U.Va. computer models help understand C. difficile, antibiotic resistance – The Cavalier Daily

U.Va.  computer models help understand C. difficile, antibiotic resistance - The Cavalier Daily

Every year, Clostridium difficile infects about half a million people in the United States, most of whom are hospitalized patients. C.difficile is a toxin producer bacteria capable of causing symptoms such as severe diarrhea and colitis. Due to the growing antibiotic resistance of C. difficile, biomedical engineering professor Jason Papin is using computer models to study and better understand the infection.

The increased prevalence of C. difficile stems from the bacteria’s ability to rebound after antibiotic treatment, contributing to making it the leading cause of nosocomial infections. Antibiotics are a common treatment for patients with bacterial infections given their ability to slow the growth of the bacteria or kill it. Bacteria, however, can develop strategies to defend themselves against antibiotics, known as resistance mechanisms. The presence of these resistance mechanisms can lead to antibiotic resistance in which antibiotics are ineffective and infections can become untreatable.

Matthew Jenior, a postdoctoral researcher in Papin’s lab, said the drugs used to treat the bacteria create a self-perpetuating cycle.

“Unfortunately, the drugs that we used to treat C. difficile are now increasing the number of cases of resistance to it, leading to this cycle of recurring infection where you get infected, you treat it, you stop the antibiotics and he comes back,” Jénior said.

Due to the growing antibiotic resistance of C. difficile, the Papin lab uses computers to model the systems that occur in bacteria to better understand specific cell cells. metabolic pathwaysor a series of interactions between genes and their products that form and break down molecules for essential cellular processes.

“The objective of the laboratory is [on] build computer models of protein and metabolite networks inside cells,” Papin said.

These computer models are mostly specific to C. difficile, but the laboratory’s past and current research on similar bacteria and other pathogens, or pathogenic agents, contributed to research on C. difficile.

For example, the Papin laboratory compares the hypervirulent forms to the virulent forms of C. difficile. Hypervirulent strains of bacteria, viruses or fungi are extremely pathogenic and capable of producing severe disease compared to ordinary virulent strains which produce typical disease. Jenior describes comparing the two forms as taking what they’ve learned about virulent strains and seeing if that applies to hypervirulent strains and if they’re associated with poorer patient outcomes.

Papin’s lab initially studied the C. difficile bacterium in isolation, but began including its interactions and behavior using advanced computer models within a host about four years ago when Jenior joined. the team.

These advanced computer models are called GENRES, or reconstructions of genome-wide metabolic networks. To develop the computer models, Papin said complex mathematical equations are programmed into the computer.

In sum, the mathematical equations represent different types of chemical reactions that could occur, which helps researchers predict the conditions they should test with C. difficile. By testing various substances with C. difficile and how it reacts to it, researchers in Papin’s lab can find out which compounds are necessary for the bacteria to grow and survive.

A recent article by Papin and his lab noted that the expression of specific genes in the bacterium in response to different metabolic conditions provided information about C. difficile.

“[The] different ways in which C. difficile activates its ability to be a pathogen [and cause infections] were related in different ways to metabolism,” Jenior wrote.

Because of the correlation between C. difficile metabolism and its pathogenic properties, Papin’s lab was able to test these conditions in the real world. Papin explains how the laboratory is able to hypothesize whether or not the bacteria can survive under particular conditions.

“You make predictions about whether this bacteria can grow if I give it this type of compound or sugar or not,” Papin said.

Using the computer’s prediction capabilities, Jenior mentions how the lab was able to manipulate the bacteria’s environment to compare whether what the computer predicted was accurate.

Computer models require a lot of computing power and therefore the lab benefits from collaborations with other research labs. The laboratory team has collaborated with several colleaguesincluding Rita Tamayo of the University of North Carolina, Chapel Hill and Bill Petri Jr. of the University’s Division of Infectious Diseases and International Health, to confirm the accuracy of their computer models after they were developed.

Fourth-year engineering student Mary Dickenson worked on this C. difficile research, as well as projects on other bacteria and pathogens.

“The C.diff. project is what I walked in and started working on initially,” Dickenson said. “So I did a lot of the modeling and troubleshooting…and not just computational or experimental, there’s this nice hybrid approach.”

Dickenson said his research experience was an entirely hands-on process involving several key players.

“[Papin’s lab] is a very collaborative environment, so we all work together to resolve any issues that arise,” Dickenson said.

Through his research experience, Dickenson was able to learn several new skills in the laboratory, including different computational techniques, introduced by computational models of C. difficile.

These computer models and others dry lab Tools, such as applied mathematical or computational analysis, can increase the efficiency of wet labs that involve the use and analysis of drugs, chemicals, or other types of biological materials using various liquids .

“[Computer models] can really speed up testing in a humid lab environment, [by] reduce many of the possibilities available to you, [so you can have] a more targeted approach to performing wet lab testing,” Dickenson said.

Papin said there is potential for the use of computer models more generally in the field of medicine.

“The computer model can hopefully predict which genes are essential for the microbe, and so if you know which genes are essential, you know the different targets you would hit with a drug,” Papin said.

For example, if a drug can alter or destroy the genes needed for C. difficile to reproduce, then that drug could be an effective treatment option for the infection.

Papin’s modeling is essential not only to understand the reproduction of C. difficile, but also the survival of the bacterium. Computational models have recently been able to predict a specific cellular process that C. difficile relies heavily on to utilize carbon as a food source.

“If we find the most important carbon sources among [the bacteria species in the healthy gut microbiome]”, said Jenior. “Then we can start looking for the species present in healthy people who are the best to take [carbon sources] away from C. diff. then build proto-targeted probiotics [with these good bacteria to] avoid antibiotics altogether” – ultimately depriving C. difficile of its needed carbon.

A 2021 study the use of probiotics to treat C. difficile found that the rate of nosocomial Clostridioides difficile infection improved by 39% in a Quebec hospital when 70% of antibiotic users took a three-strain Lactobacillus probiotic in the framework of a pharmacy-based protocol.

Given the growing understanding of the capabilities of computer models, Papin and his lab hope that doctors of the future will be able to treat patients more effectively based on new predictions generated by computer models.

By studying the biochemical processes of the bacterium C. difficile, the Papin Laboratory makes it possible to apply the understanding of the nature of the bacterium to new treatment options for the bacterial infection C. difficile.