Growth-restricting effects of siRNA transfections: a largely deterministic combination of off-target binding and hybridization-independent competition.

Perturbation of gene expression by means of synthetic small interfering RNAs (siRNAs) is a powerful way to uncover gene function. However, siRNA technology suffers from sequence-specific off-target effects and from limitations in knock-down efficiency. In this study, we assess a further problem: unintended effects of siRNA transfections on cellular fitness/proliferation. We show that the nucleotide compositions of siRNAs at specific positions have reproducible growth-restricting effects on mammalian cells in culture. This is likely distinct from hybridization-dependent off-target effects, since each nucleotide residue is seen to be acting independently and additively. The effect is robust and reproducible across different siRNA libraries and also across various cell lines, including human and mouse cells. Analyzing the growth inhibition patterns in correlation to the nucleotide sequence of the siRNAs allowed us to build a predictor that can estimate growth-restricting effects for any arbitrary siRNA sequence. Competition experiments with co-transfected siRNAs further suggest that the growth-restricting effects might be linked to an oversaturation of the cellular miRNA machinery, thus disrupting endogenous miRNA functions at large. We caution that competition between siRNA molecules could complicate the interpretation of double-knockdown or epistasis experiments, and potential interactions with endogenous miRNAs can be a factor when assaying cell growth or viability phenotypes.

Improved pathway reconstruction from RNA interference screens by exploiting off-target effects.

Motivation: Pathway reconstruction has proven to be an indispensable tool for analyzing the molecular mechanisms of signal transduction underlying cell function. Nested effects models (NEMs) are a class of probabilistic graphical models designed to reconstruct signalling pathways from high-dimensional observations resulting from perturbation experiments, such as RNA interference (RNAi). NEMs assume that the short interfering RNAs (siRNAs) designed to knockdown specific genes are always on-target. However, it has been shown that most siRNAs exhibit strong off-target effects, which further confound the data, resulting in unreliable reconstruction of networks by NEMs. Results: Here, we present an extension of NEMs called probabilistic combinatorial nested effects models (pc-NEMs), which capitalize on the ancillary siRNA off-target effects for network reconstruction from combinatorial gene knockdown data. Our model employs an adaptive simulated annealing search algorithm for simultaneous inference of network structure and error rates inherent to the data. Evaluation of pc-NEMs on simulated data with varying number of phenotypic effects and noise levels as well as real data demonstrates improved reconstruction compared to classical NEMs. Application to Bartonella henselae infection RNAi screening data yielded an eight node network largely in agreement with previous works, and revealed novel binary interactions of direct impact between established components. Availability and implementation: The software used for the analysis is freely available as an R package at https://github.com/cbg-ethz/pcNEM.git. Supplementary information: Supplementary data are available at Bioinformatics online.

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

BACKGROUND: 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. RESULTS: We show that PMM gains statistical power for hit detection due to parallel screening. PMM allows incorporating siRNA weights that can be assigned according to available information on RNAi quality. Moreover, PMM is able to estimate a sharedness score that can be used to focus follow-up efforts on generic or specific gene regulators. By fitting a PMM model to our data, we found several novel hit genes for most of the pathogens studied. CONCLUSIONS: Our results show parallel RNAi screening can improve the results of individual screens. This is currently particularly interesting when large-scale parallel datasets are becoming more and more publicly available. Our comprehensive siRNA dataset provides a public, freely available resource for further statistical and biological analyses in the high-content, high-throughput siRNA screening field.

Bartonella entry mechanisms into mammalian host cells.

The Gram-negative genus Bartonella comprises arthropod-borne pathogens that typically infect mammals in a host-specific manner. Bartonella bacilliformis and Bartonella quintana are human-specific pathogens, while several zoonotic bartonellae specific for diverse animal hosts infect humans as an incidental host. Clinical manifestations of Bartonella infections range from mild symptoms to life-threatening disease. Following transmission by blood-sucking arthropods or traumatic contact with infected animals, bartonellae display sequential tropisms towards endothelial and possibly other nucleated cells and erythrocytes, the latter in a host-specific manner. Attachment to the extracellular matrix (ECM) and to nucleated cells is mediated by surface-exposed bacterial adhesins, in particular trimeric autotransporter adhesins (TAAs). The subsequent engulfment of the pathogen into a vacuolar structure follows a unique series of events whereby the pathogen avoids the endolysosomal compartments. For Bartonella henselae and assumingly most other species, the infection process is aided at different steps by Bartonella effector proteins (Beps). They are injected into host cells through the type IV secretion system (T4SS) VirB/D4 and subvert host cellular functions to favour pathogen uptake. Bacterial binding to erythrocytes is mediated by Trw, another T4SS, in a strictly host-specific manner, followed by pathogen-forced uptake involving the IalB invasin and subsequent replication and persistence within a membrane-bound intra-erythrocytic compartment.

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

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-ß signaling. gespeR is available as a Bioconductor R-package.

Systems-level analysis of host-pathogen interaction using RNA interference.

Hand-in-hand with the availability of full genome sequences for eukaryotic model organisms and humans the demand for analysis of gene function on a system level has grown. In a process called RNA interference (RNAi) specific mRNA species can be degraded by introduction of double-stranded small interfering RNAs (siRNAs) that are complementary to the targeted transcript sequence. This enables the selective impairment of gene function. During the past decade RNAi has been exploited in many different eukaryotic cell types and model organisms. Large-scale and eventually genome-wide RNAi screens ablating gene functions in a systematic manner have delivered an overwhelming amount of data on the requirement of distinct gene products for major cellular pathways. A large part of the RNAi field is dedicated to disease states such as cancer or infection with the prospect of discovering pathways suitable for new therapeutic interventions. Here some of the major steps in the development of the RNAi technology will be outlined and exemplified with a focus on the progress made in the field of mammalian host-pathogen interactions.