Abstract
Rapid advances in high-throughput sequencing-based technologies and computational tools have opened up entirely new strategies for extensively characterizing the microbial ecology of human body habitats, independent of laboratory cultivation. Several large-scale seminal studies have revealed that various human diseases are closely associated with compositional changes in the intestinal microbiota. However, the causal connection between these microbial imbalances and clinical symptomology and the underlying pathophysiological mechanisms of microbial-host interactions are still essentially unknown for many pathologies. The transfer of findings from basic biomedical research into clinical application is one of the major challenges in microbiome research and is impeded by large interindividual variations and the lack of knowledge about potential confounding factors such as diet or host and environmental influences. Clinical application of microbiome analyses requires a diligent implementation of quality-controlled standardized wet lab and bioinformatic protocols, as well as continuous quality monitoring and accreditation in addition to well-controlled cohort studies. Furthermore, additional tools for the functional analysis of microbiome signatures are needed. Only if these conditions are met can high-throughput sequencing-based quantitative metagenomics be successfully applied as a prognostic tool in clinical practice or for improving the development of individualized therapies based on microbiota profiles.
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References
Bahl, M. I., Laursen, M. F., & Dalgaard, M. D. (2017). Genomic GC-content affects the accuracy of 16S rRNA gene sequencing based microbial profiling due to PCR bias. Frontiers in Microbiology, 8, 1–8.
Barb, J. J., Oler, A. J., Kim, H. S., Chalmers, N., Wallen, G. R., Cashion, A., et al. (2016). Development of an analysis pipeline characterizing multiple hypervariable regions of 16S rRNA using mock samples. PLoS One, 11, 1–18.
Benitez-Paez, A., Portune, K., & Sanz, Y. (2015). Species-level resolution of 16S rRNA gene amplicons sequenced through MinIONTM portable nanopore sequencer. Gigascience, 5, 4.
Burke, C. M., & Darling, A. E. (2016). A method for high precision sequencing of near full-length 16S rRNA genes on an Illumina MiSeq. PeerJ, 4, e2492.
Callahan, B. J., McMurdie, P. J., & Holmes, S. P. (2017). Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. The ISME Journal, 11, 2639–2643.
Choo, J. M., Leong, L. E. X., & Rogers, G. B. (2015). Sample storage conditions significantly influence faecal microbiome profiles. Scientific Reports, 5, 16350.
Costea, P. I., Zeller, G., Sunagawa, S., Pelletier, E., Alberti, A., Levenez, F., et al. (2017). Towards standards for human fecal sample processing in metagenomic studies. Nat. Biotechnology, 35, 1069–1076.
Davido, B., Batista, R., Michelon, H., Lepainteur, M., Bouchand, F., Lepeule, R., et al. (2017). Is faecal microbiota transplantation an option to eradicate highly drug-resistant enteric bacteria carriage? The Journal of Hospital Infection, 95, 433–437.
Donaldson, G. P., Lee, S. M., & Mazmanian, S. K. (2015). Gut biogeography of the bacterial microbiota. Nature Reviews. Microbiology, 14, 20–32.
Duvallet, C., Gibbons, S. M., Gurry, T., Irizarry, R. A., & Alm, E. J. (2017). Meta-analysis of gut microbiome studies identifies diseasespecific and shared responses. Nature Communications, 8, 1784.
Forslund, K., Hildebrand, F., Nielsen, T., Falony, G., Le Chatelier, E., Sunagawa, S., et al. (2015). Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature, 528, 262–266.
Fouhy, F., Clooney, A. G., Stanton, C., Claesson, M. J., & Cotter, P. D. (2016). 16S rRNA gene sequencing of mock microbial populations- impact of DNA extraction method, primer choice and sequencing platform. BMC Microbiology, 16, 123.
Franzosa, E. A., Morgan, X. C., Segata, N., Waldron, L., Reyes, J., Earl, A. M., et al. (2014). Relating the metatranscriptome and metagenome of the human gut. Proceedings of the National Academy of Sciences of the United States of America, 111, E2329–E2338.
Gagan, J., & Van Allen, E. M. (2015). Next-generation sequencing to guide cancer therapy. Genome Medicine, 7, 80.
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V., & Egozcue, J. J. (2017). Microbiome datasets are compositional: And this is not optional. Frontiers in Microbiology, 8, 2224.
Gohl, D. M., Vangay, P., Garbe, J., MacLean, A., Hauge, A., Becker, A., et al. (2016). Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nature Biotechnology, 34, 942–949.
Hiergeist, A., Gläsner, J., Reischl, U., & Gessner, A. (2015). Analyses of intestinal microbiota: Culture versus sequencing. ILAR Journal, 56, 228–240.
Hiergeist, A., Reischl, U., Priority Program 1656 Intestinal Microbiota Consortium/Quality Assessment Participants, & Gessner, A. (2016). Multicenter quality assessment of 16S ribosomal DNA-sequencing for microbiome analyses reveals high inter-center variability. International Journal of Medical Microbiology, 306, 334–342.
Jones, M. B., Highlander, S. K., Anderson, E. L., Li, W., Dayrit, M., Klitgord, N., et al. (2015). Library preparation methodology can influence genomic and functional predictions in human microbiome research. Proceedings of the National Academy of Sciences of the United States of America, 112, 14024–14029.
Kearney, S. M., Gibbons, S. M., Poyet, M., Gurry, T., Bullock, K., Allegretti, J., et al. (2017). Endospores and other lysisresistant bacteria comprise a widely shared core community within the human microbiota. BioRxiv, 221713.
Kim, H. J., Huh, D., Hamilton, G., & Ingber, D. E. (2012). Human gut-on-a-chip inhabited by microbial flora that experiences intestinal peristalsis-like motions and flow. Lab on a Chip, 12, 2165.
Kim, D., Hofstaedter, C. E., Zhao, C., Mattei, L., Tanes, C., Clarke, E., et al. (2017). Optimizing methods and dodging pitfalls in microbiome research. Microbiome, 5, 52.
Klindworth, A., Pruesse, E., Schweer, T., Peplies, J., Quast, C., Horn, M., et al. (2013). Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Research, 41, e1.
Koppel, N., Maini Rekdal, V., & Balskus, E. P. (2017). Chemical transformation of xenobiotics by the human gut microbiota. Science, 356, eaag2770.
Lagier, J. C., Armougom, F., Million, M., Hugon, P., Pagnier, I., Robert, C., et al. (2012). Microbial culturomics: Paradigm shift in the human gut microbiome study. Clinical Microbiology and Infection, 18, 1185–1193.
Lagkouvardos, I., Joseph, D., Kapfhammer, M., Giritli, S., Horn, M., Haller, D., et al. (2016). IMNGS: A comprehensive open resource of processed 16S rRNA microbial profiles for ecology and diversity studies. Scientific Reports, 6, 33721.
Leclercq, S., Mian, F. M., Stanisz, A. M., Bindels, L. B., Cambier, E., Ben-Amram, H., et al. (2017). Low-dose penicillin in early life induces long-term changes in murine gut microbiota, brain cytokines and behavior. Nature Communications, 8, 15062.
Lee, S. T. M., Kahn, S. A., Delmont, T. O., Shaiber, A., Esen, ö. C., Hubert, N. A., et al. (2017). Tracking microbial colonization in fecal microbiota transplantation experiments via genome-resolved metagenomics. Microbiome, 5.
Leinonen, R., Sugawara, H., & Shumway, M. (2011). The sequence read archive. Nucleic Acids Research, 39.
Maurice, C. F., Haiser, H. J., & Turnbaugh, P. J. (2013). Xenobiotics shape the physiology and gene expression of the active human gut microbiome. Cell, 152, 39–50.
Molly, K., Vande Woestyne, M., & Verstraete, W. (1993). Development of a 5-step multi-chamber reactor as a simulation of the human intestinal microbial ecosystem. Applied Microbiology and Biotechnology, 39, 254–258.
Neville, B. A., Forster, S. C., & Lawley, T. D. (2018). Commensal Koch’s postulates: Establishing causation in human microbiota research. Current Opinion in Microbiology, 42, 47–52.
Nguyen, T. L. A., Vieira-Silva, S., Liston, A., & Raes, J. (2015). How informative is the mouse for human gut microbiota research? Disease Models and Mechanisms, 8, 1–16.
Nielsen, H. B., Almeida, M., Juncker, A. S., Rasmussen, S., Li, J., Sunagawa, S., et al. (2014). Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nature Biotechnology, 32, 822–828.
Raju, S., Ellonen, P., De Vos, W. M., Eriksson, J. G., Weiderpass, E., Rounge, T. B., et al. (2018). Reproducibility and repeatability of six high-throughput 16S rDNA sequencing protocols for microbiota profiling. J Microbiol Methods, 147, 76–86.
Santiago, A., Panda, S., Mengels, G., Martinez, X., Azpiroz, F., Dore, J., et al. (2014). Processing faecal samples: A step forward for standards in microbial community analysis. BMC Microbiology, 14, 112.
Schirmer, M., Ijaz, U. Z., D’Amore, R., Hall, N., Sloan, W. T., & Quince, C. (2015). Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. Nucleic Acids Research, 43, e37.
Sinha, R., Abu-Ali, G., Vogtmann, E., Fodor, A.A., Ren, B., Amir, A., et al., Microbiome Quality Control Project Consortium. (2017). Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium. Nature Biotechnology, 35, 1077–1086.
Song, S. J., Amir, A., Metcalf, J. L., Amato, K. R., Xu, Z. Z., Humphrey, G., et al. (2016). Preservation methods differ in fecal microbiome stability, affecting suitability for field studies. mSystems, 1, e00021-16.
Stämmler, F., Gläsner, J., Hiergeist, A., Holler, E., Weber, D., Oefner, P. J., et al. (2016). Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome, 4, 28.
Surana, N. K., & Kasper, D. L. (2017). Moving beyond microbiome-wide associations to causal microbe identification. Nature, 552(7684), 244–247.
Taur, Y., & Pamer, E. G. (2014). Harnessing microbiota to kill a pathogen: Fixing the microbiota to treat Clostridium difficile infections. Nature Medicine, 20, 246–247.
Thaiss, C. A., Levy, M., Korem, T., DohnalovĂ¡, L., Shapiro, H., Jaitin, D. A., et al. (2016). Microbiota diurnal rhythmicity programs host transcriptome oscillations. Cell, 167, 1495–1510.e12.
VelĂ¡squez-MejĂa, E. P., de la Cuesta-Zuluaga, J., & Escobar, J. S. (2018). Impact of DNA extraction, sample dilution, and reagent contamination on 16S rRNA gene sequencing of human feces. Applied Microbiology and Biotechnology, 102, 403–411.
Wagner, J., Coupland, P., Browne, H. P., Lawley, T. D., Francis, S. C., & Parkhill, J. (2016). Evaluation of PacBio sequencing for full-length bacterial 16S rRNA gene classification. BMC Microbiology, 16, 1–17.
Walker, A. W., Martin, J. C., Scott, P., Parkhill, J., Flint, H. J., & Scott, K. P. (2015). 16S rRNA gene-based profiling of the human infant gut microbiota is strongly influenced by sample processing and PCR primer choice. Microbiome, 3, 26.
Wang, J., & Jia, H. (2016). Metagenome-wide association studies: Fine-mining the microbiome. Nature Reviews. Microbiology, 14, 508–522.
Weiss, S., Xu, Z. Z., Peddada, S., Amir, A., Bittinger, K., Gonzalez, A., et al. (2017). Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome, 5, 27.
Wilson, M. R., Suan, D., Duggins, A., Schubert, R. D., Khan, L. M., Sample, H. A., et al. (2017). A novel cause of chronic viral meningoencephalitis: Cache Valley virus. Annals of Neurology, 82, 105–114.
Yang, C., & Iwasaki, W. (2014). MetaMetaDB: A database and analytic system for investigating microbial habitability. PLoS One, 9, e87126.
Zoetendal, E. G., Von Wright, A., Vilpponen-Salmela, T., Ben-Amor, K., Akkermans, A. D. L., & De Vos, W. M. (2002). Mucosa-associated bacteria in the human gastrointestinal tract are uniformly distributed along the colon and differ from the community recovered from feces. Applied and Environmental Microbiology, 68, 3401–3407.
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Hiergeist, A., Gessner, A. (2018). Clinical Implementation of High-Throughput Sequencing. In: Haller, D. (eds) The Gut Microbiome in Health and Disease. Springer, Cham. https://doi.org/10.1007/978-3-319-90545-7_19
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DOI: https://doi.org/10.1007/978-3-319-90545-7_19
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