Comparison of Transcriptional Responses and Metabolic Alterations in Three Multidrug-Resistant Model Microorganisms, Staphylococcus aureus ATCC BAA-39, Escherichia coli ATCC BAA-196, and Acinetobacter baumannii ATCC BAA-1790, on Exposure to Iodine-Containing Nano-micelle Drug FS-1

Abstract

Iodine is one of the oldest antimicrobial agents. Until now, there have been no reports on acquiring resistance to iodine. Recent studies showed promising results on application of iodine-containing nano-micelles, FS-1, against antibiotic-resistant pathogens as a supplement to antibiotic therapy. The mechanisms of the action, however, remain unclear. The aim of this study was to perform a holistic analysis and comparison of gene regulation in three phylogenetically distant multidrug-resistant reference strains representing pathogens associated with nosocomial infections from the ATCC culture collection: Escherichia coli BAA-196, Staphylococcus aureus BAA-39, and Acinetobacter baumannii BAA-1790. These cultures were treated by a 5-min exposure to sublethal concentrations of the iodine-containing drug FS-1 applied in the late lagging phase and the middle of the logarithmic growth phase. Complete genome sequences of these strains were obtained in the previous studies. Gene regulation was studied by total RNA extraction and Ion Torrent sequencing followed by mapping the RNA reads against the reference genome sequences and statistical processing of read counts using the DESeq2 algorithm. It was found that the treatment of bacteria with FS-1 profoundly affected the expression of many genes involved in the central metabolic pathways; however, alterations of the gene expression profiles were species specific and depended on the growth phase. Disruption of respiratory electron transfer membrane complexes, increased penetrability of bacterial cell walls, and osmotic and oxidative stresses leading to DNA damage were the major factors influencing the treated bacteria.IMPORTANCE Infections caused by antibiotic-resistant bacteria threaten public health worldwide. Combinatorial therapy in which antibiotics are administered together with supplementary drugs improving susceptibility of pathogens to the regular antibiotics is considered a promising way to overcome this problem. An induction of antibiotic resistance reversion by the iodine-containing nano-micelle drug FS-1 has been reported recently. This drug is currently under clinical trials in Kazakhstan against multidrug-resistant tuberculosis. The effects of released iodine on metabolic and regulatory processes in bacterial cells remain unexplored. The current work provides an insight into gene regulation in the antibiotic-resistant nosocomial reference strains treated with iodine-containing nanoparticles. This study sheds light on unexplored bioactivities of iodine and the mechanisms of its antibacterial effect when applied in sublethal concentrations. This knowledge will aid in the future design of new drugs against antibiotic-resistant infections.

Keywords: Acinetobacter baumannii; Escherichia coli; Staphylococcus aureus; antibiotic resistance; iodine; nano-micelle; transcriptomics.

FIG 1

Plots of coregulation of genes in the Lag and Log growth phases. (A) E. coli BAA-196 (EC-BAA-196 Lag/Log); (B) S. aureus BAA-39 (SA-BAA-39 Lag/Log); (C) A. baumannii BAA-1790 (AB-BAA-1790 Lag/Log). Circles represent protein coding genes (CDS) plotted according to their negative and positive log₂(fold change) values calculated in the Lag experiment (x axis) and Log experiment (y axis). The outermost regulated genes are labeled by their names or locus tag numbers. Thin vertical and horizontal lines within the plots separate genes with 2-fold or higher regulation and split the plots into sectors of genes of different categories depending on their coregulation. Numbers of CDS falling into different sectors are shown. Up- and down-coregulated genes, oppositely regulated genes, and the genes regulated only in one experiment are depicted by different colors. Statistical reliability of the fold change predictions is depicted by sizes of circles as explained in the legend at the bottom of the figure. Estimated Pearson correlation coefficients are given on the top of each plot.

FIG 2

Metabolic pathways affected by the treatment of the model microorganisms E. coli BAA-196 (A), S. aureus BAA-39 (B), and A. baumannii BAA-1790 (C) with FS-1 as predicted by gene expression patterning in the Lag and Log experiments. Up- and downregulation of metabolic pathways discovered in both experiments, or only in one experiment, are depicted by arrows of different colors and widths as explained in the legend at the bottom right of the figure. Cell membrane and cell wall-associated proteins are shown by blocks depicted by the same color scheme. Individual pathways and key compounds are labeled.

FIG 3

PheNetic network of the regulated genes of E. coli BAA-196 identified in the Lag experiment and clustered according to their regulation by higher-level transcriptional regulators. Upregulated genes are depicted by pink nodes, and downregulated genes are indicated by green nodes (vertices). Color intensity indicates levels of the expression fold changes. Gray nodes are transcriptional regulators involved in the network, whose expression was not reliably changed. Orange edges show regulation by transcriptional activators, and blue edges show regulation by transcriptional repressors. Direct regulation by the transcriptional regulators is indicated by arrowheads.

FIG 4

Plots of coregulation of homologous genes shared by pairs of model microorganisms in the Lag experiment. (A) E. coli BAA-196 and S. aureus BAA-39 (EC-BAA-196/SA-BAA-39); (B) E. coli BAA-196 and A. baumannii BAA-1790 (EC-BAA-196/AB-BAA-1790); (C) S. aureus BAA-39 and A. baumannii BAA-1790 (SA-BAA-39/AB-BAA-1790). Circles represent protein coding genes (CDS) plotted according to their negative and positive log₂(fold change) values calculated in the Lag experiments for different microorganisms shown along x and y axes. The outermost regulated genes are labeled by their names. Thin vertical and horizontal lines within the plots separate genes with 2-fold or higher regulation and split the plots into sectors of genes of different categories depending on their coregulation. Numbers of CDS falling into different sectors are shown. Up- and down-coregulated genes and oppositely regulated genes are depicted by different colors. Statistical reliability of the fold change predictions is depicted by sizes of the circles as explained in the legend at the bottom of the figure. Estimated Pearson correlation coefficients are given on the top of each plot.