Summary

A medRxiv preprint introduces EMERGENe, a phylogenetic framework that estimates how transmissible bacterial traits are acquired and expand. Tests using simulations and 3,745 Shigella sonnei genomes detected known resistance trends and additional emerging traits.

Researchers have introduced EMERGENe, a phylogenetic framework designed to measure how epidemiologically relevant traits in bacteria are acquired and then expand through descendant lineages. The method was evaluated in evolutionary simulations and applied to a national genomic-surveillance dataset containing 3,745 Shigella sonnei isolates, according to a medRxiv preprint posted on September 14, 2026.

This is a computational surveillance study rather than a clinical trial. Its main outcomes are estimates of trait acquisition and expansion in bacterial populations, with particular relevance to antimicrobial resistance (AMR).

Contents

Why prevalence alone can miss rapid expansion

Genomic surveillance commonly measures prevalence: the proportion of sampled bacteria carrying a resistance gene or other trait at a particular time. That measure shows how widespread a trait is, but it can obscure whether the trait is stable, declining or undergoing rapid growth within a genetically diverse population.

This distinction matters for AMR. A newly acquired transmissible resistance trait may initially be rare, yet expand quickly through bacterial descendants. A trait that is already widespread may have a higher prevalence but be changing more slowly. Treating both situations as equivalent can make it harder to identify which traits are gaining epidemiological importance.

EMERGENe is intended to add that evolutionary dimension to surveillance. It combines ancestral-state reconstruction—the process of inferring the likely traits of earlier bacterial ancestors—with analysis of branching patterns in a phylogenetic tree. A phylogeny represents relationships among sampled bacterial genomes; a time-scaled phylogeny also places those relationships along a timeline.

How EMERGENe analyses bacterial traits

The framework uses a time-scaled phylogeny together with binary presence data, recording whether each genome carries a specified trait. It identifies independent events in which a trait appears in a lineage and is subsequently inherited by that lineage's descendants.

From these events, EMERGENe estimates two interpretable quantities. Entry rates describe the introduction of populations carrying a particular trait, while Emergence rates capture the expansion of those trait-specific populations after introduction. The distinction allows surveillance teams to compare traits according to the processes producing their observed distribution, rather than relying only on their current frequency.

The authors first tested the method with phylogenetic simulations representing different population-growth patterns. Across those simulated scenarios, they report that EMERGENe was better than prevalence at distinguishing traits undergoing rapid population growth from traits with slower growth or no expansion.

Findings in Shigella sonnei

The researchers then applied EMERGENe to 3,745 S. sonnei isolates from a national genomic-surveillance dataset. The analysis detected previously known epidemiological acquisitions of resistance to azithromycin, ciprofloxacin and third-generation cephalosporins, while also providing information about how those resistance populations emerged and expanded.

Temporal analysis showed progressive expansion of ceftriaxone resistance during a period that overlapped with the increasing replacement of previously highly disseminated azithromycin resistance. This illustrates the type of shift that a prevalence snapshot may describe incompletely: the important signal is not only which resistance trait is common, but also which trait is gaining ground and how its population is changing over time.

EMERGENe also identified traits the authors describe as emerging or overlooked in the dataset. These included a recently described epidemiologically relevant phage plasmid and the qnrS1 gene.

The framework could therefore help genomic surveillance identify early signals from traits that are still uncommon but expanding rapidly. Earlier recognition could support closer investigation and prioritisation of rapidly emerging AMR threats. That possible public-health use remains prospective: the preprint evaluates the method through simulations and genomic data, rather than reporting a surveillance intervention or patient outcome.

The work is presented as a medRxiv preprint. Its reported application covers one bacterial species and one national dataset, so performance across other organisms, settings and trait types will require further evaluation.

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