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Bacterial small RNAs are key mediators of post-transcriptional gene regulation. An increasing number of sRNAs have been implicated in the regulation of virulence programs of pathogenic bacteria. Recently, in the enteric pathogen Salmonella Typhimurium, the PinT sRNA has gained increased importance as it is the most upregulated sRNA as Salmonella infects mammalian host cells (Westermann et al., 2016). PinT acts as a temporal regulator of Salmonella‘s two major pathogenicity islands, SPI-1 and SPI-2 (Kim et al., 2019; Westermann et al., 2016). However, the complete set of PinT targets, its role in Salmonella infection and host response is not yet fully understood. Building on the MS2 affinity purification and RNA- seq (MAPS) method (Lalaouna et al., 2015), we here set out to globally identify direct RNA ligands of PinT, relevant to Salmonella infection. We transferred the classical MAPS technique, based on sRNA-bait overexpression, to more physiological conditions, using endogenous levels of the sRNA. Making the henceforth identified targets, less likely to represent artefacts of the overexpression. More importantly, we progressed the MAPS technique to in vivo settings and by doing so, we were able pull-down bacterial RNA transcripts bound by PinT during macrophage infection. While we validate previously known PinT targets, our integrated data revealed novel virulence relevant target. These included mRNAs for the SPI-2 effector SteC, the PhoQ activator UgtL and the 30S ribosomal protein S22 RpsV. Next, we follow up on SteC, the best characterized virulence relevant PinT target. Using genetic and biochemical assays, we demonstrate that PinT represses steC mRNA by direct base-pairing and translational interference. PinT-mediated regulation of SteC leads to alterations in the host response to Salmonella infection. This regulation impacts the cytokine response of infected macrophages, by altering IL10 production, and possibly driving the macrophages to an anti-inflammatory state, more permise to infection. SteC is responsible for F-actin meshwork rearrangements around the SCV (Poh et al., 2008). Here we demonstrate that PinT-mediated regulation of SteC, impacts the formation of this actin meshwork in infected cells. Our results demonstrate that SteC expression is very tightly regulated by PinT in two layers; indirectly, by repressing ssrB and crp; and directly by binding to steC 5’UTR. PinT contributes to post-transcriptional cross-talk between invasion and intracellular replication programs of Salmonella, by controlling the expression of both SPI-1 and SPI-2 genes (directly and indirectly). Together, our collective data makes PinT the first sRNA in Gram-negatives with a pervasive role in virulence, at the center of Salmonella virulence programs and provide molecular input that could help explain the attenuation of pinT-deficient Salmonella strains in whole animal models of infection.
Development and application of computational tools for RNA-Seq based transcriptome annotations
(2019)
In order to understand the regulation of gene expression in organisms, precise genome annotation is essential. In recent years, RNA-Seq has become a potent method for generating and improving genome annotations. However, this Approach is time consuming and often inconsistently performed when done manually. In particular, the discovery of non-coding RNAs benefits strongly from the application of RNA-Seq data but requires significant amounts of expert knowledge and is labor-intensive. As a part of my doctoral study, I developed a modular tool called ANNOgesic that can detect numerous transcribed genomic features, including non-coding RNAs, based on RNA-Seq data in a precise and automatic fashion with a focus on bacterial and achaeal species. The software performs numerous analyses and generates several visualizations. It can generate annotations of high-Resolution that are hard to produce using traditional annotation tools that are based only on genome sequences. ANNOgesic can detect numerous novel genomic Features like UTR-derived small non-coding RNAs for which no other tool has been developed before. ANNOgesic is available under an open source license (ISCL) at https://github.com/Sung-Huan/ANNOgesic.
My doctoral work not only includes the development of ANNOgesic but also its application to annotate the transcriptome of Staphylococcus aureus HG003 - a strain which has been a insightful model in infection biology. Despite its potential as a model, a complete genome sequence and annotations have been lacking for HG003. In order to fill this gap, the annotations of this strain, including sRNAs and their functions, were generated using ANNOgesic by analyzing differential RNA-Seq data from 14 different samples (two media conditions with seven time points), as well as RNA-Seq data generated after transcript fragmentation. ANNOgesic was
also applied to annotate several bacterial and archaeal genomes, and as part of this its high performance was demonstrated. In summary, ANNOgesic is a powerful computational tool for RNA-Seq based annotations and has been successfully applied to several species.