Scientists have developed a scalable method to discover bioactive molecules from previously inaccessible microbes, opening new possibilities in microbiology and antibiotic development.

This innovative approach allows researchers to find bioactive compounds from unculturable microbes, paving the way for new antibiotics and other applications.

Using long-read nanopore sequencing, the team assembled complete genomes from complex soil microbiomes by producing DNA sequences tens of thousands of base pairs long.

The research highlights the immense microbial diversity in soil, which holds promise for discovering new therapeutics and understanding microbial roles in ecosystems and climate.

Researchers at Rockefeller University have devised a method to access the genetic diversity of uncultured soil bacteria by extracting large DNA fragments directly from soil samples, eliminating the need for lab cultivation.

Decoding unculturable microbial genomes through this method can lead to the discovery of novel antibiotics and provide deeper insights into microbial functions in ecosystems.

The team identified two new antibiotics, erutacidin and trigintamicin, effective against drug-resistant bacteria by applying a bioinformatics approach called synBNP to predict and synthesize natural products.

Erutacidin disrupts bacterial membranes, while trigintamicin targets a protein-unfolding motor, both showing promise against resistant bacterial strains.

This approach can be adapted to other environments beyond soil, potentially accelerating the discovery of natural products for various applications.

The study marks a significant technological breakthrough in microbiology, enabling scientists to explore microbial dark matter and speed up drug discovery.

Using this technique, researchers generated hundreds of complete bacterial genomes from a single forest soil sample, with over 99% being new to science, revealing vast microbial diversity.

The method integrates large DNA extraction, long-read nanopore sequencing, and bioinformatics to assemble genomes and predict natural product structures efficiently.