Proteomic and transcriptomic experiments reveal an essential role of RNA degradosome complexes in shaping the transcriptome of Mycobacterium tuberculosis

Abstract

The phenotypic adjustments of Mycobacterium tuberculosis are commonly inferred from the analysis of transcript abundance. While mechanisms of transcriptional regulation have been extensively analysed in mycobacteria, little is known about mechanisms that shape the transcriptome by regulating RNA decay rates. The aim of the present study is to identify the core components of the RNA degradosome of M. tuberculosis and to analyse their function in RNA metabolism. Using an approach involving cross-linking to 4-thiouridine-labelled RNA, we mapped the mycobacterial RNA-bound proteome and identified degradosome-related enzymes polynucleotide phosphorylase (PNPase), ATP-dependent RNA helicase (RhlE), ribonuclease E (RNase E) and ribonuclease J (RNase J) as major components. We then carried out affinity purification of eGFP-tagged recombinant constructs to identify protein-protein interactions. This identified further interactions with cold-shock proteins and novel KH-domain proteins. Engineering and transcriptional profiling of strains with a reduced level of expression of core degradosome ribonucleases provided evidence of important pleiotropic roles of the enzymes in mycobacterial RNA metabolism highlighting their potential vulnerability as drug targets.

Genome-wide identification of the context-dependent sRNA expression in Mycobacterium tuberculosis

“…We used RNA-Seq data of the mid-exponential phase culture to develop a methodology for identifying sRNAs in the intergenic regions (IGRs) of M. tuberculosis [21]. Initially, we quantified the expression of different functional elements in the genome and tested if the RNA-Seq data is sufficient to detect the IGR sRNA expression.…”

“…MultiBamcov in Bedtools was used to obtain read counts which were further normalised using RPKM (Reads Per Kilobase Million) [44]. In the mid-exponential phase growth culture, top 1000 genes with high expression, which correspond to more than third quartile (Q3) of the dataset were identified as highly expressed (HE) genes [21]. Similarly, bottom 1000 genes with the least expression, which correspond to less than first quartile (Q1) of the dataset were identified as less expressed (LE) genes [21].…”

“…In the mid-exponential phase growth culture, top 1000 genes with high expression, which correspond to more than third quartile (Q3) of the dataset were identified as highly expressed (HE) genes [21]. Similarly, bottom 1000 genes with the least expression, which correspond to less than first quartile (Q1) of the dataset were identified as less expressed (LE) genes [21]. For comparison, experimentally validated sRNAs in M. tuberculosis were curated from the available literature (Additional file 8: Table S2).…”

Quasi-essentiality of RNase Y inBacillus subtilisis caused by its critical role in the control of mRNA homeostasis

“…In bacteria, rapid growth requires high transcription rates accompanied by rapid RNA degradation. The association between RNA polymerase and components of the RNA degrading machinery, as shown for B. subtilis and Mycobacterium tuberculosis might be a factor to achieve this coupling between RNA synthesis and degradation (83,86).…”

Biobank Lodz – population based biobank at the University of Lodz, Lodz, Poland

“…DNA, RNA tests (DNA and RNA isolation from various biological materials), study of genetic variation using classical methods of molecular biology such as: PCR, real-time PCR, Sanger sequencing [18], high-resolution melting of PCR products (HRM) [3-4, 17, 21, 26, 44-45], Taq-Man probes, measurements of selected parameters of the tested material (fluorometric testing of the DNA concentration), separation of nucleic acids using horizontal, vertical, capillary electrophoresis, genomic DNA testing using microarrays (e.g. GWAS) [15-16, 20, 22-23], next generation sequencing (Illumina Platform, Nanopore Sequencing), gene expression testing (RNA-seq, microarray and qPCR) [46][47][48][49][50][51], epigenetic analysis (Chip-Seq, DNA methylation analysis, methylation RNA), microorganism genome analysis (de novo sequencing, variant calling) [44,[52][53][54][55][56][57], eukaryotes whole genome and targeting sequencing, aDNA analysis [58][59], human genome analysis (variant calling, NGS Target Enrichment, whole exome sequencing), amplicon sequencing, metagenomics and metabarcoding, bioinformatics analysis.…”