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Mechanisms for Differential Protein Production in Toxin–Antitoxin Systems

Department of Physics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0435, USA
Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0435, USA
Department of Biology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0435, USA
Quantitative Biosciences, Inc., Solana Beach, CA 92075, USA
Department of Biology and Microbiology, South Dakota State University, Brookings, SD 57006, USA
Author to whom correspondence should be addressed.
Academic Editor: Thomas Keith Wood
Toxins 2017, 9(7), 211;
Received: 25 May 2017 / Revised: 19 June 2017 / Accepted: 23 June 2017 / Published: 4 July 2017
(This article belongs to the Section Bacterial Toxins)
Toxin–antitoxin (TA) systems are key regulators of bacterial persistence, a multidrug-tolerant state found in bacterial species that is a major contributing factor to the growing human health crisis of antibiotic resistance. Type II TA systems consist of two proteins, a toxin and an antitoxin; the toxin is neutralized when they form a complex. The ratio of antitoxin to toxin is significantly greater than 1.0 in the susceptible population (non-persister state), but this ratio is expected to become smaller during persistence. Analysis of multiple datasets (RNA-seq, ribosome profiling) and results from translation initiation rate calculators reveal multiple mechanisms that ensure a high antitoxin-to-toxin ratio in the non-persister state. The regulation mechanisms include both translational and transcriptional regulation. We classified E. coli type II TA systems into four distinct classes based on the mechanism of differential protein production between toxin and antitoxin. We find that the most common regulation mechanism is translational regulation. This classification scheme further refines our understanding of one of the fundamental mechanisms underlying bacterial persistence, especially regarding maintenance of the antitoxin-to-toxin ratio. View Full-Text
Keywords: toxin–antitoxin systems; persister; RNA-seq; ribosome profiling; Ribo-Seq toxin–antitoxin systems; persister; RNA-seq; ribosome profiling; Ribo-Seq
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    Description: Fig. S1. RNA-seq coverage from experiment SRX1424838 (GSE74809, [1]) mapped to mazEF (Class 1), and rnlAB (Class 4). The mazEF genes do not have greater than a twofold difference in coverage (number of RNA-seq reads mapped to the genome) or an internal promoter according to the EcoCyc database [2]. The coverage of rnlAB shows an increase of transcription at the transcriptional start site for the internal promoter (P4) (indicated as a dashed line) approximately 280 nt upstream from the antitoxin start codon [3]. Comparison of the coverage between toxin (rnlA) and antitoxin (rnlB) using number of reads mapped to each gene would misrepresent the ratio of mRNA due to the transcriptional start site located within the rnlB gene. The coverage before the internal promoter is twofold lower than the coverage after the internal promoter, indicating that functional toxin mRNA is two-fold less than functional antitoxin mRNA. Top: Wire diagram of the indicated genes above the corresponding line graph of the coverage on a log2 scale versus nucleotide position in the genome. P: promoter B) Table indicating the mean coverage and coverage ratios for a given gene or region. Fig. S2: Biological replicate values for antitoxin and toxin coverage across RNA-seq datasets. Shown is a comparison of coverage for biological replicates from the datasets GSE48829 [4] (left) and GSE74809 [1] (right), the former of which contains triplicate data in one growth condition, and the latter of which contains duplicate data across five growth conditions. Replicates are shown as small symbols, while the mean of their log10 coverage is shown as a corresponding larger transparent symbol. The dashed line represents antitoxin to toxin coverage ratio 1:1 (equal coverage), while the dotted lines represent antitoxin to toxin coverage ratios equal to 1:2 and 2:1. Units of coverage are Reads Per Kilobase Million (RPKM), and the major directions of ratio and magnitude are also included (see Methods). Fig. S3. Representative error estimates for RNA-seq datasets. (A) Biological replicates in the datasets GSE48829 [4] (left) and GSE74809 [1] (right) were used to estimate the standard error of the log-coverage (natural logarithm of the coverage) for each gene in the dataset (red dots). This logarithmic error measurement is natural for data represented in log-log coordinates. Antitoxin and toxin genes belonging to the TA systems listed in the legend are represented using their own symbols. A smooth global error estimate is plotted as a blue line. This global error estimate 〈σ〉_i for a gene with index i is derived from the formula 〈σ〉_i=∑_j▒σ_j ρ_ji (summation over all indices j), where ρ_ji is a normalized weighting factor proportional to exp⁡(-2(x_i-x_j )^2 ), with x_i the log-coverage for a gene with index i. (B) The mean log-coverage for each condition from panel A are plotted, with error bars corresponding to their global error estimates. Supplementary Table 1. Selected RNA-seq experiment numbers and conditions for GSE48829 (grey background) and GSE74809. Supplementary Table 2. Ratios of the calculated antitoxin to toxin translation initiation rates. All translation initiation rates (TIR) were calculated using translation rate calculators, as outlined in the main text (see Methods). TIR is in arbitrary units (AU). The toxin is underlined.
MDPI and ACS Style

Deter, H.S.; Jensen, R.V.; Mather, W.H.; Butzin, N.C. Mechanisms for Differential Protein Production in Toxin–Antitoxin Systems. Toxins 2017, 9, 211.

AMA Style

Deter HS, Jensen RV, Mather WH, Butzin NC. Mechanisms for Differential Protein Production in Toxin–Antitoxin Systems. Toxins. 2017; 9(7):211.

Chicago/Turabian Style

Deter, Heather S., Roderick V. Jensen, William H. Mather, and Nicholas C. Butzin. 2017. "Mechanisms for Differential Protein Production in Toxin–Antitoxin Systems" Toxins 9, no. 7: 211.

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