Machine learning identifies antibiotic resistance genes in tuberculosis-causing bacteria

October 25, 2018, University of California - San Diego
Credit: CC0 Public Domain

Researchers at the University of California San Diego have developed an approach that uses machine learning to identify and predict which genes make infectious bacteria resistant to antibiotics. The approach was tested on strains of Mycobacterium tuberculosis—the bacteria that cause tuberculosis (TB) in humans. It identified 33 known and 24 new antibiotic resistance genes in these bacteria.

The researchers say the approach can be used on other infection-causing pathogens, including staph and bacteria that cause urinary tract infections, pneumonia and meningitis. The work was recently published in Nature Communications.

"Knowing which are conferring 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 UC 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 it's resistant to and which ones it's susceptible to, then prescribe the right antibiotic for that strain."

"This could open up opportunities for personalized treatment for your pathogen. Every strain is different and should potentially be treated differently," said co-senior author Bernhard Palsson, Galletti Professor of Bioengineering at the UC San Diego Jacobs School of Engineering. "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."

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 —of more than 1,500 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. 33 were validated with known , the remaining 24 were new predictions that have not yet been experimentally tested.

The researchers further analyzed the algorithm's predictions and identified combinations of alleles that could be interacting together and causing a strain to be antibiotic resistant. They also mapped these alleles onto crystal structures of M. tuberculosis proteins (published in the Protein Data Bank). They 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," said first author 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."

The results of this study are all computational, so the team is looking to work with experimental researchers to test whether the 24 new genes predicted by the algorithm indeed confer antibiotic resistance in M. tuberculosis.

Future studies will involve applying the team's machine learning approach to the leading infectious bacteria, known as the ESKAPE pathogens: Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter species. As a next step, the team is integrating genome-scale models of metabolic networks with their machine learning approach to understand mechanisms underlying the evolution of antibiotic resistance.

Explore further: Five of the scariest antibiotic-resistant bacteria in the past five years

More information: Erol S. Kavvas et al, Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance, Nature Communications (2018). DOI: 10.1038/s41467-018-06634-y

Related Stories

The hidden hazards of antibiotic resistance genes in air

July 25, 2018

People are often notified about poor air quality by weather apps, and this happens frequently in urban areas, where levels of outdoor pollution containing particulates and soot are high. But now scientists are reporting in ...

New antibiotic resistance genes found

October 16, 2017

Researchers at Chalmers University of Technology and the University of Gothenburg, Sweden, have found several previously unknown genes that make bacteria resistant to last-resort antibiotics. The genes were found by searching ...

Fish food for marine farms harbor antibiotic resistance genes

August 30, 2017

From isolated caves to ancient permafrost, antibiotic-resistant bacteria and genes for resistance have been showing up in unexpected places. As scientists puzzle over how genes for antibiotic resistance arise in various environments ...

Distinguishing deadly Staph bacteria from harmless strains

June 6, 2016

Staphylococcus aureus bacteria are the leading cause of skin, soft tissue and several other types of infections. Staph is also a global public threat due to the rapid rise of antibiotic-resistant strains, including methicillin-resistant ...

Recommended for you

Competing species can both survive, study finds

January 21, 2019

When species compete for limited resources, structures in their environment can be the difference between coexistence or one eliminating another. Relationships between species also are important, according to new research ...

0 comments

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.