Tag Archives: microbiota

Human genetics, gut microbiome and evolution.

I have never made a big secret of my passion for microbiota biology. The interaction between microbial communities and host organism is definitely one of the best topics to take stock of  the amazing complexity of biological systems. In fact, I have already discussed about some possible applications for theoretical biology in this field, and introduced a couple of bioinformatics methods aimed at metagenomics analyses. Today, I return to this topic to report a brand- new amazing paper on Cell, authored by Julia K. Goodrich and co. from the Ithaca University (New York).

How are human genetics and microbiome composition interlinked? This study proposes a strong genetic determination in the abundance of many microbial taxa in human gut microbiota, after an investigation on more than 1,000 fecal samples from 416 twin pairs (homo- and heterozygotes) living in the UK. Intriguingly, “the most heritable taxon, the family Christensenellaceae, formed a co-occurrence network with other heritable Bacteria and with methanogenic Archaea”, and data prove its strong role in the onset of obesity, suggesting new possible therapeutic applications.

As I skip a well- detailed discussion, letting you insight this paper on Cell’s website, I limit to rattle off a couple of considerations on the *theoretical side*. The interplay between gut microbiota and host genome, even if strongly supported in this work, is not a big novelty. Under an evolutionary point of view, the importance of symbiosis in organisms’ life and adaptation, gave rise, in 2008, to the very debated “hologenome theory of evolution“, that propose the “holobiont” (organism + associated microbial communities) as the subject of selection instead of the mere organism.

Actually, I never focus on genomic evolution only, sensing that, if defined as the change of dynamic and replicating complex systems over generations, evolution cannot be properly studied at genomic level only. In host- microbiota interaction, we appreciate a complete mutual dependency between organisms belonging to very different domains. The microbial community behavior, the immune response, the cell-to-cell communication, epigenomic regulation, protein interactions, and the effects of genetic determination found in microbiome studies, indicate a very complex and multi- level process as determinant in any eukaryote- microbial symbiosis. As we get totally aware about the relation between human genetics and microbiome composition, the open questions are still  in which way, and to which extent, host genome can affect microbial communities, and how much the symbiosis influenced adaptation and evolution of higher eukaryotes.

And this explains fairly well why I am so interested in everything about microbioma. Studying the way how microbes live along with animals and plants, makes a perfect framework to tackle questions of general interest in theoretical biology. As in any study, we can recognize an interplay between complex systems at a different organization level, and to understand the evolution, we need to accept complexity as a fact, and try to understand in which extent any level contribute to adaptation and evolution. Though, differently than other fields, I sense that the microbiome constituted a very effective framework to do this.

Metagenomics: exploring the Phyloseq package

Sometimes, I wonder if our limited ability to cultivate bacterial species represented an advantage. In fact, this lack has boosted the development of methods for the analysis of environmental samples, and the question is what would have happened if we had the chance to grow each species, would we have been able to understand the complexity of microbial communities, their emergent features and the importance of an holistic approach?

Tough question, and I fear that this post would be too long to fulfill a proper dissertation, that is a matter on how methods development drives the way we do science, and vice versa. In the meanwhile, as many out there are enjoying the challenging benefits of holistic approaches in microbiology, let’s have a look on what’s going on in the development of a rapidly growing project for data analysis in metagenomics: the Phlyoseq project.

Published by Paul J. McMurdie and Susan Holmes at Stanford University in 2013, Phyloseq is an object- oriented R package, designed to ease the life of anyone dealing with analysis of microbiome census data. Those analysis can be made by several techniques, and a variety of file formats are thus generated. That is why the authors underline the importance of reproducible research, often reported to be quite rare in microbiome census data, and provide some example of reproducible protocols.

The workflow displaying down here, illustrates the functioning of Phyoloseq. As shown, OTU clustering and independently- measured sample data are accepted as input, pre- processed and submitted to analytic procedures available in R for inference and validation. Rounded rectangles and diamond shapes represent functions and data objects respectively.


Some days ago, the Phyloseq team published a new package, an interactive web application that provides a graphical user interface to analyze Phyloseq data. As the name suggests, Shiny- Phyloseq is developed within the Shiny framework, a R- dedicated web framework. This front- end interface complete the back- end Phyloseq implementation, providing a comprehensive and easy-to-use software for genome scientists working on microbiome.

Further information, tutorial and downloads are to be found on GitHub.

Phyloseq on GitHub.io

Shiny Phyloseq on Github.io