Introduction
Drug discovery, in particular for anti-infective therapies, is in a deep crisis due to low and further declining efficacy in principle
Traditional gene expression and network-based target identification has so far provided only few validated therapeutic approaches
Such network temporality is often modeled over time through dynamic links that can be active or nonactive (binary). We hypothesize that it is better modeled quantitatively and based on the temporal expressions profiles of the nodes (i.e., node dynamics rather than edge dynamics). Big “omics” data sets, including an increasing number of time-resolved data sets, as well as molecular networks, have been curated already. However, suitable approaches to analyze these data types together are lacking. Thus, an integrated analysis to illuminate systemic response patterns of temporal resolution has been infeasible so far.
We developed time course network enrichment (TiCoNE), a novel human-augmented time series clustering method combined with a temporal network enricher (
Principle of TiCoNE. TiCoNE is a novel temporal systems medicine tool for drug target discovery, network enrichment, and subnetwork cross talk detection over time. After an initial clustering of a given set of temporal expression profiles, the partitioning is continuously refined through human-augmented operations. We call this novel strategy “human-augmented clustering.” Clusters are represented by prototypes (triangles), and objects (circles) are assigned to the most similar cluster prototype. Given an interaction network of biological entities such as genes (circles), TiCoNE may further unravel subnetworks significantly enriched with disease genes (de novo network enrichment), compare these temporal network modules between different phenotypes, and identify enrichment or depletion of interactions (cross talk) between such temporal modules (temporal cross talk). We developed tailored statistical models to assess enrichment significance at different steps of data analysis (p*). TiCoNE, time course network enrichment.
TiCoNE works with most kinds of biological entities (genes, proteins, RNAs, etc.) and most types of molecular measures acquirable for them (transcriptomics, proteomics, etc.). To increase readability, in the remainder of this article we simply refer to genes and gene expression as we focus on applying TiCoNE to influenza A virus (IAV) transcriptomics data.
Materials and Methods
In this study, we briefly outline the computational approach of TiCoNE and the utilized data sets. For an exhausting and more formal description of the methodology, we refer to Computational Methods section in
Overview
TiCoNE is a human-augmented clustering method for time series data combined with a temporal network enricher. As any clustering approach it seeks to partition objects of a data set into groups such that (1) objects of the same group are similar and (2) objects of different groups are dissimilar to each other. In this study, we group objects based on their time course. Since TiCoNE is human augmented, it allows the user to interfere with the clustering process and, thus, can incorporate valuable domain knowledge. After the clustering process, TiCoNE offers a sophisticated set of methods for an enrichment of the clustering with molecular networks paving the way for unprecedented systems medicine analyses of time series data.
Clustering
The objective of clustering is to cluster biomolecular entities (e.g., genes) in such a way that the time courses of genes assigned to one cluster are more similar to each other than to time courses of genes assigned to different clusters. To establish the similarity between two time courses, the user can choose between the Euclidean distance and the Pearson correlation coefficient, depending on whether one wants to emphasize the time course amplitudes or their shapes, respectively. Note that TiCoNE can also directly cope with (biological or technical) replicates.
With this information and some optional data cleaning (e.g., removing genes with baseline time courses behavior), TiCoNE produces an initial clustering using one of several common clustering approaches. One may choose between CLARA,
- 1.
Assign all genes (based on their time courses) to the most similar prototype.
- 2.
Update the prototypes accordingly.
This process can be repeated automatically until convergence (i.e., no genes reassigned to a different cluster/prototype). The essential working mode of TiCoNE, however, is to allow the user to interfere with the clustering after each iteration in various ways. We term this approach human-augmented clustering.
Human-augmented clustering
The result of each clustering iteration is presented to the user in a sophisticated graphical user interface allowing for the efficient inspection and manipulation of the intermediate clustering results. The user has the following main options:
Merge clusters of genes with apparently too similar prototypes.
Split clusters in cases wherein a cluster appears to the user to be the union of multiple different time courses.
Delete clusters in case a cluster is perceived as noise or uninteresting.
We refer to the Computational Methods section in
Network enrichment
After clustering, TiCoNE offers several novel network enrichment methods to boost the significance of the derived clusters and put them in a systems biological context. The most straightforward but naive analysis is the extraction of the node-induced subnetworks consisting of the genes presented in a selected cluster of interest. This approach is naive, as it will fail to find connected networks if the genes of a cluster are not directly linked in the network. In a biomedical setting, it is reasonable to assume that not all functionally related objects show a very similar time behavior and end up in the same cluster (e.g., genes in a negative feedback loop). Thus, one may want to allow for a certain number of exception nodes that are not in the selected clusters but connect other objects that are. We use KeyPathwayMiner
Time course network cross talk
TiCoNE allows for temporal cross talk enrichment by scanning the network for pairs of clusters that are connected more (less) often than expected by chance in a given network. We utilize randomly permuted networks to assess significance levels.
Phenotype comparison
Different conditions may be compared over time. First, the data for both conditions are treated independently for clustering. After clustering, TiCoNE identifies significantly overlapping clusters of the two conditions and evaluates the similarity of their prototypes. We regard those cluster pairs as most interesting that have a significant overlap and a very similar (or dissimilar) prototype to investigate the commonalities (or the differences) of the conditions. These clusters may afterward be inspected using the aforementioned network enrichment analysis.
TiCoNE as Cytoscape app and web application
We implemented TiCoNE in a Cytoscape app as well as a feature reduced interactive web application. The TiCoNE Cytoscape app is full featured and includes all approaches described in this article. In this study, TiCoNE is complemented by Cytoscape's network visualization and analysis functionalities. The TiCoNE web application includes only a limited feature set, most importantly the formation of clusters of time series data sets and the visualization and enrichment of identified clusters on a biological network. Temporal cross talk identification and phenotype comparison are not included.
IAV and RV data
We applied the TiCoNE Cytoscape App to human gene expression data measured with an Affymetrix Human Genome U219 Array containing expression levels for 49,386 probes for 10 time points (baseline and at 2, 4, 8, 12, 24, 36, 48, 60, and 72 h after infection) under three different experimental conditions: BEAS-2B lung cells have been infected with (1) IAV, (2) rhinovirus (RV), or (3) coinfected with both. The data set contained five biological replicates for all time points except the first one, for which it contained six. See Kim et al.
Clustering of IAV and RV data
We clustered both the expression data of the IAV and the RV infection experiments with CLARA and 20 negative/positive discretization steps into 500 initial clusters. We chose CLARA as it designed to cluster large data sets efficiently. We used the Pearson correlation as similarity function, cluster aggregation function [formula omitted--see PDF], and removed genes not present in the used interaction network (see the section
Biological network
We extracted the human interactome from the Interologous Interaction Database (I2D) protein–protein interaction (PPI) database,
Results
To demonstrate how TiCoNE can facilitate temporal systems medicine drug target discovery, we analyzed human whole-genome time series transcriptomics data of BEAS-2B lung cells after infection with IAV or RV together with the human protein interactome.
Antivirals normally attack viral functions, such as antibodies that bind to viral proteins, or drugs that impair viral functions. As an RNA virus, IAV possesses high genetic flexibility, allowing for quick adaptation to selective pressures imposed by these antiviral attacks. The result is that neither a lasting immunization nor an effective therapy has been developed. IAV exploits the host cell after infection by hijacking a variety of fundamental intracellular signaling cascades.
The transcriptomics data contain expression levels for 49,386 probes at baseline and over nine time points between 2 and 72 h after infection with five replicates for each time point. TiCoNE clustered the expression data of IAV infection and RV infection experiments into 88 and 71 significant ([formula omitted--see PDF]) clusters, respectively (
For either clustering, we then performed a time course network coenrichment analysis. p-Values were derived using a permutation test with 1000 permutations. When comparing the distributions of undirected edge counts between cluster pairs (
In general, IAV cluster pairs with significant cross talk (either depleted or enriched) form a large connected complex along the timeline potentially representing the gene program response cascades to IAV infection over time. In contrast, such significant RV cluster pairs seem to be more fragmented and form multiple separated cluster complexes possibly indicating that no such tightly controlled process of gene program response cascades is activated. Out of the 61 significant gene cluster pairs for the IAV infection, we see more often an enriched number of edges (39/61) than for the 34 significant gene cluster pairs for the RV infection (19/34). These results highlight that both conditions exhibit fundamentally different behavior over time on a systems biology level.
We then compared the systems medicine response between the two infection types (IAV vs. RV) to identify de novo pathways in the network that behave differently over time under the two conditions. p-Values were derived using a permutation test with 1000 permutations. We identified 30 such pathways (subnetworks) of highly significant size ([formula omitted--see PDF]). When enriching for the three most significant pathways ([formula omitted--see PDF]) in the interactome, we find a connected component consisting of 50 genes and 101 interactions (
Schematic representation of intracellular signaling pathways that are involved in the regulation of innate immune responses against IAV. IAV infection is associated with unique PAMPs (red), which are recognized by PRRs such as TLRs, RLRs, and NLRs leading to the induction of different signaling pathways such as PI3K-Akt pathway, PKR signaling, OAS1/RNaseL pathway, TLR/RLRs/NLRs signaling, MAPK pathways (JNK pathway/p38 pathway), NF-κB pathways (canonical), and ERK1/2 pathway. After the secretion of interferons (IFN-α, -β) and tumor necrosis factors (TNF-α, -β), the noncanonical NF-κB pathway and STAT1/STAT pathways are stimulated, leading to the expression of several ISGs. At later time points, IAV-induced apoptosis is mainly mediated by TRIAL- and FASL-dependent activation of DRs and subsequent activation of casp3. Factors that were identified by TiCoNE analysis to be significantly enriched in IAV-infected mammalian cells are marked in blue (
Querying the established drug target databases DrugBank and Therapeutic Target Database for the identified genes confirms that TiCoNE recovered several known drug targets (
TiCoNE analysis identified several genes, such as phospholipid scramblase 1 (PLSCR1), FK506 binding protein like (FKPBL), and helicase with zinc finger (HELZ2), which were not previously known to be specifically activated upon IAV infection. PLSCR1 encodes an interferon (IFN)-inducible protein that mediates antiviral activity against DNA and RNA viruses in vitro including hepatitis B viruses,
To investigate whether PLSCR1 activity indeed affects also IAV propagation in an either anti- or proviral manner, we analyzed propagation of the human influenza virus strain A/Puerto Rico/8/1934 (PR8, H1N1) in human lung cells (A549) and in human bronchial epithelial cells (BEAS-2B) in the presence or absence of different concentrations of the PLSCR1 inhibitor R5421 (
Influence of scramblase inhibitor R5421 on cell viability and IAV replication. A549 (A) and BEAS-2B (B) cells were treated with R5421 at indicated concentration for 24 h and cell viability was checked using PrestoBlue reagent (MolecularProbes). Data represent means±SEM (n=8). A 24-h-old monolayer of A549 (C) or BEAS-2B (D) cells was infected with PR8 virus at an MOI=1. At 45 min p.i., viral inoculum was removed and cells were overlaid with corresponding medium containing R5421 at indicated concentration or DMSO as a control. Twenty-four hours p.i. supernatants were collected, and virus titers were determined by focus forming assay. Data represent means±SEM (n=3). Denoted significance levels are: p ≤ 0.05 (*) and p ≤ 0.01 (**). p.i., postinfection; SEM, standard error of the mean.
Discussion and Conclusion
We developed TiCoNE, the first approach to seamlessly identify regions in biomolecular networks that are enriched in genes (or proteins, metabolites) with significantly overrepresented time series behavior or time series coexpression behavior. TiCoNE offers a human-augmented cluster optimization strategy and allows the user to iteratively refine the clustering automatically or visually by applying operations such as adding, deleting, merging, or splitting clusters. Our approach can compare different phenotypes and identify differentially behaving network complexes. TiCoNE computes empirical p-values for clusters, phenotype comparisons and network co-enrichment based on different kinds of permutation tests (see
We demonstrate the power of TiCoNE by processing time series gene expression data of human host lung cells, infected with either RV or IAV. In this study, we use our coenrichment analysis approach to construct complexes of clusters along a timeline that are biologically meaningful and may explain the systemic unraveling of host immune response to influenza virus infection. We find specific properties of coenrichments of IAV and RV data clusters, which may help explain the large difference in severity of the two viruses on a systems biology level.
We discovered de novo groups of genes that behave consistently but show temporally different behavior under the two conditions. By integrating these candidates with the human PPI network, we discovered a complex of 50 genes, out of which 30 have been previously associated with IAV infection.
Among the identified host genes not previously known to be specifically activated upon IAV infection were PLSCR1, FKPBL, and helicase with zinc finger (HELZ2). Through experimental validation we could confirm that we have identified PLSCR1 as a novel host factor, which is acting as a negative regulator for IAV infection. These data provide proof-of-principle that a systems medicine approach to analyze the virus and host interactome provides novel and possibly more effective therapeutic approaches in line with network pharmacology
With TiCoNE, we provide the first integrated temporal systems medicine drug target identification approach. It extends time series expression data directly to temporal disease-specific subnetworks. It also identifies cross talk between them, and we show that it can identify novel drug targets for IAV infection. TiCoNE is publicly available in the Cytoscape app store
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Abbreviations Used
Clustering Large Applications
FK506-binding protein like
Interologous Interaction Database
influenza A virus
interferon-stimulated genes
Kyoto Encyclopedia of Genes and Genomes
Partitioning Around Medoids
plasmacytoid dendritic cells
phospholipid scramblase 1
protein–protein interaction
rhinovirus
Short Time-Series Expression Miner
time course network enrichment
vesicular stomatitis virus
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Abstract
Introduction: Drug-resistant infections are becoming increasingly frequent worldwide, causing hundreds of thousands of deaths annually. This is partly due to the very limited set of protein drug targets known for human-infecting viral genomes. The eleven influenza virus proteins, for instance, exploit host cell factors for replication and suppression of the antiviral immune responses. A systems medicine approach to identify relevant and druggable host factors would dramatically expand therapeutic options. Therapeutic target identification, however, has hitherto relied on static molecular networks, whereas in reality the interactome, in particular during an infection, is subject to constant change.
Methods: We developed time-course network enrichment (TiCoNE), an expert-centered approach for discovering temporal response pathways. In the first stage of TiCoNE, time-series expression data is clustered in a human-augmented manner to identify groups of biological entities with coherent temporal responses. Throughout this process, the expert can add, remove, merge, or split temporal patterns. The resulting groups can then be mapped to an interaction network to identify enriched pathways and to analyze cross-talk enrichments and depletions between groups. Finally, temporal response groups of two experiments can be intersected, to identify condition-variant response patterns that represent promising drug-target candidates.
Results: We applied TiCoNE to human gene expression data for influenza A virus infection and rhino virus infection, respectively. We then identified coherent temporal response patterns and employed our cross-talk analysis to establish two potential timelines of systems-level host responses for either infection. Next, we compared the two phenotypes and unraveled condition-variant temporal groups interacting on a networks level. The highest-ranking ones we then validated via literature search and wet-lab experiments. This not only confirmed many of our candidates as previously known, but we also identified phospholipid scramblase 1 (encoded by PLSCR1) as a previously not recognized host factor that is essential for influenza A virus infection.
Conclusion: With TiCoNE we developed a novel approach for conjointly analyzing molecular networks with time-series expression data and demonstrated its power by identifying temporal drug-targets. We provide proof-of-concept that not only novel targets can be identified using our approach, but also that anti-infective drug target discovery can be enhanced by investigating temporal molecular networks of the host in response to viral infection.
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Details
1 Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
2 Institute of Medical Virology, Justus Liebig University Giessen, Giessen, Germany
3 Institute of Medical Virology, Justus Liebig University Giessen, Giessen, Germany; Center of Scientific Excellence for Influenza Viruses, National Research Centre, Cairo, Egypt
4 Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
5 Department of Pharmacology and Personalized Medicine, Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
6 Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark; Department of Experimental Bioinformatics, Technical University of Munich, Freising, Germany