Unveiling the Microbial Universe Within: Navigating Gut Microbiome Analysis for Optimal Health and Wellness

 In Genomics Innovation, Genomics News, Genomics Research

In this final installment of our three-article series on Bioinformatics, let’s embark on an exploration of gut microbiome analysis – a pivotal pathway to understanding and elevating your personal health and well-being.

Picture your gut as a thriving ecosystem, teeming with a diverse community of both beneficial and potentially harmful bacteria. These minute inhabitants, numbering a staggering 100 trillion, call your gastrointestinal tract their home (Cresci & Izzo, 2019; Valdes et al., 2018). Intriguingly, elements such as your dietary choices, antibiotic history, age, and physical activity level all join forces to sculpt this vibrant microbial ensemble (Rinninella et al., 2019).

In recent times, scientists have delved deep into the intricate dance between your gut’s microbiome and your body, a dance that influences metabolism, immune responses, and even the workings of your nervous system (Cresci & Izzo, 2019). Given the profound sway this microbial community holds over your well-being and immune defenses, it becomes paramount to possess effective means of gauging its state.

As we draw the curtain on our voyage through the realm of Bioinformatics, let’s unlock the concealed mysteries of your gut and empower you to steer your health journey.

Two Pathways of Sequencing Revelation

The journey into the microbial cosmos within your gut is paved with two principal sequencing methods: 16s rRNA sequencing and metagenomic/metatranscriptomic sequencing.

Figure 1. 16s rRNA analysis workflow (Gao et al., 2021)

16s rRNA Sequencing: The genetic code of 16s ribosomal RNA is like an address shared among bacterial species. This method helps us identify and characterize bacterial communities. It involves amplifying variable regions of this conserved gene using PCR. This process unfolds in three stages: data preprocessing and quality assurance, taxonomic assignment, and community characterization. Taxonomic assignment, a pivotal step, can be achieved using operational taxonomic units (OTUs) based on sequence similarity. Sequences with at least 97% similarity are grouped and then assigned a taxonomic label (Gao et al., 2021).

Metagenomic Sequencing: A glimpse into the whole metagenome – the genetic content of the microbial community – reveals insights into genes, functions, and the organisms present. It offers a window into both the structure and function of the community, unveiling the functional pathways enoded by its genes.

Figure 2. Metagenomics workflow (Gao et al., 2021)

A specific form of this is shotgun metagenomic sequencing. The process encompasses sample collection, nucleic acid extraction, metagenomic library preparation, quality control, and data analysis. The data can be processed using alignment-based approaches or assembly-based methods, combining both for utmost accuracy. This method has been used to explore shifts in the gut microbiome linked to conditions like inflammatory bowel disease, melanoma, and alcohol-associated liver disease (Gao et al., 2021).

Divergence and Discovery

While both methods – 16s rRNA sequencing and shotgun metagenomic sequencing – are pivotal for dissecting bacterial communities, a study by Durazzi et al. (2021) uncovers a captivating contrast. 16s rRNA sequencing unveiled merely a fragment of the gut microbiota, compared to the comprehensive snapshot provided by shotgun metagenomic sequencing. The latter not only discovered less common taxa missed by the former but also displayed higher sensitivity, identifying a broader array of bacterial genera (Durazzi et al., 2021).

As we conclude our odyssey, remember that these methods are not just tools; they’re keys to unraveling the mysteries within you. The deeper our understanding of the microbial realm, the better equipped we are to chart our course towards well-being.

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References

Colwell, R. K. (2009). III.1 Biodiversity: Concepts, Patterns, and Measurement. In The Princeton Guide to  Ecology (pp. 257–263). Princeton University Press. https://doi.org/10.1515/9781400833023.257 

Cresci, G. A. M., & Izzo, K. (2019). Gut Microbiome. In Adult Short Bowel Syndrome (pp. 45–54). Elsevier.  https://doi.org/10.1016/B978-0-12-814330-8.00004-4 

Durazzi, F., Sala, C., Castellani, G., Manfreda, G., Remondini, D., & De Cesare, A. (2021). Comparison  between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut  microbiota. Scientific Reports, 11(1), 3030. https://doi.org/10.1038/s41598-021-82726-y 

Frankel, A. E., Coughlin, L. A., Kim, J., Froehlich, T. W., Xie, Y., Frenkel, E. P., & Koh, A. Y. (2017).  Metagenomic Shotgun Sequencing and Unbiased Metabolomic Profiling Identify Specific Human  Gut Microbiota and Metabolites Associated with Immune Checkpoint Therapy Efficacy in  Melanoma Patients. Neoplasia, 19(10), 848–855. https://doi.org/10.1016/j.neo.2017.08.004 

Franzosa, E. A., Sirota-Madi, A., Avila-Pacheco, J., Fornelos, N., Haiser, H. J., Reinker, S., Vatanen, T., Hall,  A. B., Mallick, H., McIver, L. J., Sauk, J. S., Wilson, R. G., Stevens, B. W., Scott, J. M., Pierce, K., Deik,  A. A., Bullock, K., Imhann, F., Porter, J. A., … Xavier, R. J. (2018). Gut microbiome structure and  metabolic activity in inflammatory bowel disease. Nature Microbiology, 4(2), 293–305.  https://doi.org/10.1038/s41564-018-0306-4 

Gao, B., Chi, L., Zhu, Y., Shi, X., Tu, P., Li, B., Yin, J., Gao, N., Shen, W., & Schnabl, B. (2021). An  Introduction to Next Generation Sequencing Bioinformatic Analysis in Gut Microbiome Studies.  Biomolecules, 11(4), 530. https://doi.org/10.3390/biom11040530 

Rinninella, E., Raoul, P., Cintoni, M., Franceschi, F., Miggiano, G., Gasbarrini, A., & Mele, M. (2019). What  is the Healthy Gut Microbiota Composition? A Changing Ecosystem across Age, Environment, Diet,  and Diseases. Microorganisms, 7(1), 14. https://doi.org/10.3390/microorganisms7010014 

Sala, C., Vitali, S., Giampieri, E., do Valle, Ì. F., Remondini, D., Garagnani, P., Bersanelli, M., Mosca, E.,  Milanesi, L., & Castellani, G. (2016). Stochastic neutral modelling of the Gut Microbiota’s relative  species abundance from next generation sequencing data. BMC Bioinformatics, 17(S2), S16.  https://doi.org/10.1186/s12859-015-0858-8 Valdes, A. M., Walter, J., Segal, E., & Spector, T. D. (2018). Role of the gut microbiota in nutrition and  health. BMJ, k2179. https://doi.org/10.1136/bmj.k2179

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