An Operational Taxonomic Unit (OTU) is a method scientists use to classify microorganisms by grouping them based on genetic similarity. This is particularly useful for categorizing closely related individuals that cannot be identified using traditional methods. This approach is effective for studying complex microbial communities, like those in the soil or the human gut, where many species are unknown.
By grouping similar genetic sequences, researchers can get a snapshot of the microbial diversity present in an environmental sample. OTUs act as proxies for species, allowing for the consistent analysis of microbial populations across different studies and environments.
How OTUs Are Defined
Defining an OTU begins with collecting an environmental sample, such as soil, water, or a gut swab, and extracting the total DNA. Scientists then amplify, or make many copies of, a specific marker gene. For bacteria and archaea, the most common marker is the 16S ribosomal RNA (rRNA) gene, while the 18S rRNA or Internal Transcribed Spacer (ITS) genes are used for eukaryotes like fungi.
This marker gene is chosen because it contains both highly conserved regions, which are similar across most species, and variable regions, which differ between species. These differences in the variable regions allow scientists to distinguish between different types of microorganisms. After amplification, these gene fragments are sequenced, a process that determines the precise order of nucleotides (the A, C, G, and T’s) in the DNA.
The core of defining an OTU is the process of clustering these sequences. Using bioinformatics software, the sequences are compared to one another and grouped based on their similarity. A predefined similarity threshold is used to determine which sequences belong together in a single OTU. The most widely accepted threshold for species-level classification of bacteria is 97% similarity, which means that all DNA sequences within an OTU are at least 97% identical.
Each resulting cluster, or OTU, is treated as a single taxonomic unit for further analysis. A representative sequence is chosen for each OTU and compared against established databases to assign a likely taxonomic identity, such as a genus or family. This process transforms a complex dataset of raw genetic sequences into a manageable table of OTUs and their relative abundances in the sample.
The Role of OTUs in Microbiology
The use of OTUs became widespread due to a challenge known as the “Great Plate Count Anomaly.” This term describes the discrepancy between the number of microbial cells observed in a sample and the number that can be grown (cultured) in a lab. It is estimated that only 1-5% of microorganisms from most environments can be cultivated using standard techniques, meaning most microbial life was hidden from culture-based methods.
OTU analysis bypasses this limitation. Instead of needing to grow organisms, scientists can directly survey the genetic material from an entire community. This culture-independent approach provides a more comprehensive picture of microbial diversity. It allows researchers to count and categorize microbes, even those that have never been cultured or formally named.
OTUs allow scientists to quantify the biodiversity within a single sample, a measure known as alpha diversity, which includes calculating the richness (number of different OTUs) and evenness (distribution of OTUs). They also enable the comparison of microbial community structures between different samples or environments, a measure called beta diversity. This helps researchers understand how microbial populations vary in response to different conditions, such as disease, diet, or environmental pollution.
Applications in Scientific Studies
OTU analysis has made complex microbial communities accessible for study in many fields. In human health, these studies explore the connection between the gut microbiome and various diseases. For example, researchers use OTUs to compare the gut microbes of healthy individuals with those of patients with Inflammatory Bowel Disease (IBD), identifying microbial signatures associated with the condition.
In environmental science, OTU analysis is used to monitor the health of ecosystems. Scientists can track how different agricultural practices, such as the use of certain fertilizers or pesticides, impact the diversity and composition of soil microbial communities. Similarly, OTUs have helped characterize the microbial life in some of the most extreme environments on Earth, from deep-sea hydrothermal vents to the ice caps of the polar regions.
OTU analysis also helps in understanding ecological processes. By examining the OTU profiles of different environments, researchers can identify which microbial groups are responsible for nutrient cycling or decomposition. This allows them to understand how environmental changes, such as pollution or climate shifts, might affect these processes.
The Shift Toward Amplicon Sequence Variants
While OTUs have been a standard tool, microbiology is evolving toward newer methods. The main limitation of the OTU approach is its clustering step. The use of a fixed 97% similarity threshold is somewhat arbitrary and can cause a loss of resolution by grouping distinct but similar sequences. Because clustering is performed per-study, comparing OTU results across different projects is also challenging.
To address these issues, many researchers are now shifting toward an alternative: Amplicon Sequence Variants (ASVs). Unlike OTUs, ASVs resolve sequences down to the level of a single nucleotide difference. Instead of clustering similar sequences, ASV methods use an error-correction algorithm to identify and remove erroneous sequences generated during the sequencing process. The result is a collection of unique, error-corrected sequences that are considered to be the true biological sequences present in the sample.
This shift represents a progression from a method that approximates species to one that detects fine-scale genetic variation. ASVs offer higher resolution and reproducibility, as exact sequences can be directly compared across studies without re-clustering. This transition does not render OTUs obsolete. They paved the way for these techniques and are still valid for many broad-scale ecological studies and for comparing new data to historical datasets.