Exploring the Microbiome (bio)diversity

Microbes are microorganisms, i.e. organisms which are microscopic (invisible to the naked eye). They can be both multi and unicellular. A microbe could be a bacteria, a fungi, an algae, or other small living organism. Viruses, though not alive, are sometimes lumped into microbes.

Microbiota and microbiome are often used interchangeably. Microbiota is a collective term for microorganisms found within a specific environment. For example, gut microbiota contains mainly bacteria but also fungi, yeasts, archaea and viruses (1). In several cases, microflora is used with the same definition (e.g., skin microflora). The term microbiome has two slighty different definitions. The first, which comes from microbi-Ome, means the collection of microbial genomes in an environment (catalog of these microbes and their genes) (2). The second, based on micro-Biome, refers to the ecological definition of a biotic area (biome) in a broader dimension than microbi-Ome.

Metagenomics or metabarcoding?

As a consequence of the Next Generation Sequencing (NGS) revolution, the two main nucleic-acid-based approaches for analyzing the microbiome are currently the 16S ribosomal RNA (rRNA) gene amplicon analysis and the shotgun metagenomics. Metagemonics look at the entire genomic content of an environment (gut, soil, wate,…) and so originally referred to shotgun characterization of total DNA (full sequencing of the genomes of the microbes using NGS technology). By analyzing whole genome, metagenomics give answers to “who are there?” and “what could they do?”. The term targeted-metagenomics is also now increasingly being applied to studies of marker genes such as the 16S rRNA gene. However, if just a subset of genes (a “barcode”) or a reporter gene such as 16S rRNA gene is sequenced, the appropriate terms would rather be metabarcoding or even metagenetics. This second approach only gives answer to “who are there?”.

Sequence, read, OTU and taxonomic rank

In all living organisms, genetic information is included in the DNA molecule as a succession of 4 bases (A/T/G/C) that form sequences and that can be “read” by NGS technology. One “read” corresponds to one portion of DNA sequence of various sizes depending of the used technology. So after the sequencing step, “reads” is somewhat digital version of the DNA molecule and is stored in a file with quality scores per base.

In the 16S approach, and once obtaining reads (several millions!), a way to manage this huge amount of information is to group reads into Operational Taxonomic Units (OTU). An OTU is typically defined as a cluster of reads with 97% similarity, motivated by the expectation that these correspond approximately to species.

These species-like will also be linked to the taxonomy at various ranks: Kingdom, Phylum, Class, Order, Family and Genus by using comparison to reference taxonomy 16S databases. The community can so be described in terms of which OTUs, genera, families… are present, their relative abundance, and/or their phylogenetic relationships, giving answer to “who are there?”

The simplest visual representation of the community composition is a barplot of relative abundance values. Every sample is mapped individually to the horizontal axis and relative abundance values are mapped to the vertical axis. In other way, heatmaps are useful for the visual display of microbiome communities. Basically, they are false colour images where cells in the matrix with high relative values (higher abundances) are coloured differently from those with low relative values (lower abundances).

Measurements of biodiversity

Biodiversity, contraction of biological diversity, is basically the variety within and among life forms on a site, ecosystem or landscape. Quantifying biodiversity remains problematic because there is no single index that adequately summarizes the concept. Diversity indices are statistics used to summarize the diversity of a population in which each member belongs to a unique group. The basic idea of a diversity index is to obtain a quantitative estimate of biological variability that can be used to compare biological entities in space or in time. Biodiversity is defined and measured through three components: richness, evenness and disparity.

Basically, the richness refers to number of species (or OTUs in NGS approach). Richness is the simplest metric used to represent diversity and it remains the most commonly applied. Comparing two ecosystems, less different species (or OTUs) means lower richness.

The evenness (or equitability) refers to the proportion of species present on a site.  In ecology, the species evenness refers to how evenly the individuals in a community are distributed among the different species. The more equal species are in proportion to each other the greater the evenness of the site is. A site with low evenness indicates that a few species dominate the site.

The disparity includes a third dimension by taking into account the difference between the species. In two defined communities of organisms with the same richness and evenness, the one that contains closer species will be consider having a poor disparity.

Based on one or a combination of these 3 dimensions, the diversity indices are calculated estimators of diversity that describe the ecosystem. In microbiome studies, they can apply to describe 2 main spatial scales:

– The alpha diversity refers to a habitat unit (a sample, a community, an ecosystem…). Some commonly used indices to describe alpha diversity include Shannon and Chao1 indices. The Chao1 index estimates the number of OTUs (= richness) from abundance data based on the importance of rare OTUs (OTUs that are present only once or twice within the biological sample). The Shannon index refers to both richness and evenness. The higher are these indices, the higher is the biodiversity.

– The beta diversity refers to diversity between habitats (or samples, or time-points); this involves comparing the number of taxa that are unique to each of the habitat. Some commonly used indices to describe beta diversity include Bray-Curtis dissimilarity and Jaccard index. Visual display of beta-diversity can be done within a cluster dendrogram. The individual samples are arranged along the bottom of the dendrogram. Sample clusters are formed by joining individual samples or existing sample clusters with the join point. The vertical axis refers to a distance measure between samples or sample clusters.

Other approaches can be used to explore alpha and beta-diversity of the microbiome: other indices (i.e. Fisher alpha, Simpson…) and other methods (i.e. Principal Coordinates Analysis (PCoA)). All of them have the same goal: exploring, and understanding the microbiome biodiversity!

To answer the growing demand of studying mechanisms of interactions between these bacterial flora and their environment and how microflora impact biological functions, Biofortis offers complete services in microbiota monitoring in several areas: gut, skin and oral health. 

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In this context, Biofortis has developed an R [1] and Shiny [2] web based platform called BioMAn™ (Biofortis Metagenomics Analysis) which mixes the statistical power of dedicated R packages (metagenomeSeq, mixOmics…) and a user friendly web design. 

– References –

1.  Mai V, Draganov PV. Recent advances and remaining gaps in our knowledge of associations between gut microbiota and human health. World J Gastroenterol. 2009;15:81–85.

2. Ursell LK, Metcalf JL, Parfrey LW, Knight R. Defining the human microbiome. Nutr Rev. 2012 Aug;70 Suppl 1:S38-44.