imaging-inlc-secretion-to-investigate-cellular-infection-bacterial

Imaging InlC Secretion to Investigate Cellular Infection by the Bacterial Pathogen <em>Listeria monocytogenes</em>

Bacterial intracellular  pathogens can be conceived as molecular tools to dissect cellular signaling  cascades due to their capacity to exquisitely ...

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 ...

University of Basel

BioDataAnalysis GmbH

High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation.

Multi-scale Gaussian representation and outline-learning based cell image segmentation.

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.

Nested effects models have been used successfully for learning subcellular networks from high-dimensional perturbation effects that result from RNA interference (RNAi) experiments. Here, we further develop the basic nested effects model using high-content single-cell imaging data from RNAi screens of cultured cells infected with human rhinovirus. RNAi screens with single-cell readouts are becoming increasingly common, and they often reveal high cell-to-cell variation. As a consequence of this cellular heterogeneity, knock-downs result in variable effects among cells and lead to weak average phenotypes on the cell population level. To address this confounding factor in network inference, we explicitly model the stimulation status of a signaling pathway in individual cells. We extend the framework of nested effects models to probabilistic combinatorial knock-downs and propose NEMix, a nested effects mixture model that accounts for unobserved pathway activation. We analyzed the identifiability of NEMix and developed a parameter inference scheme based on the Expectation Maximization algorithm. In an extensive simulation study, we show that NEMix improves learning of pathway structures over classical NEMs significantly in the presence of hidden pathway stimulation. We applied our model to single-cell imaging data from RNAi screens monitoring human rhinovirus infection, where limited infection efficiency of the assay results in uncertain pathway stimulation. Using a subset of genes with known interactions, we show that the inferred NEMix network has high accuracy and outperforms the classical nested effects model without hidden pathway activity. NEMix is implemented as part of the R/Bioconductor package 'nem' and available at www.cbg.ethz.ch/software/NEMix.

NEMix: single-cell nested effects models for probabilistic pathway stimulation.

Listeria monocytogenes enters nonphagocytic cells by a receptor-mediated mechanism that is dependent on a clathrin-based molecular machinery and actin rearrangements. Bacterial intra- and intercellular movements are also actin dependent and rely on the actin nucleating Arp2/3 complex, which is activated by host-derived nucleation-promoting factors downstream of the cell receptor Met during entry and by the bacterial nucleation-promoting factor ActA during comet tail formation. By genome-wide small interfering RNA (siRNA) screening for host factors involved in bacterial infection, we identified diverse cellular signaling networks and protein complexes that support or limit these processes. In addition, we could precise previously described molecular pathways involved in Listeria invasion. In particular our results show that the requirements for actin nucleators during Listeria entry and actin comet tail formation are different. Knockdown of several actin nucleators, including SPIRE2, reduced bacterial invasion while not affecting the generation of comet tails. Most interestingly, we observed that in contrast to our expectations, not all of the seven subunits of the Arp2/3 complex are required for Listeria entry into cells or actin tail formation and that the subunit requirements for each of these processes differ, highlighting a previously unsuspected versatility in Arp2/3 complex composition and function.

Genome-Wide siRNA Screen Identifies Complementary Signaling Pathways Involved in Listeria Infection and Reveals Different Actin Nucleation Mechanisms during Listeria Cell Invasion and Actin Comet Tail Formation.

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.

gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens.

Erratum to: gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens.

Salmonella Typhimurium (S.Tm) is an enteropathogen requiring multiple virulence factors, including two type three secretion systems (T1 and T2). T1 triggers epithelium invasion in which the bacteria are taken up into endosomes that mature into Salmonella-containing vacuoles (SCV) and trigger T2 induction upon acidification. Mechanisms controlling endosome membrane integrity or pathogen egress into the cytosol are incompletely understood. We screened for host factors affecting invasion and SCV maturation and identified a role for autophagy in sealing endosomal membranes damaged by T1 during host cell invasion. S.Tm-infected autophagy-deficient (atg5(-/-)) cells exhibit reduced SCV dye retention and lower T2 expression but no effects on steps preceding SCV maturation. However, in the absence of T1, autophagy is dispensable for T2 induction. These findings establish a role of autophagy at early stages of S.Tm infection and suggest that autophagy-mediated membrane repair might be generally important for invasive pathogens and endosomal membrane function.

ArticleTotal : 2	Year
Publication title Cited by 7	2013
Publication title Cited by 6	2016

Article	Year
Multi-scale Gaussian representation and outline-learning based cell image segmentation. BMC bioinformatics\| PubMed ID: 24267488	2013
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
NEMix: single-cell nested effects models for probabilistic pathway stimulation. PLoS computational biology\| PubMed ID: 25879530	2015
Genome-Wide siRNA Screen Identifies Complementary Signaling Pathways Involved in Listeria Infection and Reveals Different Actin Nucleation Mechanisms during Listeria Cell Invasion and Actin ... mBio\| PubMed ID: 25991686	2015
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
Autophagy Proteins Promote Repair of Endosomal Membranes Damaged by the Salmonella Type Three Secretion System 1. Cell host & microbe\| PubMed ID: 26567507	2015

Mario Emmenlauer

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