Normalizing for individual cell population context in the analysis of high-content cellular screens

<p>Abstract</p> <p>Background</p> <p>High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cell's population context significantly influences results. However, standard analysis methods for cellular screens do not currently take individual cell data into account unless this is important for the phenotype of interest, i.e. when studying cell morphology.</p> <p>Results</p> <p>We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell's individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a non-virus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach.</p> <p>Conclusions</p> <p>Using a cell-based analysis and normalization for population context, we achieve improved sensitivity and specificity not only on a individual protein level, but especially also on a pathway level. This leads to the identification of new host dependency factors of the hepatitis C and dengue viruses and higher reproducibility of results.</p

Computational Analysis of RNAi Screening Data to Identify Host Factors Involved in Viral Infection and to Characterize Protein-Protein Interactions

The study of gene functions in a variety of different treatments, cell lines and organisms has been facilitated by RNA interference (RNAi) technology that tracks the phenotype of cells after silencing of particular genes. In this thesis, I describe two computational approaches developed to analyze the image data from two different RNAi screens. Firstly, I developed an alternative approach to detect host factors (human proteins) that support virus growth and replication of cells infected with the Hepatitis C virus (HCV). To identify the human proteins that are crucial for the efficiency of viral infection, several RNAi experiments of viral-infected cells have been conducted. However, the target lists from different laboratories have shown only little overlap. This inconsistency might be caused not only by experimental discrepancies, but also by not fully explored possibilities of the data analysis. Observing only viral intensity readouts from the experiments might be insufficient. In this project, I describe our computational development as a new alternative approach to improve the reliability for the host factor identification. Our approach is based on characterizing the clustering of infected cells. The idea is that viral infection is spread by cell-cell contacts, or at least advantaged by the vicinity of cells. Therefore, clustering of the HCV infected cells is observed during spreading of the infection. We developed a clustering detection method basing on a distance-based point pattern analysis (K-function) to identify knockdown genes in which the clusters of HCV infected cells were reduced. The approach could significantly separate between positive and negative controls and found good correlations between the clustering score and intensity readouts from the experimental screens. In comparison to another clustering algorithm, the K-function method was superior to Quadrat analysis method. Statistical normalization approaches were exploited to identify protein targets from our clustering-based approach and the experimental screens. Integrating results from our clustering method, intensity readout analysis and secondary screen, we finally identified five promising host factors that are suitable candidate targets for drug therapy. Secondly, a machine learning based approach was developed to characterize protein-protein interactions (PPIs) in a signaling network. The characterization of each PPI is fundamental to our understanding of the complex signaling system of a human cell. Experiments for PPI identification, such as yeast two-hybrid and FRET analysis, are resource-intensive, and, therefore, computational approaches for analysing large-scale RNAi knockdown screens have become an important pursuit of inferring the functional similarities from the phenotypic similarities of the down-regulated proteins. However, these methods did not provide a more detailed characterization of the PPIs. In this project, I developed a new computational approach that is based on a machine learning technique which employs the mitotic phenotypes of an RNAi screen. It enables the identification of the nature of a PPI, i.e., if it is of rather activating or inhibiting nature. We established a systematic classification using Support Vector Machines (SVMs) that was based on the phenotypic descriptors and used it to classify the interactions that activate or inhibit signal transduction. The machines yielded promising results with good performance when integrating different sets of published descriptors and our own developed descriptors calculated from fractions of specific phenotypes, linear classification of phenotypes, and phenotypic distance to distinct proteins. A comprehensive model generated from the machines was used for further predictions. We investigated the nature of pairs of interacting proteins and generated a consistency score that enhanced the precisions of the classification results. We predicted the activating/inhibiting nature for 214 PPIs with high confidence in signaling pathways and enabled to identify a new subgroup of chemokine receptors. These findings might facilitate an enhanced understanding of the cellular mechanisms during inflammation and immunologic responses. In summary, two computational approaches were developed to analyze the image data of the different RNAi screens: 1) a clustering-based approach was used to identify the host factors that are crucial for HCV infection; and 2) a machine learning-based approach with various descriptors was employed to characterize PPI activities. The results from the host factor analysis revealed novel target proteins that are involved in the spread of the HCV. In addition, the results of the characterization of the PPIs lead to a better understanding of the signaling pathways. The two large-scale RNAi data were successfully analyzed by our established approaches to obtain new insights into virus biology and cellular signaling

Discovering antiviral restriction factors and pathways using genetic screens

Research in the Hughes lab is supported by a grant from the Academy of Medical Sciences (SFB003/1028), a grant from Tenovus Scotland (T20/63), and The Wellcome Trust Institutional Strategic Support Fund (ISSF). Research in the Gray lab is supported Medical Research Council (MR/N001796/1) and the Biotechnology and Biological Sciences Research Council (BBS/E/D/20002172). C. E. J. is supported by a University of St Andrews Ph.D. scholarship.Viral infections activate the powerful interferon (IFN) response that induces the expression of several hundred IFN stimulated genes (ISGs). The principal role of this extensive response is to create an unfavourable environment for virus replication and to limit spread; however, untangling the biological consequences of this large response is complicated. In addition to a seemingly high degree of redundancy, several ISGs are usually required in combination to limit infection as individual ISGs often have low to moderate antiviral activity. Furthermore, what ISG or combination of ISGs are antiviral for a given virus is usually not known. For these reasons, and that the function(s) of many ISGs remains unexplored, genome-wide approaches are well placed to investigate what aspects of this response results in an appropriate, virus-specific phenotype. This review discusses the advances screening approaches have provided for the study of host defence mechanisms, including CRISPR/Cas9, ISG expression libraries and RNAi technologies.Publisher PDFPeer reviewe

RNA Interference Data: from a Statistical Analysis to Network Inference

Viruses are the cause of many severe human diseases such as Hepatitis C, Dengue fever, AIDS, Infuenza and even cancer. In consequence of viral diseases several millions of people die every year all over the world. Due to the rapid evolution of viruses their drug development and treatment are especially difficult. The present work aims at getting a better understanding of the ongoing signaling processes of certain diseases. To do this, methods for the analysis and network inference of RNA interference (RNAi) data are presented. Recent biological and technological advances in the fi eld of RNAi enable the knockdown of individual genes in a high-content high-throughput manner. Thereby, a detailed quantifi cation of perturbation e ffects on specifi c phenotypes can be assessed using multiparametric imaging. This in turn allows the identi fication of genes which are involved in certain biological processes such as virus-host factors used in the viral life-cycles. However, hit lists of already published RNAi screens show only a small overlap, even for studies of the same virus. This may be due to insufficient data analysis where the potential of microscopic screening data is not fully tapped since individual cell measurements are not taken into account for data normalization and hit scoring. This thesis shows that for RNAi data studying Hepatitis C and Dengue virus the phenotypic e ffect after a perturbation is highly influenced by each cell's population context. Therefore, novel methodologies are proposed which use the individual cell measurements for the data analysis and statistical scoring. This results in an increased sensitivity and speci ficity in comparison to already existing methods where these factors are disregarded. The method proposed here allows the identifi cation of already existing as well as new hit genes which are signi ficantly involved in the respective viral life-cycles. The spatial and temporal placement of these hits, however, still remains unknown, and the ongoing signaling processes are only poorly understood. To understand the underlying biology from a system wide view it is necessary to infer the signaling cascade of involved factors in detail. One of the challenges of network inference is the exponentially increasing dimensionality with an increasing number of nodes. The method proposed in this thesis is formulated as a linear optimization problem which can be solved efficiently even for large data sets. The model can incorporate data of single or multiple perturbations at the same time. The aim is to defend the network topology which best represents the given data. Based on simulated data for an small artificial five-node example the robustness of the model against noisy or incomplete data is demonstrated. Furthermore, for this small as well as for larger networks with 10 to 52 nodes it is shown that the model achieves superior results than random guessing. In addition, the performance and the computation time of large networks are better than another approach which has been recently published. Moreover, the network inference method presented here has been applied to data measuring the signaling of ErbB proteins. These proteins are associated with the development of many human cancers. The results of the network inference show that already known signaling cascades can be successfully reconstructed from the data. Additionally, newly learned protein-protein interactions indicate that there are several still unknown feedback and feedforward loops. The proteins of these loops may serve as potential targets to control ErbB signaling. The knowledge about these factors is an important step towards the development of new drugs and therefore,this helps to fi ght ErbB related diseases

In silico design of selective high affinity ligands against HCV using novel computational diology tools

Background: Hepatitis C virus (HCV) infects 170 million patients worldwide. The absence of an effective mean of treatment or prophylaxis makes HCV infection a serious public health problem. Generating selective high affinity ligands (SHALs) against HCV could provide a solution to this public health burden. HCV E2 glycoprotein is required for HCV entry into host cells. It binds to CD81, a host receptor protein that belongs to the tetraspanin family and plays a critical role in viral invasion. HCV protease is essential for the cleavage of non structural proteins, in addition to preventing the phosphorylation of human interferon regulatory factor 3 and thus prevents the anti-viral response. Objective: To design a cocktail of SHAL-based inhibitors against several target proteins such as CD81, HCV E2, and HCV protease and optimize the currently available E2 homology models. Methods and findings: Different homology modeling techniques such as AS2TS, Phyre, ROSETTA and TASSER, were used to obtain reliable models of HCV NS3 serine protease and HCV E2 glycoprotein. LGA was used to structurally analyze the models in addition of clustering the obtained models and finding the closest structural templates using StralCP. Auto Dock Tools 1.5.6 was used to prepare the crystal structures of the CD81-LEL protein (1G8Q and 1IV5), HCV protease, HCV polymerase and the homology model of HCV E2 by deleting water molecules, adding polar hydrogens, and assigning Gasteiger charges and to create a grid bounding box, which provided the desired grid parameter file using 0.375 A spacing. Autoligand, an AutoDock tool, was used to identify several binding sites on the protein targets. Fill points were created using a 1 A grid, and the calculations were performed using 10 to 210 fill points. AutoDock 4.2 was used to screen 30,000 ligands obtained from different libraries (NCI_DSII, Sigma and Asinex) and identify small molecules that might bind to each site. The docking results were analyzed and the top 20 ligands for each binding site on the target proteins were ranked according to selection criteria required for the design of promising SHALs. Distances between pairs of bound ligands were estimated and used to design several SHALs that should bind selectively to the target proteins. Conclusion: New computational tools have been used to design in silico several SHAL-based inhibitors that might have the potential to prevent both HCV entry into hepatocytes and the production of inflammatory cytokines that accelerate liver damage when targeting E2-CD81 interaction. It might alter protein processing and viral replication if HCV protease was targeted. By targeting HCV RNA dependent RNA polymerase, the HCV replication could be blocked. In addition, blocking the complexation of NS3 with the NS4A co-factor will render it non functional and thus block the replication process and disrupt the HCV life cycle. If a reliable E2 homology model was developed based on different studies and validations, it could help generate selective high affinity ligands that block the earliest phase of the HCV life cycle

Role of Host Restriction Factors on Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) Replication

In this study, the role of IFITM3 on PRRSV replication was studied in vitro by expressing exogenous IFITM3 in MARC-145 cells. An average of 31% reduction in PRRSV N protein expression and an average of 5.4 fold decrease in virus titer in the supernatant were observed in IFITM3 overexpressing cells as compared to vector control cells at 24 hours post infection (hpi). Moreover, there was a positive correlation between interferon- induced IFITM3 up-regulation and reduced PRRSV replication. To determine the role of endogenous IFITM3 in PRRSV replication, siRNA induced knockdown of IFITM3 was employed. RT-PCR validated the successful silencing of IFITM3 in MARC- 145 cells, with an average knockdown of 50%. PRRSV RNA copies were 1.28-fold higher in IFITM3 silenced cells as compared to control silencing, suggesting that knockdown of endogenous IFITM3 only slightly enhanced PRRSV replication. Taken together, these results suggest antiviral role of IFITM3 against PRRSV in vitro. In this study, we tested if the antifungal drug Amphotericin B restores PRRSV replication in IFITM3 overexpressing MARC-145 cells. Amphotericin B only partially restored PRRSV replication as confirmed by flow cytometry. Interestingly, more colocalization of PRRSV with early endosome marker was observed at 3 hpi (37.9%) than at 1 hpi (23.8%) and 6 hpi (31.8%). To further investigate the stage of PRRSV infection restricted by IFITM3 over-expression, colocalization study was performed at 3 and 24 hpi. Our results showed that IFITM3 expressing cells were positive for PRRSV at both 3 and 24 hpi. The percentage of IFITM3 positive cells with positive PRRSV staining was significantly higher at 3 hpi as compared to 24 hpi. Collectively, our data suggest that IFITM3 may restrict PRRSV via multiple post-entry mechanisms. The role of restriction factor, ZMPSTE24, on PRRSV replication was also studied. The ZMPSTE24 exerted antiviral effect against PRRSV as confirmed by both ZMPSTE24 overexpression and silencing experiments. A reduced expression of PRRSV N protein by approximately 3% and a 146-fold decrease in virus titer in the supernatant in the ZMPSTE24 overexpressing cells compared to vector control were observed. To further determine the role of endogenous ZMPSTE24 in PRRSV replication, we performed siRNA induced silencing of ZMPSTE24 in MARC-145 cells. RT-PCR validated highly successful silencing of ZMPSTE24, with an average knockdown of 74%. Knockdown of endogenous ZMPSTE24 slightly affected PRRSV replication, as only 1.2 fold increase in PRRSV RNA copies was observed. To study the stage of PRRSV infection impeded by ZMPSTE24, colocalization study was performed at 3 and 24 hpi. There were no significant differences in the number of PRRSV positive cells or total viral RNA copies between the vector control and ZMPSTE24 over-expressing cells at 3 hpi. The colocalization of PRRSV with ZMPSTE24 was significantly higher at 3 hpi as compared to 24 hpi, suggesting that ZMPSTE24 does not affect PRRSV entry into endosomes and restriction occurs after 3 hpi. Taken together, our results suggest that IFITM3 and ZMPSTE24 likely restrict PRRSV at multiple post-entry steps

Novel antiviral strategies for feline coronavirus and feline calicivirus

Feline coronavirus (FCoV) and feline calicivirus (FCV) commonly infect domestic cats, and are an important cause of morbidity and mortality. There are currently no effective antiviral agents for these pathogens. Studies reported herein attempt address this therapeutic shortfall through the testing of a panel of small molecule compounds and specifically designed small interfering RNAs (siRNAs) for antiviral effects against both viruses. Initial compound screening identified chloroquine, mefloquine, and hexamethylene amiloride as effective inhibitors of FCoV, whilst mefloquine effectively inhibited FCV. Efficacy at low micromolar concentration was confirmed with orthogonal testing, albeit with relatively narrow selective indices. Preliminary experiments performed to inform the antiviral mechanism of the compounds against FCoV demonstrated all three compounds acted at an early stage of viral replication. For FCV, mefloquine exhibited potent inhibition of a panel of recent field isolates and demonstrated additive effects in combination with recombinant feline interferon omega. For both FCoV and FCV, a number of siRNAs demonstrated potent and specific inhibition of viral replication. These were effective at low nanomolar concentrations, when used in combination, and against high viral loads. A structural siRNA variant, Dicer-substrate siRNA, demonstrated similar or better efficacy, depending on the target, over canonical siRNAs directed at the same FCoV motif. Limitations of antiviral siRNAs in terms of antiviral resistance were investigated. FCoV serially passaged through siRNA treated cells rapidly acquired resistance, however combination therapy with three siRNAs was able to delay this considerably. For FCV, siRNAs effective against the reference strain were broadly efficacious against field isolates, although some variability was noted. Taken together these results provide important information regarding potential antiviral therapies against these important pathogens

Novel antiviral strategies for feline coronavirus and feline calicivirus

Feline coronavirus (FCoV) and feline calicivirus (FCV) commonly infect domestic cats, and are an important cause of morbidity and mortality. There are currently no effective antiviral agents for these pathogens. Studies reported herein attempt address this therapeutic shortfall through the testing of a panel of small molecule compounds and specifically designed small interfering RNAs (siRNAs) for antiviral effects against both viruses. Initial compound screening identified chloroquine, mefloquine, and hexamethylene amiloride as effective inhibitors of FCoV, whilst mefloquine effectively inhibited FCV. Efficacy at low micromolar concentration was confirmed with orthogonal testing, albeit with relatively narrow selective indices. Preliminary experiments performed to inform the antiviral mechanism of the compounds against FCoV demonstrated all three compounds acted at an early stage of viral replication. For FCV, mefloquine exhibited potent inhibition of a panel of recent field isolates and demonstrated additive effects in combination with recombinant feline interferon omega. For both FCoV and FCV, a number of siRNAs demonstrated potent and specific inhibition of viral replication. These were effective at low nanomolar concentrations, when used in combination, and against high viral loads. A structural siRNA variant, Dicer-substrate siRNA, demonstrated similar or better efficacy, depending on the target, over canonical siRNAs directed at the same FCoV motif. Limitations of antiviral siRNAs in terms of antiviral resistance were investigated. FCoV serially passaged through siRNA treated cells rapidly acquired resistance, however combination therapy with three siRNAs was able to delay this considerably. For FCV, siRNAs effective against the reference strain were broadly efficacious against field isolates, although some variability was noted. Taken together these results provide important information regarding potential antiviral therapies against these important pathogens

Functional role of HCMV miRNAs

miRNAs are a species of small-regulatory RNA that post-transcriptionally regulate gene expression via the RNA induced silencing complex (RISC). They are encoded ubiquitously among animals and plants, and have recently been shown to be encoded by the majority of herpesviruses. It seems likely that herpesvirus encoded miRNAs have evolved as a tool for the manipulation of host-cellular and viral-gene expression during infection. Human cytomegalovirus (HCMV) is a clinically important herpesvirus that represents a significant cause of morbidity and mortality in the immune-compromised. HCMV encodes as many as 25 miRNAs during infection, but the function of the majority of these is not known. Identifying the targets of HCMV miRNAs will not only establish a basis for understanding the role of miRNAs within the context of HCMV infection, but also provide a means for discovering novel host-virus interactions. Using RISC immunoprecipitation and siRNA screening, host-cellular targets of viral miRNAs that play important roles in the biology of HCMV were identified. ATP6VOC, a key component of the vacuolar-ATPase, was shown to be a target of miR-US25-1 and subsequent siRNA knockdown of ATP6VOC resulted in the almost complete inhibition of infectious virion production. Despite this, ATP6VOC knock-down did not inhibit viral entry, DNA synthesis, or gene expression, highlighting a possible role for ATP6VOC in the assembly and egress of HCMV. A critical step in HCMV assembly and egress is the formation of the juxta-nuclear virion assembly compartment (VAC). The HCMV VAC is derived from host-cellular endocytic and secretory vacuoles, and is crucial for the efficient nuclear egress of nucleocapsids, cyotplasmic tegumentation, final envelopment, and the egress of mature virions. Using siRNA knock-down, immunofluorescence-microscopy, and western-blot analysis, a crucial role for ATP6VOC and v-ATPase function in the formation of the VAC was demonstrated. siRNA knock-down of ATP6VOC resulted in a failure in the reorganisation of trans-golgi and early-endosomal compartments during infection, resulting in a failure in VAC formation. These findings demonstrate a crucial role for ATP6VOC during infection, and in so doing identify a novel host factor that is required for HCMV assembly