Antibiotic Resistance May Depend on Environment, Danish Researchers Argue

For more than half a century, the diagnosis of bacterial infection has rested on a deceptively simple binary: a laboratory test declares a bacterium either susceptible to a given antibiotic or resistant to it. That single determination shapes which drug a physician prescribes, how a veterinarian treats livestock, and how public health agencies track one of the most serious threats in modern medicine. Now, a team of Danish researchers argues that this foundational assumption is due for a radical revision. Writing in the journal Trends in Microbiology, scientists from the Technical University of Denmark, DTU Bioengineering, the University of Copenhagen, and Copenhagen University Hospital propose a paradigm shift in which antibiotic resistance is understood not as a fixed property of a bacterium, but as a dynamic state that emerges only under specific environmental conditions.

The World Health Organization ranks antimicrobial resistance among the leading global health challenges, and the stakes of getting this science right could hardly be higher. When resistance is misjudged, antibiotics fail in the clinic, infections spread, and the pipeline of effective drugs narrows further. The new opinion piece, authored by Professor Frank Møller Aarestrup of the DTU National Food Institute and colleagues including Professor Thomas Bjarnsholt of the University of Copenhagen and Copenhagen University Hospital, draws on recent experimental findings suggesting that factors such as temperature, oxygen availability, and pH can determine whether a bacterium carrying a resistance gene actually behaves in a resistant manner.

Two underlying studies anchor the argument. One examined how the contrasting pH optima of the beta-lactamase enzymes CTX-M and CMY influence the fitness and resistance ecology of Escherichia coli. The other showed that environmental factors, specifically pH and redox potential, modulate beta-lactamase phenotypes and thereby alter beta-lactam resistance. Together, these findings indicate that the expression of resistance is not an intrinsic, unvarying trait encoded neatly in a gene, but a phenotype that depends on the microenvironment in which the bacterium finds itself at a given moment.

According to Aarestrup, this realization demands that researchers ask a far more granular set of questions. Is a bacterium carrying a resistance gene resistant at a pH of 5 or 8, when standard laboratory testing is performed at a fixed pH of 7.2? What oxygen levels and temperatures permit resistance to manifest? Does a patient’s fever change the answer? Is the bacterium residing in the oxygen-poor environment of the gut or in the oxygenated bloodstream? Current diagnostic protocols, he notes, take none of these biological conditions into account, even though they are precisely the conditions a bacterium encounters inside the human body.

Bjarnsholt frames the conceptual shift in equally stark terms. Resistance genes, he emphasizes, do not function in a vacuum. They exist in different bacterial species and in varying microenvironments, and the scientific community must therefore stop asking only which resistance genes a bacterium possesses and start asking when those genes actually render the bacterium resistant. The distinction is more than semantic. It implies that a bacterium classified as resistant in a standardized assay could behave as fully susceptible in the tissue of a patient, and vice versa, potentially explaining the frustrating clinical scenarios in which a course of antibiotics fails despite a laboratory report predicting success, or succeeds when the laboratory predicted failure.

The researchers acknowledge that embracing this context-dependent view of resistance will make laboratory measurement considerably harder. For decades, enormous effort has gone into standardizing susceptibility testing worldwide, so that a result produced in Copenhagen can be compared with one produced in Nairobi or São Paulo. Measuring resistance across a matrix of pH values, oxygen concentrations, temperatures, and host conditions multiplies the experimental burden many times over. Yet the researchers contend that the payoff justifies the effort, because a richer dataset would capture the true behavior of resistance genes under the conditions that matter in real infections.

Perhaps the most provocative implication concerns treatment strategy. If resistance manifests only when certain conditions are met, then it may become possible to select antibiotics that trigger the development of resistance only under circumstances that never occur in the human body, for example at a temperature far above physiological norms. Such a strategy would, in principle, allow clinicians to treat infections while deliberately choosing drugs whose resistance pathways remain switched off in the patient. Aarestrup suggests that this line of thinking could open entirely new approaches to combating resistance in humans, animals, and the wider environment, moving beyond the current reactive cycle of drug use followed by resistance emergence.

The environmental dimension of the proposal is equally significant. Resistance genes circulate through soil, water, wastewater, and animal populations, and monitoring efforts have long struggled to interpret what the presence of a gene in these reservoirs actually means for human health. If gene presence no longer equates to functional resistance, environmental surveillance could be refined to identify the conditions under which environmental resistance genes become clinically relevant, offering a better chance of intervening before dangerous resistance traits migrate into human pathogens. More targeted treatment of animals could similarly reduce the selective pressure that farms currently exert on bacterial communities.

Realizing this vision will require an enormous collective undertaking. The researchers argue that large volumes of new data on resistance across a range of biological conditions must be collected, effectively restarting resistance gene research from square one. Aarestrup envisions grouping resistance genes into categories and then deploying computational models and artificial intelligence to identify the relationships between genes and varying conditions, a task well suited to machine learning given the combinatorial complexity of gene-by-environment interactions. Such an effort would revisit and reassess much of the knowledge accumulated over decades of research built on the old binary model.

The opinion piece, titled From resistance genes to resistance states and enzymatic context-dependence in antimicrobial resistance, was published on 16 September 2026 and declares no competing interests. Whether the field adopts the proposed framework will depend on whether laboratories, regulators, and diagnostic manufacturers are willing to abandon a century of standardized practice in favor of a messier but potentially far more accurate picture of microbial behavior. If the Danish team is right, the future of antibiotic stewardship may lie not in discovering new drugs alone, but in understanding precisely when, where, and under what conditions the drugs already in use lose their power, and in engineering treatment strategies that keep resistance from ever switching on.

Subject of Research: Context-dependent antibiotic resistance and the environmental conditions that determine whether resistance genes make bacteria resistant

Article Title: Researchers in Denmark aim to find new solutions in the fight against antibiotic resistance

Article References: Researchers in Denmark aim to find new solutions in the fight against antibiotic resistance. (n.d.). Original publication

Image Credits: AI Generated

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Keywords: antibiotic resistance, antimicrobial resistance, Trends in Microbiology, Technical University of Denmark, beta-lactamase, pH, microenvironment, diagnostics, Escherichia coli, resistance genes, public health, WHO