Bacteria employ small non-coding RNAs (sRNAs) to regulate gene expression. Ms1 is an sRNA that binds to the RNA polymerase (RNAP) core and affects the intracellular level of this essential enzyme. Ms1 is structurally related to 6S RNA that binds to a different form of RNAP, the holoenzyme bearing the primary sigma factor. 6S RNAs are widespread in the bacterial kingdom except for the industrially and medicinally important Actinobacteria. While Ms1 RNA was identified in Mycobacterium, it is not clear whether Ms1 RNA is present also in other Actinobacteria species. Here, using a computational search based on secondary structure similarities combined with a linguistic gene synteny approach, we identified Ms1 RNA in Streptomyces. In S. coelicolor, Ms1 RNA overlaps with the previously annotated scr3559 sRNA with an unknown function. We experimentally confirmed that Ms1 RNA/scr3559 associates with the RNAP core without the primary sigma factor HrdB in vivo. Subsequently, we applied the computational approach to other Actinobacteria and identified Ms1 RNA candidates in 824 Actinobacteria species, revealing Ms1 RNA as a widespread class of RNAP binding sRNAs, and demonstrating the ability of our multifactorial computational approach to identify weakly conserved sRNAs in evolutionarily distant genomes.

• Keywords
• 6S RNA, Actinobacteria, Ms1 RNA, Mycobacterium, Streptomyces, gene synteny, sRNA,
• Publication type
• Journal Article MeSH

Regulatory RNAs control a number of physiological processes in bacterial cells. Here we report on a 6S-like RNA transcript (scr3559) that affects both development and antibiotic production in Streptomyces coelicolor. Its expression is enhanced during the transition to stationary phase. Strains that over-expressed the scr3559 gene region exhibited a shortened exponential growth phase in comparison with a control strain; accelerated aerial mycelium formation and spore maturation; alongside an elevated production of actinorhodin and undecylprodigiosin. These observations were supported by LC-MS analyses of other produced metabolites, including: germicidins, desferrioxamines, and coelimycin. A subsequent microarray differential analysis revealed increased expression of genes associated with the described morphological and physiological changes. Structural and functional similarities between the scr3559 transcript and 6S RNA, and its possible employment in regulating secondary metabolite production are discussed.

• Keywords
• 6S RNA, Streptomyces, antibiotics, secondary metabolism, small RNA,
• Publication type
• Journal Article MeSH

HrdB in streptomycetes is a principal sigma factor whose deletion is lethal. This is also the reason why its regulon has not been investigated so far. To overcome experimental obstacles, for investigating the HrdB regulon, we constructed a strain whose HrdB protein was tagged by an HA epitope. ChIP-seq experiment, done in 3 repeats, identified 2137 protein-coding genes organized in 337 operons, 75 small RNAs, 62 tRNAs, 6 rRNAs and 3 miscellaneous RNAs. Subsequent kinetic modeling of regulation of protein-coding genes with HrdB alone and with a complex of HrdB and a transcriptional cofactor RbpA, using gene expression time series, identified 1694 genes that were under their direct control. When using the HrdB-RbpA complex in the model, an increase of the model fidelity was found for 322 genes. Functional analysis revealed that HrdB controls the majority of gene groups essential for the primary metabolism and the vegetative growth. Particularly, almost all ribosomal protein-coding genes were found in the HrdB regulon. Analysis of promoter binding sites revealed binding motif at the -10 region and suggested the possible role of mono- or di-nucleotides upstream of the -10 element.

• MeSH
• Bacterial Proteins genetics   metabolism   MeSH
• RNA, Bacterial genetics   MeSH
• Chromatin Immunoprecipitation MeSH
• DNA, Bacterial chemistry   metabolism   MeSH
• DNA-Binding Proteins metabolism   MeSH
• Gene Expression MeSH
• Genes, rRNA MeSH
• Kinetics MeSH
• Models, Genetic MeSH
• Promoter Regions, Genetic MeSH
• Gene Expression Regulation, Bacterial MeSH
• Regulon * MeSH
• RNA, Transfer genetics   MeSH
• Sequence Analysis, DNA MeSH
• Sigma Factor metabolism   MeSH
• Streptomyces coelicolor genetics   metabolism   MeSH
• Binding Sites MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Bacterial Proteins MeSH
• RNA, Bacterial MeSH
• DNA, Bacterial MeSH
• DNA-Binding Proteins MeSH
• HrdB protein, Streptomyces MeSH Browser
• RNA, Transfer MeSH
• Sigma Factor MeSH

cis-Antisense RNAs (asRNAs) provide very simple and effective gene expression control due to the perfect complementarity between regulated and regulatory transcripts. In Streptomyces, the antibiotic-producing clade, the antisense control system is not yet understood, although it might direct the organism's complex development. Initial studies in Streptomyces have found a number of asRNAs. Apart from this, hundreds of mRNAs have been shown to bind RNase III, the double strand-specific endoribonuclease. In this study, we tested 17 mRNAs that have been previously co-precipitated with RNase III for antisense expression. Our RACE mapping showed that all of these mRNAs possess cognate asRNA. Additional tests for antisense expression uncovered as-adpA, as-rnc, as3983, as-sigB, as-sigH, and as-sigR RNAs. Northern blots detected the expression profiles of 18 novel transcripts. Noteworthy, we also found that only a minority of asRNAs respond to the absence of RNase III enzyme by increasing their cellular levels. Our findings suggest that antisense expression is widespread in Streptomyces, including genes of such important developmental regulators, as AdpA, RNase III, and sigma factors.

• Keywords
• RNase III, Streptomyces, antibiotics, cis-antisense RNA, gene expression control,
• Publication type
• Journal Article MeSH

A computational model of gene expression was applied to a novel test set of microarray time series measurements to reveal regulatory interactions between transcriptional regulators represented by 45 sigma factors and the genes expressed during germination of a prokaryote Streptomyces coelicolor. Using microarrays, the first 5.5 h of the process was recorded in 13 time points, which provided a database of gene expression time series on genome-wide scale. The computational modeling of the kinetic relations between the sigma factors, individual genes and genes clustered according to the similarity of their expression kinetics identified kinetically plausible sigma factor-controlled networks. Using genome sequence annotations, functional groups of genes that were predominantly controlled by specific sigma factors were identified. Using external binding data complementing the modeling approach, specific genes involved in the control of the studied process were identified and their function suggested.

• MeSH
• Transcription, Genetic MeSH
• Gene Regulatory Networks * MeSH
• Kinetics MeSH
• Models, Genetic * MeSH
• Computer Simulation MeSH
• Gene Expression Regulation, Bacterial * MeSH
• Oligonucleotide Array Sequence Analysis MeSH
• Sigma Factor metabolism   MeSH
• Spores, Bacterial genetics   growth & development   metabolism   MeSH
• Gene Expression Profiling * MeSH
• Streptomyces coelicolor genetics   metabolism   physiology   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Sigma Factor MeSH

Non-coding RNAs (ncRNAs) are regulatory molecules encoded in the intergenic or intragenic regions of the genome. In prokaryotes, biocomputational identification of homologs of known ncRNAs in other species often fails due to weakly evolutionarily conserved sequences, structures, synteny and genome localization, except in the case of evolutionarily closely related species. To eliminate results from weak conservation, we focused on RNA structure, which is the most conserved ncRNA property. Analysis of the structure of one of the few well-studied bacterial ncRNAs, 6S RNA, demonstrated that unlike optimal and consensus structures, suboptimal structures are capable of capturing RNA homology even in divergent bacterial species. A computational procedure for the identification of homologous ncRNAs using suboptimal structures was created. The suggested procedure was applied to strongly divergent bacterial species and was capable of identifying homologous ncRNAs.

• MeSH
• RNA, Bacterial chemistry   MeSH
• Nucleic Acid Conformation MeSH
• Molecular Sequence Data MeSH
• Mycobacterium genetics   MeSH
• RNA, Untranslated chemistry   MeSH
• Base Sequence MeSH
• Sequence Homology, Nucleic Acid MeSH
• Streptomyces genetics   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• 6S RNA MeSH Browser
• RNA, Bacterial MeSH
• RNA, Untranslated MeSH

This review summarizes the main results obtained in the fields of general and molecular microbiology and microbial genetics at the Institute of Microbiology of the Academy of Sciences of the Czech Republic (AS CR) [formerly Czechoslovak Academy of Sciences (CAS)] over more than 50 years. Contribution of the founder of the Institute, academician Ivan Málek, to the introduction of these topics into the scientific program of the Institute of Microbiology and to further development of these studies is also included.

• MeSH
• Academies and Institutes history   MeSH
• History, 20th Century MeSH
• Genetics, Microbial history   MeSH
• Molecular Biology history   MeSH
• Check Tag
• History, 20th Century MeSH
• Publication type
• Journal Article MeSH
• Historical Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH
• Geographicals
• Czech Republic MeSH