Proteostasis Environment Shapes Higher-Order Epistasis Operating on Antibiotic Resistance

Abstract

Recent studies have affirmed that higher-order epistasis is ubiquitous and can have large effects on complex traits. Yet, we lack frameworks for understanding how epistatic interactions are influenced by central features of cell physiology. In this study, we assess how protein quality control machinery-a critical component of cell physiology-affects epistasis for different traits related to bacterial resistance to antibiotics. Specifically, we disentangle the interactions between different protein quality control genetic backgrounds and two sets of mutations: (i) SNPs associated with resistance to antibiotics in an essential bacterial enzyme (dihydrofolate reductase, or DHFR) and (ii) differing DHFR bacterial species-specific amino acid background sequences (Escherichia coli, Listeria grayi, and Chlamydia muridarum). In doing so, we improve on generic observations that epistasis is widespread by discussing how patterns of epistasis can be partly explained by specific interactions between mutations in an essential enzyme and genes associated with the proteostasis environment. These findings speak to the role of environmental and genotypic context in modulating higher-order epistasis, with direct implications for evolutionary theory, genetic modification technology, and efforts to manage antimicrobial resistance.

Keywords: antibiotic resistance; epistasis; proteostasis.

Figure 1

Phenotypic variation of DHFR mutants across proteostasis contexts. ${\text{IC}}_{\text{50}}$ (A) and abundance (B) depend on protein quality control context (panel rows) and species background (panel columns). DHFR mutations at three amino acid positions are represented by closed circles (first site = P21L, second site = A26T, and third site = L28T). DHFR, dihydrofolate reductase; WT, wild-type.

Figure 2

Widespread presence of higher-order interactions for (A)${\text{IC}}_{\text{50}}$ and (B) protein abundance. Distribution of regression coefficients from a LASSO regression allowing interactions across species, protein quality control context, and DHFR mutations for two phenotypes of interest. Bars represent the signs and magnitudes of the 20 largest coefficients in the best-fit model. Coefficients are arranged from top to bottom by their magnitude and their color represents their order (gray for main effects and increasing darkness of red for terms of order two through five). The treemaps in the bottom right corner of each panel represent the sum of all nonzero coefficients by order (the area of each box is the total effect of terms of that order). DHFR, dihydrofolate reductase; LASSO, least absolute shrinkage and selection operator.

Figure 3

Epistatic effects are correlated between ${\text{IC}}_{\text{50}}$ and protein abundance traits, with several important higher-order outliers that demonstrate pleiotropic effects. Highlighted are the five terms with the largest discrepancies in value between the two phenotypes. DHFR, dihydrofolate reductase.

Figure 4

Magnitude, direction, and order of epistatic effects between SNPs across PQC-species backgrounds for (A) ${\text{IC}}_{\text{50}}$ and (B) DHFR abundance. The estimated effect of single-amino acid substitutions (P21L, A26T, and L28R) and their interactions vary across PQC backgrounds, indicating higher-order epistasis. Effect sizes were estimated using a LASSO regression within PQC-species background (see Materials and Methods). Dashed lines are drawn for clarity only. The bars at the bottom of each panel summarize the relative contribution of each order (main effects in gray, pair-wise interactions in light red, and third-order in dark red) to the total of (absolute) coefficients estimated in each model. DHFR, dihydrofolate reductase; LASSO, least absolute shrinkage and selection operator; PQC, protein quality control; WT, wild-type.