When microbial DNA is scarce, new profiling method helps separate genuine signals from contamination

In acute, life-threatening infections, rapidly characterizing the microorganisms in a patient sample can help guide diagnosis and treatment. Computational methods known as taxonomic profilers can analyze metagenome sequencing data generated from the microorganisms’ genomic information and compare it with reference genomes of individual microorganisms. However, taxonomic profilers are still under development and are not yet in common use. Current methods can produce false-positive results or inaccurate abundance estimates.

Researchers at the Helmholtz Centre for Infection Research (HZI) have developed a new taxonomic profiler called Metax. By using information about how sequencing reads are distributed across microbial reference genomes, Metax can distinguish true microbial signals from artifacts more reliably and improve both taxonomic identification and abundance estimation. The study was published in the journal Cell.

“In clinical samples, for example, microbial profiling can provide important information about microorganisms that may be relevant for an infection,” explains Alice McHardy, a professor and head of the research group “Computational Biology for Infection Research” at HZI. “Such information can complement established diagnostic approaches and help researchers and clinicians investigate potential pathogens. But taxonomic profiling is equally important far beyond clinical applications, from human microbiome research to environmental monitoring.”

Reading beyond sequence matches

Most taxonomic profilers compare sequencing reads from a sample with reference genomes stored in databases. This seemingly straightforward task is complicated by the fact that different microorganisms can share highly similar DNA sequences. Reference genomes may also contain contaminating sequences.

In addition, traces of microbial DNA can sometimes come from laboratory reagents used during sample preparation—a source of contamination often referred to as the “kitome.” As a result, sequencing reads may match reference genomes of microorganisms that are not actually present in the sample.

“An important piece of information is where these matching reads occur across the genome,” says Dr. Zhi-Luo Deng, a scientist in McHardy’s research group and first author of the study. “If a microorganism is truly present, we generally expect reads to be distributed across multiple regions of its genome. False-positive signals, in contrast, are often restricted to only one or a few local regions, with little or no support elsewhere.”

Metax was designed to look beyond a simple sequence match. It also considers how sequencing reads are distributed across a microorganism’s genome. By using this genome-wide coverage information, Metax can more reliably distinguish genuine microbial signals from false-positive results.

Broad coverage, more reliable estimates

Metax can analyze a wide range of microorganisms, including bacteria, archaea, fungi and other eukaryotic microorganisms, as well as viruses. It therefore provides a more complete picture of the microbial community in a sample and can estimate how abundant the different microorganisms are.

The researchers tested Metax on simulated and real-world samples, including samples from the human microbiome, the environment, wastewater and clinical settings. “Overall, Metax identified microorganisms more accurately and estimated their abundances more reliably than the other methods tested. This is particularly important for low-biomass samples, where microbial signals can be overwhelmed by large amounts of other genetic material, for example, from the host or contaminants,” says McHardy. “Metax can, in a sense, better find the microbial needle in the haystack.”

Uses from surveillance to diagnosis

Accurately identifying which microorganisms are present in a sample and how abundant they are can be useful in many areas. In the human microbiome, more reliable profiling can help uncover microbial patterns linked to health and disease, including potential biomarkers. In wastewater surveillance, it can help track pathogens circulating in a community and provide early warning of outbreaks that might otherwise go unnoticed.

In environmental research, it can help scientists better understand microbial diversity and how microorganisms interact with each other and their surroundings. In clinical metagenomics, more accurate profiling can support the detection of potential pathogens, particularly in samples containing only very small amounts of microbial material.

Further testing and research access

The researchers plan to further develop Metax and evaluate its performance and potential applications in additional microbiome and clinical studies. The Computational Biology for Infection Research group is based at the Braunschweig Integrated Centre of Systems Biology (BRICS), a joint research center of the Helmholtz Centre for Infection Research (HZI) and Technische Universität Braunschweig. McHardy is also a scientist at the German Center for Infection Research (DZIF) and within the RESIST Cluster of Excellence.

Metax is freely available for research use.

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