High-throughput RNA interference screens integrative analysis: Towards a comprehensive understanding of the virus-host interplay

Artificial Intelligence Methods in Infection Biology Research

Despite unprecedented achievements, the domain-specific application of artificial intelligence (AI) in the realm of infection biology was still in its infancy just a couple of years ago. This is largely attributable to the proneness of the infection biology community to shirk quantitative techniques. The so-called "sorting machine" paradigm was prevailing at that time, meaning that AI applications were primarily confined to the automation of tedious laboratory tasks. However, fueled by the severe acute respiratory syndrome coronavirus 2 pandemic, AI-driven applications in infection biology made giant leaps beyond mere automation. Instead, increasingly sophisticated tasks were successfully tackled, thereby ushering in the transition to the "Swiss army knife" paradigm. Incentivized by the urgent need to subdue a raging pandemic, AI achieved maturity in infection biology and became a versatile tool. In this chapter, the maturation of AI in the field of infection biology from the "sorting machine" paradigm to the "Swiss army knife" paradigm is outlined. Successful applications are illustrated for the three data modalities in the domain, that is, images, molecular data, and language data, with a particular emphasis on disentangling host-pathogen interactions. Along the way, fundamental terminology mentioned in the same breath as AI is elaborated on, and relationships between the subfields these terms represent are established. Notably, in order to dispel the fears of infection biologists toward quantitative methodologies and lower the initial hurdle, this chapter features a hands-on guide on software installation, virtual environment setup, data preparation, and utilization of pretrained models at its very end.

Systematic Comparison of CRISPR and shRNA Screens to Identify Essential Genes Using a Graph-Based Unsupervised Learning Model

Generally, essential genes identified using shRNA and CRISPR are not always the same, raising questions about the choice between these two screening platforms. To address this, we systematically compared the performance of CRISPR and shRNA to identify essential genes across different gene expression levels in 254 cell lines. As both platforms have a notable false positive rate, to correct this confounding factor, we first developed a graph-based unsupervised machine learning model to predict common essential genes. Furthermore, to maintain the unique characteristics of individual cell lines, we intersect essential genes derived from the biological experiment with the predicted common essential genes. Finally, we employed statistical methods to compare the ability of these two screening platforms to identify essential genes that exhibit differential expression across various cell lines. Our analysis yielded several noteworthy findings: (1) shRNA outperforms CRISPR in the identification of lowly expressed essential genes; (2) both screening methodologies demonstrate strong performance in identifying highly expressed essential genes but with limited overlap, so we suggest using a combination of these two platforms for highly expressed essential genes; (3) notably, we did not observe a single gene that becomes universally essential across all cancer cell lines.

Generation of Combinatorial Lentiviral Vectors Expressing Multiple Anti-Hepatitis C Virus shRNAs and Their Validation on a Novel HCV Replicon Double Reporter Cell Line

Despite the introduction of directly acting antivirals (DAAs), for the treatment of hepatitis C virus (HCV) infection, their cost, patient compliance, and viral resistance are still important issues to be considered. Here, we describe the generation of a novel JFH1-based HCV subgenomic replicon double reporter cell line suitable for testing different antiviral drugs and therapeutic interventions. This cells line allowed a rapid and accurate quantification of cell growth/viability and HCV RNA replication, thus discriminating specific from unspecific antiviral effects caused by DAAs or cytotoxic compounds, respectively. By correlating cell number and virus replication, we could confirm the inhibitory effect on the latter of cell over confluency and characterize an array of lentiviral vectors expressing single, double, or triple cassettes containing different combinations of short hairpin (sh)RNAs, targeting both highly conserved viral genome sequences and cellular factors crucial for HCV replication. While all vectors were effective in reducing HCV replication, the ones targeting viral sequences displayed a stronger antiviral effect, without significant cytopathic effects. Such combinatorial platforms as well as the developed double reporter cell line might find application both in setting-up anti-HCV gene therapy approaches and in studies aimed at further dissecting the viral biology/pathogenesis of infection.

Host factor prioritization for pan-viral genetic perturbation screens using random intercept models and network propagation

Genetic perturbation screens using RNA interference (RNAi) have been conducted successfully to identify host factors that are essential for the life cycle of bacteria or viruses. So far, most published studies identified host factors primarily for single pathogens. Furthermore, often only a small subset of genes, e.g., genes encoding kinases, have been targeted. Identification of host factors on a pan-pathogen level, i.e., genes that are crucial for the replication of a diverse group of pathogens has received relatively little attention, despite the fact that such common host factors would be highly relevant, for instance, for devising broad-spectrum anti-pathogenic drugs. Here, we present a novel two-stage procedure for the identification of host factors involved in the replication of different viruses using a combination of random effects models and Markov random walks on a functional interaction network. We first infer candidate genes by jointly analyzing multiple perturbations screens while at the same time adjusting for high variance inherent in these screens. Subsequently the inferred estimates are spread across a network of functional interactions thereby allowing for the analysis of missing genes in the biological studies, smoothing the effect sizes of previously found host factors, and considering a priori pathway information defined over edges of the network. We applied the procedure to RNAi screening data of four different positive-sense single-stranded RNA viruses, Hepatitis C virus, Chikungunya virus, Dengue virus and Severe acute respiratory syndrome coronavirus, and detected novel host factors, including UBC, PLCG1, and DYRK1B, which are predicted to significantly impact the replication cycles of these viruses. We validated the detected host factors experimentally using pharmacological inhibition and an additional siRNA screen and found that some of the predicted host factors indeed influence the replication of these pathogens.

Host-Induced Gene Silencing of an Adenylate Kinase Gene Involved in Fungal Energy Metabolism Improves Plant Resistance to Verticillium dahliae

Verticillium wilt, caused by the ascomycete fungus Verticillium dahliae (Vd), is a devastating disease of numerous plant species. However, the pathogenicity/virulence-related genes in this fungus, which may be potential targets for improving plant resistance, remain poorly elucidated. For the study of these genes in Vd, we used a well-established host-induced gene silencing (HIGS) approach and identified 16 candidate genes, including a putative adenylate kinase gene (VdAK). Transiently VdAK-silenced plants developed milder wilt symptoms than control plants did. VdAK-knockout mutants were more sensitive to abiotic stresses and had reduced germination and virulence on host plants. Transgenic Nicotiana benthamiana and Arabidopsis thaliana plants that overexpressed VdAK dsRNAs had improved Vd resistance than the wild-type. RT-qPCR results showed that VdAK was also crucial for energy metabolism. Importantly, in an analysis of total small RNAs from Vd strains isolated from the transgenic plants, a small interfering RNA (siRNA) targeting VdAK was identified in transgenic N. benthamiana. Our results demonstrate that HIGS is a promising strategy for efficiently screening pathogenicity/virulence-related genes of Vd and that VdAK is a potential target to control this fungus.

Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy

High-throughput genome-wide RNAi (RNA interference) screening technology has been widely used for discovering host factors that impact virus replication. Here we present the application of this technology to uncovering host targets that specifically modulate the replication of Maraba virus, an oncolytic rhabdovirus, and vaccinia virus with the goal of enhancing therapy. While the protocol has been tested for use with oncolytic Maraba virus and oncolytic vaccinia virus, this approach is applicable to other oncolytic viruses and can also be utilized for identifying host targets that modulate virus replication in mammalian cells in general. This protocol describes the development and validation of an assay for high-throughput RNAi screening in mammalian cells, the key considerations and preparation steps important for conducting a primary high-throughput RNAi screen, and a step-by-step guide for conducting a primary high-throughput RNAi screen; in addition, it broadly outlines the methods for conducting secondary screen validation and tertiary validation studies. The benefit of high-throughput RNAi screening is that it allows one to catalogue, in an extensive and unbiased fashion, host factors that modulate any aspect of virus replication for which one can develop an in vitro assay such as infectivity, burst size, and cytotoxicity. It has the power to uncover biotherapeutic targets unforeseen based on current knowledge.

CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes

High-throughput genetic screens have become essential tools for studying a wide variety of biological processes. Here we experimentally compare systems based on clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) or its transcriptionally repressive variant, CRISPR-interference (CRISPRi), with a traditional short hairpin RNA (shRNA)-based system for performing lethality screens. We find that the CRISPR technology performed best, with low noise, minimal off-target effects and consistent activity across reagents.

Evaluating network inference methods in terms of their ability to preserve the topology and complexity of genetic networks

Network inference is a rapidly advancing field, with new methods being proposed on a regular basis. Understanding the advantages and limitations of different network inference methods is key to their effective application in different circumstances. The common structural properties shared by diverse networks naturally pose a challenge when it comes to devising accurate inference methods, but surprisingly, there is a paucity of comparison and evaluation methods. Historically, every new methodology has only been tested against gold standard (true values) purpose-designed synthetic and real-world (validated) biological networks. In this paper we aim to assess the impact of taking into consideration aspects of topological and information content in the evaluation of the final accuracy of an inference procedure. Specifically, we will compare the best inference methods, in both graph-theoretic and information-theoretic terms, for preserving topological properties and the original information content of synthetic and biological networks. New methods for performance comparison are introduced by borrowing ideas from gene set enrichment analysis and by applying concepts from algorithmic complexity. Experimental results show that no individual algorithm outperforms all others in all cases, and that the challenging and non-trivial nature of network inference is evident in the struggle of some of the algorithms to turn in a performance that is superior to random guesswork. Therefore special care should be taken to suit the method to the purpose at hand. Finally, we show that evaluations from data generated using different underlying topologies have different signatures that can be used to better choose a network reconstruction method.

An integrative approach for a network based meta-analysis of viral RNAi screens

BACKGROUND

Big data is becoming ubiquitous in biology, and poses significant challenges in data analysis and interpretation. RNAi screening has become a workhorse of functional genomics, and has been applied, for example, to identify host factors involved in infection for a panel of different viruses. However, the analysis of data resulting from such screens is difficult, with often low overlap between hit lists, even when comparing screens targeting the same virus. This makes it a major challenge to select interesting candidates for further detailed, mechanistic experimental characterization.

RESULTS

To address this problem we propose an integrative bioinformatics pipeline that allows for a network based meta-analysis of viral high-throughput RNAi screens. Initially, we collate a human protein interaction network from various public repositories, which is then subjected to unsupervised clustering to determine functional modules. Modules that are significantly enriched with host dependency factors (HDFs) and/or host restriction factors (HRFs) are then filtered based on network topology and semantic similarity measures. Modules passing all these criteria are finally interpreted for their biological significance using enrichment analysis, and interesting candidate genes can be selected from the modules.

CONCLUSIONS

We apply our approach to seven screens targeting three different viruses, and compare results with other published meta-analyses of viral RNAi screens. We recover key hit genes, and identify additional candidates from the screens. While we demonstrate the application of the approach using viral RNAi data, the method is generally applicable to identify underlying mechanisms from hit lists derived from high-throughput experimental data, and to select a small number of most promising genes for further mechanistic studies.

Reprogramming the host: Modification of cell functions upon viral infection

Viruses and their hosts have co-evolved for million years. In order to successfully replicate their genome, viruses need to usurp the biosynthetic machinery of the host cell. Depending on the complexity and the nature of the genome, replication might involve or not a relatively large subset of viral products, in addition to a number of host cell factors, and take place in several subcellular compartments, including the nucleus, the cytoplasm, as well as virus-induced, rearranged membranes. Therefore viruses need to ensure the correct subcellular localization of their effectors and to be capable of disguising from the cellular defensive mechanisms. In addition, viruses are capable of exploiting host cell activities, by modulating their post-translational modification apparatus, resulting in profound modifications in the function of cellular and viral products. Not surprisingly infection of host cells by these parasites can lead to alterations of cellular differentiation and growing properties, with important pathogenic consequences. In the present hot topic highlight entitled “Reprogramming the host: modification of cell functions upon viral infection”, a number of leading virologists and cell biologist thoroughly describe recent advances in our understanding of how viruses modulate cellular functions to achieve successful replication and propagation at the expenses of human cells.