Inferring the selection window in antimicrobial resistance using deep mutational scanning data and biophysics-based fitness models
Abstract Mutant-selection window (MSW) hypothesis in antimicrobial resistance implies a range for antimicrobial concentration that promotes selection of single-step resistant mutants. Since the inception and experimental verification, MSW has been at the forefront of strategies to minimize development of antimicrobial resistance (AR). Setting the upper and lower limits of MSW requires an understanding of the dependence of selection coefficient of arising mutations to antimicrobial concentration. In this work, we employed a biophysics-based and experimentally calibrated fitness model to estimate MSW in the case of Ampicillin and Cefotaxime resistance in E.coli TEM-1 beta lactamase. In line with experimental observations, we show that selection is active at very low levels of antimicrobials. Furthermore, we elucidate the dependence of MSW to catalytic efficiency of mutants, fraction of mutants in the population and discuss the role of population genetic parameters such as population size and mutation rate. Altogether, our analysis and formalism provide a predictive model of MSW with direct implications in the design of dosage strategies..
| Medienart: |
|
|---|
Preprint| Erscheinungsjahr: |
2022 |
|---|---|
| Erschienen: |
2022 |
| Enthalten in: |
bioRxiv.org - (2022) vom: 02. Aug. Zur Gesamtaufnahme - year:2022 |
|---|
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Dasmeh, Pouria [VerfasserIn] |
|---|
| Links: |
Volltext [kostenfrei] |
|---|
| Themen: |
|---|
| doi: |
10.1101/189019 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
XBI000172634 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | XBI000172634 | ||
| 003 | DE-627 | ||
| 005 | 20230429092139.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 200312s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1101/189019 |2 doi | |
| 035 | |a (DE-627)XBI000172634 | ||
| 035 | |a (biorXiv)10.1101/189019 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Dasmeh, Pouria |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Inferring the selection window in antimicrobial resistance using deep mutational scanning data and biophysics-based fitness models |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Abstract Mutant-selection window (MSW) hypothesis in antimicrobial resistance implies a range for antimicrobial concentration that promotes selection of single-step resistant mutants. Since the inception and experimental verification, MSW has been at the forefront of strategies to minimize development of antimicrobial resistance (AR). Setting the upper and lower limits of MSW requires an understanding of the dependence of selection coefficient of arising mutations to antimicrobial concentration. In this work, we employed a biophysics-based and experimentally calibrated fitness model to estimate MSW in the case of Ampicillin and Cefotaxime resistance in E.coli TEM-1 beta lactamase. In line with experimental observations, we show that selection is active at very low levels of antimicrobials. Furthermore, we elucidate the dependence of MSW to catalytic efficiency of mutants, fraction of mutants in the population and discuss the role of population genetic parameters such as population size and mutation rate. Altogether, our analysis and formalism provide a predictive model of MSW with direct implications in the design of dosage strategies. | ||
| 650 | 4 | |a Biology |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 570 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Ton, Anh-Tien |e verfasserin |4 aut | |
| 700 | 1 | |a Quach, Caroline |e verfasserin |4 aut | |
| 700 | 1 | |a Serohijos, Adrian W.R. |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t bioRxiv.org |g (2022) vom: 02. Aug. |
| 773 | 1 | 8 | |g year:2022 |g day:02 |g month:08 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1101/189019 |z kostenfrei |3 Volltext |
| 912 | |a GBV_XBI | ||
| 912 | |a SSG-OLC-PHA | ||
| 951 | |a AR | ||
| 952 | |j 2022 |b 02 |c 08 | ||