OMICS approaches stand for collective characterization and quantification of pools of biological molecules. A gram of feces contains 10 to 12th of microbes. Not all of them are the same, with some of them being present in higher quantities and other in lower quantities. If you visualize them as a ping-pong balls, 10 to 12th of the room is occupied by them balls, each of them of different colors, each color representing a microbial species. If you pick 10 balls, you cannot have an accurate representation of the composition, but if you pick one million, you can be close to it.
OMICS techniques in microbiome research are doing that. By taking a sample and analyzing, you can get a snapshot of the composition at a certain point. The most widely used technique to study the microbiome is 16s rRNA DNA profiling, and by using primers to amplify the 16s rRNA gene of all microbes, you can create a database of DNA sequences. From one fecal sample, you can generate 1000 to 1000000 of 16s DNA sequences. The microbe most abundant in the sample will be found back the most in terms of 16s DNA sequence reads, the microbes that are not very abundant will be not detected or will generate only few of those 16s DNA sequences. This can be used to find out what microbes are there, what microbes are well represented and what microbes are present in low numbers.
To study the genetic potential of the microbiome we can use the information of the complete genomes of all microbes in the fecal sample. This technique is called metagenomics, where a set of primers is used to amplify any gene of the microorganism, in this case all the ping-pong balls are colored by gene, with many more different colors. After sequencing, a database of DNA is generated, that can hold again millions of reads. The reads can be grouped in batches, depending on the information you are interested like in batches of similar genes, for example. This way you could tell how much antibiotic genes are present in the microbiome. Another advantage of metagenomics is that the genes can be used to assemble genomes. This way you can extrapolate complete genomes from your fecal sample. This also makes it possible to discover complete genomes of bacteria that are not yet cultured. The genome information could be used again to understand what a microbe needs for its physiology, and this information can help to design a culture media for an uncultured microbiota member.
Another approach is used with metagenomic databases, to compare set of genes of microbiome between groups of individuals. This way differences in microbiota composition can be taken to the next level, you can compare health individuals with ones having diabetes and see the difference, for example. This can help to understand the functional role of the microbiome in states of disease. The creation of big microbial databases products, by sequencing DNA, RNA, proteins and metabolites, can be done if needed.
Each of this different approaches can give us clues about the function of microbiota, it will tell you what is the most abundant microbe and what are the differences between samples. But we do not need to forget that the generated results are just predictions based of a snapshot of a certain sampling moment. OMICS approaches are techniques that generate big databases of molecular data and are a big advantage in culture independent studies of the microbiome.
Next post will be about microbiome research and causality.
Have a nice day!