Flaws with genotypic microbial identification methods

 

Current microbial analyses may falsely detect species that are not actually present. This is based on a study of simulated microbial communities. This shows analyses are flawed by incomplete DNA databases.

 

This is based on research conducted by Sequentia Biotech SL and the Centro de Investigaciones Biologicas Margarita Salas, with the results reported to PLOS ONE (“The virtual microbiome: A computational framework to evaluate microbiome analyses.”)

 

The findings have particular significance to studies of microbiomes. The most commonly used methods for studying microbial communities rely on comparing the DNA obtained from a biological sample to sequences in genome databanks.

 

This approach means that researchers can only identify DNA sequences that are already in the databases. This fact that may severely compromise the reliability of microbiome data.

 

 

The most common methodologies to study the microbiome of a chosen animal species are amplicon and whole-genome sequencing. The former is based on the identification of taxonomical markers such as the ribosomal gene 16S for bacteria.

 

The relatively short gene (∼1500 bp) is universal among bacteria and archaea; however, some studies have indicated that 16S rRNA gene does not show precise phylogenetic relationships within particular taxa. The natural variability in short 16S rRNA fragments may not be sufficiently large to use conservative annotation methods. In contrast, 23S ribosomal RNA (rRNA) offers more diagnostic sequence stretches and greater sequence variation.

 

To test the consistency of current methods of microbiome analysis, the researchers used computer simulations to create virtual microbiome communities that imitate real-world bacterial populations.

 

Following this, the researchers used standard techniques to analyze the virtual communities and compared the results with the original composition. The experiment showed that results from DNA analyses can bear little resemblance to the actual composition of the community, and that a large number of the species "detected" by the analysis are not actually present in the community.

 

The study demonstrates significant flaws in the techniques currently used to identify microbial communities.

 

The researchers conclude that there is a need for increased efforts to collect genome information from microbes and to make that information available in public databases to improve the accuracy of microbiome analysis.

 

In the meantime, the researchers argue, the results of microbiome studies should be interpreted with caution, especially in cases where the available genomic information from those environments is still scarce.

 

According to the research paper: "This study reveals intrinsic constraints in metagenomic analysis stemming from current database limitations and how genomic information is used. To enhance the reliability of metagenomic data, a research effort is necessary to improve both database contents and analysis methods. Meanwhile, metagenomic data should be approached with great care."

 

Posted by Dr. Tim Sandle, Pharmaceutical Microbiology Resources (http://www.pharmamicroresources.com/)