AI may offer a better way to ID drug-resistant superbugs

Date:
August 4, 2020
Source:
Duke University
Summary:
Biomedical engineers have shown that different strains of
the same bacterial pathogen can be distinguished by a machine
learning analysis of their growth dynamics alone, which can
then also accurately predict other traits such as resistance
to antibiotics. The demonstration could point to methods for
identifying diseases and predicting their behaviors that are
faster, simpler, less expensive and more accurate than current
standard techniques.



FULL STORY ========================================================================== Biomedical engineers at Duke University have shown that different strains
of the same bacterial pathogen can be distinguished by a machine learning analysis of their growth dynamics alone, which can then also accurately
predict other traits such as resistance to antibiotics. The demonstration
could point to methods for identifying diseases and predicting their
behaviors that are faster, simpler, less expensive and more accurate
than current standard techniques.


==========================================================================
The results appear online on August 3 in the Proceedings of the National Academy of Sciences.

For most of the history of microbiology, bacteria identification
has relied on growing cultures and analyzing the physical traits and
behaviors of the resulting bacterial colony. It wasn't until recently
that scientists could simply run a genetic test.

Genetic sequencing, however, isn't universally available and can often
take a long time. And even with the ability to sequence entire genomes,
it can be difficult to tie specific genetic variations to different
behaviors in the real world.

For example, even though researchers know the genetic mutations that
help shield/protect bacteria from beta-lactam antibiotics -- the most
commonly used antibiotic in the world -- sometimes the DNA isn't the
whole story. While a single resistant bacteria usually can't survive a
dose of antibiotics on its own, large populations often can.

Lingchong You, professor of biomedical engineering at Duke, and his
graduate student, Carolyn Zhang, wondered if a new twist on older
methods might work better. Maybe they could amplify one specific physical characteristic and use it to not only identify the pathogen, but to make
an educated guess about other traits such as antibiotic resistance.



==========================================================================
"We thought that the slight variance in the genes between strains of
bacteria might have a subtle effect on their metabolism," You said. "But because bacterial growth is exponential, that subtle effect could be
amplified enough for us to take advantage of it. To me, that notion is
somewhat intuitive, but I was surprised at how well it actually worked."
How quickly a bacterial culture grows in a laboratory depends on the
richness of the media it is growing in and its chemical environment. But
as the population grows, the culture consumes nutrients and produces
chemical byproducts. Even if different strains start with the exact
same environmental conditions, subtle differences in how they grow and influence their surroundings accumulate over time.

In the study, You and Zhang took more than 200 strains of bacterial
pathogens, most of which were variations of E. coli, put them into
identical growth environments, and carefully measured their population
density as it increased.

Because of their slight genetic differences, the cultures grew in fits
and starts, each possessing a unique temporal fluctuation pattern. The researchers then fed the growth dynamics data into a machine learning
program, which taught itself to identify and match the growth profiles
to the different strains.

To their surprise, it worked really well.

"Using growth data from only one initial condition, the model was able
to identify a particular strain with more than 92 percent accuracy,"
You said.

"And when we used four different starting environments instead of one,
that accuracy rose to about 98 percent." Taking this idea one step
further, You and Zhang then looked to see if they could use growth
dynamic profiles to predict another phenotype -- antibiotic resistance.



==========================================================================
The researchers once again loaded a machine learning program with the
growth dynamic profiles from all but one of the various strains, along
with data about their resilience to four different antibiotics. They then tested to see if the resulting model could predict the final strain's antibiotic resistances from its growth profile. To bulk up their dataset,
they repeated this process for all of the other strains.

The results showed that the growth dynamic profile alone could
successfully predict a strain's resistance to antibiotics 60 to 75
percent of the time.

"This is actually on par or better than some of the current techniques
in the literature, including many that use genetic sequencing data,"
said You. "And this was just a proof of principle. We believe that with higher-resolution data of the growth dynamics, we could do an even better
job in the long term." The researchers also looked to see if the strains exhibiting similar growth curves also had similar genetic profiles. As
it turns out, the two are completely uncorrelated, demonstrating once
again how difficult it can be to map cellular traits and behaviors to
specific stretches of DNA.

Moving forward, You plans to optimize the growth curve procedure to reduce
the time it takes to identify a strain from 2 to 3 days to perhaps 12
hours. He's also planning on using high-definition cameras to see if
mapping how bacterial colonies grow in space in a Petri dish can help
make the process even more accurate.


========================================================================== Story Source: Materials provided by Duke_University. Original written
by Ken Kingery. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Carolyn Zhang, Wenchen Song, Helena R. Ma, Xiao Peng, Deverick J.

Anderson, Vance G. Fowler, Joshua T. Thaden, Minfeng Xiao,
Lingchong You.

Temporal encoding of bacterial identity and traits in growth
dynamics.

Proceedings of the National Academy of Sciences, 2020; 202008807
DOI: 10.1073/pnas.2008807117 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200804122213.htm

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