Bacterial Energy Model Unveils Antimicrobial Spread

Bacteria can sneakily evade our best efforts at eradication by developing resistance to various pressures in their environment. For example, you may be familiar with antibiotic-resistant bacteria that stubbornly survive the usual deadly effects of antibiotics. However, the trade-off for developing a better defense is that it consumes a precious resource: energy. Bacteria must decide how to allocate limited energy resources to develop these resistances and spread them to neighbouring bacteria (through a process called conjugative transfer). There is a certain tipping point where it is worth the energy expenditure, but the details about these thresholds in aquatic environments are poorly understood.

A research team led by Assistant Professor Katayoun Amirfard of the Graduate School of Environmental Studies and Professor Daisuke Sano of the Graduate School of Engineering at Tohoku University analyzed how bacteria in aquatic environments distribute energy across diverse functions such as growth, biofilm formation (a protective barrier), conjugative transfer of antimicrobial resistance genes, and heavy‑metal tolerance. By clarifying bacterial energy investment strategies, this study is expected to contribute to improved water‑environment management.

"These findings offer vital clues to help us stop antibiotic resistant bacteria from spreading in water environment," says Sano.

The findings were published in Water Research on December 18, 2025.

This figure illustrates how bacteria in water environments redistribute their limited energy when exposed to zinc oxide (ZnO). The model shows that ZnO exposure reduces the energy available for conjugation, weakens biofilm development at high concentrations, and increases energy investment in metal resistance during the early exposure period. ©Katayoun et al., 2026

The team employed a mathematical framework based on the Dynamic Energy Budget (DEB) theory. In particular, the researchers focused on how bacterial energy allocation changes over time when exposed to zinc oxide (ZnO), a widely used material commonly found in water environments. Using experimentally measured values, including substrate concentration, biofilm biomass, bacterial density, and conjugation efficiency, they estimated the parameters of the DEBbased model.

Conceptual simplified DEB-based model of bacterial energy allocation in the recipient bacteria. Substrate (S) is assimilated as the primary energy source through the assimilation energy flux (Jassimilation), stored as a reserve energy pool (E), and then mobilized through mobilization energy flux (Jmobilization) to support maintenance (Jmaintenance), growth (Jgrowth), metal resistance (Jmetal), biofilm formation (Jbiofilm), and conjugation (Jconjugation) under metal stress and the presence of potential donor bacteria, which pose external stress leading to the structural biomass (V). Energy fluxes are represented with dashed-lined blue squares, while only reserve and structure are treated as energy pools (solid-line blue squares). ©Katayoun et al., 2026

This study reveals, for the first time, how bacteria in aquatic environments strategically allocate their limited energy among multiple physiological functions under environmental stress. This mechanistic understanding provides a new scientific foundation for assessing how pollutants influence the spread of antimicrobial resistance in the environment, which could inform more effective water‑quality management, pollution control, and public‑health strategies.

"Antimicrobial resistance is a growing global health concern, and its environmental dimensions are less understood than those in clinical settings," remarks Amirfard. "We hope this study helps to fill in that knowledge gap."

Time-resolved conjugation-associated energy flux (Jconjugation) from 0-60 h under ZnO treatments, including the control. Simulations were performed using the ODE-based DEB model. Lines connect the means at 0, 12, 24, 36, 48, and 60 h while shaded bands show ±1 SD across five simulation replicates generated by ±5% Gaussian perturbations of kinetic or yield parameters to capture model uncertainty. ©Katayoun et al., 2026
Publication Details:

Title: Energy allocation trade-offs among conjugative transfer, biofilm formation, and heavy metal resistance: A dynamic energy budget theory perspective

Authors: Katayoun Dadeh Amirfard, Mohan Amarasiri, Daisuke Sano

Journal: Water Research

DOI: 10.1016/j.watres.2025.125216

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