Cross-feeding modulates the rate and mechanism of antibiotic resistance evolution in a model microbial community of Escherichia coli and Salmonella enterica

Abstract

With antibiotic resistance rates on the rise, it is critical to understand how microbial species interactions influence the evolution of resistance. In obligate mutualisms, the survival of any one species (regardless of its intrinsic resistance) is contingent on the resistance of its cross-feeding partners. This sets the community antibiotic sensitivity at that of the 'weakest link' species. In this study, we tested the hypothesis that weakest link dynamics in an obligate cross-feeding relationship would limit the extent and mechanisms of antibiotic resistance evolution. We experimentally evolved an obligate co-culture and monoculture controls along gradients of two different antibiotics. We measured the rate at which each treatment increased antibiotic resistance, and sequenced terminal populations to question whether mutations differed between mono-and co-cultures. In both rifampicin and ampicillin treatments, we observed that resistance evolved more slowly in obligate co-cultures of E. coli and S. enterica than in monocultures. While we observed similar mechanisms of resistance arising under rifampicin selection, under ampicillin selection different resistance mechanisms arose in co-cultures and monocultures. In particular, mutations in an essential cell division protein, ftsI, arose in S. enterica only in co-culture. A simple mathematical model demonstrated that reliance on a partner is sufficient to slow the rate of adaptation, and can change the distribution of adaptive mutations that are acquired. Our results demonstrate that cooperative metabolic interactions can be an important modulator of resistance evolution in microbial communities.

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

“…In recent years, evolution-based strategies for impeding drug resistance have gained significant attention. These approaches have identified a number of different factors that could modulate resistance evolution, including spatial heterogeneity ( Zhang et al, 2011 ; Baym et al, 2016a ; Greulich et al, 2012 ; Hermsen et al, 2012 ; Moreno-Gamez et al, 2015 ; Gokhale et al, 2018 ; De Jong and Wood, 2018 ; Santos-Lopez et al, 2019 ); competitive ( Read et al, 2011 ; Hansen et al, 2017 ; Hansen et al, 2020 ), cooperative ( Meredith et al, 2015b ; Artemova et al, 2015 ; Sorg et al, 2016 ; Tan et al, 2012 ; Karslake et al, 2016 ; Yurtsev et al, 2016 ; Hallinen et al, 2020 ), or metabolic ( Adamowicz et al, 2018 ; Adamowicz et al, 2020 ) interactions between bacterial cells; synergy with the immune system, especially in the context of adaptive treatment ( Gjini and Brito, 2016 ); epistasis between resistance mutations ( Trindade et al, 2009 ; Borrell et al, 2013 ; Lukačišinová et al, 2020 ); plasmid dynamics ( Lopatkin et al, 2016 ; Lopatkin et al, 2017 ; Cooper et al, 2017 ); precise tuning of drug doses ( Lipsitch and Levin, 1997 ; Yoshida et al, 2017 ; Meredith et al, 2015a ; Nichol et al, 2015 ; Fuentes-Hernandez et al, 2015 ; Coates et al, 2018 ; Iram et al, 2021 ); cycling or mixing drugs at the hospital level ( Bergstrom et al, 2004 ; Beardmore et al, 2017 ); and statistical correlations between resistance profiles for different drugs ( Imamovic and Sommer, 2013 ; Kim et al, 2014 ; Pál et al, 2015 ; Barbosa et al, 2017 ; Rodriguez de Evgrafov et al, 2015 ; Nichol et al, 2019 ; Podnecky et al, 2018 ; Imamovic et al, 2018 ; Barbosa et al, 2019 ; Rosenkilde et al, 2019 ; Apjok et al, 2019 ; …”

Microbial communities form rich extracellular metabolomes that foster metabolic interactions and promote drug tolerance

“…In parallel, there is an active discussion regarding whether and how the exchange of metabolites between cells is influencing the evolution of resistance genes. ‘Weakest links’ in metabolite exchange chains can slow the spread of drug resistance genes if their growth is impaired by antimicrobial exposure 25 , 26 .…”

Tracking Antimicrobial Resistance Determinants in Diarrheal Pathogens: A Cross-Institutional Pilot Study

“…However, a key limitation of any molecular technology approach is that genotype may not always be predictive of phenotype if differential expression and/or functionality of expressed proteins is not known or fully characterized. Furthermore, cooperative interactions amongst members of an ecosystem consortium can significantly affect individual species' phenotypic traits and survivability in native environments [22,[99][100][101][102]. Therefore, while molecular techniques are useful for AMR surveillance, they should be complemented with approaches that address additional mechanisms for AMR and survival and (potentially uncharacterized) contributions of components of the ecosystem consortium.…”