Response network analysis of differential gene expression in human epithelial lung cells during avian influenza infections

Examining the role of EGR1 during viral infections

Early growth response 1 (EGR1) is a multifunctional mammalian transcription factor capable of both enhancing and/or inhibiting gene expression. EGR1 can be activated by a wide array of stimuli such as exposure to growth factors, cytokines, apoptosis, and various cellular stress states including viral infections by both DNA and RNA viruses. Following induction, EGR1 functions as a convergence point for numerous specialized signaling cascades and couples short-term extracellular signals to influence transcriptional regulation of genes required to initiate the appropriate biological response. The role of EGR1 has been extensively studied in both physiological and pathological conditions of the adult nervous system where it is readily expressed in various regions of the brain and is critical for neuronal plasticity and the formation of memories. In addition to its involvement in neuropsychiatric disorders, EGR1 has also been widely examined in the field of cancer where it plays paradoxical roles as a tumor suppressor gene or oncogene. EGR1 is also associated with multiple viral infections such as Venezuelan equine encephalitis virus (VEEV), Kaposi's sarcoma-associated herpesvirus (KSHV), herpes simplex virus 1 (HSV-1), human polyomavirus JC virus (JCV), human immunodeficiency virus (HIV), and Epstein-Barr virus (EBV). In this review, we examine EGR1 and its role(s) during viral infections. First, we provide an overview of EGR1 in terms of its structure, other family members, and a brief overview of its roles in non-viral disease states. We also review upstream regulators of EGR1 and downstream factors impacted by EGR1. Then, we extensively examine EGR1 and its roles, both direct and indirect, in regulating replication of DNA and RNA viruses.

Comparative Investigation of Gene Regulatory Processes Underlying Avian Influenza Viruses in Chicken and Duck

Simple Summary

Avian influenza poses a great risk to gallinaceous poultry, while mallard ducks can withstand most virus strains. To date, the mechanisms underlying the susceptibility of chicken and the effective immune response of duck have not been completely understood. In this study, our aim is to investigate the transcriptional gene regulation governing the expression of important avian-influenza-induced genes and to reveal the master regulators stimulating an effective immune response after virus infection in ducks while dysfunctioning in chicken.

Abstract

The avian influenza virus (AIV) mainly affects birds and not only causes animals’ deaths, but also poses a great risk of zoonotically infecting humans. While ducks and wild waterfowl are seen as a natural reservoir for AIVs and can withstand most virus strains, chicken mostly succumb to infection with high pathogenic avian influenza (HPAI). To date, the mechanisms underlying the susceptibility of chicken and the effective immune response of duck have not been completely unraveled. In this study, we investigate the transcriptional gene regulation underlying disease progression in chicken and duck after AIV infection. For this purpose, we use a publicly available RNA-sequencing dataset from chicken and ducks infected with low-pathogenic avian influenza (LPAI) H5N2 and HPAI H5N1 (lung and ileum tissues, 1 and 3 days post-infection). Unlike previous studies, we performed a promoter analysis based on orthologous genes to detect important transcription factors (TFs) and their cooperation, based on which we apply a systems biology approach to identify common and species-specific master regulators. We found master regulators such as EGR1, FOS, and SP1, specifically for chicken and ETS1 and SMAD3/4, specifically for duck, which could be responsible for the duck’s effective and the chicken’s ineffective immune response.

Weighted Gene Co-Expression Network Analysis Combined with Machine Learning Validation to Identify Key Modules and Hub Genes Associated with SARS-CoV-2 Infection

The coronavirus disease-2019 (COVID-19) pandemic has caused an enormous loss of lives. Various clinical trials of vaccines and drugs are being conducted worldwide; nevertheless, as of today, no effective drug exists for COVID-19. The identification of key genes and pathways in this disease may lead to finding potential drug targets and biomarkers. Here, we applied weighted gene co-expression network analysis and LIME as an explainable artificial intelligence algorithm to comprehensively characterize transcriptional changes in bronchial epithelium cells (primary human lung epithelium (NHBE) and transformed lung alveolar (A549) cells) during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Our study detected a network that significantly correlated to the pathogenicity of COVID-19 infection based on identified hub genes in each cell line separately. The novel hub gene signature that was detected in our study, including PGLYRP4 and HEPHL1, may shed light on the pathogenesis of COVID-19, holding promise for future prognostic and therapeutic approaches. The enrichment analysis of hub genes showed that the most relevant biological process and KEGG pathways were the type I interferon signaling pathway, IL-17 signaling pathway, cytokine-mediated signaling pathway, and defense response to virus categories, all of which play significant roles in restricting viral infection. Moreover, according to the drug-target network, we identified 17 novel FDA-approved candidate drugs, which could potentially be used to treat COVID-19 patients through the regulation of four hub genes of the co-expression network. In conclusion, the aforementioned hub genes might play potential roles in translational medicine and might become promising therapeutic targets. Further in vitro and in vivo experimental studies are needed to evaluate the role of these hub genes in COVID-19.

Revealing nuclear receptor hub modules from Basal-like breast cancer expression networks

Nuclear receptors are a class of transcriptional factors. Together with their co-regulators, they regulate development, homeostasis, and metabolism in a ligand-dependent manner. Their ability to respond to environmental stimuli rapidly makes them versatile cellular components. Their coordinated activities regulate essential pathways in normal physiology and in disease. Due to their complexity, the challenge remains in understanding their direct associations in cancer development. Basal-like breast cancer is an aggressive form of breast cancer that often lacks ER, PR and Her2. The absence of these receptors limits the treatment for patients to the non-selective cytotoxic and cytostatic drugs. To identify potential drug targets it is essential to identify the most important nuclear receptor association network motifs in Basal-like subtype progression. This research aimed to reveal the transcriptional network patterns, in the hope to capture the underlying molecular state driving Basal-like oncogenesis. In this work, we illustrate a multidisciplinary approach of integrating an unsupervised machine learning clustering method with network modelling to reveal unique transcriptional patterns (network motifs) underlying Basal-like breast cancer. The unsupervised clustering method provides a natural stratification of breast cancer patients, revealing the underlying heterogeneity in Basal-like. Identification of gene correlation networks (GCNs) from Basal-like patients in both the TCGA and METABRIC databases revealed three critical transcriptional regulatory constellations that are enriched in Basal-like. These represent critical NR components implicated in Basal-like breast cancer transcription. This approach is easily adaptable and applicable to reveal critical signalling relationships in other diseases.

Natural selection shaped the rise and fall of passenger pigeon genomic diversity

The extinct passenger pigeon was once the most abundant bird in North America, and possibly the world. Although theory predicts that large populations will be more genetically diverse, passenger pigeon genetic diversity was surprisingly low. To investigate this disconnect, we analyzed 41 mitochondrial and 4 nuclear genomes from passenger pigeons and 2 genomes from band-tailed pigeons, which are passenger pigeons' closest living relatives. Passenger pigeons' large population size appears to have allowed for faster adaptive evolution and removal of harmful mutations, driving a huge loss in their neutral genetic diversity. These results demonstrate the effect that selection can have on a vertebrate genome and contradict results that suggested that population instability contributed to this species's surprisingly rapid extinction.

Gene regulation in the immediate-early response process

Immediate-early genes (IEGs) can be activated and transcribed within minutes after stimulation, without the need for de novo protein synthesis, and they are stimulated in response to both cell-extrinsic and cell-intrinsic signals. Extracellular signals are transduced from the cell surface, through receptors activating a chain of proteins in the cell, in particular extracellular-signal-regulated kinases (ERKs), mitogen-activated protein kinases (MAPKs) and members of the RhoA-actin pathway. These communicate through a signaling cascade by adding phosphate groups to neighboring proteins, and this will eventually activate and translocate TFs to the nucleus and thereby induce gene expression. The gene activation also involves proximal and distal enhancers that interact with promoters to simulate gene expression. The immediate-early genes have essential biological roles, in particular in stress response, like the immune system, and in differentiation. Therefore they also have important roles in various diseases, including cancer development. In this paper we summarize some recent advances on key aspects of the activation and regulation of immediate-early genes.

Systematic identification of transcriptional and post-transcriptional regulations in human respiratory epithelial cells during influenza A virus infection

Background

Respiratory epithelial cells are the primary target of influenza virus infection in human. However, the molecular mechanisms of airway epithelial cell responses to viral infection are not fully understood. Revealing genome-wide transcriptional and post-transcriptional regulatory relationships can further advance our understanding of this problem, which motivates the development of novel and more efficient computational methods to simultaneously infer the transcriptional and post-transcriptional regulatory networks.

Results

Here we propose a novel framework named SITPR to investigate the interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes. Briefly, a background regulatory network on a genome-wide scale (~23,000 nodes and ~370,000 potential interactions) is constructed from curated knowledge and algorithm predictions, to which the identification of transcriptional and post-transcriptional regulatory relationships is anchored. To reduce the dimension of the associated computing problem down to an affordable size, several topological and data-based approaches are used. Furthermore, we propose the constrained LASSO formulation and combine it with the dynamic Bayesian network (DBN) model to identify the activated regulatory relationships from time-course expression data. Our simulation studies on networks of different sizes suggest that the proposed framework can effectively determine the genuine regulations among TFs, miRNAs and target genes; also, we compare SITPR with several selected state-of-the-art algorithms to further evaluate its performance. By applying the SITPR framework to mRNA and miRNA expression data generated from human lung epithelial A549 cells in response to A/Mexico/InDRE4487/2009 (H1N1) virus infection, we are able to detect the activated transcriptional and post-transcriptional regulatory relationships as well as the significant regulatory motifs.

Conclusion

Compared with other representative state-of-the-art algorithms, the proposed SITPR framework can more effectively identify the activated transcriptional and post-transcriptional regulations simultaneously from a given background network. The idea of SITPR is generally applicable to the analysis of gene regulatory networks in human cells. The results obtained for human respiratory epithelial cells suggest the importance of the transcriptional, post-transcriptional regulations as well as their synergies in the innate immune responses against IAV infection.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-336) contains supplementary material, which is available to authorized users.

Prediction of dynamical drug sensitivity and resistance by module network rewiring-analysis based on transcriptional profiling

Revealing functional reorganization or module rewiring between modules at network levels during drug treatment is important to systematically understand therapies and drug responses. The present article proposed a novel model of module network rewiring to characterize functional reorganization of a complex biological system, and described a new framework named as module network rewiring-analysis (MNR) for systematically studying dynamical drug sensitivity and resistance during drug treatment. MNR was used to investigate functional reorganization or rewiring on the module network, rather than molecular network or individual molecules. Our experiments on expression data of patients with Hepatitis C virus infection receiving Interferon therapy demonstrated that consistent module genes derived by MNR could be directly used to reveal new genotypes relevant to drug sensitivity, unlike the other differential analyses of gene expressions. Our results showed that functional connections and reconnections among consistent modules bridged by biological paths were necessary for achieving effective responses of a drug. The hierarchical structures of the temporal module network can be considered as spatio-temporal biomarkers to monitor the efficacy, efficiency, toxicity, and resistance of the therapy. Our study indicates that MNR is a useful tool to identify module biomarkers and further predict dynamical drug sensitivity and resistance, characterize complex dynamic processes for therapy response, and provide biologically systematic clues for pharmacogenomic applications.

Network biomarkers, interaction networks and dynamical network biomarkers in respiratory diseases

Identification and validation of interaction networks and network biomarkers have become more critical and important in the development of disease-specific biomarkers, which are functionally changed during disease development, progression or treatment. The present review headlined the definition, significance, research and potential application for network biomarkers, interaction networks and dynamical network biomarkers (DNB). Disease-specific interaction networks, network biomarkers, or DNB have great significance in the understanding of molecular pathogenesis, risk assessment, disease classification and monitoring, or evaluations of therapeutic responses and toxicities. Protein-based DNB will provide more information to define the differences between the normal and pre-disease stages, which might point to early diagnosis for patients. Clinical bioinformatics should be a key approach to the identification and validation of disease-specific biomarkers.

Protective efficacy of orally administered, heat-killed Lactobacillus pentosus b240 against influenza A virus

Influenza A(H1N1)pdm virus caused the first human pandemic of the 21st century. Although various probiotic Lactobacillus species have been shown to have anti-microbial effects against pneumonia-inducing pathogens, the prophylactic efficacy and mechanisms behind their protection remain largely unknown. Here, we evaluated the prophylactic efficacy of heat-killed Lactobacillus pentosus b240 against lethal influenza A(H1N1)pdm virus infection in a mouse model. To further define the protective responses induced by b240, we performed virologic, histopathologic, and transcriptomic analyses on the mouse lungs. Although we did not observe an appreciable effect of b240 on virus growth, cytokine production, or histopathology, gene expressional analysis revealed that oral administration of b240 differentially regulates antiviral gene expression in mouse lungs. Our results unveil the possible mechanisms behind the protection mediated by b240 against influenza virus infection and provide new insights into probiotic therapy.

Mechanisms of severe acute respiratory syndrome coronavirus-induced acute lung injury

Systems biology offers considerable promise in uncovering novel pathways by which viruses and other microbial pathogens interact with host signaling and expression networks to mediate disease severity. In this study, we have developed an unbiased modeling approach to identify new pathways and network connections mediating acute lung injury, using severe acute respiratory syndrome coronavirus (SARS-CoV) as a model pathogen. We utilized a time course of matched virologic, pathological, and transcriptomic data within a novel methodological framework that can detect pathway enrichment among key highly connected network genes. This unbiased approach produced a high-priority list of 4 genes in one pathway out of over 3,500 genes that were differentially expressed following SARS-CoV infection. With these data, we predicted that the urokinase and other wound repair pathways would regulate lethal versus sublethal disease following SARS-CoV infection in mice. We validated the importance of the urokinase pathway for SARS-CoV disease severity using genetically defined knockout mice, proteomic correlates of pathway activation, and pathological disease severity. The results of these studies demonstrate that a fine balance exists between host coagulation and fibrinolysin pathways regulating pathological disease outcomes, including diffuse alveolar damage and acute lung injury, following infection with highly pathogenic respiratory viruses, such as SARS-CoV.

Severe acute respiratory syndrome coronavirus (SARS-CoV) emerged in 2002 and 2003, and infected patients developed an atypical pneumonia, acute lung injury (ALI), and acute respiratory distress syndrome (ARDS) leading to pulmonary fibrosis and death. We identified sets of differentially expressed genes that contribute to ALI and ARDS using lethal and sublethal SARS-CoV infection models. Mathematical prioritization of our gene sets identified the urokinase and extracellular matrix remodeling pathways as the most enriched pathways. By infecting Serpine1-knockout mice, we showed that the urokinase pathway had a significant effect on both lung pathology and overall SARS-CoV pathogenesis. These results demonstrate the effective use of unbiased modeling techniques for identification of high-priority host targets that regulate disease outcomes. Similar transcriptional signatures were noted in 1918 and 2009 H1N1 influenza virus-infected mice, suggesting a common, potentially treatable mechanism in development of virus-induced ALI.

Digital gene expression analysis of early root infection resistance to Sporisorium reilianum f. sp. zeae in maize

The maize smut fungus, Sporisorium reilianum f. sp. zeae, which is an important biotrophic pathogen responsible for extensive crop losses, can infect maize by invading the root during the early seedling stage. In order to investigate disease-resistance mechanisms at this early seedling stage, digital gene expression analysis, which applies a dual-enzyme approach, was used to identify the transcriptional changes in the roots of Huangzao4 (susceptible) and Mo17 (resistant) after root inoculation with S. reilianum. During the infection in the roots, the expression pattern of pathogenesis-related genes in Huangzao4 and Mo17 were significantly differentially regulated at different infection stages. The glutathione S-transferase enzyme activity and reactive oxygen species levels also showed changes before and after inoculation. The total lignin contents and the pattern of lignin depositions in the roots differed during root colonization of Huangzao4 and Mo17. These results suggest that the interplay between S. reilianum and maize during the early infection stage involves many important transcriptional and physiological changes, which offer several novel insights to understanding the mechanisms of resistance to the infection of biotrophic fungal pathogens.

Influenza infection and therapy: a systems approach

Seasonal flu affects 5–20% of the human population each year. Although mortality rates are typically <0.1% and the pandemic 2009 H1N1 influenza strain has been well contained by vaccination and strict hygiene, particularly virulent pandemic forms have emerged three times in the last century, resulting in millions of deaths. Current vaccine and therapeutic strategies are limited by the ability of the virus to generate variants that evade vaccine-induced immune responses and resist the therapeutic effects of antiviral drugs. Host genetic variations affect immune responses and may induce adverse effects during drug treatment or against vaccines. To develop new, first-in-class therapeutics, new antiviral targets and new chemical entities must be identified in the context of the immunogenomic repertoire of the patient. Since influenza and so many other viruses need to escape innate immunity to become pathogenic, the viral proteins responsible for this, as well as the host cell molecular pathways that lead to the antiviral response, are an excellent potential source of new therapeutic targets within a systems approach against influenza infections.

Differential intestinal M-cell gene expression response to gut commensals

Different rates of bacterial translocation across the gut mucosa have been reported but few studies have examined translocation of commensals at the level of the gut epithelial microfold (M) cell. We used an in vitro M-cell model to quantify translocation and determine the transcriptional response of M cells to various commensal bacteria. The transport kinetics and gene expression profile of M cells in response to different bacterial strains, namely Lactobacillus salivarius, Escherichia coli and Bacteroides fragilis, was assessed. Bacterial strains translocated across M cells with different efficiencies; E. coli and B. fragilis translocated with equal efficiency whereas L. salivarius translocated with less efficiency. Microarray analysis of the M cell response showed both common and differential gene expression changes between the bacterial strains. In the presence of bacteria, but not control beads, up-regulated genes were mainly involved in transcription regulation whereas pro-inflammatory and stress response genes were primarily up-regulated by E. coli and B. fragilis, but not L. salivarius nor beads. Translocation of bacteria and M-cell gene expression responses were confirmed in murine M cells following bacterial challenge in vivo. These results demonstrate that M cells have the ability to discriminate between different commensal bacteria and modify subsequent immune responses.

Inhibition of pyrimidine synthesis reverses viral virulence factor-mediated block of mRNA nuclear export

The NS1 protein of influenza virus is a major virulence factor essential for virus replication, as it redirects the host cell to promote viral protein expression. NS1 inhibits cellular messenger ribonucleic acid (mRNA) processing and export, down-regulating host gene expression and enhancing viral gene expression. We report in this paper the identification of a nontoxic quinoline carboxylic acid that reverts the inhibition of mRNA nuclear export by NS1, in the absence or presence of the virus. This quinoline carboxylic acid directly inhibited dihydroorotate dehydrogenase (DHODH), a host enzyme required for de novo pyrimidine biosynthesis, and partially reduced pyrimidine levels. This effect induced NXF1 expression, which promoted mRNA nuclear export in the presence of NS1. The release of NS1-mediated mRNA export block by DHODH inhibition also occurred in the presence of vesicular stomatitis virus M (matrix) protein, another viral inhibitor of mRNA export. This reversal of mRNA export block allowed expression of antiviral factors. Thus, pyrimidines play a necessary role in the inhibition of mRNA nuclear export by virulence factors.