6S rRNA phylogeny outperforms similarity thresholds for species identification and reveals genus-dependent underestimation of Lactobacillales diversity
This study demonstrates that phylogenetic analysis of 6S rRNA sequences outperforms traditional similarity thresholds and database methods for accurately identifying species within the Lactobacillales order, while also validating Oxford Nanopore sequencing as a cost-effective tool for high-resolution microbial diversity assessment in underexplored habitats.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer
Every living thing carries a biological history written in its DNA, a molecular record that tells scientists who an organism is and how it is related to others. For bacteria, which are too small to see clearly and often look identical under a microscope, this genetic record is the only reliable way to tell one species from another. For decades, researchers have relied on a specific gene, known as the 16S rRNA gene, to act as a universal barcode for identifying these microscopic life forms. The standard method has been to compare a new bacterial sample against a massive library of known sequences, looking for a match based on how similar the letters in the genetic code are. If the similarity is high enough, usually above a certain percentage, the sample is assigned to a known species. This approach has been the workhorse of microbiology, but it has a blind spot: when two different species have nearly identical genetic codes, the simple comparison method often fails to distinguish between them, leaving scientists unsure of exactly what they are studying.
This uncertainty matters deeply because bacteria are not just abstract names; they are the engines of our food, our health, and our environment. In the world of fermentation, for instance, different species of lactic acid bacteria perform very different jobs. Some are essential for making safe, tasty yogurt and cheese, while others can cause illness or spoilage. If a researcher cannot tell the difference between a helpful bacterium and a harmful one, the consequences can range from a ruined batch of food to a missed medical diagnosis. The challenge is particularly acute in places with rich biodiversity but fewer resources, where scientists need affordable, accurate tools to map the microbial life around them. A team of researchers at Universidad EAFIT in Colombia set out to test whether a more sophisticated way of reading these genetic barcodes could solve this problem, using a diverse collection of bacteria found in traditional fermented foods across their country.
The researchers gathered hundreds of bacterial strains from a wide variety of sources, including dairy products, fermented fruits, and plant-based foods collected from the mountains, valleys, and coasts of Colombia. They extracted the DNA from these samples and sequenced the 16S rRNA gene using a portable, affordable device called the MinION, which allows for high-quality genetic analysis without needing a massive, centralized laboratory. Once they had the genetic sequences, they did not simply look for the closest match in a database. Instead, they built a detailed family tree for the bacteria using a method called Maximum Likelihood. This approach does not just count how many letters are the same between two sequences; it analyzes the specific patterns of change that occurred over time to determine how the bacteria are related. It is a way of reconstructing the evolutionary history of the group, looking for shared traits that define a specific family or species.
When the team compared this evolutionary approach to the standard method of simply checking for similarity, the difference in results was stark. The standard method, which relies on a simple percentage match, often returned dozens of possible species for a single bacterial sample, leaving the researchers with no clear answer. In many cases, the bacteria were so similar that the standard tool could not tell them apart, effectively grouping distinct species together. In contrast, the evolutionary tree method successfully identified more than half of the bacterial strains to the species level. The researchers found that for many groups, the tree method could pinpoint exactly which species a strain belonged to by finding unique genetic signatures that the simple comparison missed. However, the study also revealed that this higher resolution is not a magic bullet for every situation. In some genera, or groups of related bacteria, the genetic differences between species are so small that even the advanced tree method could not distinguish them, meaning that for certain types of bacteria, the species count remains uncertain.
The findings suggest that the way scientists identify bacteria needs to shift from simple matching to understanding relationships. The study demonstrated that by using a more rigorous, evolutionary framework, researchers can uncover a much clearer picture of microbial diversity, identifying dozens of species that were previously hidden or misidentified. This level of precision is crucial for industries that rely on specific bacteria, such as food production and probiotics, where the wrong species could lead to safety issues or failed products. Furthermore, the research proved that this high-level analysis can be done affordably and locally. By using the portable sequencing device and building their own analysis pipelines, the team showed that countries with rich biodiversity do not need to wait for expensive, centralized facilities to understand their own microbial resources. They can identify the specific bacteria in their traditional foods and environments with confidence, ensuring that the unique biological heritage of their region is accurately documented and protected.
Drowning in papers in your field?
Get daily digests of the most novel papers matching your research keywords — with technical summaries, in your language.