Data-driven insights on the dissemination of antibiotic resistance genes
Doktorsavhandling, 2025

Antibiotic resistance is increasing among pathogens, representing a serious threat to public health. Bacteria often become resistant by acquiring mobile antibiotic resistance genes (ARGs), which are disseminated via horizontal gene transfer. To anticipate the emergence of new ARGs and limit their spread, we must increase our knowledge about resistance genes that exist in different environments and about their horizontal dissemination among bacteria. The six papers presented in this thesis aim to provide an extensive characterization of the resistome and an analysis of horizontal ARG dissemination. In Paper I, a previously unseen diversity of genes giving resistance to aminoglycoside antibiotics was identified, including 50 previously unknown mobile ARGs carried by human pathogens. In Paper II, the abundance of ARGs, both well-studied and computationally predicted, was estimated in different microbiomes, revealing a widespread presence of previously unknown ARGs across all analyzed environments. In Paper III, a detailed characterization of the resistomes of the human gut and wastewater microbiomes was performed, highlighting the relationship between ARG prevalence in these microbial communities and potential implications for human health. Papers IV and V present a phylogenetic method to identify horizontal ARG transfer between evolutionarily divergent bacteria, which was used to analyze inter-phyla ARG transfers, and combined with machine learning to quantify the impact of different factors on horizontal ARG dissemination. Finally, in Paper VI, the potential use of machine learning to predict the dissemination of emerging ARGs was evaluated. The resulting models showed promise but need further refinement to inform clinical decision-making. Together, the findings presented in this thesis increase our understanding of how ARGs transfer between bacterial species and communities, highlighting the presence in anthropogenic microbiomes and genetic compatibility as key factors associated with successful ARG dissemination. Moreover, the results demonstrate the utility provided by data-driven methods for improving surveillance and diagnostics of antibiotic resistance.

antibiotic resistance

phylogenetic analysis

hidden Markov model

microbiome

random forest

horizontal gene transfer

Pascal, Chalmers tvärgata 3, Göteborg
Opponent: Professor Sofia Kirke Forslund-Startceva, Max Delbrük Center, Berlin

Författare

David Lund

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Extensive screening reveals previously undiscovered aminoglycoside resistance genes in human pathogens

Communications Biology,;Vol. 6(2023)

Artikel i vetenskaplig tidskrift

Lund, D., Johnning, A., Holmström, M., Varghaei, L., Inda-Díaz, J. S., Bengtsson-Palme, J., Kristiansson, E. Community-promoted antibiotic resistance genes show increased dissemination among pathogens.

Parras-Moltó, M., Lund, D., Ebmeyer, S., Larsson, D. G. J., Johnning, A., Kristiansson, E. The transfer of antibiotic resistance genes between evolutionarily distant bacteria.

Genetic compatibility and ecological connectivity drive the dissemination of antibiotic resistance genes

Nature Communications,;Vol. 16(2025)

Artikel i vetenskaplig tidskrift

Lund, D., Axillus, S., Larsson, D. G. J., Johnning, A., Kristiansson, E. Can we predict the spread of emerging antibiotic resistance genes?

Antibiotic resistance is a global public health crisis, associated with millions of deaths yearly. Bacteria develop resistance from genetic changes, often by acquiring antibiotic resistance genes from other bacteria. Most of these genes originate in harmless bacteria living in the environment or our bodies, but our antibiotic use has incentivized disease-causing bacteria to pick up these genes to survive. Unfortunately, we still lack knowledge about the resistance genes that exist in different environments, and the factors that influence their spread between bacteria. This hampers our ability to limit and anticipate the spread of new resistance genes among disease-causing bacteria. However, recent years have seen an explosion in the amount of available data, which presents new opportunities to answer these questions.

This thesis applies computational methods to analyze the presence and spread of resistance genes in different environments. Our results reveal a vast number of previously unknown resistance genes in different environments. We also show that the human gut and wastewater environments are connected to the spread of antibiotic resistance genes, and that this process is influenced by the genetic similarity between bacteria. Finally, we show that machine learning can potentially be used to anticipate the spread of new resistance genes. Together, our results provide new knowledge that can inform strategies to combat the spread of antibiotic resistance.

Nya resistensgener mot antibiotika och deras spridning i miljön

Vetenskapsrådet (VR) (2019-03482), 2020-01-01 -- 2023-12-31.

Ämneskategorier (SSIF 2025)

Bioinformatik och beräkningsbiologi

ISBN

978-91-8103-222-2

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5680

Utgivare

Chalmers

Pascal, Chalmers tvärgata 3, Göteborg

Opponent: Professor Sofia Kirke Forslund-Startceva, Max Delbrük Center, Berlin

Mer information

Senast uppdaterat

2025-05-19