Microbiome RNA Sequencing: Introduction, Methods, and Advantages

Introduction to Microbial Profiling using 16s rRNA Sequencing

The use of 16S ribosomal RNA (rRNA) sequencing for microbial profiling is a common method for studying bacterial phylogeny and taxonomy. Because it contains both highly conserved and hypervariable regions, the 16S rRNA gene is the most widely used genetic marker for bacterial identification and classification. The conserved regions can act as universal primer binding sites for amplification of whole genes or gene fragments, whereas the hypervariable regions contain species-specific sequences that can distinguish between bacteria and archaea. Similarly, for taxonomic profiling of fungi, the internal transcribed spacer (ITS) region is used.

With public databases containing sequencing data for the 16S rRNA gene and the ITS region, rDNA amplification and sequencing can be used to semi-quantitatively define bacteria, archaea, and fungi found in complex biological samples. This method is widely regarded as the gold standard for examining the whole bacterial, archaeal, and fungal communities, as well as their members. rRNA gene sequencing can be used to process environmental and clinical samples and can be used for a variety of purposes, including food quality assurance and research into the gut microbiota.

Microbiome RNA Sequencing: Introduction, Methods, and AdvantagesFigure 1. Different sequencing and bioinformatic strategies for human microbiome analysis. (Bikel, 2015)

Methods and Technologies

Amplicon sequencing includes 16S rRNA, 18S rRNA, and ITS region gene sequencing. More specifically, universal primers specific to conserved areas are used to amplify all or parts of the gene. The primer sets are made to capture as many different microorganisms as possible. Next-generation sequencing is used to examine the amplified fragments (NGS).

The reads are then quantified after being clustered with other related sequences at a pre-defined level of identity. Operational taxonomic units are groups of sequencing reads that have a lot in common (OTUs). The data is used for downstream processing after the OTU counts are summarized in a table of relative abundances for bacteria in each sample. A level of 97 percent sequence identity for a species and 95 percent for a genus is frequently chosen as representative. Microbial identification is done by comparing sequences to databases of known bacteria.

Advantages of using 16s rRNA Sequencing in Microbial Profiling

For the taxonomic classification of organisms found in microbiomes, three techniques are commonly utilized. The traditional method for characterizing microbial communities is Sanger sequencing of the rRNA genes. NGS is another common targeted method for determining the sequence of ribosomal genes. The third method, metagenomic sequencing, entails isolating total DNA from the starting material and then reassembling NGS-derived sequencing reads into the origin microbial genomes.

Metagenomic sequencing may be less effective at detecting rare species in a microbial community than rRNA sequencing, which can exhibit biases by amplifying species unequally. In comparison to metagenomic data, rRNA gene data can capture a broader range of microbiome diversity, but with lower sensitivity and resolution. Sanger sequencing is only useful for a small number of low-complexity samples, and the method has length restrictions and time-consuming workflows. Frequently, the research project's goal determines which method of microbial profiling will be used.

References:

  1. Kim H, Kim S, Jung S. Instruction of microbiome taxonomic profiling based on 16S rRNA sequencing. Journal of Microbiology. 2020 Mar;58(3).
  2. Bharti R, Grimm DG. Current challenges and best-practice protocols for microbiome analysis. Briefings in bioinformatics. 2021 Jan;22(1).
  3. Sangal V, Goodfellow M, Blom J, et al. Revisiting the taxonomic status of the biomedically and industrially important genus Amycolatopsis, using a phylogenomic approach. Frontiers in microbiology. 2018 Sep 27;9:2281.
  4. Bikel S, Valdez-Lara A, Cornejo-Granados F, et al. Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: towards a systems-level understanding of human microbiome. Computational and structural biotechnology journal. 2015 Jan 1;13.
* For Research Use Only. Not for use in diagnostic procedures.


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