Inferring host gene subnetworks involved in viral replication

Ubiquitin-specific proteinase 1 stabilizes PRRSV nonstructural protein Nsp1β to promote viral replication by regulating K48 ubiquitination

The porcine reproductive and respiratory syndrome virus (PRRSV) can lead to severe reproductive problems in sows, pneumonia in weaned piglets, and increased mortality, significantly negatively impacting the economy. Post-translational changes are essential for the host-dependent replication and long-term infection of PRRSV. Uncertainty surrounds the function of the ubiquitin network in PRRSV infection. Here, we screened 10 deubiquitinating enzyme inhibitors and found that the ubiquitin-specific proteinase 1 (USP1) inhibitor ML323 significantly inhibited PRRSV replication in vitro. Importantly, we found that USP1 interacts with nonstructural protein 1β (Nsp1β) and deubiquitinates its K48 to increase protein stability, thereby improving PRRSV replication and viral titer. Among them, lysine at position 45 is essential for Nsp1β protein stability. In addition, deficiency of USP1 significantly reduced viral replication. Moreover, ML323 loses antagonism to PRRSV rSD16-K45R. This study reveals the mechanism by which PRRSV recruits the host factor USP1 to promote viral replication, providing a new target for PRRSV defense.IMPORTANCEDeubiquitinating enzymes are critical factors in regulating host innate immunity. The porcine reproductive and respiratory syndrome virus (PRRSV) nonstructural protein 1β (Nsp1β) is essential for producing viral subgenomic mRNA and controlling the host immune system. The host inhibits PRRSV proliferation by ubiquitinating Nsp1β, and conversely, PRRSV recruits the host protein ubiquitin-specific proteinase 1 (USP1) to remove this restriction. Our results demonstrate the binding of USP1 to Nsp1β, revealing a balance of antagonism between PRRSV and the host. Our research identifies a brand-new PRRSV escape mechanism from the immune response.

The ubiquitin proteasome system is necessary for efficient proliferation of porcine reproductive and respiratory syndrome virus

The ubiquitin-proteasome system (UPS) plays a vital role in cellular protein homeostasis by ensuring protein quality control and maintaining a critical level of important regulatory proteins. Thus, it is not surprising that the functional UPS is manipulated by viruses to assist in viral propagation. Porcine reproductive and respiratory syndrome virus (PRRSV) is an economically significant swine disease that has been devastating the swine industry worldwide. However, the role of UPS in PRRSV infection is unknown. In this study, we found that treatment with the proteasome inhibitor MG132 significantly inhibited PRRSV proliferation in a dose-dependent manner. The anti-PRRSV effect of MG132 was most significant in the middle stage of the PRRSV lifecycle, which is achieved via inhibition of viral attachment and replication. Interestingly, the expression of poly-ubiquitin was drastically decreased and the accumulation of free-ubiquitin was obviously elevated in the middle stage of PRRSV infection. Furthermore, the ectopic expression of ubiquitin in MG132-treated cells partially reversed the inhibitory effect of MG132 on PRRSV proliferation. Taken together, these results suggest that PRRSV manipulates UPS to promote self-proliferation by cheating or taking advantage of the host proteasome, degrading intracellular poly-ubiquitin and increasing the accumulation of free ubiquitin.

Augmenting subnetwork inference with information extracted from the scientific literature

Many biological studies involve either (i) manipulating some aspect of a cell or its environment and then simultaneously measuring the effect on thousands of genes, or (ii) systematically manipulating each gene and then measuring the effect on some response of interest. A common challenge that arises in these studies is to explain how genes identified as relevant in the given experiment are organized into a subnetwork that accounts for the response of interest. The task of inferring a subnetwork is typically dependent on the information available in publicly available, structured databases, which suffer from incompleteness. However, a wealth of potentially relevant information resides in the scientific literature, such as information about genes associated with certain concepts of interest, as well as interactions that occur among various biological entities. We contend that by exploiting this information, we can improve the explanatory power and accuracy of subnetwork inference in multiple applications. Here we propose and investigate several ways in which information extracted from the scientific literature can be used to augment subnetwork inference. We show that we can use literature-extracted information to (i) augment the set of entities identified as being relevant in a subnetwork inference task, (ii) augment the set of interactions used in the process, and (iii) support targeted browsing of a large inferred subnetwork by identifying entities and interactions that are closely related to concepts of interest. We use this approach to uncover the pathways involved in interactions between a virus and a host cell, and the pathways that are regulated by a transcription factor associated with breast cancer. Our experimental results demonstrate that these approaches can provide more accurate and more interpretable subnetworks. Integer program code, background network data, and pathfinding code are available at https://github.com/Craven-Biostat-Lab/subnetwork_inference

There is a multitude of publicly available databases that contain information about biological entities (i.e., genes, proteins, and other small molecules) as well as information about how these entities interact together. However, these databases are often incomplete. There is a wealth of information present in the text of the scientific literature that is not yet available in these databases. Using tools that mine the scientific literature we are able to extract some of this potentially relevant information. In this work we show how we can use publicly available databases in conjunction with the information extracted from the scientific literature to infer the networks that are involved in specific biological processes, such as viral replication and cancer tumor growth.

Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks

Transcriptional regulatory networks specify the regulatory proteins of target genes that control the context-specific expression levels of genes. With our ability to profile the different types of molecular components of cells under different conditions, we are now uniquely positioned to infer regulatory networks in diverse biological contexts such as different cell types, tissues, and time points. In this chapter, we cover two main classes of computational methods to integrate different types of information to infer genome-scale transcriptional regulatory networks. The first class of methods focuses on integrative methods for specifically inferring connections between transcription factors and target genes by combining gene expression data with regulatory edge-specific knowledge. The second class of methods integrates upstream signaling networks with transcriptional regulatory networks by combining gene expression data with protein-protein interaction networks and proteomic datasets. We conclude with a section on practical applications of a network inference algorithm to infer a genome-scale regulatory network.

Systems Biology-Based Investigation of Cellular Antiviral Drug Targets Identified by Gene-Trap Insertional Mutagenesis

Viruses require host cellular factors for successful replication. A comprehensive systems-level investigation of the virus-host interactome is critical for understanding the roles of host factors with the end goal of discovering new druggable antiviral targets. Gene-trap insertional mutagenesis is a high-throughput forward genetics approach to randomly disrupt (trap) host genes and discover host genes that are essential for viral replication, but not for host cell survival. In this study, we used libraries of randomly mutagenized cells to discover cellular genes that are essential for the replication of 10 distinct cytotoxic mammalian viruses, 1 gram-negative bacterium, and 5 toxins. We herein reported 712 candidate cellular genes, characterizing distinct topological network and evolutionary signatures, and occupying central hubs in the human interactome. Cell cycle phase-specific network analysis showed that host cell cycle programs played critical roles during viral replication (e.g. MYC and TAF4 regulating G0/1 phase). Moreover, the viral perturbation of host cellular networks reflected disease etiology in that host genes (e.g. CTCF, RHOA, and CDKN1B) identified were frequently essential and significantly associated with Mendelian and orphan diseases, or somatic mutations in cancer. Computational drug repositioning framework via incorporating drug-gene signatures from the Connectivity Map into the virus-host interactome identified 110 putative druggable antiviral targets and prioritized several existing drugs (e.g. ajmaline) that may be potential for antiviral indication (e.g. anti-Ebola). In summary, this work provides a powerful methodology with a tight integration of gene-trap insertional mutagenesis testing and systems biology to identify new antiviral targets and drugs for the development of broadly acting and targeted clinical antiviral therapeutics.

Infectious diseases result in millions of deaths and cost billions of dollars annually. Hence, there is urgency for developing more innovative and effective antiviral therapeutics. In this study, we used libraries of randomly mutagenized cells to discover cellular genes that are essential for the replication of 10 distinct cytotoxic mammalian viruses. We herein reported over 700 candidate cellular genes, over 20% of which were independently selected by multiple viruses in one or more cell types. Using systems biology-based analysis, we found that host genes associated with viral replication tended to occupy central hubs in the human protein interactome and to be ancient genes with low evolutionary rates, compared to non-virus-associated genes. Cell cycle phase-specific sub-network analysis showed that host cell cycle program played important roles during viral replication by regulating specific cell cycle phases. Moreover, we presented novel evidences to suggest that host genes supporting viral replication were frequently implicated in Mendelian and orphan diseases, or played critical roles in cancer. Importantly, we found approximately 110 new putative druggable antiviral targets by merging genome-wide gene-trap insertional mutagenesis, drug-gene network, and bioinformatics data. Furthermore, we have demonstrated the use of a computable representation of genetic testing to effectively identify new potential antiviral indications for existing drugs. In summary, this study presents new and important methodologies for developing broadly active antiviral therapeutics.

Network-based approaches for analysis of complex biological systems

Cells function and respond to changes in their environment by the coordinated activity of their molecular components, including mRNAs, proteins and metabolites. At the heart of proper cellular function are molecular networks connecting these components to process extra-cellular environmental signals and drive dynamic, context-specific cellular responses. Network-based computational approaches aim to systematically integrate measurements from high-throughput experiments to gain a global understanding of cellular function under changing environmental conditions. We provide an overview of recent methodological developments toward solving two major computational problems within this field in the past two years (2013-2015): network reconstruction and network-based interpretation. Looking forward, we envision development of methods that can predict phenotypes with high accuracy as well as provide biologically plausible mechanistic hypotheses.