This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: When scientists assess how dangerous a bacterium is, the first question often seems simple: Can it be stopped or killed? In microbiology and antibiotic research, one of the classic readouts has been whether a bacterium can grow in the presence of a given treatment.
The bacterium either grows or it does not. The drug either stops it or it does not. But infection is not just a survival contest.
What bacteria do near human cells can be just as important as whether they remain alive. Do they enter host cells? Do they spread across many cells or accumulate in large numbers in only a few?
Do they trigger DNA damage that may contribute to inflammation, tissue damage or longer-term disease processes? To address these questions, researchers at the HUN-REN Biological Research Center, Szeged, and the Hungarian Center of Excellence for Molecular Medicine developed MALVINA, short for Machine Learning-Based Virulence Interaction Analysis. The method detects fluorescently labeled bacteria inside human cells and uses machine learning to analyze microscopy images.
The paper is published in the journal Nature Communications. This allows researchers to measure, in the same experimental system, how efficiently bacteria enter cells, how strongly they accumulate inside them and what kind of DNA damage they induce. "One of the strengths of the method is that we can see the details of infection cell by cell.
We can distinguish whether a few bacteria enter many cells or whether large numbers accumulate in only a few cells, while also measuring the damage they cause," said Bence Bognár, co-first author of the study. MALVINA does more than produce more detailed images of infected cells. It changes the type of questions researchers can ask about bacterial virulence.
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