Host-pathogen protein-nucleic acid interactions: A comprehensive review.

Recognition of pathogen-derived nucleic acids by host cells is an effective host strategy to detect pathogenic invasion and trigger immune responses. In the context of pathogen-specific pharmacology, there is a growing interest in mapping the interactions between pathogen-derived nucleic acids and host proteins. Insight into the principles of the structural and immunological mechanisms underlying such interactions and their roles in host defense is necessary to guide therapeutic intervention. Here, we discuss the newest advances in studies of molecular interactions involving pathogen nucleic acids and host factors, including their drug design, molecular structure and specific patterns. We observed that two groups of nucleic acid recognizing molecules, Toll-like receptors (TLRs) and the cytoplasmic retinoic acid-inducible gene (RIG)-I-like receptors (RLRs) form the backbone of host responses to pathogen nucleic acids, with additional support provided by absent in melanoma 2 (AIM2) and DNA-dependent activator of Interferons (IFNs)-regulatory factors (DAI) like cytosolic activity. We review the structural, immunological, and other biological aspects of these representative groups of molecules, especially in terms of their target specificity and affinity and challenges in leveraging host-pathogen protein-nucleic acid interactions (HP-PNI) in drug discovery.

Integrative Network Modeling Highlights the Crucial Roles of Rho-GDI Signaling Pathway in the Progression of non-Small Cell Lung Cancer.

Non-small cell lung cancer (NSCLC) is the most prevalent form of lung cancer and a leading cause of cancer-related deaths worldwide. Using an integrative approach, we analyzed a publicly available merged NSCLC transcriptome dataset using machine learning, protein-protein interaction (PPI) networks and bayesian modeling to pinpoint key cellular factors and pathways likely to be involved with the onset and progression of NSCLC. First, we generated multiple prediction models using various machine learning classifiers to classify NSCLC and healthy cohorts. Our models achieved prediction accuracies ranging from 0.83 to 1.0, with XGBoost emerging as the best performer. Next, using functional enrichment analysis (and gene co-expression network analysis with WGCNA) of the machine learning feature-selected genes, we determined that genes involved in Rho GTPase signaling that modulate actin stability and cytoskeleton were likely to be crucial in NSCLC. We further assembled a PPI network for the feature-selected genes that was partitioned using Markov clustering to detect protein complexes functionally relevant to NSCLC. Finally, we modeled the perturbations in RhoGDI signaling using a bayesian network; our simulations suggest that aberrations in ARHGEF19 and/or RAC2 gene activities contributed to impaired MAPK signaling and disrupted actin and cytoskeleton organization and were arguably key contributors to the onset of tumorigenesis in NSCLC. We hypothesize that targeted measures to restore aberrant ARHGEF19 and/or RAC2 functions could conceivably rescue the cancerous phenotype in NSCLC. Our findings offer promising avenues for early predictive biomarker discovery, targeted therapeutic intervention and improved clinical outcomes in NSCLC.

Flavivirus recruits the valosin-containing protein-NPL4 complex to induce stress granule disassembly for efficient viral genome replication.

Flaviviruses are human pathogens that can cause severe diseases, such as dengue fever and Japanese encephalitis, which can lead to death. Valosin-containing protein (VCP)/p97, a cellular ATPase associated with diverse cellular activities (AAA-ATPase), is reported to have multiple roles in flavivirus replication. Nevertheless, the importance of each role still has not been addressed. In this study, the functions of 17 VCP mutants that are reportedly unable to interact with the VCP cofactors were validated using the short-interfering RNA rescue experiments. Our findings of this study suggested that VCP exerts its functions in replication of the Japanese encephalitis virus by interacting with the VCP cofactor nuclear protein localization 4 (NPL4). We show that the depletion of NPL4 impaired the early stage of viral genome replication. In addition, we demonstrate that the direct interaction between NPL4 and viral nonstructural protein (NS4B) is critical for the translocation of NS4B to the sites of viral replication. Finally, we found that Japanese encephalitis virus and dengue virus promoted stress granule formation only in VCP inhibitor-treated cells and the expression of NS4B or VCP attenuated stress granule formation mediated by protein kinase R, which is generally known to be activated by type I interferon and viral genome RNA. These results suggest that the NS4B-mediated recruitment of VCP to the virus replication site inhibits cellular stress responses and consequently facilitates viral protein synthesis in the flavivirus-infected cells.

Network modeling and inference of peroxisome proliferator-activated receptor pathway in high fat diet-linked obesity.

Systems biology aims to understand how holistic systems theory can be used to explain the observable living system characteristics, and mathematical modeling tools have been successful in understanding the intricate relationships underlying cellular functions. Lately, researchers have been interested in understanding molecular mechanisms underlying obesity, which is a major health concern worldwide and has been linked to several diseases. Various mechanisms such as peroxisome proliferator-activated receptors (PPARs) are known to modulate obesity-induced inflammation and its consequences. In this study, we have modeled the PPAR pathway using a Bayesian model and inferred the sub-pathways that are potentially responsible for the activation of the output processes that are associated with high fat diet (HFD)-induced obesity. We examined a previously published dataset from a study that compared gene expression profiles of 40 mice maintained on HFD against 40 mice fed with chow diet (CD). Our simulations have highlighted that GPCR and FATCD36 sub-pathways were aberrantly active in HFD mice and are therefore favorable targets for anti-obesity strategies. We further cross-validated our observations with experimental results from the literature. We believe that mathematical models such as those presented in the present study can help in inferring other pathways and deducing significant biological relationships.

Integrative Analysis Reveals Common and Unique Roles of Tetraspanins in Fibrosis and Emphysema.

While both chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are multifactorial disorders characterized by distinct clinical and pathological features, their commonalities and differences have not been fully elucidated. We sought to investigate the preventive roles of tetraspanins Cd151 and Cd9 -that are involved in diverse cellular processes in lung pathophysiology- in pulmonary fibrosis and emphysema, respectively, and to obtain a deeper understanding of their underlying molecular mechanisms toward facilitating improved therapeutic outcomes. Using an integrative approach, we examined the transcriptomic changes in the lungs of Cd151- and Cd9-deficient mice using functional-enrichment-analysis, pathway-perturbation-analysis and protein-protein-interaction (PPI) network analysis. Circadian-rhythm, extracellular-matrix (ECM), cell-adhesion and inflammatory responses and associated factors were prominently influenced by Cd151-deletion. Conversely, cellular-junctions, focal-adhesion, vascular-remodeling, and TNF-signaling were deeply impacted by Cd9-deletion. We also highlighted a "common core" of factors and signaling cascades that underlie the functions of both Cd151 and Cd9 in lung pathology. Circadian dysregulation following Cd151-deletion seemingly facilitated progressive fibrotic lung phenotype. Conversely, TGF-ß signaling attenuation and TNF-signaling activation emerged as potentially novel functionaries of Cd9-deletion-induced emphysema. Our findings offer promising avenues for developing novel therapeutic treatments for pulmonary fibrosis and emphysema.

The TargetMine Data Warehouse: Enhancement and Updates.

Biological data analysis is the key to new discoveries in disease biology and drug discovery. The rapid proliferation of high-throughput 'omics' data has necessitated a need for tools and platforms that allow the researchers to combine and analyse different types of biological data and obtain biologically relevant knowledge. We had previously developed TargetMine, an integrative data analysis platform for target prioritisation and broad-based biological knowledge discovery. Here, we describe the newly modelled biological data types and the enhanced visual and analytical features of TargetMine. These enhancements have included: an enhanced coverage of gene-gene relations, small molecule metabolite to pathway mappings, an improved literature survey feature, and in silico prediction of gene functional associations such as protein-protein interactions and global gene co-expression. We have also described two usage examples on trans-omics data analysis and extraction of gene-disease associations using MeSH term descriptors. These examples have demonstrated how the newer enhancements in TargetMine have contributed to a more expansive coverage of the biological data space and can help interpret genotype-phenotype relations. TargetMine with its auxiliary toolkit is available at https://targetmine.mizuguchilab.org. The TargetMine source code is available at https://github.com/chenyian-nibio/targetmine-gradle.

CLINE: a web-tool for the comparison of biological dendrogram structures.

BACKGROUND: When visually comparing the results of hierarchical clustering, the differences in the arrangements of components are of special interest. However, in a biological setting, identifying such differences becomes less straightforward, as the changes in the dendrogram structure caused by permuting biological replicates, do not necessarily imply a different biological interpretation. Here, we introduce a visualization tool to help identify biologically similar topologies across different clustering results, even in the presence of replicates. RESULTS: Here we introduce CLINE, an open-access web application that allows users to visualize and compare multiple dendrogram structures, by visually displaying the links between areas of similarity across multiple structures. Through the use of a single page and a simple user interface, the user is able to load and remove structures form the visualization, change some aspects of their display and set the parameters used to match cluster topology across consecutive pairs of dendrograms. CONCLUSIONS: We have implemented a web-tool that allows the users to visualize different dendrogram structures, showing not only the structures themselves, but also linking areas of similarity across multiple structures. The software is freely available at http://mizuguchilab.org/tools/cline/ . Also, the source code, documentation and installation instructions are available on GitHub at https://github.com/RodolfoAllendes/cline/ .

Data Warehousing with TargetMine for Omics Data Analysis.

Most biological processes including diseases are multifactorial and determined by a complex interplay of various genetic and environmental factors. This chapter aims to provide a user guide to data querying, analysis, and visualization with TargetMine and the associated auxiliary toolkit. We have also discussed some of the commonly used data queries for the researchers who are interested in gene set analysis within a data warehouse framework. Overall, TargetMine provides a convenient web browser-based interface that enables the discovery of new hypotheses interactively, by performing analysis of omics data using complicated searches without any scripting and programming efforts on the part of the user and also by providing the results in an easy-to-comprehend output format.

Double deletion of tetraspanins CD9 and CD81 in mice leads to a syndrome resembling accelerated aging.

Chronic obstructive pulmonary disease (COPD) has been recently characterized as a disease of accelerated lung aging, but the mechanism remains unclear. Tetraspanins have emerged as key players in malignancy and inflammatory diseases. Here, we found that CD9/CD81 double knockout (DKO) mice with a COPD-like phenotype progressively developed a syndrome resembling human aging, including cataracts, hair loss, and atrophy of various organs, including thymus, muscle, and testis, resulting in shorter survival than wild-type (WT) mice. Consistent with this, DNA microarray analysis of DKO mouse lungs revealed differential expression of genes involved in cell death, inflammation, and the sirtuin-1 (SIRT1) pathway. Accordingly, expression of SIRT1 was reduced in DKO mouse lungs. Importantly, siRNA knockdown of CD9 and CD81 in lung epithelial cells additively decreased SIRT1 and Foxo3a expression, but reciprocally upregulated the expression of p21 and p53, leading to reduced cell proliferation and elevated apoptosis. Furthermore, deletion of these tetraspanins increased the expression of pro-inflammatory genes and IL-8. Hence, CD9 and CD81 might coordinately prevent senescence and inflammation, partly by maintaining SIRT1 expression. Altogether, CD9/CD81 DKO mice represent a novel model for both COPD and accelerated senescence.

Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism.

DNA-binding proteins (DBPs) perform diverse biological functions ranging from transcription to pathogen sensing. Machine learning methods can not only identify DBPs de novo but also provide insights into their DNA-recognition dynamics. However, it remains unclear whether available methods that can accurately predict DNA-binding sites in known DBPs can also identify novel DBPs. Moreover, sequence information is blind to the cellular- and disease-specific contexts of DBP activities, whereas the under-utilized knowledge from public gene expression data offers great promise. To address these issues, we have developed novel methods for predicting DBPs by integrating sequence and gene expression-derived features and applied them to explore human, mouse and Arabidopsis proteomes. While our sequence-based models outperformed the gene expression-based ones, some proteins with weaker DBP-like sequence features were correctly predicted by gene expression-based features, suggesting that these proteins acquire a tangible DBP functionality in a conducive gene expression environment. Analysis of motif enrichment among the co-expressed genes of top 100 candidates DBPs from hitherto unannotated genes provides further avenues to explore their functional associations.

Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery.

Protein-protein interactions (PPIs) are vital to maintaining cellular homeostasis. Several PPI dysregulations have been implicated in the etiology of various diseases and hence PPIs have emerged as promising targets for drug discovery. Surface residues and hotspot residues at the interface of PPIs form the core regions, which play a key role in modulating cellular processes such as signal transduction and are used as starting points for drug design. In this review, we briefly discuss how PPI networks (PPINs) inferred from experimentally characterized PPI data have been utilized for knowledge discovery and how in silico approaches to PPI characterization can contribute to PPIN-based biological research. Next, we describe the principles of in silico PPI prediction and survey the existing PPI and PPI site prediction servers that are useful for drug discovery. Finally, we discuss the potential of in silico PPI prediction in drug discovery.

Classification of idiopathic interstitial pneumonias using anti-myxovirus resistance-protein 1 autoantibody.

Chronic fibrosing idiopathic interstitial pneumonia (IIP) can be divided into two main types: idiopathic pulmonary fibrosis (IPF), a steroid-resistant and progressive disease with a median survival of 2-3 years, and idiopathic non-specific interstitial pneumonia (INSIP), a steroid-sensitive and non-progressive autoimmune disease. Although the clinical courses of these two diseases differ, they may be difficult to distinguish at diagnosis. We performed a comprehensive analysis of serum autoantibodies from patients definitively diagnosed with IPF, INSIP, autoimmune pulmonary alveolar proteinosis, and sarcoidosis. We identified disease-specific autoantibodies and enriched KEGG pathways unique to each disease, and demonstrated that IPF and INSIP are serologically distinct. Furthermore, we discovered a new INSIP-specific autoantibody, anti-myxovirus resistance-1 (MX1) autoantibody. Patients positive for anti-MX1 autoantibody constituted 17.5% of all cases of chronic fibrosing IIPs. Notably, patients rarely simultaneously carried the anti-MX1 autoantibody and the anti-aminoacyl-transfer RNA synthetase autoantibody, which is common in chronic fibrosing IIPs. Because MX1 is one of the most important interferon-inducible anti-viral genes, we have not only identified a new diagnostic autoantibody of INSIP but also obtained new insight into the pathology of INSIP, which may be associated with viral infection and autoimmunity.

An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework.

Data analysis is one of the most critical and challenging steps in drug discovery and disease biology. A user-friendly resource to visualize and analyse high-throughput data provides a powerful medium for both experimental and computational biologists to understand vastly different biological data types and obtain a concise, simplified and meaningful output for better knowledge discovery. We have previously developed TargetMine, an integrated data warehouse optimized for target prioritization. Here we describe how upgraded and newly modelled data types in TargetMine can now survey the wider biological and chemical data space, relevant to drug discovery and development. To enhance the scope of TargetMine from target prioritization to broad-based knowledge discovery, we have also developed a new auxiliary toolkit to assist with data analysis and visualization in TargetMine. This toolkit features interactive data analysis tools to query and analyse the biological data compiled within the TargetMine data warehouse. The enhanced system enables users to discover new hypotheses interactively by performing complicated searches with no programming and obtaining the results in an easy to comprehend output format. Database URL: http://targetmine.mizuguchilab.org.

Interpreting functional effects of coding variants: challenges in proteome-scale prediction, annotation and assessment.

Accurate assessment of genetic variation in human DNA sequencing studies remains a nontrivial challenge in clinical genomics and genome informatics. Ascribing functional roles and/or clinical significances to single nucleotide variants identified from a next-generation sequencing study is an important step in genome interpretation. Experimental characterization of all the observed functional variants is yet impractical; thus, the prediction of functional and/or regulatory impacts of the various mutations using in silico approaches is an important step toward the identification of functionally significant or clinically actionable variants. The relationships between genotypes and the expressed phenotypes are multilayered and biologically complex; such relationships present numerous challenges and at the same time offer various opportunities for the design of in silico variant assessment strategies. Over the past decade, many bioinformatics algorithms have been developed to predict functional consequences of single nucleotide variants in the protein coding regions. In this review, we provide an overview of the bioinformatics resources for the prediction, annotation and visualization of coding single nucleotide variants. We discuss the currently available approaches and major challenges from the perspective of protein sequence, structure, function and interactions that require consideration when interpreting the impact of putatively functional variants. We also discuss the relevance of incorporating integrated workflows for predicting the biomedical impact of the functionally important variations encoded in a genome, exome or transcriptome. Finally, we propose a framework to classify variant assessment approaches and strategies for incorporation of variant assessment within electronic health records.

Altered Actions of Memantine and NMDA-Induced Currents in a New Grid2-Deleted Mouse Line.

Memantine is a non-competitive antagonist of the N-methyl-D-aspartate (NMDA) receptor, and is an approved drug for the treatment of moderate-to-severe Alzheimer's disease. We identified a mouse strain with a naturally occurring mutation and an ataxic phenotype that presents with severe leg cramps. To investigate the phenotypes of these mutant mice, we screened several phenotype-modulating drugs and found that memantine (10 mg/kg) disrupted the sense of balance in the mutants. Moreover, the mutant mice showed an attenuated optokinetic response (OKR) and impaired OKR learning, which was also observed in wild-type mice treated with memantine. Microsatellite analyses indicated that the Grid2 gene-deletion is responsible for these phenotypes. Patch-clamp analysis showed a relatively small change in NMDA-dependent current in cultured granule cells from Grid2 gene-deleted mice, suggesting that GRID2 is important for correct NMDA receptor function. In general, NMDA receptors are activated after the activation of non-NMDA receptors, such as AMPA receptors, and AMPA receptor dysregulation also occurs in Grid2 mutant mice. Indeed, the AMPA treatment enhanced memantine susceptibility in wild-type mice, which was indicated by balance sense and OKR impairments. The present study explores a new role for GRID2 and highlights the adverse effects of memantine in different genetic backgrounds.

Integrated pathway clusters with coherent biological themes for target prioritisation.

Prioritising candidate genes for further experimental characterisation is an essential, yet challenging task in biomedical research. One way of achieving this goal is to identify specific biological themes that are enriched within the gene set of interest to obtain insights into the biological phenomena under study. Biological pathway data have been particularly useful in identifying functional associations of genes and/or gene sets. However, biological pathway information as compiled in varied repositories often differs in scope and content, preventing a more effective and comprehensive characterisation of gene sets. Here we describe a new approach to constructing biologically coherent gene sets from pathway data in major public repositories and employing them for functional analysis of large gene sets. We first revealed significant overlaps in gene content between different pathways and then defined a clustering method based on the shared gene content and the similarity of gene overlap patterns. We established the biological relevance of the constructed pathway clusters using independent quantitative measures and we finally demonstrated the effectiveness of the constructed pathway clusters in comparative functional enrichment analysis of gene sets associated with diverse human diseases gathered from the literature. The pathway clusters and gene mappings have been integrated into the TargetMine data warehouse and are likely to provide a concise, manageable and biologically relevant means of functional analysis of gene sets and to facilitate candidate gene prioritisation.

Understanding the biological context of NS5A-host interactions in HCV infection: a network-based approach.

Hepatitis C virus (HCV) is a major cause of chronic liver disease. HCV NS5A protein plays an important role in HCV infection through its interactions with other HCV proteins and host factors. In an attempt to further our understanding of the biological context of protein interactions between NS5A and host factors in HCV pathogenesis, we generated an extensive physical interaction map between NS5A and cellular factors. By combining a yeast two-hybrid assay with comprehensive literature mining, we built the NS5A interactome composed of 132 human proteins that interact with NS5A. These interactions were integrated into a high-confidence human protein interactome (HPI) with the help of the TargetMine data warehouse system to infer an overall protein interaction map linking NS5A with the components of the host cellular networks. The NS5A-host interactions that were integrated with the HPI were shown to participate in compact and well-connected cellular networks. Functional analysis of the NS5A "infection" network using TargetMine highlighted cellular pathways associated with immune system, cellular signaling, cell adhesion, cellular growth and death among others, which were significantly targeted by NS5A-host interactions. In addition, cellular assays with in vitro HCV cell culture systems identified two ER-localized host proteins RTN1 and RTN3 as novel regulators of HCV propagation. Our analysis builds upon the present understanding of the role of NS5A protein in HCV pathogenesis and provides potential targets for more effective anti-HCV therapeutic intervention.

Prediction and experimental validation of a putative non-consensus binding site for transcription factor STAT3 in serum amyloid A gene promoter.

We previously demonstrated that though the human SAA1 gene shows no typical STAT3 response element (STAT3-RE) in its promoter region, STAT3 and the nuclear factor (NF-κB) p65 first form a complex following interleukin IL-1 and IL-6 (IL-1+6) stimulation, after which STAT3 interacts with a region downstream of the NF-κB RE in the SAA1 promoter. In this study, we employed a computational approach based on indirect read outs of protein-DNA contacts to identify a set of candidates for non-consensus STAT3 transcription factor binding sites (TFBSs). The binding of STAT3 to one of the predicted non-consensus TFBSs was experimentally confirmed through a dual luciferase assay and DNA affinity chromatography. The present study defines a novel STAT3 non-consensus TFBS at nt -75/-66 downstream of the NF-κB RE in the SAA1 promoter region that is required for NF-κB p65 and STAT3 to activate SAA1 transcription in human HepG2 liver cells. Our analysis builds upon the current understanding of STAT3 function, suggesting a wider array of mechanisms of STAT3 function in inflammatory response, and provides a useful framework for investigating novel TF-target associations with potential therapeutic implications.

A combined proteomics and computational approach provides a better understanding of HCV-induced liver disease.

HCV is a major cause of chronic liver disease worldwide and is a formidable therapeutic challenge. Recently, Diamond et al. analyzed the proteomic profiles of liver samples from HCV-positive liver transplant recipients, supplemented with an independent metabolite analysis. They used a computational approach, which highlighted the enriched functional themes and topological attributes associated with the protein association network based on their clinical data and suggested a crucial role of oxidative stress in fibrosis progression in HCV infection. Their findings provide new insights into the mechanisms that regulate the progression of HCV-associated liver fibrosis, which may be useful for identification of suitable biomarkers to evaluate the onset and severity of hepatic fibrosis and the development of new therapeutic and anti-HCV strategies.

Proteomic analysis of hepatitis C virus (HCV) core protein transfection and host regulator PA28γ knockout in HCV pathogenesis: a network-based study.

Hepatitis C virus (HCV) causes chronic liver disease worldwide. HCV Core protein (Core) forms the viral capsid and is crucial for HCV pathogenesis and HCV-induced hepatocellular carcinoma, through its interaction with the host factor proteasome activator PA28γ. Here, using BD-PowerBlot high-throughput Western array, we attempt to further investigate HCV pathogenesis by comparing the protein levels in liver samples from Core-transgenic mice with or without the knockout of PA28γ expression (abbreviated PA28γ(-/-)CoreTG and CoreTG, respectively) against the wild-type (WT). The differentially expressed proteins integrated into the human interactome were shown to participate in compact and well-connected cellular networks. Functional analysis of the interaction networks using a newly developed data warehouse system highlighted cellular pathways associated with vesicular transport, immune system, cellular adhesion, and cell growth and death among others that were prominently influenced by Core and PA28γ in HCV infection. Follow-up assays with in vitro HCV cell culture systems validated VTI1A, a vesicular transport associated factor, which was upregulated in CoreTG but not in PA28γ(-/-)CoreTG, as a novel regulator of HCV release but not replication. Our analysis provided novel insights into the Core-PA28γ interplay in HCV pathogenesis and identified potential targets for better anti-HCV therapy and potentially novel biomarkers of HCV infection.