Aaron Behr

All Publications

• 'Jumping genes' help a bacterium that causes hospital infections to adapt quickly NATURE Behr, A. A., Grieshop, M. P. 2026

  View details for DOI 10.1038/d41586-026-01274-5

  View details for Web of Science ID 001747101200001

  View details for PubMedID 42020586

• Transposable elements are driving rapid adaptation of Enterococcus faecium. Nature Grieshop, M. P., Behr, A. A., Bowden, S., Lin, J. D., Molari, M., Reynolds, G. Z., Brooks, E. F., Doyle, B., Moore, A. A., Rodriguez-Nava, G., Salinas, J. L., Banaei, N., Bhatt, A. S. 2026

  Abstract

  Bacterial pathogens adapt rapidly to clinical and within-host selective pressures1. Insertion sequences (IS) are transposable elements that can contribute to pathogenic adaptation2, but their activity and consequences in contemporary clinical populations are not well characterized. Here, combining large-scale genomic surveys with long-read sequencing of clinical isolates and longitudinal gut metagenomes, we quantify pathogen IS dynamics from global patterns to within-host evolution. Across 19,485 publicly available high-contiguity ESKAPEE pathogen genomes, Enterococcus faecium genomes are the most IS dense, dominated by replicative ISL3 family elements, which have proliferated in clinical lineages over the past 30 years. We find extensive chromosomal structural variation, largely involving ISL3, within a new single-hospital collection of bloodstream isolates. Long-read metagenomic sequencing of 28 longitudinal stool samples from 12 haematopoietic cell transplantation (HCT) recipients demonstrates within-host IS dynamics and their regulatory consequences. In one patient, an ISL3 insertion upstream of a folate transporter formed a strong promoter, increasing transcription and improving relative fitness under folate limitation. Enhanced folate scavenging may enable E. faecium to thrive in the setting of microbiome collapse, which is common in HCT and other critically ill patients3. Together, these results show that a recent ISL3 expansion is driving rapid evolution in healthcare-associated E. faecium, with consequences for its metabolic fitness that may help explain its increasing clinical burden. Several other pathogens also show elevated IS loads in our survey, which suggests that IS expansion-mediated evolution might be more broadly relevant.

  View details for DOI 10.1038/s41586-026-10373-2

  View details for PubMedID 42020750

  View details for PubMedCentralID 7190074

• pong: fast analysis and visualization of latent clusters in population genetic data. Bioinformatics (Oxford, England) Behr, A. A., Liu, K. Z., Liu-Fang, G., Nakka, P., Ramachandran, S. 2016; 32 (18): 2817-23

  Abstract

  A series of methods in population genetics use multilocus genotype data to assign individuals membership in latent clusters. These methods belong to a broad class of mixed-membership models, such as latent Dirichlet allocation used to analyze text corpora. Inference from mixed-membership models can produce different output matrices when repeatedly applied to the same inputs, and the number of latent clusters is a parameter that is often varied in the analysis pipeline. For these reasons, quantifying, visualizing, and annotating the output from mixed-membership models are bottlenecks for investigators across multiple disciplines from ecology to text data mining.We introduce pong, a network-graphical approach for analyzing and visualizing membership in latent clusters with a native interactive D3.js visualization. pong leverages efficient algorithms for solving the Assignment Problem to dramatically reduce runtime while increasing accuracy compared with other methods that process output from mixed-membership models. We apply pong to 225 705 unlinked genome-wide single-nucleotide variants from 2426 unrelated individuals in the 1000 Genomes Project, and identify previously overlooked aspects of global human population structure. We show that pong outpaces current solutions by more than an order of magnitude in runtime while providing a customizable and interactive visualization of population structure that is more accurate than those produced by current tools.pong is freely available and can be installed using the Python package management system pip. pong's source code is available at https://github.com/abehr/pongaaron_behr@alumni.brown.edu or sramachandran@brown.eduSupplementary data are available at Bioinformatics online.

  View details for DOI 10.1093/bioinformatics/btw327

  View details for PubMedID 27283948

  View details for PubMedCentralID PMC5018373