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. 2017 Jul 19;7(1):5868.
doi: 10.1038/s41598-017-06003-7.

Co-expression Network Analysis of Toxin-Antitoxin Loci in Mycobacterium Tuberculosis Reveals Key Modulators of Cellular Stress

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Free PMC article

Co-expression Network Analysis of Toxin-Antitoxin Loci in Mycobacterium Tuberculosis Reveals Key Modulators of Cellular Stress

Amita Gupta et al. Sci Rep. .
Free PMC article

Erratum in

Abstract

Research on toxin-antitoxin loci (TA loci) is gaining impetus due to their ubiquitous presence in bacterial genomes and their observed roles in stress survival, persistence and drug tolerance. The present study investigates the expression profile of all the seventy-nine TA loci found in Mycobacterium tuberculosis. The bacterium was subjected to multiple stress conditions to identify key players of cellular stress response and elucidate a TA-coexpression network. This study provides direct experimental evidence for transcriptional activation of each of the seventy-nine TA loci following mycobacterial exposure to growth-limiting environments clearly establishing TA loci as stress-responsive modules in M. tuberculosis. TA locus activation was found to be stress-specific with multiple loci activated in a duration-based response to a particular stress. Conditions resulting in arrest of cellular translation led to greater up-regulation of TA genes suggesting that TA loci have a primary role in arresting translation in the cell. Our study identifed higBA2 and vapBC46 as key loci that were activated in all the conditions tested. Besides, relBE1, higBA3, vapBC35, vapBC22 and higBA1 were also upregulated in multpile stresses. Certain TA modules exhibited co-activation across multiple conditions suggestive of a common regulatory mechanism.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Toxin Antitoxin loci encoded by Mycobacterium tuberculosis H37Rv The 79 TA loci present in M. tuberculosis H37Rv genome tabulated with the locus name, gene name for toxin and antitoxin, gene identifier (Rv number), genomic location as Start base position and End base position, orientation in the genome and the type of TA locus. The uncharacterized TA loci are given the nomenclature as ucAT followed by a number for the series. The number in the blue bar indicates the total number of experimental conditions wherein differential expression was observed for the toxin gene and the number in the pink bar indicates the same for the antitoxin gene.
Figure 2
Figure 2
Differentially expressed TA Loci across experimental conditions. Line Graph depicts the number of TA genes that are upregulated and downregulated in each experimental condition at 1.5 and 2.0 fold change. Total number of up and down regulated toxin and antitoxin genes for each condition were plotted using Microsoft excel. The Y-axis indicates total number of toxin and antitoxin genes differentially regulated and X-axis indicates the experimental conditions and time points of the study. Ethambutol (ETH), Isoniazid (INH), Rifampicin (RIF), Streptomycin (STR), Starvation (STRV). 4 H, 6 H, 24 H, 72 H denotes the duration of treatment as 4 hours, 6 hours, 24 hours and 72 hours. 1WK denotes treatment for 1 week.
Figure 3
Figure 3
Differential gene expression matrix showing the number of up and down regulated TA genes across the experimental conditions. TA genes up and down regulated at varying sensitivity of detection were subjected to GeneMatrix software which resulted in distribution of up and down regulated genes across 18 conditions profiled. The upregulated genes at 1.5 FC (A) and 2.0 FC (B) are shown in increasing color of red while the downregulated genes at 1.5 FC (C) and 2.0 FC (D) are shown in green.
Figure 4
Figure 4
Unsupervised hierarchical clustering analysis of Differentially expressed TA Loci TA genes 1.5 fold up and down regulated along with their fold change were subjected to unsupervised hierarchical clustering. Up regulated genes are in red color gradient and down regulated genes are in green colour gradient. Fold change of 2 was given as a cutoff to render the cluster image to identify patterns of up and down regulated genes.
Figure 5
Figure 5
(A) Line Graph depicting the approach used to calculate activation and deactivation scores for TA loci (B) Number of TA loci that are activated, deactivated and co-expressed in each of the experimental conditions profiled. Fold change of toxin from cognate antitoxin was subtracted in early and late time point. The subtracted scores were subjected to biclustering/K-means clustering using Pearson Uncentered algorithm in GeneSpring Gx v 12.8 to provide 3 clusters of all the 78 TA loci, with clusters representing activation, deactivation and co-expression status. Similar approach was followed for each of the experimental conditions and TA loci activated, deactivated and co-expressed in each condition were identified.
Figure 6
Figure 6
Unsupervised hierarchical clustering analysis of TA loci based on their activation and deactivation status in response to stress. All the 79 TA loci with their activation-deactivation and co-expression status defined as 2 for activation, −2 for deactivation and 0 for co-expression were subjected to unsupervised hierarchical clustering using Cluster 3.0 software by applying Pearson Uncentered algorithm with average linkage rule. The resultant cluster file was imported into Java Treeview software to visualize the cluster. Activated TA loci are in red color gradient and Deactivated TA loci are in green color gradient. A score of 2 was given as a cutoff to render the cluster image to identify patterns of activation and deactivation of TA loci across conditions profiled.
Figure 7
Figure 7
Biological Network of TA loci. Condition-based list of TA loci activated and deactivated was provided to BridgeIsland software. This software identifies the nodes and edges that come together to form a network of connections that could be representative of the TA gene regulation. The nodes (TA loci) are colored based on the activation and deactivation levels with activation being colored in red gradient and deactivation being colored in green gradient and co-expression colored in yellow gradient. The nodes are sized based on their connectivity scores with larger the size indicating a TA is activated/deactivated in as many conditions profiled.
Figure 8
Figure 8
(A) Unsupervised hierarchical clustering analysis of protease genes based on their activation and deactivation status in response to stress. (B) Unsupervised hierarchical clustering analysis of nuclease genes based on their activation and deactivation status in response to stress Fold change of genes in late time point was subtracted from early time point to calculate the activation deactivation score. The cluster was created as described for Figs 5 and 6.

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