The distinction between resistance and tolerance is central to the argument. Resistant bacteria grow even in the presence of an antibiotic, and the mutations that confer resistance are readily detected because they allow measurable growth under drug pressure. Tolerant bacteria, by contrast, survive antibiotic exposure without necessarily growing, and they do so in ways that can be temporary and hard to distinguish from ordinary physiological stasis. That has historically made tolerance difficult to study: if a phenotype is transient and non-heritable, it leaves little trace in the kinds of experiments that classical microbiology was built to perform. The result has been a research landscape in which resistance mechanisms are well catalogued while tolerance mechanisms remain comparatively obscure.

Kobras’s review contends that this obscurity is no longer tenable, because the tools now exist to interrogate tolerance at the level of the genome. Genome-wide analyses, combined with laboratory evolution experiments and functional genetic screens, have begun to reveal the pathways that allow bacteria to survive antibiotic exposure beyond the familiar model of dormant, metabolically quiescent cells. Laboratory evolution, in which bacterial populations are propagated under antibiotic stress over many generations, allows tolerance-associated variants to accumulate and be identified through whole-genome sequencing of the evolved lineages. Functional screens, including genome-scale knockout and overexpression libraries, allow researchers to ask systematically which genes, when disrupted or amplified, change how long cells survive a drug.

The technical power of these approaches lies in their convergence. Sequencing an evolved population identifies candidate mutations, but it cannot by itself distinguish causal changes from hitchhikers. Functional screens provide the complementary evidence: if a mutation identified by evolution also produces a tolerance phenotype when introduced deliberately, the case for causality strengthens. Combining the two, alongside precise reconstruction of variants in clean genetic backgrounds, allows the genetic architecture of tolerance to be resolved, including cases where multiple loci interact. This is where the concept of epistasis becomes important. The review highlights that the effects of tolerance variants may depend on the presence of other variants, meaning that the same mutation can produce different survival outcomes in different genetic backgrounds. Untangling these interactions is essential if tolerance is to be predicted from sequence data alone.

One of the most consequential shifts described in the review is conceptual. The dominant model of antibiotic tolerance has centred on persisters, a subpopulation of cells that enter a dormant or slow-growing state and thereby evade antibiotics that kill actively growing bacteria. Dormancy remains an important part of the picture, but the genomics-driven work summarised by Kobras reveals survival pathways that operate independently of the dormant-cell model. Bacteria can survive antibiotic exposure through active physiological responses, stress-response programmes and metabolic adjustments that are encoded in the genome and subject to evolutionary modification. Recognising this breadth matters because it expands the set of potential intervention points: if tolerance is not solely a property of dormant cells, then strategies aimed solely at waking persisters up may address only part of the problem.

The clinical stakes are considerable. Treatment failure in bacterial infections is usually attributed to resistance, but tolerance can plausibly explain cases in which patients fail therapy despite isolates that test as susceptible in the laboratory. Standard susceptibility testing measures growth inhibition at fixed drug concentrations, a framework that can miss survival phenotypes expressed over longer time scales or under the fluctuating drug concentrations that occur in treated patients. Tolerant populations also buy time for resistance to emerge, because cells that survive initial exposure constitute the population in which resistance mutations can subsequently arise. The review therefore frames tolerance not only as a direct cause of treatment failure but as a factor that shapes the evolution of resistance itself, an influence that genomic approaches are now positioned to quantify.

That connection between tolerance and resistance evolution is one of the areas the review identifies as ripe for further work. If particular tolerance genotypes accelerate the emergence of resistance, then detecting those genotypes in patient-derived isolates could inform treatment choices before resistance becomes detectable by conventional means. Genome-wide analyses of tolerance offer a route to identifying candidate genes and variants in clinical isolates, extending the insights of laboratory systems to the strains that actually cause disease. Population genomics of clinical collections could, in principle, reveal whether tolerance-associated variants recur in specific lineages, correlate with treatment outcomes, or precede the acquisition of classical resistance determinants.

Kobras is careful, however, about the limits of the current evidence base. Most of what is known about the genetics of tolerance comes from laboratory systems: reference strains, defined media, controlled drug exposures and evolution experiments designed to make phenotypes measurable. Whether the pathways identified under those conditions operate to the same extent, or in the same combinations, in the environments of actual infections is unresolved. Infection sites present bacteria with nutrient limitation, host immune pressures, biofilm structures and antibiotic concentrations that vary over space and time, and any of these factors could modify the contribution of a given tolerance variant. The review presents the translation of laboratory insights into patient-derived isolates as the key outstanding challenge, and as the step that will determine whether genomic tolerance research changes clinical practice.

The potential payoffs of meeting that challenge are spelled out in diagnostic and therapeutic terms. On the diagnostic side, a better understanding of tolerance genetics could support assays that flag tolerant genotypes in isolates, complementing conventional susceptibility testing and giving clinicians a more complete picture of how a pathogen is likely to behave during therapy. On the therapeutic side, the pathways revealed by genome-wide analyses define candidate targets for drugs that would sensitize tolerant bacteria to existing antibiotics, an attractive strategy because it would extend the useful life of drugs already in the clinic rather than requiring entirely new classes of antimicrobials. Combination therapy designed to eliminate tolerant subpopulations could also reduce the reservoir from which resistance emerges, addressing both problems simultaneously.

The review, which is open access and was published on 2 October 2026 after being received on 28 May and accepted on 24 August, arrives at a moment when antimicrobial resistance remains one of the most pressing threats to global health, and when the research community is increasingly aware that resistance alone does not explain every therapeutic failure. By consolidating the genomics-driven transformation of tolerance research, Kobras provides a roadmap for a field moving from phenomenology toward mechanism. The core message is that antibiotic tolerance has a genetics, that this genetics is now tractable with modern tools, and that resolving it, first in the laboratory and then in patient-derived isolates, offers a realistic path toward diagnostics and therapies that account for the full range of bacterial survival strategies. If that programme succeeds, the stubborn gap between a susceptible laboratory test and a failed course of treatment may finally begin to close.

Subject of Research: Genomic analysis of antibiotic tolerance and its genetic basis in bacteria

Article Title: Transforming antibiotic tolerance research through genomics

Article References: Kobras, C. M. (2026). Transforming antibiotic tolerance research through genomics. Genome Biology. https://doi.org/10.1186/s13059-026-04257-x

Image Credits: AI Generated

DOI: 10.1186/s13059-026-04257-x

Keywords: antibiotic tolerance, antimicrobial resistance, bacterial genomics, persistence, epistasis, laboratory evolution, functional genomics, treatment failure, population genomics, bacterial survival, Genome Biology, microbial genetics

The team combined a microfluidic device known as the dual input mother machine with time-lapse fluorescence microscopy and automated image analysis to follow thousands of individual cells through an abrupt switch from glucose-rich growth medium to a carbon-free version of the same medium. Each strain carried a transcriptional fluorescent reporter for one of 22 different promoters, spanning native genes and synthetic constitutive promoters chosen to cover a wide range of expression levels and regulatory behaviors. This multiplexed setup allowed the researchers to quantify, for every single cell, its size, growth rate and the rate at which it produced a fluorescent protein from each promoter, corrected for the confounding effects of photobleaching, fluorescent protein maturation and, crucially, protein degradation.

The first finding is a matter of minutes. Upon removal of the carbon source, every cell stopped growing essentially immediately, within the three-minute resolution of the microscopy. Contrary to any picture of the stationary phase as a mixture of growing and dying cells, the population entered growth arrest as one. Most remarkably, after growth had already ceased, roughly 38 percent of cells went on to divide during the following ten hours, undergoing what biologists call reductive division. These divisions shrank the average cell size of the population and occurred preferentially in longer cells, likely those that had already progressed furthest in their cell cycle, perhaps having started or completed DNA replication. Even after 60 hours without food, more than 90 percent of cells were still capable of regrowing when nutrients returned.

With growth arrested, attention turned to gene expression. Here the diversity across promoters was dramatic. Ribosomal protein promoters such as rplN, which drive the cell’s protein synthesis machinery during exponential growth, were shut down almost completely within an hour of the switch. Other promoters, like hupA, showed a sharp initial drop followed by a slower, sustained trickle of production. A third class, exemplified by hslV and the RpoS-regulated promoter bolA, did something counterintuitive: production briefly increased, sometimes above its level during growth, before decaying exponentially over the following hours. By late starvation, the relative production rates across the 22 promoters spanned more than two orders of magnitude, from about 20 percent down to 0.1 percent of their growth-phase values, underscoring how profoundly the regulatory network is rewired when nutrients vanish.

What makes these dynamics especially noteworthy is their consistency across cells. The researchers had anticipated that individual bacteria might respond to starvation in idiosyncratic ways, as has been observed for other stresses such as the SOS response to DNA damage, where the population-level induction conceals almost no coordination between cells. Instead, during early starvation, single cells followed nearly identical trajectories. For promoters showing a burst of expression, more than half of all the variance observed across cells and time was captured simply by the population average, a degree of coordination rarely seen in bacterial gene regulation. This homogeneity suggests that entry into starvation triggers a tightly controlled program rather than a stochastic drift into dormancy.

Production rates, however, are only half the story; protein concentrations are what determine physiology. Because growth arrest halts the dilution of existing proteins while degradation continues, and because production does not stop instantly, many proteins accumulated substantially even as their synthesis slowed. The researchers measured the degradation rate of fluorescent proteins directly and found that it too decays exponentially during starvation, dropping from initial values around 0.02 to 0.03 per hour. This slowdown makes mechanistic sense: most protein degradation in E. coli is carried out by ATP-dependent proteases, and ATP levels plummet when carbon is gone. Integrating production, dilution and degradation in a simple mathematical model, the team showed that promoter concentrations either fell by as much as half or rose by up to twelvefold, with nearly all of these changes occurring within the first five to ten hours. After that, protein levels were essentially frozen, locking in a phenotype that persisted for days.

The model also revealed an elegant set of principles governing the final proteome. The concentration fold-change for each gene depends on just three effective ratios: the total degradation capacity, the balance between the fold-change in production and the fold-change in degradation relative to growth rate, and the relative time scales over which production and degradation decay. Even without a burst of expression, a promoter whose production decays gradually over about five hours can achieve a fourfold increase in protein concentration, simply because dilution has stopped while synthesis continues. For RpoS-regulated promoters such as bolA, an initial burst pushes the increase further, to around sevenfold. Experiments with an rpoS deletion strain confirmed the central role of this alternative sigma factor, although not all starvation-induced promoters depended on it, pointing to additional regulators still to be identified.

Importantly, the response did not require cells to anticipate starvation in advance. In a separate set of experiments, the researchers let a batch culture exhaust its glucose gradually while flowing the same medium through the microfluidic chip, so that cells inside the chip experienced a slower, roughly 45-minute transition into growth arrest instead of an abrupt five-minute switch. The gene expression dynamics and the resulting protein concentration changes were remarkably similar between the gradual and abrupt transitions, suggesting that the program is triggered by the cessation of growth itself rather than by sensing dwindling nutrients. Even when protein synthesis was temporarily blocked with chloramphenicol during the first five hours of starvation, the expression program simply resumed once the drug was removed, implying that the eventual decline in production reflects exhaustion of internal resources rather than a specific transcriptional shutoff.

The most consequential result concerns what this early remodeling is for. When the researchers subjected starving cells to a strong oxidative stress with hydrogen peroxide at the 20-hour mark, nearly 98 percent of normally starved cells survived. But when translation was inhibited from the very start of starvation, survival fell to 19 percent, whereas inhibiting protein production only after the first five hours was far less damaging, allowing 65 percent of cells to survive. The protective early expression tracked the accumulation of RpoS-regulated reporters, and a strain lacking rpoS altogether fared even worse than translation-inhibited wild-type cells, with survival dropping almost to zero. Together, these experiments demonstrate that the protein production of the first five hours is what arms the cells against stresses arriving much later, when their capacity to respond has essentially vanished.

The findings also suggest how starving cells manage to keep producing proteins at all. With ribosomal gene expression shut off, the reduced translation capacity is redirected away from growth machinery toward protective proteins, effectively raising the relative output of many genes even as total synthesis falls. The energy likely comes from endogenous metabolism, the controlled self-digestion of abundant cellular constituents: the team’s degradation measurements imply that roughly 40 percent of the proteome may be broken down within the first day of starvation, releasing metabolites and fueling ATP production. In this light, the first hours of starvation emerge as a resource-limited but precisely coordinated investment phase, in which a bacterium cannibalizes part of itself to build the molecular toolkit it will need to endure an uncertain and potentially hostile future. For a organism long caricatured as a simple growth machine, E. coli turns out to be a far more deliberate survivor than its reputation suggests.

Subject of Research: Proteome remodeling and gene expression dynamics in single E. coli cells during carbon starvation

Article Title: E. coli prepares for starvation by dramatically remodeling its proteome in the first hours after loss of nutrients

Article References: E. coli prepares for starvation by dramatically remodeling its proteome in the first hours after loss of nutrients. (n.d.). https://doi.org/10.1038/s44320-026-00226-5

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00226-5

Keywords: E. coli, carbon starvation, proteome remodeling, gene expression, microfluidics, single-cell microscopy, RpoS, stationary phase, protein degradation, stress tolerance, bacterial survival, Molecular Systems Biology