New Tool Assesses Bacteria to Combat Antibiotic Resistance

A promising new tool is helping the ongoing fight against antibiotic resistance by analyzing bacteria populations, according to a team from Icahn School of Medicine at Mount Sinai in New York City.

While the primary nucleotide sequences ae A’s, G’s, C’s, and T’s, individual DNA bases can be chemically modified as well. Gang Fang, PhD, and his colleagues were able to identify epigenetic mechanisms behind virulence by evaluating DNA methylations — the most prevalent base modifications in the bacterial kingdom.

“We created a technique for the detection and phasing of DNA methylation at the single molecule level,” said Fang, an assistant professor of genetics and genomics at the Icahn School of Medicine at Mount Sinai, in a news release.

Existing methods look at molecule-aggregated, single-nucleotide scores while the innovative tool, called single-molecule modification analysis of long reads (SMALR), is able to analyze a single molecule, single nucleotide. SMALR detects the phasing and functions of DNA methylation which reveals the impact on the bacteria make up.

“Given that phenotypic heterogeneity within a bacterial population can increase its advantage of survival under stress conditions such as antibiotic treatment,” Fang explained, “this new technique is quite promising for future treatment of bacterial pathogens, as it enables de novo detection and characterization of epigenetic heterogeneity in a bacterial population.”

The team assessed 7 bacterial strains using SMALR. According to the findings published in Nature Communications, the bacterium Helicobacter pylori — which is established in 40% of the world population – can divide from a single cell and express distinct gene patterns. This may offer insight to why the bacterium has continued to build a tolerance to antibiotics.

Furthermore, SMALR provides the ability to more precisely look at DNA viruses in order to determine sufficient ways to combat them.

“Resolving nucleotide modifications at the single molecule, single nucleotide level, especially when integrated with other single molecule- or single cell-level data, such as RNA and protein expression, will help resolve regulatory relationships that govern higher order phenotypes such as drug resistance,” one of the authors Eric Schadt, PhD, founding director of the Icahn Institute, concluded.