Researchers have developed "smart tweezers" that can single out specific strains of bacteria from the trillions of microbiomes and sequence their genomes in a more cost- and resource-effective way than existing methods. This versatile tool enables precise study of the microbiome and leads to breakthroughs in disease diagnosis and treatment.
Bacterial genome sequencing has greatly improved our understanding of the biology of many bacterial pathogens and identified novel antibiotic targets. When it comes to the microbiome, researchers often only want to study one type of bacteria, rather than all of them. The problem is that a given bacterium is only part of a complex environment that includes other bacteria, viruses, fungi and host cells, each with its own equally complex DNA.
Currently, scientists need to isolate a specific strain of bacteria from a given sample and use culture media to selectively grow that strain. This is a time- and resource-consuming process that does not work for all bacteria. However, researchers at the Icahn School of Medicine at Mount Sinai in the United States have launched an innovative method, mEnrich-seq, designed to significantly improve the level of microbiome research.
"Imagine you are a scientist and need to study a specific type of bacteria in a complex environment," said Fang Gang, the study's corresponding author. "mEnrich-seq basically provides researchers with a 'smart tweezers' that allows them to pick up the parts they are interested in."
In developing mEnrich-seq, the researchers aimed to distinguish bacteria from each other before sequencing to enrich for bacteria of interest and remove background DNA. To do this, the tool exploits naturally occurring bacterial DNA methylation motifs, the "secret codes" on bacterial DNA that bacteria use to distinguish themselves as part of their own immune system. In fact, in the name "mEnrich-seq", "m" stands for methylation and "seq" stands for sequencing.
Once pinched out by the "smart tweezers," researchers can assemble one or more genomes of the target bacteria to study them more precisely. The researchers demonstrated the power of mEnrich-seq by using mEnrich-seq to reconstruct the genome of E. coli by analyzing urine samples from three patients with urinary tract infections (UTI). They found that the tool covered more than 99.97% of the genomes in all three samples, enabling a comprehensive analysis of antibiotic resistance genes in each genome. mEnrich-seq facilitates culture-free studies of E. coli genomes in the urine microbiome with higher sensitivity (lower relative abundance of bacteria) compared to standard methods.
They then turned their attention to Akkermansiamuciniphila, a bacteria that colonizes the gut and is associated with conditions such as obesity and type 2 diabetes. Isolating this bacterium from stool samples is also notoriously difficult. However, the researchers did this using mEnrich-seq technology, covering more than 99.7% of the Shigella dysenteriae genome across the three samples.
The researchers say that mEnrich-seq provides a more economical method for microbiome research, which is especially beneficial for large-scale studies with limited resources, thus opening up new horizons in various research fields. They said mEnrich-seq can focus on a variety of bacteria and is a versatile tool for research and clinical applications. Through more targeted microbiome studies, mEnrich-seq can accelerate the development of new diagnostic tools and treatments.
"The most exciting thing about mEnrich-seq is its potential to uncover previously missed details, such as antibiotic resistance genes that traditional sequencing methods were unable to detect due to insufficient sensitivity," said Fang. "This could be an important step in combating the global problem of antibiotic resistance."
The researchers plan to improve the tool to further increase its efficiency and expand its application. It is envisaged that mEnrich-seq will become a sensitive and versatile tool in future microbiome research and clinical applications.
The research was published in the journal Nature Methods.