. Dietary Fibers and Their Importance in the Diet. IntechOpen https://doi.org/10.5772/intechopen.115461 (2024).

  11. Shterzer, N. et al. Vertical transmission of gut bacteria in commercial chickens is limited. Anim. Microbiome 5, 50 (2023).

    Google Scholar 

  12. Novoa Rama, E., Bailey, M., Kumar, S., Leone, C., den Bakker, H. C., Thippareddi, H. & Singh, M. Characterizing the gut microbiome of broilers raised under conventional and no antibiotics ever practices. Poult. Sci. 102, 102832 (2023).

    Google Scholar 

  13. Jamroz, D., Jakobsen, K., Knudsen, K. E. B., Wiliczkiewicz, A. & Orda, J. Digestibility and energy value of non-starch polysaccharides in young chickens, ducks and geese, fed diets containing high amounts of barley. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 131, 657–668 (2002).

    Google Scholar 

  14. Ocejo, M., Oporto, B. & Hurtado, A. 16S rRNA amplicon sequencing characterization of caecal microbiome composition of broilers and free-range slow-growing chickens throughout their productive lifespan. Sci. Rep. 9, 2506 (2019).

    Google Scholar 

  15. Xia, Y. et al. Dietary inulin supplementation modulates the composition and activities of carbohydrate-metabolizing organisms in the cecal microbiota of broiler chickens. PLoS One 16, e0258663 (2021).

    Google Scholar 

  16. Mirzaie, S., Zaghari, M., Aminzadeh, S. & Shivazad, M. The effects of non-starch polysaccharides content of wheat and xylanase supplementation on the intestinal amylase, amino peptidase and lipase activities, ileal viscosity and fat digestibility in layer diet. 208–214 (2012).

  17. de Sousa, L. S. et al. Cecal microbial composition and serum concentration of short-chain fatty acids in laying hens fed different fiber sources. Brazilian J. Microbiol. 1–14 https://doi.org/10.1007/s42770-024-01606-5 (2025).

  18. Qiu, M. et al. Research Note: The gut microbiota varies with dietary fiber levels in broilers. Poult. Sci. 101, 101922 (2022).

    Google Scholar 

  19. Hou, L., Sun, B. & Yang, Y. Effects of added dietary fiber and rearing system on the gut microbial diversity and gut health of chickens. Animals 10, 107 (2020).

    Google Scholar 

  20. Jian, Z. et al. Species and functional composition of cecal microbiota and resistance gene diversity in different Yunnan native chicken breeds: A metagenomic analysis. Poult. Sci. 104, 105138 (2025).

    Google Scholar 

  21. Chen, S. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta 2, e107 (2023).

    Google Scholar 

  22. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv Prepr. arXiv 1303, 3997 (2013).

    Google Scholar 

  23. Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).

    Google Scholar 

  24. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Google Scholar 

  25. Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).

    Google Scholar 

  26. Chklovski, A., Parks, D. H., Woodcroft, B. J. & Tyson, G. W. Author Correction: CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat. Methods 21, 735 (2024).

    Google Scholar 

  27. Evans, J. T. & Denef, V. J. To Dereplicate or Not To Dereplicate? mSphere 5, e00971–19 (2020).

  28. Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics 38, 5315–5316 (2022).

    Google Scholar 

  29. Aroney, S. T. N. et al. CoverM: Read alignment statistics for metagenomics. arXiv Prepr. arXiv 2501, 11217 (2025).

    Google Scholar 

  30. Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).

    Google Scholar 

  31. Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).

    Google Scholar 

  32. Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. bioinformatics 31, 166–169 (2015).

    Google Scholar 

  33. Gemayel, K., Lomsadze, A. & Borodovsky, M. MetaGeneMark-2: improved gene prediction in metagenomes. BioRxiv 2022–2027 https://doi.org/10.1101/2022.07.25.500264 (2022).

  34. Shen, W., Le, S., Li, Y. & Hu, F. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS One 11, e0163962 (2016).

    Google Scholar 

  35. Suzek, B. E. et al. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31, 926–932 (2015).

    Google Scholar 

  36. McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).

    Google Scholar 

  37. Lin, H. & Peddada, S. Das. Analysis of compositions of microbiomes with bias correction. Nat. Commun. 11, 3514 (2020).

    Google Scholar 

  38. Zheng, J. et al. dbCAN3: automated carbohydrate-active enzyme and substrate annotation. Nucleic Acids Res 51, W115–W121 (2023).

    Google Scholar 

  39. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 11, 1–11 (2010).

    Google Scholar 

  40. Lu, J., Breitwieser, F. P., Thielen, P. & Salzberg, S. L. Bracken: estimating species abundance in metagenomics data. PeerJ Comput. Sci. 3, e104 (2017).

    Google Scholar 

  41. Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 1–13 (2019).

    Google Scholar 

  42. Cheng, K. et al. MetaLab: an automated pipeline for metaproteomic data analysis. Microbiome 5, 157 (2017).

    Google Scholar 

  43. Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D. & Nesvizhskii, A. I. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics. Nat. Methods 14, 513–520 (2017).

    Google Scholar 

  44. Cheng, K. et al. MetaLab-MAG: A Metaproteomic Data Analysis Platform for Genome-Level Characterization of Microbiomes from the Metagenome-Assembled Genomes Database. J. Proteome Res. 22, 387–398 (2023).

    Google Scholar 

  45. Ewels, P. A. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat. Biotechnol. 38, 276–278 (2020).

    Google Scholar 

  46. Ginestet, C. ggplot2: elegant graphics for data analysis. J. R. Stat. Soc. Ser. A Stat. Soc. 174, 245–246 (2011).

    Google Scholar 

  47. Makki, K., Deehan, E. C., Walter, J. & Bäckhed, F. The impact of dietary fiber on gut microbiota in host health and disease. Cell Host Microbe 23, 705–715 (2018).

    Google Scholar 

  48. Jha, R. & Mishra, P. Dietary fiber in poultry nutrition and their effects on nutrient utilization, performance, gut health, and on the environment: a review. J. Anim. Sci. Biotechnol. 12, 51 (2021).

    Google Scholar 

  49. Anand, A., Manjula, S. N., Fuloria, N. K., Sharma, H. & Mruthunjaya, K. Inulin as a Prebiotic and Its Effect on Gut Microbiota. in Inulin for Pharmaceutical Applications 113–135 (Springer Nature Singapore, Singapore, https://doi.org/10.1007/978-981-97-9056-2_6 (2025).

  50. Yasmeen, R. & Ahmad, F. Microbial fermented agricultural waste-based broiler feed: a sustainable alternative to conventional feed. Worlds Poult. Sci. J. 81, 271–287 (2025).

    Google Scholar 

  51. Chen, M. et al. Effect of inulin supplementation in maternal fecal microbiota transplantation on the early growth of chicks. Microbiome 13, 98 (2025).

    Google Scholar 

  52. Song, J. et al. Dietary inulin supplementation modulates short-chain fatty acid levels and cecum microbiota composition and function in chickens infected with salmonella. Front. Microbiol. 11, 584380 (2020).

  53. Abdelqader, A., Al-Fataftah, A.-R. & Daş, G. Effects of dietary Bacillus subtilis and inulin supplementation on performance, eggshell quality, intestinal morphology and microflora composition of laying hens in the late phase of production. Anim. Feed Sci. Technol. 179, 103–111 (2013).

    Google Scholar 

  54. Liu, H. Y., Hou, R., Yang, G. Q., Zhao, F. & Dong, W. G. In vitro effects of inulin and soya bean oligosaccharide on skatole production and the intestinal microbiota in broilers. J. Anim. Physiol. Anim. Nutr. (Berl.) 102, 706–716 (2018).

    Google Scholar 

  55. Zeng, C., Zeng, X., Xia, S. & Ye, G. Caproicibacterium argilliputei sp. nov., a novel caproic acid producing anaerobic bacterium isolated from pit clay. Int. J. Syst. Evol. Microbiol. 74, https://doi.org/10.1099/ijsem.0.006246 (2024).

  56. Farkas, V. et al. Even low amounts of amorphous lignocellulose affect some upper gut parameters, but they do not modify ileal microbiota in young broiler chickens. Animals 15, 851 (2025).

    Google Scholar 

  57. Feng, Y. et al. Advances in understanding dietary fiber: Classification, structural characterization, modification, and gut microbiome interactions. Compr. Rev. Food Sci. Food Saf. 24, e70092 (2025).

  58. Abdel-Hamid, A. M., Attwood, M. M. & Guest, J. R. Pyruvate oxidase contributes to the aerobic growth efficiency of Escherichia coli. Microbiol. (N. Y) 147, 1483–1498 (2001).

    Google Scholar 

  59. Chastain, C. J. et al. Functional evolution of C4 pyruvate, orthophosphate dikinase. J. Exp. Bot. 62, 3083–3091 (2011).

    Google Scholar 

  60. Pouyez, J. et al. First crystal structure of an endo-inulinase, INU2, from Aspergillus ficuum: Discovery of an extra-pocket in the catalytic domain responsible for its endo-activity. Biochimie 94, 2423–2430 (2012).

    Google Scholar 

  61. Kaenying, W. et al. Structural and mutational analysis of glycoside hydrolase family 1 Br2 β-glucosidase derived from bovine rumen metagenome. Heliyon 9, e21923 (2023).

    Google Scholar 

  62. Nicholls, C., Li, H. & Liu, J. GAPDH: A common enzyme with uncommon functions. Clin. Exp. Pharmacol. Physiol. 39, 674–679 (2012).

    Google Scholar 

  63. Kasaeizadeh, R., Salari, S., Abdollahi, M. R. & Baghban, F. Changes in performance, apparent ileal digestibility and intestinal morphology of broiler chickens fed diets containing sunflower hulls with full-fat canola seed. Anim. Feed Sci. Technol. 327, 116417 (2025).

    Google Scholar 

  64. Cui, X. et al. Dietary fiber modulates abdominal fat deposition associated with cecal microbiota and metabolites in yellow chickens. Poult. Sci. 101, 101721 (2022).

    Google Scholar 

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