Estimating emergence rates of epidemiologically relevant traits in bacteria with EMERGENe
- 1Department of Genetics, University of Cambridge, Cambridge, United Kingdom, CB2 3EH
Abstract
Genomic surveillance has transformed our ability to identify and track bacterial lineages and epidemiologically relevant traits. However, surveillance approaches are mainly based on prevalence-based measures, which can obscure the dynamics of traits undergoing rapid expansion within genetically and temporally heterogeneous populations. This limitation is particularly relevant for antimicrobial resistance (AMR), where the emergence and subsequent expansion of newly acquired transmissible traits can generate substantial changes in population-level prevalence. Here, we introduce EMERGENe (https://github.com/gbatbiff/EMERGENe), a phylogenetic framework that combines ancestral-state reconstruction and analysis of phyletic patterns to quantify the emergence dynamics of gained and transmissible traits. Using time-scaled phylogenies and binary presence data, EMERGENe identifies independent phyletic events in which a trait is acquired and subsequently inherited by its descendants, and estimates interpretable Entry and Emergence rates that capture the introduction and expansion of trait-specific populations. We evaluated the performance of EMERGENe using phylogenetic simulations across evolutionary trajectories with different population growth dynamics, and applied our framework to a national genomic surveillance dataset comprising 3,745 Shigella sonnei isolates. Across simulated evolutionary scenarios, EMERGENe was superior to prevalence for discriminating traits undergoing rapid population growth from those with slower or no expansion. Applied to S. sonnei, our method detected previously known epidemiological acquisition of resistance to azithromycin, ciprofloxacin and third-generation cephalosporins, while providing information on their underlying emergence dynamics. Temporal analyses also revealed the progressive expansion of ceftriaxone resistance, overlapping with the increasing replacement of previously highly disseminated azithromycin resistance. EMERGENe also detected emerging and overlooked traits, including a recently described epidemiologically relevant phage-plasmid and the qnrS1 gene. EMERGENe provides a complementary approach to genomic surveillance by shifting the focus from static trait prevalence towards the evolutionary processes underlying trait emergence and expansion. Thus, EMERGENe provides a robust quantification method for comparison of trait emergence, and by identifying early signals of rapidly emerging traits, also has the potential to improve longitudinal surveillance, facilitating earlier intervention in the onward transmission of AMR in bacterial populations.
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BibTeX
@misc{biffignandi2026estimating,
title = {{Estimating emergence rates of epidemiologically relevant traits in bacteria with EMERGENe}},
author = {G. Batisti Biffignandi and K. C. Wei and J. Hellewell and C. Jenkins and J. Corander and J. A. Lees and K. S. Baker},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.10.26362639},
url = {https://www.medrxiv.org/content/10.64898/2026.09.10.26362639v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Estimating emergence rates of epidemiologically relevant traits in bacteria with EMERGENe
AU - G. Batisti Biffignandi
AU - K. C. Wei
AU - J. Hellewell
AU - C. Jenkins
AU - J. Corander
AU - J. A. Lees
AU - K. S. Baker
PY - 2026
DA - 2026/09/10
PB - medRxiv
DO - 10.64898/2026.09.10.26362639
UR - https://www.medrxiv.org/content/10.64898/2026.09.10.26362639v1
AB - Genomic surveillance has transformed our ability to identify and track bacterial lineages and epidemiologically relevant traits. However, surveillance approaches are mainly based on prevalence-based measures, which can obscure the dynamics of traits undergoing rapid expansion within genetically and temporally heterogeneous populations. This limitation is particularly relevant for antimicrobial resistance (AMR), where the emergence and subsequent expansion of newly acquired transmissible traits can generate substantial changes in population-level prevalence. Here, we introduce EMERGENe (https://github.com/gbatbiff/EMERGENe), a phylogenetic framework that combines ancestral-state reconstruction and analysis of phyletic patterns to quantify the emergence dynamics of gained and transmissible traits. Using time-scaled phylogenies and binary presence data, EMERGENe identifies independent phyletic events in which a trait is acquired and subsequently inherited by its descendants, and estimates interpretable Entry and Emergence rates that capture the introduction and expansion of trait-specific populations. We evaluated the performance of EMERGENe using phylogenetic simulations across evolutionary trajectories with different population growth dynamics, and applied our framework to a national genomic surveillance dataset comprising 3,745 Shigella sonnei isolates. Across simulated evolutionary scenarios, EMERGENe was superior to prevalence for discriminating traits undergoing rapid population growth from those with slower or no expansion. Applied to S. sonnei, our method detected previously known epidemiological acquisition of resistance to azithromycin, ciprofloxacin and third-generation cephalosporins, while providing information on their underlying emergence dynamics. Temporal analyses also revealed the progressive expansion of ceftriaxone resistance, overlapping with the increasing replacement of previously highly disseminated azithromycin resistance. EMERGENe also detected emerging and overlooked traits, including a recently described epidemiologically relevant phage-plasmid and the qnrS1 gene. EMERGENe provides a complementary approach to genomic surveillance by shifting the focus from static trait prevalence towards the evolutionary processes underlying trait emergence and expansion. Thus, EMERGENe provides a robust quantification method for comparison of trait emergence, and by identifying early signals of rapidly emerging traits, also has the potential to improve longitudinal surveillance, facilitating earlier intervention in the onward transmission of AMR in bacterial populations.
ER -