Characterizing and controlling the inflammatory network during influenza A virus infection

scRDEN: single-cell dynamic gene rank differential expression network and robust trajectory inference

The remarkable advancement of single-cell RNA sequencing (scRNA-seq) technology has empowered researchers to probe gene expression at the single-cell level with unprecedented precision. To gain a profound understanding of the heterogeneity inherent in cell fate determination, a central challenge lies in the comprehensive analysis of the dynamic regulatory alterations that underlie transcriptional differences and the accurate inference of the differentiation trajectory. Here, we propose the method scRDEN, a robust framework that infers important cell sub-populations and differential expression networks of multiple genes along the differentiation directions of each branch by converting the unstable gene expression values in cells into relatively stable gene-gene interactions (global features) and extracting the order of differential expression (network features), and further integrating the expression features of different dimension reduction methods. When applied to five published scRNA-seq datasets from human and mouse cell differentiation, scRDEN not only successfully captures the stable cell subpopulations with potential marker genes, measures the transcriptional differences of gene pairs to identify the rank differential expression network along the differentiation direction of each branch. In addition, in multiple gene rank differential expression networks, the rank expression directly related to transcription factors/marker genes shows a significant strengthening and weakening trend along with their expression changes, and the distribution of diversity and cluster coefficient show a non-monotonic change trend, including the cases of increasing first and then decreasing or decreasing first and then increasing. This may correspond to the mechanism of cells gradually differentiating into stable functions. It is particularly noteworthy that scRDEN method yielded exceptional results when applied to the large-scale, multi-branched, double-batch mouse dentate gyrus data. This outstanding performance provides novel and valuable insights into large-scale, multi-batch trajectory inference and the study of transcriptional mechanism regulation during the processes of differentiation and development.

QIGTD: identifying critical genes in the evolution of lung adenocarcinoma with tensor decomposition

BACKGROUND

Identifying critical genes is important for understanding the pathogenesis of complex diseases. Traditional studies typically comparing the change of biomecules between normal and disease samples or detecting important vertices from a single static biomolecular network, which often overlook the dynamic changes that occur between different disease stages. However, investigating temporal changes in biomolecular networks and identifying critical genes is critical for understanding the occurrence and development of diseases.

METHODS

A novel method called Quantifying Importance of Genes with Tensor Decomposition (QIGTD) was proposed in this study. It first constructs a time series network by integrating both the intra and inter temporal network information, which preserving connections between networks at adjacent stages according to the local similarities. A tensor is employed to describe the connections of this time series network, and a 3-order tensor decomposition method was proposed to capture both the topological information of each network snapshot and the time series characteristics of the whole network. QIGTD is also a learning-free and efficient method that can be applied to datasets with a small number of samples.

RESULTS

The effectiveness of QIGTD was evaluated using lung adenocarcinoma (LUAD) datasets and three state-of-the-art methods: T-degree, T-closeness, and T-betweenness were employed as benchmark methods. Numerical experimental results demonstrate that QIGTD outperforms these methods in terms of the indices of both precision and mAP. Notably, out of the top 50 genes, 29 have been verified to be highly related to LUAD according to the DisGeNET Database, and 36 are significantly enriched in LUAD related Gene Ontology (GO) terms, including nuclear division, mitotic nuclear division, chromosome segregation, organelle fission, and mitotic sister chromatid segregation.

CONCLUSION

In conclusion, QIGTD effectively captures the temporal changes in gene networks and identifies critical genes. It provides a valuable tool for studying temporal dynamics in biological networks and can aid in understanding the underlying mechanisms of diseases such as LUAD.

Exosomes Released by Influenza-Virus-Infected Cells Carry Factors Capable of Suppressing Immune Defense Genes in Naïve Cells

Background: Exosomes are involved in intercellular communication and can transfer regulatory molecules between cells. Consequently, they can participate in host immune response regulation. For the influenza A virus (IAV), there is very limited information on changes in exosome composition during cell infection shedding light on the potential role of these extracellular membrane vesicles. Thus, the aim of our work was to study changes in exosomal composition following IAV infection of cells, as well as to evaluate their effect on uninfected cells. Methods: To characterize changes in the composition of cellular miRNAs and mRNAs of exosomes during IAV infection of A549 cells, NGS was used, as well as PCR to identify viral genes. Naïve A549 cells were stimulated with infected-cell-secreted exosomes for studying their activity. Changes in the expression of genes associated with the cell's immune response were shown using PCR. The effect of exosomes on IAV replication was shown in MDCK cells using In-Cell ELISA and PCR of the supernatants. Results: A change in the miRNA composition (miR-21-3p, miR-26a-5p, miR-23a-5p, miR-548c-5p) and mRNA composition (RPL13A, MKNK2, TRIB3) of exosomes under the influence of the IAV was shown. Many RNAs were involved in the regulation of the immune response of the cell, mainly by suppressing it. After exosome stimulation of naïve cells, a significant decrease in the expression of genes involved in the immune response was shown (RIG1, IFIT1, MDA5, COX2, NFκB, AnxA1, PKR, IL6, IL18). When infecting MDCK cells, a significant decrease in nucleoprotein levels was observed in the presence of exosomes secreted by mock-infected cells. Viral levels in supernatants also decreased. Conclusions: Exosomes secreted by IAV-infected cells could reduce the immune response of neighboring intact cells, leading to more effective IAV replication. This may be associated both with regulatory functions of cellular miRNAs and mRNAs carried by exosomes, or with the presence of viral mRNAs encoding proteins with an immunosuppressive function.

Dynamic Host Immune and Transcriptomic Responses to Respiratory Syncytial Virus Infection in a Vaccination-Challenge Mouse Model

Respiratory syncytial virus (RSV) is the major cause of lower respiratory tract infections in children. Inactivated RSV vaccine was developed in the late 1960's, but the vaccine-enhanced disease (VED) occurred to vaccinated infants upon subsequent natural RSV infection. The excessive inflammatory immunopathology in the lungs might be involved in the VED, but the underlying mechanisms remain not fully understood. In this study, we utilized UV-inactivated RSV in the prime/boost approach followed by RSV challenge in BALB/c mice to mimic RSV VED. The dynamic virus load, cytokines, histology and transcriptome profiles in lung tissues of mice were investigated from day 1 to day 6 post-infection. Compared to PBS-treated mice, UV-RSV vaccination leads to a Th2 type inflammatory response characterized by enhanced histopathology, reduced Treg cells and increased IL4+CD4 T cells in the lung. Enhanced production of several Th2 type cytokines (IL-4, IL-5, IL-10) and TGF-β,  reduction of IL-6 and IL-17 were observed in UV-RSV vaccinated mice. A total of 5582 differentially expressed (DE) genes between PBS-treated or vaccinated mice and naïve mice were identified by RNA-Seq. Eleven conserved high-influential modules (HMs) were recognized, majorly grouped into regulatory networks related to cell cycle and cell metabolism, signal transduction, immune and inflammatory responses. At an early time post-infection, the vaccinated mice showed obvious decreased expression patterns of DE genes in 11 HMs compared to PBS-treated mice. The extracellular matrix (HM5) and immune responses (HM8) revealed tremendous differences in expression and regulation characteristics of transcripts between PBS-treated and vaccinated mice at both early and late time points. The highly connected genes in HM5 and HM8 networks were further validated by RT-qPCR. These findings reveal the relationship between RSV VED and immune responses, which could benefit the development of novel RSV vaccines.

The Potential Role of Cytokine Storm Pathway in the Clinical Course of Viral Respiratory Pandemic

The "cytokine storm" (CS) consists of a spectrum of different immune dysregulation disorders characterized by constitutional symptoms, systemic inflammation and multiorgan dysfunction triggered by an uncontrolled immune response. Particularly in respiratory virus infections, the cytokine storm plays a primary role in the pathogenesis of respiratory disease and the clinical outcome of respiratory diseases, leading to complications such as alveolar edema and hypoxia. In this review, we wanted to analyze the different pathogenetic mechanisms involved in the various respiratory viral pandemics (COVID-19; SARS; MERS; H1N1 influenza A and Spanish flu) which have affected humans in this and last century, with particular attention to the phenomenon of the "cytokine storm" which determines the clinical severity of the respiratory disease and consequently its lethality.

The cytokine storms of COVID-19, H1N1 influenza, CRS and MAS compared. Can one sized treatment fit all?

An analysis of published data appertaining to the cytokine storms of COVID-19, H1N1 influenza, cytokine release syndrome (CRS), and macrophage activation syndrome (MAS) reveals many common immunological and biochemical abnormalities. These include evidence of a hyperactive coagulation system with elevated D-dimer and ferritin levels, disseminated intravascular coagulopathy (DIC) and microthrombi coupled with an activated and highly permeable vascular endothelium. Common immune abnormalities include progressive hypercytokinemia with elevated levels of TNF-α, interleukin (IL)-6, and IL-1β, proinflammatory chemokines, activated macrophages and increased levels of nuclear factor kappa beta (NFκB). Inflammasome activation and release of damage associated molecular patterns (DAMPs) is common to COVID-19, H1N1, and MAS but does not appear to be a feature of CRS. Elevated levels of IL-18 are detected in patients with COVID-19 and MAS but have not been reported in patients with H1N1 influenza and CRS. Elevated interferon-γ is common to H1N1, MAS, and CRS but levels of this molecule appear to be depressed in patients with COVID-19. CD4+ T, CD8+ and NK lymphocytes are involved in the pathophysiology of CRS, MAS, and possibly H1N1 but are reduced in number and dysfunctional in COVID-19. Additional elements underpinning the pathophysiology of cytokine storms include Inflammasome activity and DAMPs. Treatment with anakinra may theoretically offer an avenue to positively manipulate the range of biochemical and immune abnormalities reported in COVID-19 and thought to underpin the pathophysiology of cytokine storms beyond those manipulated via the use of, canakinumab, Jak inhibitors or tocilizumab. Thus, despite the relative success of tocilizumab in reducing mortality in COVID-19 patients already on dexamethasone and promising results with Baricitinib, the combination of anakinra in combination with dexamethasone offers the theoretical prospect of further improvements in patient survival. However, there is currently an absence of trial of evidence in favour or contravening this proposition. Accordingly, a large well powered blinded prospective randomised controlled trial (RCT) to test this hypothesis is recommended.

Evodiamine Enhanced the Anti-Inflammation Effect of Clindamycin in the BEAS-2B Cells Infected with H5N1 and Pneumoniae D39 Through CREB-C/EBPβ Signaling Pathway

Pneumonia is a pulmonary disease among children. Evodiamine, a traditional Chinese medicine, is known for anti-inflammatory effect. This study aimed to investigate the impact of evodiamine on severe pneumonia-like cells and the underlying mechanism involved. H5N1 and pneumoniae D39 was used to induce severe pneumonia-like conditions in BEAS-2B cells. The cell viability in BEAS-2B cells after treatments with 0, 20, 40, 60, 80, and 100 μM evodiamine was examined using MTT assays. The protein concentrations of inflammatory cytokines tumor necrosis factor (TNF)-α, interleukin (IL)-6 and IL-1β, and Toll-like receptors (TLRs) were measured by enzyme-linked immunosorbent assay methods and the protein and mRNA changes in C/EBPβ/CREB were measured using Real Time-quantitative polymerase chain reaction and Western blot methods. Our results revealed that Evodiamine significantly decreased TNF-α, IL-6, and IL-1β in BEAS-2B cells. Moreover, evodiamine markedly reduced TLR2,3,4 protein expression and the phosphorylated protein of C/EBPβ and CREB. Besides, evodiamine combined with clindamycin exerted more significant effects than clindamycin alone. Taken together, our results demonstrated that evodiamine enhanced the anti-inflammation effect of clindamycin in the BEAS-2B cells infected with H5N1 and pneumoniae D39 through CREB-C/EBPβ signaling pathway.

A network-based computational framework to predict and differentiate functions for gene isoforms using exon-level expression data

MOTIVATION

Alternative splicing makes significant contributions to functional diversity of transcripts and proteins. Many alternatively spliced gene isoforms have been shown to perform specific biological functions under different contexts. In addition to gene-level expression, the advances of high-throughput sequencing offer a chance to estimate isoform-specific exon expression with a high resolution, which is informative for studying splice variants with network analysis.

RESULTS

In this study, we propose a novel network-based analysis framework to predict isoform-specific functions from exon-level RNA-Seq data. In particular, based on exon-level expression data, we firstly propose a unified framework, referred to as Iso-Net, to integrate two new mathematical methods (named MINet and RVNet) that infer co-expression networks at different data scenarios. We demonstrate the superior prediction accuracy of Iso-Net over the existing methods for most simulation data, especially in two extreme cases: sample size is very small and exon numbers of two isoforms are quite different. Furthermore, by defining relevant quantitative measures (e.g., Jaccard correlation coefficient) and combining differential co-expression network analysis and GO functional enrichment analysis, a co-expression network analysis framework is developed to predict functions of isoforms and further, to discover their distinct functions within the same gene. We apply Iso-Net to study gene isoforms for several important transcription factors in human myeloid differentiation with the exon-level RNA-Seq data from three different cell lines.

AVAILABILITY AND IMPLEMENTATION

Iso-Net is open source and freely available from https://github.com/Dingjie-Wang/Iso-Net.

A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection

A biological host response to an external stimulus or intervention such as a disease or infection is a dynamic process, which is regulated by an intricate network of many genes and their products. Understanding the dynamics of this gene regulatory network allows us to infer the mechanisms involved in a host response to an external stimulus, and hence aids the discovery of biomarkers of phenotype and biological function. In this article, we propose a modeling/analysis pipeline for dynamic gene expression data, called Pipeline4DGEData, which consists of a series of statistical modeling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository. This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies. We apply the proposed pipeline to influenza infection data from nine studies and demonstrate that interesting biological findings can be discovered with its implementation.

Transcriptome Profiling Reveals Differential Effect of Interleukin-17A Upon Influenza Virus Infection in Human Cells

Influenza A virus (IAV) has developed elegant strategies to utilize cellular proteins and pathways to promote replication and evade the host antiviral response. Identification of these sabotaged host factors could increase the number of potential antiviral drug targets. Here, IAV A/PR/8/34 (PR8)- and A/California/04/2009-infected A549 and 293T cells displayed differential virus replication. To determine the host cellular responses of A549 and 293T cells to IAV infection, RNA-seq was used to identify differentially expressed genes. Our data revealed that IAV-infected A549 cells activated stronger virus-sensing signals and highly expressed cytokines, which play significant roles in initiating the innate immune and inflammatory responses. In addition, IAV-infected 293T cells displayed weak immune signaling and cytokine production. Remarkably, IL-17A and associated genes were highly enriched in IAV-infected 293T cells. Furthermore, IL-17A can partially facilitate A549 cell infection by the PR8 strain and PR8-infected IL-17A knock-out mice consistently exhibited decreased weight loss and reduced lung immunopathology, as compared to controls. This work uncovered the differential responses of cells infected with two H1N1 IAV strains and the potential roles of IL-17A in modulating virus infection.

Mathematical modeling and computational prediction of cancer drug resistance

Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic–pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of computational methods for studying drug resistance, including inferring drug-induced signaling networks, multiscale modeling, drug combinations and precision medicine.

Control of multilayer biological networks and applied to target identification of complex diseases

BACKGROUND

Networks have been widely used to model the structures of various biological systems. The ultimate aim of research on biological networks is to steer biological system structures to desired states by manipulating signals. Despite great advances in the linear control of single-layer networks, it has been observed that many complex biological systems have a multilayer networked structure and extremely complicated nonlinear processes.

RESULT

In this study, we propose a general framework for controlling nonlinear dynamical systems with multilayer networked structures by formulating the problem as a minimum union optimization problem. In particular, we offer a novel approach for identifying the minimal driver nodes that can steer a multilayered nonlinear dynamical system toward any desired dynamical attractor. Three disease-related biology multilayer networks are used to demonstrate the effectiveness of our approaches. Moreover, in the set of minimum driver nodes identified by the algorithm we proposed, we confirmed that some nodes can act as drug targets in the biological experiments. Other nodes have not been reported as drug targets; however, they are also involved in important biological processes from existing literature.

CONCLUSIONS

The proposed method could be a promising tool for determining higher drug target enrichment or more meaningful steering nodes for studying complex diseases.

Network-Guided Discovery of Influenza Virus Replication Host Factors

Integrating virus-host interactions with host protein-protein interactions, we have created a method using these established network practices to identify host factors (i.e., proteins) that are likely candidates for antiviral drug targeting. We demonstrate that interaction cascades between host proteins that directly interact with viral proteins and host factors that are important to influenza virus replication are enriched for signaling and immune processes. Additionally, we show that host proteins that interact with viral proteins are in network locations of power. Finally, we demonstrate a new network methodology to predict novel host factors and validate predictions with an siRNA screen. Our results show that integrating virus-host proteins interactions is useful in the identification of antiviral drug target candidates.

The positions of host factors required for viral replication within a human protein-protein interaction (PPI) network can be exploited to identify drug targets that are robust to drug-mediated selective pressure. Host factors can physically interact with viral proteins, be a component of virus-regulated pathways (where proteins do not interact with viral proteins), or be required for viral replication but unregulated by viruses. Here, we demonstrate a method of combining human PPI networks with virus-host PPI data to improve antiviral drug discovery for influenza viruses by identifying target host proteins. Analysis shows that influenza virus proteins physically interact with host proteins in network positions significant for information flow, even after the removal of known abundance-degree bias within PPI data. We have isolated a subnetwork of the human PPI network that connects virus-interacting host proteins to host factors that are important for influenza virus replication without physically interacting with viral proteins. The subnetwork is enriched for signaling and immune processes distinct from those associated with virus-interacting proteins. Selecting proteins based on subnetwork topology, we performed an siRNA screen to determine whether the subnetwork was enriched for virus replication host factors and whether network position within the subnetwork offers an advantage in prioritization of drug targets to control influenza virus replication. We found that the subnetwork is highly enriched for target host proteins—more so than the set of host factors that physically interact with viral proteins. Our findings demonstrate that network positions are a powerful predictor to guide antiviral drug candidate prioritization.

Biological mechanisms of non-linear dose-response for respirable mineral fibers

Sufficiently high and prolonged inhalation exposures to some respirable elongated mineral particles (REMPs), notably including amphibole asbestos fibers, can increase risk of inflammation-mediated diseases including malignant mesothelioma, pleural diseases, fibrosis, and lung cancer. Chronic inflammation involves ongoing activation of the NLRP3 inflammasome, which enables immune cells to produce potent proinflammatory cytokines IL-1β and IL-18. Reactive oxygen species (ROS) (in particular, mitochondrial ROS) contribute to NRLP3 activation via a well-elucidated mechanism involving oxidation of reduced thioredoxin and association of thioredoxin-interacting protein with NLRP3. Lysosomal destabilization, efflux of cytosolic potassium ions and influx of calcium ions, signals from damaged mitochondria, both translational and post-translational controls, and prion-like polymerization have increasingly clear roles in regulating NLRP3 activation. As the molecular biology of inflammation-mediated responses to REMP exposure becomes clearer, a practical question looms: What do these mechanisms imply for the shape of the dose-response function relating exposure concentrations and durations for EMPs to risk of pathological responses? Dose-response thresholds or threshold-like nonlinearities can arise from (a) Cooperativity in assembly of supramolecular signaling complexes; (b) Positive feedback loops and bistability in regulatory networks; (c) Overwhelming of defensive barriers maintaining homeostasis; and (d) Damage thresholds, as in lysosome destabilization-induced activation of NLRP3. Each of these mechanisms holds for NLRP3 activation in response to stimuli such as REMP exposures. It is therefore timely to consider the implications of these advances in biological understanding for human health risk assessment with dose-response thresholds.

Cytokine-Mediated Tissue Injury in Non-human Primate Models of Viral Infections

Viral infections trigger robust secretion of interferons and other antiviral cytokines by infected and bystander cells, which in turn can tune the immune response and may lead to viral clearance or immune suppression. However, aberrant or unrestricted cytokine responses can damage host tissues, leading to organ dysfunction, and even death. To understand the cytokine milieu and immune responses in infected host tissues, non-human primate (NHP) models have emerged as important tools. NHP have been used for decades to study human infections and have played significant roles in the development of vaccines, drug therapies and other immune treatment modalities, aided by an ability to control disease parameters, and unrestricted tissue access. In addition to the genetic and physiological similarities with humans, NHP have conserved immunologic properties with over 90% amino acid similarity for most cytokines. For example, human-like symptomology and acute respiratory syndrome is found in cynomolgus macaques infected with highly pathogenic avian influenza virus, antibody enhanced dengue disease is common in neotropical primates, and in NHP models of viral hepatitis cytokine-induced inflammation induces severe liver damage, fibrosis, and hepatocellular carcinoma recapitulates human disease. To regulate inflammation, anti-cytokine therapy studies in NHP are underway and will provide important insights for future human interventions. This review will provide a comprehensive outline of the cytokine-mediated exacerbation of disease and tissue damage in NHP models of viral infections and therapeutic strategies that can aid in prevention/treatment of the disease syndromes.

Proteome-wide analysis of human motif-domain interactions mapped on influenza a virus

Background

The influenza A virus (IAV) is a constant threat for humans worldwide. The understanding of motif-domain protein participation is essential to combat the pathogen.

Results

In this study, a data mining approach was employed to extract influenza-human Protein-Protein interactions (PPI) from VirusMentha,Virus MINT, IntAct, and Pfam databases, to mine motif-domain interactions (MDIs) stored as Regular Expressions (RegExp) in 3DID database. A total of 107 RegExp related to human MDIs were searched on 51,242 protein fragments from H1N1, H1N2, H2N2, H3N2 and H5N1 strains obtained from Virus Variation database. A total 46 MDIs were frequently mapped on the IAV proteins and shared between the different strains. IAV kept host-like MDIs that were associated with the virus survival, which could be related to essential biological process such as microtubule-based processes, regulation of cell cycle check point, regulation of replication and transcription of DNA, etc. in human cells. The amino acid motifs were searched for matches in the immune epitope database and it was found that some motifs are part of experimentally determined epitopes on IAV, implying that such interactions exist.

Conclusion

The directed data-mining method employed could be used to identify functional motifs in other viruses for envisioning new therapies.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2237-8) contains supplementary material, which is available to authorized users.

Differential network as an indicator of osteoporosis with network entropy

Osteoporosis is a common skeletal disorder characterized by a decrease in bone mass and density. The peak bone mass (PBM) is a significant determinant of osteoporosis. To gain insights into the indicating effect of PBM to osteoporosis, this study focused on characterizing the PBM networks and identifying key genes. One biological data set with 12 monocyte low PBM samples and 11 high PBM samples was derived to construct protein-protein interaction networks (PPINs). Based on clique-merging, module-identification algorithm was used to identify modules from PPINs. The systematic calculation and comparison were performed to test whether the network entropy can discriminate the low PBM network from high PBM network. We constructed 32 destination networks with 66 modules divided from monocyte low and high PBM networks. Among them, network 11 was the only significantly differential one (P<0.05) with 8 nodes and 28 edges. All genes belonged to precursors of osteoclasts, which were related to calcium transport as well as blood monocytes. In conclusion, based on the entropy in PBM PPINs, the differential network appears to be a novel therapeutic indicator for osteoporosis during the bone monocyte progression; these findings are helpful in disclosing the pathogenetic mechanisms of osteoporosis.

DyNetViewer: a Cytoscape app for dynamic network construction, analysis and visualization

SUMMARY

The molecular interactions in a cell are varying with time and surrounded environmental cues. The construction and analysis of dynamic molecular networks can elucidate dynamic cellular mechanisms of different biological functions and provide a chance to understand complex diseases at the systems level. Here, we develop DyNetViewer, a Cytoscape application that provides a range of functionalities for the construction, analysis and visualization of dynamic protein-protein interaction networks. The current version of DyNetViewer consists of four different dynamic network construction methods, twelve topological variation analysis methods and four clustering algorithms. Moreover, visualization of different topological variation of nodes and clusters over time enables users to quickly identify the most variations across many network states.

AVAILABILITY AND IMPLEMENTATION

DyNetViewer is freely available with tutorials at the Cytoscape (3.4+) App Store (http://apps.cytoscape.org/apps/dynetviewer).

CONTACT

limin@mail.csu.edu.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Immune responses in influenza A virus and human coronavirus infections: an ongoing battle between the virus and host

Respiratory viruses, especially influenza A viruses and coronaviruses such as MERS-CoV, represent continuing global threats to human health. Despite significant advances, much needs to be learned. Recent studies in virology and immunology have improved our understanding of the role of the immune system in protection and in the pathogenesis of these infections and of co-evolution of viruses and their hosts. These findings, together with sophisticated molecular structure analyses, omics tools and computer-based models, have helped delineate the interaction between respiratory viruses and the host immune system, which will facilitate the development of novel treatment strategies and vaccines with enhanced efficacy.

Identification of dysregulated modules based on network entropy in type 1 diabetes

Type 1 diabetes is a prevalent autoimmune disease of which the underlying mechanisms remain to be elucidated. The aim of the study was to identify dysregulated modules of type 1 diabetes. After microarray data were preprocessed, 20,545 genes were obtained. By integrating gene expression data and protein-protein interactions (PPI) data, 48,778 new networks were obtained, including 7,953 genes. After simplifying networks, we obtained 24 target networks. By ranking networks with P-values, two modules with P<0.05 were identified, including the genes, CCNB1, CDC45, GINS2, NDC80, FBXO5, NCAPG and DLGAP5. Module 2 was part of module 1. The identified modules and genes may provide new insights into the underlying biological mechanisms that drive the progression of type 1 diabetes.

Mathematical modeling and quantitative analysis of HIV-1 Gag trafficking and polymerization

Gag, as the major structural protein of HIV-1, is necessary for the assembly of the HIV-1 sphere shell. An in-depth understanding of its trafficking and polymerization is important for gaining further insights into the mechanisms of HIV-1 replication and the design of antiviral drugs. We developed a mathematical model to simulate two biophysical processes, specifically Gag monomer and dimer transport in the cytoplasm and the polymerization of monomers to form a hexamer underneath the plasma membrane. Using experimental data, an optimization approach was utilized to identify the model parameters, and the identifiability and sensitivity of these parameters were then analyzed. Using our model, we analyzed the weight of the pathways involved in the polymerization reactions and concluded that the predominant pathways for the formation of a hexamer might be the polymerization of two monomers to form a dimer, the polymerization of a dimer and a monomer to form a trimer, and the polymerization of two trimers to form a hexamer. We then deduced that the dimer and trimer intermediates might be crucial in hexamer formation. We also explored four theoretical combined methods for Gag suppression, and hypothesized that the N-terminal glycine residue of the MA domain of Gag might be a promising drug target. This work serves as a guide for future theoretical and experimental efforts aiming to understand HIV-1 Gag trafficking and polymerization, and might help accelerate the efficiency of anti-AIDS drug design.

The human immunodeficiency virus (HIV-1) is a retrovirus that causes acquired immunodeficiency syndrome (AIDS), an infectious disease with high annual mortality. Gag protein is the major structural protein of HIV-1 and can self-assemble into the HIV-1 sphere shell. Therefore, an in-depth understanding of Gag protein trafficking and polymerization is important for gaining further insights into the mechanisms of HIV-1 replication and the design of antiviral drugs. Through mathematical modeling, optimization and quantitative analysis, we hypothesized the budding and release time of virus-like particles and revealed that the dimer and trimer intermediates might be crucial in hexamer formation. We also concluded that the predominant pathways in hexamer formation might be the polymerization of two monomers to form a dimer, the polymerization of a dimer and a monomer to form a trimer, and the polymerization of two trimers to form a hexamer. Our analysis also suggested that the N-terminal glycine residue of the MA domain of Gag might be a promising drug target. These results serve as a guide for further theoretical and experimental efforts aiming to understand HIV-1 Gag trafficking and polymerization and could aid anti-AIDS drug design.

Several Indicators of Critical Transitions for Complex Diseases Based on Stochastic Analysis

Many complex diseases (chronic disease onset, development and differentiation, self-assembly, etc.) are reminiscent of phase transitions in a dynamical system: quantitative changes accumulate largely unnoticed until a critical threshold is reached, which causes abrupt qualitative changes of the system. Understanding such nonlinear behaviors is critical to dissect the multiple genetic/environmental factors that together shape the genetic and physiological landscape underlying basic biological functions and to identify the key driving molecules. Based on stochastic differential equation (SDE) model, we theoretically derive three statistical indicators, that is, coefficient of variation (CV), transformed Pearson's correlation coefficient (TPC), and transformed probability distribution (TPD), to identify critical transitions and detect the early-warning signals of the phase transition in complex diseases. To verify the effectiveness of these early-warning indexes, we use high-throughput data for three complex diseases, including influenza caused by either H3N2 or H1N1 and acute lung injury, to extract the dynamical network biomarkers (DNBs) responsible for catastrophic transition into the disease state from predisease state. The numerical results indicate that the derived indicators provide a data-based quantitative analysis for early-warning signals for critical transitions in complex diseases or other dynamical systems.

Identifying key nodes in multilayer networks based on tensor decomposition

The identification of essential agents in multilayer networks characterized by different types of interactions is a crucial and challenging topic, one that is essential for understanding the topological structure and dynamic processes of multilayer networks. In this paper, we use the fourth-order tensor to represent multilayer networks and propose a novel method to identify essential nodes based on CANDECOMP/PARAFAC (CP) tensor decomposition, referred to as the EDCPTD centrality. This method is based on the perspective of multilayer networked structures, which integrate the information of edges among nodes and links between different layers to quantify the importance of nodes in multilayer networks. Three real-world multilayer biological networks are used to evaluate the performance of the EDCPTD centrality. The bar chart and ROC curves of these multilayer networks indicate that the proposed approach is a good alternative index to identify real important nodes. Meanwhile, by comparing the behavior of both the proposed method and the aggregated single-layer methods, we demonstrate that neglecting the multiple relationships between nodes may lead to incorrect identification of the most versatile nodes. Furthermore, the Gene Ontology functional annotation demonstrates that the identified top nodes based on the proposed approach play a significant role in many vital biological processes. Finally, we have implemented many centrality methods of multilayer networks (including our method and the published methods) and created a visual software based on the MATLAB GUI, called ENMNFinder, which can be used by other researchers.

A network analysis of the Chinese medicine Lianhua-Qingwen formula to identify its main effective components

Chinese medicine is known to treat complex diseases with multiple components and multiple targets. However, the main effective components and their related key targets and functions remain to be identified. Herein, a network analysis method was developed to identify the main effective components and key targets of a Chinese medicine, Lianhua-Qingwen Formula (LQF). The LQF is commonly used for the prevention and treatment of viral influenza in China. It is composed of 11 herbs, gypsum and menthol with 61 compounds being identified in our previous work. In this paper, these 61 candidate compounds were used to find their related targets and construct the predicted-target (PT) network. An influenza-related protein-protein interaction (PPI) network was constructed and integrated with the PT network. Then the compound-effective target (CET) network and compound-ineffective target network (CIT) were extracted, respectively. A novel approach was developed to identify effective components by comparing CET and CIT networks. As a result, 15 main effective components were identified along with 61 corresponding targets. 7 of these main effective components were further experimentally validated to have antivirus efficacy in vitro. The main effective component-target (MECT) network was further constructed with main effective components and their key targets. Gene Ontology (GO) analysis of the MECT network predicted key functions such as NO production being modulated by the LQF. Interestingly, five effective components were experimentally tested and exhibited inhibitory effects on NO production in the LPS induced RAW 264.7 cell. In summary, we have developed a novel approach to identify the main effective components in a Chinese medicine LQF and experimentally validated some of the predictions.

Multi-Omics Studies towards Novel Modulators of Influenza A Virus-Host Interaction

Human influenza A viruses (IAVs) cause global pandemics and epidemics. These viruses evolve rapidly, making current treatment options ineffective. To identify novel modulators of IAV–host interactions, we re-analyzed our recent transcriptomics, metabolomics, proteomics, phosphoproteomics, and genomics/virtual ligand screening data. We identified 713 potential modulators targeting 199 cellular and two viral proteins. Anti-influenza activity for 48 of them has been reported previously, whereas the antiviral efficacy of the 665 remains unknown. Studying anti-influenza efficacy and immuno/neuro-modulating properties of these compounds and their combinations as well as potential viral and host resistance to them may lead to the discovery of novel modulators of IAV–host interactions, which might be more effective than the currently available anti-influenza therapeutics.

Differential Biphasic Transcriptional Host Response Associated with Coevolution of Hemagglutinin Quasispecies of Influenza A Virus

Severe influenza associated with strong symptoms and lung inflammation can be caused by intra-host evolution of quasispecies with aspartic acid or glycine in hemagglutinin position 222 (HA-222D/G; H1 numbering). To gain insights into the dynamics of host response to this coevolution and to identify key mechanisms contributing to copathogenesis, the lung transcriptional response of BALB/c mice infected with an A(H1N1)pdm09 isolate consisting HA-222D/G quasispecies was analyzed from days 1 to 12 post infection (p.i). At day 2 p.i. 968 differentially expressed genes (DEGs) were detected. The DEG number declined to 359 at day 4 and reached 1001 at day 7 p.i. prior to recovery. Interestingly, a biphasic expression profile was shown for the majority of these genes. Cytokine assays confirmed these results on protein level exemplarily for two key inflammatory cytokines, interferon gamma and interleukin 6. Using a reverse engineering strategy, a regulatory network was inferred to hypothetically explain the biphasic pattern for selected DEGs. Known regulatory interactions were extracted by Pathway Studio 9.0 and integrated during network inference. The hypothetic gene regulatory network revealed a positive feedback loop of Ifng, Stat1, and Tlr3 gene signaling that was triggered by the HA-G222 variant and correlated with a clinical symptom score indicating disease severity.

A Hierarchical Mechanism of RIG-I Ubiquitination Provides Sensitivity, Robustness and Synergy in Antiviral Immune Responses

RIG-I is an essential receptor in the initiation of the type I interferon (IFN) signaling pathway upon viral infection. Although K63-linked ubiquitination plays an important role in RIG-I activation, the optimal modulation of conjugated and unanchored ubiquitination of RIG-I as well as its functional implications remains unclear. In this study, we determined that, in contrast to the RIG-I CARD domain, full-length RIG-I must undergo K63-linked ubiquitination at multiple sites to reach full activity. A systems biology approach was designed based on experiments using full-length RIG-I. Model selection for 7 candidate mechanisms of RIG-I ubiquitination inferred a hierarchical architecture of the RIG-I ubiquitination mode, which was then experimentally validated. Compared with other mechanisms, the selected hierarchical mechanism exhibited superior sensitivity and robustness in RIG-I-induced type I IFN activation. Furthermore, our model analysis and experimental data revealed that TRIM4 and TRIM25 exhibited dose-dependent synergism. These results demonstrated that the hierarchical mechanism of multi-site/type ubiquitination of RIG-I provides an efficient, robust and optimal synergistic regulatory module in antiviral immune responses.

A new reassortment of influenza A (H7N9) virus causing human infection in Beijing, 2014

A 73-year-old man was confirmed to have an influenza A (H7N9) virus infection, and the causative agent A/Beijing/02/2014(H7N9) virus was isolated. Genetic and phylogenetic analyses revealed that the virus belonged to a novel genotype, which probably emerged and further reassorted with other H9 or H7 viruses in poultry before transmitting to humans. This virus caused a severe infection with high levels of cytokines and neutralizing antibodies. Eventually, the patient was cured after serially combined treatments. Taken together, our findings indicated that this novel genotype of the human H7N9 virus did not evolve directly from the first Beijing isolate A/Beijing/01/2013(H7N9), suggesting that the H7N9 virus has not obtained the ability for human-to-human transmissibility and the virus only evolves in poultry and then infects human by direct contact. Hence, the major measures to prevent human H7N9 virus infection are still to control and standardize the live poultry trade. Early antiviral treatment with combination therapies, including mechanical ventilation, nutrition support and symptomatic treatment, are effective for H7N9 infection.

Crosstalk between pathways enhances the controllability of signalling networks

The control of complex networks is one of the most challenging problems in the fields of biology and engineering. In this study, the authors explored the controllability and control energy of several signalling networks, which consisted of many interconnected pathways, including networks with a bow-tie architecture. On the basis of the theory of structure controllability, they revealed that biological mechanisms, such as cross-pathway interactions, compartmentalisation and so on make the networks easier to fully control. Furthermore, using numerical simulations for two realistic examples, they demonstrated that the control energy of normal networks with crosstalk is lower than in networks without crosstalk. These results indicate that the biological networks are optimally designed to achieve their normal functions from the viewpoint of the control theory. The authors' work provides a comprehensive understanding of the impact of network structures and properties on controllability.

Pathogen Cell-to-Cell Variability Drives Heterogeneity in Host Immune Responses

Encounters between immune cells and invading bacteria ultimately determine the course of infection. These interactions are usually measured in populations of cells, masking cell-to-cell variation that may be important for infection outcome. To characterize the gene expression variation that underlies distinct infection outcomes and monitor infection phenotypes, we developed an experimental system that combines single-cell RNA-seq with fluorescent markers. Probing the responses of individual macrophages to invading Salmonella, we find that variation between individual infected host cells is determined by the heterogeneous activity of bacterial factors in individual infecting bacteria. We illustrate how variable PhoPQ activity in the population of invading bacteria drives variable host type I IFN responses by modifying LPS in a subset of bacteria. This work demonstrates a causative link between host and bacterial variability, with cell-to-cell variation between different bacteria being sufficient to drive radically different host immune responses. This co-variation has implications for host-pathogen dynamics in vivo.

A New Method for Detecting Protein Complexes based on the Three Node Cliques

The identification of protein complexes in protein-protein interaction (PPI) networks is fundamental for understanding biological processes and cellular molecular mechanisms. Many graph computational algorithms have been proposed to identify protein complexes from PPI networks by detecting densely connected groups of proteins. These algorithms assess the density of subgraphs through evaluation of the sum of individual edges or nodes; thus, incomplete and inaccurate measures may miss meaningful biological protein complexes with functional significance. In this study, we propose a novel method for assessing the compactness of local subnetworks by measuring the number of three node cliques. The present method detects each optimal cluster by growing a seed and maximizing the compactness function. To demonstrate the efficacy of the new proposed method, we evaluate its performance using five PPI networks on three reference sets of yeast protein complexes with five different measurements and compare the performance of the proposed method with four state-of-the-art methods. The results show that the protein complexes generated by the proposed method are of better quality than those generated by four classic methods. Therefore, the new proposed method is effective and useful for detecting protein complexes in PPI networks.

Chronic inflammation: is it the driver or is it paving the road for malignant transformation?

Chronic inflammation in well-defined mouse models such as Giα2 knock out mouse has been shown to trigger formation and expansion of hypoxic niches and also leads to up regulation of NFĸB, offering cells which have adapted their genetic machinery to hypoxia a unique survival advantage. These adapted cells have been shown to acquire stem cell-like capabilities as shown by up regulation of stem cell markers. Such long lived cells become permanent residents in sub mucosa and acquire a malignant phenotype from long-term exposure to noxious environmental agents due to a barrier defect secondary to down regulation of barrier proteins such as Zo1 and Occludin. Indeed mitotic spindle disorientation in such mice has been proposed as another contributory factor to malignant transformation. Sterilization of bowel lumen of these mice through different techniques has prevented malignant transformation in the presence of chronic inflammation. These facts stand strongly against chronic inflammation as a true driver of carcinogenesis but clearly support its role in facilitating the emergence of the neoplastic clone.

Additional Evidence That the Polymerase Subunits Contribute to the Viral Replication and the Virulence of H5N1 Avian Influenza Virus Isolates in Mice

Genetically similar H5N1 viruses circulating in the avian reservoir exhibit different levels of pathogenicity in mice. In this study, we characterized two highly pathogenic H5N1 avian isolates—A/Hunan/316/2005 (HN05), which is highly pathogenic in mice, and A/Hubei/489/2004 (HB04), which is nonpathogenic. In mammalian cells, HN05 replicates more efficiently than HB04, although both viruses have similar growth kinetics in avian cells. We used reverse genetics to generate recombinant H5N1 strains containing genes from HN05 and HB04 and examined their virulence. HN05 genes encoding the polymerase complex determine pathogenicity and viral replication ability both in vitro and in vivo. The PB2 subunit plays an important role in enhancing viral replication, and the PB1 and PA subunits contribute mainly to pathogenicity in mice. These results can be used to elucidate host-range expansion and the molecular basis of the high virulence of H5N1 viruses in mammalian species.

Negative feedback contributes to the stochastic expression of the interferon-β gene in virus-triggered type I interferon signaling pathways

Type I interferon (IFN) signaling pathways play an essential role in the defense against early viral infections; however, the diverse and intricate molecular mechanisms of virus-triggered type I IFN responses are still poorly understood. In this study, we analyzed and compared two classes of models i.e., deterministic ordinary differential equations (ODEs) and stochastic models to elucidate the dynamics and stochasticity of type I IFN signaling pathways. Bifurcation analysis based on an ODE model reveals that the system exhibits a bistable switch and a one-way switch at high or low levels when the strengths of the negative and positive feedbacks are tuned. Furthermore, we compared the stochastic simulation results under the Master and Langevin equations. Both of the stochastic equations generate the bistable switch phenomenon, and the distance between two stable states are smaller than normal under the simulation of the Langevin equation. The quantitative computations also show that a moderate ratio between positive and negative feedback strengths is required to ensure a reliable switch between the different IFN concentrations that regulate the immune response. Moreover, we propose a multi-state stochastic model based on the above deterministic model to describe the multi-cellular system coupled with the diffusion of IFNs. The perturbation and inhibition analysis showed that the positive feedback, as well as noises, has little effect on the stochastic expression of IFNs, but the negative feedback of ISG56 on the activation of IRF7 has a great influence on IFN stochastic expression. Together, these results reveal that positive feedback stabilizes IFN gene expression, and negative feedback may be the main contribution to the stochastic expression of the IFN gene in the virus-triggered type I IFN response. These findings will provide new insight into the molecular mechanisms of virus-triggered type I IFN signaling pathways.

Optimal control strategy for abnormal innate immune response

Innate immune response plays an important role in control and clearance of pathogens following viral infection. However, in the majority of virus-infected individuals, the response is insufficient because viruses are known to use different evasion strategies to escape immune response. In this study, we use optimal control theory to investigate how to control the innate immune response. We present an optimal control model based on an ordinary-differential-equation system from a previous study, which investigated the dynamics and regulation of virus-triggered innate immune signaling pathways, and we prove the existence of a solution to the optimal control problem involving antiviral treatment or/and interferon therapy. We conduct numerical experiments to investigate the treatment effects of different control strategies through varying the cost function and control efficiency. The results show that a separate treatment, that is, only inhibiting viral replication (u₁(t)) or enhancing interferon activity (u₂(t)), has more advantages for controlling viral infection than a mixed treatment, that is, controlling both (u₁(t)) and (u₂(t)) simultaneously, including the smallest cost and operability. These findings would provide new insight for developing effective strategies for treatment of viral infectious diseases.

A new asynchronous parallel algorithm for inferring large-scale gene regulatory networks

The reconstruction of gene regulatory networks (GRNs) from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high-throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost. To overcome these difficulties, the main goal of this study is to design an effective parallel algorithm to infer large-scale GRNs based on high-performance parallel computing environments. In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE)-based optimization. The presented algorithm uses the sparsity and modularity of GRNs to split whole large-scale GRNs into many small-scale modular subnetworks. Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network. To test the performance of the proposed approach, we used well-known benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge (DREAM), experimentally determined GRN of Escherichia coli and one published dataset that contains more than 10 thousand genes to compare the proposed approach with several popular algorithms on the same high-performance computing environments in terms of both accuracy and time complexity. The numerical results demonstrate that our parallel algorithm exhibits obvious superiority in inferring large-scale GRNs.

Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis

The early diagnosis and investigation of the pathogenic mechanisms of complex diseases are the most challenging problems in the fields of biology and medicine. Network-based systems biology is an important technique for the study of complex diseases. The present study constructed dynamic protein-protein interaction (PPI) networks to identify dynamical network biomarkers (DNBs) and analyze the underlying mechanisms of complex diseases from a systems level. We developed a model-based framework for the construction of a series of time-sequenced networks by integrating high-throughput gene expression data into PPI data. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. Function and pathway analyses revealed that the identified DNBs were significantly enriched during key events in early disease development. Correlation and information flow analyses revealed that DNBs effectively discriminated between different disease processes and that dysfunctional regulation and disproportional information flow may contribute to the increased disease severity. This study provides a general paradigm for revealing the deterioration mechanisms of complex diseases and offers new insights into their early diagnoses.

Mathematical modeling for intracellular transport and binding of HIV-1 Gag proteins

This paper presents a modeling study for the intracellular trafficking and trimerization of the HIV-1 Gag proteins. A set of differential equations including initial and boundary conditions is used to characterize the transport, diffusion, association and dissociation of Gag monomers and trimers for the time period from the initial production of Gag protein monomers to the initial appearance of immature HIV-1 virions near the cell membrane (the time duration Ta). The existence and stability of the steady-state solution of the initial boundary value problems provide a quantitative characterization of the tendency and equilibrium of Gag protein movement. The numerical simulation results further demonstrate Gag trimerization near the cell membrane. Our calculations of Ta are in good agreement with published experimental data. Sensitivity analysis of Ta to the model parameters indicates that the timing of the initial appearance of HIV-1 virions on the cell membrane is affected by the diffusion and transport processes. These results provide important information and insight into the Gag protein transport and binding and HIV-1 virion formation.