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Peer-reviewedCC BY-NC-ND 4.0Nature Communications

Higher-order epistasis drives evolutionary unpredictability toward new antibiotic resistance

  • Ilona Kamila Gaszek1,2
  • Muhammed Sadik Yildiz1,2
  • Zhaoxu Meng2
  • Jose Alberto de la Paz3
  • Sophia Maria Alvarez3
  • Deniz Sezer1
  • Faruck Morcos3,4,5,6
  • Milo Lin2,7,8
  • Erdal Toprak1,2,8
8 affiliations
  1. 1Department of Pharmacology, The University of Texas Southwestern Medical Center, Dallas, TX USA
  2. 2Lyda Hill Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, TX USA
  3. 3Department of Biological Sciences, University of Texas at Dallas, Richardson, TX USA
  4. 4Department of Bioengineering, University of Texas at Dallas, Richardson, TX USA
  5. 5Department of Physics, University of Texas at Dallas, Richardson, TX USA
  6. 6Center for Systems Biology, University of Texas at Dallas, Richardson, TX USA
  7. 7Department of Biophysics, The University of Texas Southwestern Medical Center, Dallas, TX USA
  8. 8Green Center for Systems Biology, The University of Texas Southwestern Medical Center, Dallas, TX USA
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Abstract

Antibiotic resistance has become a critical public health problem, rendering many antibiotics ineffective. In particular, the evolution of extended-spectrum β-lactamases (ESBLs) threatens β-lactams, the cornerstone of bacterial infection treatment. We investigated the evolution of Escherichia coli TEM-1 β-lactamase into ESBLs by constructing a combinatorially complete library of all 55,296 TEM-1 variants from 18 clinical mutations across 13 residues. We obtained over 9,000,000 fitness measurements under native (ampicillin) and non-native (aztreonam) selection. Graph-theoretic and epistatic analyses revealed that ampicillin selection produced weak epistasis and predictable evolutionary trajectories, whereas aztreonam selection induced extensive higher-order epistasis, increasing phenotypic unpredictability. Machine learning identified interpretable epistatic rules shaping these landscapes. Evolutionary statistics, including direct coupling analysis and latent generative landscapes, showed that top-performing ESBL variants followed conserved epistatic patterns observed in natural β-lactamases. Our integrated experimental–computational framework provides a foundation for predicting ESBL evolution and quantifying mutational contributions to ESBL variants.

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Cite

BibTeXRIS
BibTeX
@article{gaszek2026higherorder,
  title = {{Higher-order epistasis drives evolutionary unpredictability toward new antibiotic resistance}},
  author = {Ilona Kamila Gaszek and Muhammed Sadik Yildiz and Zhaoxu Meng and Jose Alberto de la Paz and Sophia Maria Alvarez and Deniz Sezer and Faruck Morcos and Milo Lin and Erdal Toprak},
  journal = {Nature Communications},
  year = {2026},
  month = sep,
  doi = {10.1038/s41467-026-77182-z},
  url = {https://doi.org/10.1038/s41467-026-77182-z},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Higher-order epistasis drives evolutionary unpredictability toward new antibiotic resistance
AU  - Ilona Kamila Gaszek
AU  - Muhammed Sadik Yildiz
AU  - Zhaoxu Meng
AU  - Jose Alberto de la Paz
AU  - Sophia Maria Alvarez
AU  - Deniz Sezer
AU  - Faruck Morcos
AU  - Milo Lin
AU  - Erdal Toprak
PY  - 2026
DA  - 2026/09/04
JO  - Nature Communications
DO  - 10.1038/s41467-026-77182-z
UR  - https://doi.org/10.1038/s41467-026-77182-z
AB  - Antibiotic resistance has become a critical public health problem, rendering many antibiotics ineffective. In particular, the evolution of extended-spectrum β-lactamases (ESBLs) threatens β-lactams, the cornerstone of bacterial infection treatment. We investigated the evolution of Escherichia coli TEM-1 β-lactamase into ESBLs by constructing a combinatorially complete library of all 55,296 TEM-1 variants from 18 clinical mutations across 13 residues. We obtained over 9,000,000 fitness measurements under native (ampicillin) and non-native (aztreonam) selection. Graph-theoretic and epistatic analyses revealed that ampicillin selection produced weak epistasis and predictable evolutionary trajectories, whereas aztreonam selection induced extensive higher-order epistasis, increasing phenotypic unpredictability. Machine learning identified interpretable epistatic rules shaping these landscapes. Evolutionary statistics, including direct coupling analysis and latent generative landscapes, showed that top-performing ESBL variants followed conserved epistatic patterns observed in natural β-lactamases. Our integrated experimental–computational framework provides a foundation for predicting ESBL evolution and quantifying mutational contributions to ESBL variants.
SN  - 2041-1723
ER  -