microscopy-based-assays-for-high-throughput-screening-host-factors

Microscopy-based Assays for High-throughput Screening of Host Factors Involved in <em>Brucella</em> Infection of Hela Cells

Brucella species are facultative intracellular pathogens that infect animals as their natural hosts. Transmission to humans is most commonly caused by ...

Biofilms are communities of surface-attached, matrix-embedded microbial cells that can resist antimicrobial chemotherapy and contribute to persistent infections. Using an Escherichia coli biofilm model we found that exposure of bacteria to subinhibitory concentrations of ribosome-targeting antibiotics leads to strong biofilm induction. We present evidence that this effect is elicited by the ribosome in response to translational stress. Biofilm induction involves upregulation of the polysaccharide adhesin poly-beta-1,6-N-acetyl-glucosamine (poly-GlcNAc) and two components of the poly-GlcNAc biosynthesis machinery, PgaA and PgaD. Poly-GlcNAc control depends on the bacterial signalling molecules guanosine-bis 3', 5'(diphosphate) (ppGpp) and bis-(3'-5')-cyclic di-GMP (c-di-GMP). Treatment with translation inhibitors causes a ppGpp hydrolase (SpoT)-mediated reduction of ppGpp levels, resulting in specific derepression of PgaA. Maximal induction of PgaD and poly-GlcNAc synthesis requires the production of c-di-GMP by the dedicated diguanylate cyclase YdeH. Our results identify a novel regulatory mechanism that relies on ppGpp signalling to relay information about ribosomal performance to the Pga machinery, thereby inducing adhesin production and biofilm formation. Based on the important synergistic roles of ppGpp and c-di-GMP in this process, we suggest that interference with bacterial second messenger signalling might represent an effective means for biofilm control during chronic infections.

Second messenger signalling governs Escherichia coli biofilm induction upon ribosomal stress.

Systematic genetic perturbation screening in human cells remains technically challenging. Typically, large libraries of chemically synthesized siRNA oligonucleotides are used, each designed to degrade a specific cellular mRNA via the RNA interference (RNAi) mechanism. Here, we report on data from three genome-wide siRNA screens, conducted to uncover host factors required for infection of human cells by two bacterial and one viral pathogen. We find that the majority of phenotypic effects of siRNAs are unrelated to the intended "on-target" mechanism, defined by full complementarity of the 21-nt siRNA sequence to a target mRNA. Instead, phenotypes are largely dictated by "off-target" effects resulting from partial complementarity of siRNAs to multiple mRNAs via the "seed" region (i.e., nucleotides 2-8), reminiscent of the way specificity is determined for endogenous microRNAs. Quantitative analysis enabled the prediction of seeds that strongly and specifically block infection, independent of the intended on-target effect. This prediction was confirmed experimentally by designing oligos that do not have any on-target sequence match at all, yet can strongly reproduce the predicted phenotypes. Our results suggest that published RNAi screens have primarily, and unintentionally, screened the sequence space of microRNA seeds instead of the intended on-target space of protein-coding genes. This helps to explain why previously published RNAi screens have exhibited relatively little overlap. Our analysis suggests a possible way of identifying "seed reagents" for controlling phenotypes of interest and establishes a general strategy for extracting valuable untapped information from past and future RNAi screens.

Specific inhibition of diverse pathogens in human cells by synthetic microRNA-like oligonucleotides inferred from RNAi screens.

Large-scale RNAi screening has become an important technology for identifying genes involved in biological processes of interest. However, the quality of large-scale RNAi screening is often deteriorated by off-targets effects. In order to find statistically significant effector genes for pathogen entry, we systematically analyzed entry pathways in human host cells for eight pathogens using image-based kinome-wide siRNA screens with siRNAs from three vendors. We propose a Parallel Mixed Model (PMM) approach that simultaneously analyzes several non-identical screens performed with the same RNAi libraries.

Simultaneous analysis of large-scale RNAi screens for pathogen entry.

Small interfering RNAs (siRNAs) exhibit strong off-target effects, which confound the gene-level interpretation of RNA interference screens and thus limit their utility for functional genomics studies. Here, we present gespeR, a statistical model for reconstructing individual, gene-specific phenotypes. Using 115,878 siRNAs, single and pooled, from three companies in three pathogen infection screens, we demonstrate that deconvolution of image-based phenotypes substantially improves the reproducibility between independent siRNA sets targeting the same genes. Genes selected and prioritized by gespeR are validated and shown to constitute biologically relevant components of pathogen entry mechanisms and TGF-β signaling. gespeR is available as a Bioconductor R-package.

Article	Year
Second messenger signalling governs Escherichia coli biofilm induction upon ribosomal stress. Molecular microbiology\| PubMed ID: 19460094	2009
Specific inhibition of diverse pathogens in human cells by synthetic microRNA-like oligonucleotides inferred from RNAi screens. Proceedings of the National Academy of Sciences of the United States of America\| PubMed ID: 24616511	2014
Simultaneous analysis of large-scale RNAi screens for pathogen entry. BMC genomics\| PubMed ID: 25534632	2014
gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens. Genome biology\| PubMed ID: 26445817	2015
Erratum to: gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens. Genome biology\| PubMed ID: 26490954	2015

Alain Casanova

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