Researchers at the University of California in San Diego have developed a new machine learning tool that identifies and predicts which genes make infectious bacteria resistant to antibiotics.
The tools was tested on strains of Mycobacterium Tuberculosis - the bacteria that causes Tuberculosis (TB) in humans - and was able to identify 33 known and 24 new antibiotic resistant genes in these bacteria.
The researchers say the approach can be used on other infection-causing pathogens, including Staphylococcus Aureus, the bacteria that cause urinary tract infections, pneumonia and meningitis. The work was recently published in Nature Communications.
If there's a persistent infection of TB in the clinic, physicians can sequence that strain, look at its genes and figure out which antibiotics it's resistant to and which ones it's susceptible to
"Knowing which genes are conferring antibiotic resistance could change the way infectious diseases are treated in the future," said co-senior author Jonathan Monk, research scientist in the Department of Bioengineering at the University of California, San Diego.
"For example, if there's a persistent infection of TB in the clinic, physicians can sequence that strain, look at its genes and figure out which antibiotics it's resistant to and which ones it's susceptible to, then prescribe the right antibiotic for that strain."
It's thought that this could open up opportunities for personalised treatment for everyone's pathogen. Bernhard Palsson, Galletti Professor of Bioengineering at the UC San Diego Jacobs School of Engineering, said that every strain is different and should potentially be treated differently.
"Through this machine learning analysis of the pan-genome - the complete set of all the genes in all the strains of a bacterial species - we can better understand the properties that make these strains different," he explained.
The team trained a machine learning algorithm using the genome sequences and phenotypes of more than 1,500 strains of M. Tuberculosis.
From these inputs, the algorithm predicted a set of genes and variant forms of these genes, called 'alleles', that cause antibiotic resistance. Thirty-three were validated with known antibiotic resistance genes, while the remaining 24 were new predictions that have not yet been experimentally tested.
The team trained a machine learning algorithm using the genome sequences and phenotypes - the physical traits or characteristics that can be observed, such as antibiotic resistance - of more than 1,500 strains of M. Tuberculosis
The researchers also analysed the algorithm's predictions and identified combinations of alleles that could be interacting together and causing a strain to be antibiotic resistant. They then mapped these alleles onto crystal structures of M. Tuberculosis proteins, and found that some of these alleles appeared in certain structural regions of the proteins.
"We did interactional and structural analyses to dig deeper and develop more intricate hypotheses for how these genes could be contributing to antibiotic resistance phenotypes," added Erol Kavvas, a bioengineering Ph.D. student in Palsson's research group.
"These findings could aid future experimental investigations on whether structural grouping of these alleles plays a role in their conferral of antibiotic resistance."
As the results of this study are all computational, the team is now looking to work with experimental researchers to test whether the 24 new genes predicted by the algorithm indeed confer antibiotic resistance in M. Tuberculosis.
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