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Revisiting the Sterile Tissue Paradigm: Systematic Re-analysis of Microbial Signals in Germ-Free Mouse Tissues | ||
| Biological Data | ||
| دوره 1، شماره 1، شهریور 2026، صفحه 86-103 اصل مقاله (595.45 K) | ||
| نوع مقاله: Original Article | ||
| نویسنده | ||
| Arash Babaei* | ||
| Biology Department, Faculty of Sciences, Malayer University First of Arak Road | ||
| چکیده | ||
| The paradigm of sterile internal tissues is challenged by high-sensitivity sequencing, creating an analytical dilemma for low-biomass samples. Distinguishing genuine endogenous signals from pervasive contamination requires stringent computational frameworks. Here, we developed and applied a systematic bioinformatic pipeline to re-analyze public 16S rRNA gene sequencing data from germ-free and conventional mouse tissues. Our approach integrated rigorous contamination control, diversity analysis, and differential abundance testing. We found no significant difference in microbial community structure (PERMANOVA: R²=0.47, p=0.15) or alpha diversity (Shannon Index, p=0.63) between germ-free and conventional groups, failing to support the hypothesis of a structured endogenous microbiome in germ-free tissues. While we observed tissue-correlated profiles, these patterns could not be definitively attributed to biological colonization versus technical artifacts. The most parsimonious interpretation is that the observed signals are consistent with contamination. This study provides a replicable analytical framework and underscores the critical importance of robust bioinformatic decontamination for validating claims of microbiomes in low-biomass environments. | ||
| کلیدواژهها | ||
| germ-free؛ tissue microbiota؛ low-biomass؛ contamination control؛ 16S rRNA | ||
| مراجع | ||
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Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J. A., & Holmes, S.P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581-583. https://doi.org/10.1038/nmeth.3869
Davis, N.M., Proctor, D.M., Holmes, S.P., Relman, D.A., & Callahan, B.J. (2018). Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome, 6, 226. https://doi.org/10.1186/s40168-018-0605-2
de Goffau, M.C., Lager, S., Sovio, U., Gaccioli, F., Cook, E., Peacock, S.J., Parkhill, J., Charnock-Jones, D.S., & Smith, G.C.S. (2019). Human placenta has no microbiome but can contain potential pathogens. Nature, 572(7769), 329-334. https://doi.org/10.1038/s41586-019-1451-5
Eisenhofer, R., Minich, J.J., Marotz, C., Cooper, A., Knight, R., & Weyrich, L.S. (2019). Contamination in low microbial biomass microbiome studies: Issues and recommendations. Trends in Microbiology, 27(2), 105-117. https://doi.org/10.1016/j.tim.2018.11.003
Kennedy, K.M., de Goffau, M.C., Perez-Muñoz, M.E., Arrieta, M.C., Bäckhed, F., Bork, P., Braun, T., Bushman, F.D., Dore, J., de Vos, W.M., Earl, A.M., Eisen, J.A., Eloe-Fadrosh, E.A., Gosalbes, M. J., Haiser, H.J., Holmes, S.P., Jansson, J.K., Knights, D., Ley, R.E., ... Walter, J. (2023). Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies. Nature, 613(7945), 639-649. https://doi.org/10.1038/s41586-022-05546-8
Kurtz, Z.D., Müller, C.L., Miraldi, E.R., Littman, D.R., Blaser, M.J., & Bonneau, R.A. (2015). Sparse and compositionally robust inference of microbial ecological networks. Plos Computational Biology, 11(5), e1004226. https://doi.org/10.1371/journal.pcbi.1004226
Lin, H., & Peddada, S.D. (2020). Analysis of compositions of microbiomes with bias correction. Nature Communications, 11, 3514. https://doi.org/10.1038/s41467-020-17041-7
Marotz, C., Sanders, J.G., Zuniga, C., Zaramela, L.S., Knight, R., & Zengler, K. (2018). Improving saliva shotgun metagenomics by chemical host DNA depletion. Microbiome, 6, 42. https://doi.org/10.1186/s40168-018-0426-3
Mirzayi, C., Renson, A., Zohra, F., Elsafoury, S., Geistlinger, L., Kasselman, L.J., Eckenrode, K., van de Wijgert, J., Loughman, A., Marques, F.Z., MacIntyre, D.A., Arumugam, M., Azhar, R., Beghini, F., Berg, G., Bäckhed, F., Borenstein, E., Bork, P., Brennan, C.A., ... Waldron, L. (2021). Reporting guidelines for human microbiome research: The STORMS checklist. Nature Medicine, 27(11), 1885-1892. https://doi.org/10.1038/s41591-021-01552-x
Oksanen, J., Simpson, G.L., Blanchet, F.G., Kindt, R., Legendre, P., Minchin, P.R., O'Hara, R.B., Solymos, P., Stevens, M.H. H., Szoecs, E., Wagner, H., Barbour, M., Bedward, M., Bolker, B., Borcard, D., Carvalho, G., Chirico, M., De Caceres, M., Durand, S., ... Weedon, J. (2024). vegan: Community ecology package (Version 2.6-8) [Computer software]. Comprehensive R Archive Network. https://CRAN.R-project.org/package=vegan
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., & Glöckner, F.O. (2013). The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Research, 41(D1), D590-D596. https://doi.org/10.1093/nar/gks1219
Round, J.L., & Mazmanian, S.K. (2009). The gut microbiota shapes intestinal immune responses during health and disease. Nature Reviews Immunology, 9(5), 313-323. https://doi.org/10.1038/nri2515
Salter, S.J., Cox, M.J., Turek, E.M., Calus, S.T., Cookson, W.O., Moffatt, M.F., Turner, P., Parkhill, J., Loman, N.J., & Walker, A.W. (2014). Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biology, 12, 87. https://doi.org/10.1186/s12915-014-0087-z
Smith, K., McCoy, K.D., & Macpherson, A.J. (2007). Use of axenic animals in studying the adaptation of mammals to their commensal intestinal microbiota. Seminars in Immunology, 19(2), 59-69. https://doi.org/10.1016/j.smim.2006.10.002
Theis, K.R., Romero, R., Winters, A.D., Jobe, A.H., & Gomez-Lopez, N. (2020). Lack of evidence for microbiota in the placental and fetal tissues of rhesus macaques. mSphere, 5(3), e00210-20. https://doi.org/10.1128/mSphere.00210-20
Weiss, S., Van Treuren, W., Lozupone, C., Faust, K., Friedman, J., Deng, Y., Xia, L.C., Xu, Z.Z., Ursell, L., Alm, E.J., Birmingham, A., Cram, J.A., Fuhrman, J.A., Raes, J., Sun, F., Zhou, J., & Knight, R. (2016). Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. The ISME Journal, 10(7), 1669-1681. https://doi.org/10.1038/ismej.2015.235 | ||
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