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Mapping Resistance Evolution: From Bench to Bedside

Exploring the genetic and ecological factors driving antibiotic resistance to design effective, evolution-inspired therapeutic strategies.

Image description: A scientist, Kyle Card, in glasses examines a petri dish in a lab, surrounded by bottles on shelves. The setting conveys focus and scientific inquiry.

Credit: Kaitlin K. Walsh/AP Images for HHMI

I am dedicated to understanding and combating antibiotic resistance. My research combines experimental evolution, functional genomics, and computational modeling to identify the genetic and environmental factors that drive resistance. By integrating these approaches, I aim to develop innovative strategies to predict and guide the evolutionary trajectories of pathogens, ultimately improving treatment outcomes.

I am a disabled scientist born with a rare neurological condition called Moebius syndrome, which affects the muscles controlling facial expressions and eye movement. I also have several limb differences. Although my disabilities pose challenges, my identity as a disabled person has enriched my academic life in many ways. Through my lived experiences, I appreciate that, despite our differences, our curiosity about the natural world binds us together, each of us has a unique story that should be respected, and we all deserve a voice in science. I honor these ideals by giving my time to advocacy, mentorship, outreach, and learning.

I am committed to creating and maintaining an environment in which all are welcome and respected—one that is inclusive of race, gender, faith, sexual orientation, ability, and socioeconomic status.

Image description: White, cursive signature of Kyle Card on a black background. The handwriting is elegant and fluid, conveying a sense of sophistication and formality.

Kyle Card, Ph.D.
HHMI Hanna H. Gray Postdoctoral Fellow

Evolution, History, and Control in Antibiotic Resistance

Connecting foundational discoveries to a clinic-ready vision.

Limiting resistance in the clinic.

Imagine a scenario where a clinician strategically uses existing antibiotics to prevent or reverse drug resistance in an infection. This approach should be possible by exploiting drug tradeoffs in which the evolution of resistance to one therapy increases susceptibility to others. They would only need to alternate to one of these other drugs at the appropriate time.

Image description: 3D graph showing resistance evolution to an antibiotic, with labeled points A, B, and AB, and axis labeled "Drug 1." Dark background with descriptive text.
Image description: 3D graph representing an evolutionary tradeoff. A point labeled 'AB' with the text '...might increase a bacterium’s susceptibility to other drugs' is highlighted. Dark background with descriptive text.

However, interactions between mutations and their genetic backgrounds can open new adaptive routes or restrict others, challenging our ability to predict in which direction a population will evolve. We must, therefore, comprehensively understand how these interactions shape resistance evolution to design effective treatment strategies.

Image description: 3D graph with points A and B connected by lines, illustrating mutations leading to increased antibiotic resistance against Drug 1. Dark background with text.
Image description: 3D graph shows effects of mutations, labeled A and B, in Drug 2. Text explains that one mutation increases drug susceptibility to Drug 2, while the other increases resistance; outcomes depend on evolutionary path.

History matters.

As an HHMI Gilliam Fellow, I investigated how a bacterium's prior history influences its ability to evolve antibiotic resistance. Using E. coli strains from the Long-Term Evolution Experiment, we found that genomic differences between lines can unpredictably alter resistance and channel evolution down particular mutational pathways.

Although this work implicates genetic interactions in our ability to forecast resistance evolution, it is insufficient to focus solely on model organisms. We must systematically investigate these relationships in pathogens, bridging the divide between bench and bedside.

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Image description: The image shows the acronym "PNAS" in bold white letters on a black background, with a thick horizontal blue line underneath.

From model systems to pathogens.

As an HHMI Hanna H. Gray Fellow, I explored drug tradeoffs in S. aureus. We discovered that this opportunistic pathogen followed at least two distinct adaptive pathways as it evolved vancomycin resistance. These separate paths led to contrasting, yet predictable sensitivities to second-line antibiotics. To account for this uncertainty, we developed a framework called the Collateral Response Score (CRS) to provide a probabilistic forecast of how past antibiotic exposure might affect future treatment outcomes.

Image description: The image shows the acronym "PNAS" in bold white letters on a black background, with a thick horizontal blue line underneath.
Image description: Electron microscope image showing two rounded cells with fibrous extensions on a textured surface, resembling a network. Color tone is cool blue.

THE CARD LAB: Where persistence meets resistance.

We established that a pathogen's evolutionary history is key to forecasting its adaptability and therapeutic responses. However, evolution is context-dependent. The selective pressures inside a flask are vastly different from those inside the complex, dynamic environment of the human body, where evolution occurs. To develop effective, evolution-informed therapies, we must bridge the gap between reductionist lab experiments and the clinical reality of infections.

Image description: Four people in an office gather around a wooden desk with a computer. One person, Kyle Card, holds a paper, others smile, engaged in discussion. The mood is collaborative and upbeat.

My laboratory will pioneer the use of organ-chip technology to overcome this challenge. By integrating experimental evolution in these high-fidelity systems with functional genomics and phenotypic screens, we will dissect the molecular and evolutionary mechanisms of pathogen adaptation in real-time, creating a framework to predict and ultimately steer the evolution of antimicrobial resistance. We will focus on chronic P. aeruginosa infections in cystic fibrosis, an area with an unmet clinical need and an ideal model system for studying within-host evolution.

The Team

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    Justin Creary

    GRADUATE STUDENT, CWRU

    Justin is investigating how prior evolutionary history under varying concentrations of tobramycin affects the ability of P. aeruginosa to evolve resistance to clinically relevant second-line drugs.

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    Amira Stocks

    UNDERGRADUATE STUDENT, CWRU

    Amira is investigating the impact of biofilm formation and population bottleneck size on the evolutionary trajectories of antibiotic resistance in P. aeruginosa and how these pathways differ from planktonic populations.

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    Shiva Ayyar

    UNDERGRADUATE STUDENT, CWRU

    Shiva is examining the combined impact of genetic background and mutation supply on the potential of P. aeruginosa to evolve resistance to several first-line antibiotics.

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    Rohan Desai

    UNDERGRADUATE STUDENT, VANDERBILT

    Rohan joined our lab this summer and is working with Justin and Shiva to knock out the methyl-directed mismatch repair gene mutS in P. aeruginosa, thereby increasing its point-mutation rate. They will use these mutants in their study of
    mutation supply.

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    Aryan Agarwal

    HIGH SCHOOL STUDENT

    Aryan is a high school student with us this summer. He is working with Shiva, using previous whole-genome sequence data from experimentally evolved S. aureus lines to derive a “heterogeneity
    score,ˮ and assessing whether this metric
    correlates with particular drug trade-offs.