UT Southwestern Unveils New Method for Fighting Complex Infections

Treating complex bacterial infections with customized therapies tailored to the infection and the patient is closer to reality, thanks to researchers at UT Southwestern Medical Center.

Using technology and techniques pioneered at UT Southwestern, researchers developed an innovative approach to a long-standing challenge in the field – predicting how changes in gene expression and environment interact to control the growth rate of bacteria. The findings, published in Cell Systems, could in the future have wide-ranging applications, including predicting antibiotic sensitivity, understanding genetic factors that promote antibiotic resistance, and engineering bacterial strains to create new small-molecule compounds for use in drug discovery. 

Today, it's common at advanced institutions like UT Southwestern to treat infections by gathering DNA and RNA data about the infecting bacteria from patient samples. That data can be used to guide treatment and improve prognosis, but doing so is complicated and time-consuming. Our work creates additional tools that we hope will one day aid in interpretation of these types of complex data, facilitating the transition toward truly personalized health care."

Kimberly Reynolds, Ph.D., Associate Professor in the Lyda Hill Department of Bioinformatics and Biophysics

The study exploited a method called titratable CRISPRi, which allowed the researchers to gradually turn down, or titrate, the expression of specific genes. That process allowed researchers to compare hundreds of gene suppression options at various levels of intensity and build a model to predict how bacteria will grow in different environments.

The research team was aided by a custom-built device called a turbidostat, which precisely controls bacterial environments. That device was constructed with guidance from Erdal Toprak, Ph.D., Associate Professor of Pharmacology and in the Lyda Hill Department of Bioinformatics, who is also a Southwestern Medical Foundation Scholar in Biomedical Research at UTSW.

Powered by titratable CRISPRi, the computer model can be trained to predict a wide range of outcomes using a small number of inputs – without requiring a detailed understanding of the mechanics of bacterial biology.

"We tackled the daunting question of how numerous changes in a cell's expression profile combine to affect fitness by breaking it down into simpler parts," said first author Ryan Otto, a graduate student and member of the Reynolds Lab. "First, we modeled the interactions present between pairs of genes, then combined these interactions to complete the full picture and predict effects for multiple genes and environments." 

The study and its findings are examples of UTSW's commitment to bringing together computational biology, quantitative experiments, and translational medicine, Dr. Reynolds said.

"This study demonstrates the growing importance of predictive and quantitative studies in modern science and medicine, which is stressed continuously in our curriculum in both graduate and medical school settings," she said. "Our continued focus on microbiology and computational modeling will pave the way for more breakthroughs like this one."

The study builds on previous work at UTSW that pioneered the technical framework for large-scale bacterial growth rate assays using titratable CRISPRi.

Philip M. Brown, M.D./Ph.D. candidate at UTSW, also contributed to the study.

This research was funded by National Institutes of Health grants (R01GM136842 and 5T32GM007062-46).

Source:
Journal reference:

Otto, R. M., et al. (2024). A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment. Cell Systems. doi.org/10.1016/j.cels.2024.01.003.

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