Publications by authors named "Maksym Bobrovskyy"

Staphylococcus aureus bears a type 7b secretion system (T7bSS) that assembles in the bacterial envelope to promote the secretion of WXG-like proteins and toxic effectors bearing LXG domains. Cognate immunity proteins bind cytosolic effectors to mute their toxicity prior to secretion. T7b-secreted factors have been associated with the pathogenesis of staphylococcal disease and intraspecies competition.

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Secretion systems utilize ATPase activity to facilitate the translocation of proteins into and across membranes. In bacteria, the universally conserved SecA ATPase binds a large repertoire of preproteins and interacts with the SecYEG translocon. In contrast, the type 7b secretion system (T7bSS) of Staphylococcus aureus supports the secretion of a restricted subset of proteins.

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Base-pairing interactions mediate many intermolecular target recognition events. Even a single base-pair mismatch can cause a substantial difference in activity but how such changes influence the target search kinetics in vivo is unknown. Here, we use high-throughput sequencing and quantitative super-resolution imaging to probe the mutants of bacterial small RNA, SgrS, and their regulation of ptsG mRNA target.

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Small RNA (sRNA) regulators promote efficient responses to stress, but the mechanisms for prioritizing target mRNA regulation remain poorly understood. This study examines mechanisms underlying hierarchical regulation by the sRNA SgrS, found in enteric bacteria and produced under conditions of metabolic stress. SgrS posttranscriptionally coordinates a nine-gene regulon to restore growth and homeostasis.

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The ESAT-6-like secretion system (ESS) of is assembled in the bacterial membrane from core components that promote the secretion of WXG-like proteins (EsxA, EsxB, EsxC, and EsxD) and the EssD effector. Genes encoding the ESS secretion machinery components, effector, and WXG-like proteins are located in the locus. Here, we identify , a heretofore uncharacterized gene of the locus, whose product is secreted via an N-terminal signal peptide into the extracellular medium of staphylococcal cultures.

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The Escherichia coli small RNA SgrS controls a metabolic stress response that occurs upon accumulation of certain glycolytic intermediates. SgrS base pairs with and represses translation of ptsG and manXYZ mRNAs, which encode sugar transporters, and activates translation of yigL mRNA, encoding a sugar phosphatase. This study defines four new genes as direct targets of E.

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Over the last decade, small (often noncoding) RNA molecules have been discovered as important regulators influencing myriad aspects of bacterial physiology and virulence. In particular, small RNAs (sRNAs) have been implicated in control of both primary and secondary metabolic pathways in many bacterial species. This chapter describes characteristics of the major classes of sRNA regulators, and highlights what is known regarding their mechanisms of action.

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Bacteria adapt to ever-changing habitats through specific responses to internal and external stimuli that result in changes in gene regulation and metabolism. One internal metabolic cue affecting such changes in Escherichia coli and related enteric species is cytoplasmic accumulation of phosphorylated sugars such as glucose-6-phosphate or the non-metabolizable analog α-methylglucoside-6-phosphate. This "glucose-phosphate stress" triggers a dedicated stress response in γ-proteobacteria including several enteric pathogens.

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Bacteria live in many dynamic environments with alternating cycles of feast or famine that have resulted in the evolution of mechanisms to quickly alter their metabolic capabilities. Such alterations often involve complex regulatory networks that modulate expression of genes involved in nutrient uptake and metabolism. A great number of protein regulators of metabolism have been characterized in depth.

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Recent advances in high-throughput RNA sequencing (RNA-seq) have enabled tremendous leaps forward in our understanding of bacterial transcriptomes. However, computational methods for analysis of bacterial transcriptome data have not kept pace with the large and growing data sets generated by RNA-seq technology. Here, we present new algorithms, specific to bacterial gene structures and transcriptomes, for analysis of RNA-seq data.

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