SmCCNet 2.0: A Comprehensive Tool for Multi-omics Network Inference with Shiny Visualization.

Summary: Sparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multi-omics networks that are specific to this variable. We present the second-generation SmCCNet (SmCCNet 2.0) that adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. In addition, this new package offers a streamlined setup process that can be configured manually or automatically, ensuring a flexible and user-friendly experience. Availability: This package is available in both CRAN: https://cran.r-project.org/web/packages/SmCCNet/index.html and Github: https://github.com/KechrisLab/SmCCNet under the MIT license. The network visualization tool is available at https://smccnet.shinyapps.io/smccnetnetwork/.

Integrated variant allele frequency analysis pipeline and R package: easyVAF.

Somatic sequence variants are associated with cancer diagnosis, prognostic stratification, and treatment response. Variant allele frequency (VAF), the percentage of sequence reads with a specific DNA variant over the read depth at that locus, has been used as a metric to quantify mutation rates in these applications. VAF has the potential for feature detection by reflecting changes in tumor clonal composition across treatments or time points. Although there are several packages, including Genome Analysis Toolkit and VarScan, designed for variant calling and rare mutation identification, there is no readily available package for comparing VAFs among and between groups to identify loci of interest. To this end, we have developed the R package easyVAF, which includes parametric and nonparametric tests to compare VAFs among multiple groups. It is accompanied by an interactive R Shiny app. With easyVAF, the investigator has the option between three statistical tests to maximize power while maintaining an acceptable type I error rate. This paper presents our proposed pipeline for VAF analysis, from quality checking to group comparison. We evaluate our method in a wide range of simulated scenarios and show that choosing the appropriate test to limit the type I error rate is critical. For situations where data is sparse, we recommend comparing VAFs with the beta-binomial likelihood ratio test over Fisher's exact test and Pearson's χ2 test.

Interferon hyperactivity impairs cardiogenesis in Down syndrome via downregulation of canonical Wnt signaling.

Congenital heart defects (CHDs) are frequent in children with Down syndrome (DS), caused by trisomy of chromosome 21. However, the underlying mechanisms are poorly understood. Here, using a human-induced pluripotent stem cell (iPSC)-based model and the Dp(16)1Yey/+ (Dp16) mouse model of DS, we identified downregulation of canonical Wnt signaling downstream of increased dosage of interferon (IFN) receptors (IFNRs) genes on chromosome 21 as a causative factor of cardiogenic dysregulation in DS. We differentiated human iPSCs derived from individuals with DS and CHDs, and healthy euploid controls into cardiac cells. We observed that T21 upregulates IFN signaling, downregulates the canonical WNT pathway, and impairs cardiac differentiation. Furthermore, genetic and pharmacological normalization of IFN signaling restored canonical WNT signaling and rescued defects in cardiogenesis in DS in vitro and in vivo. Our findings provide insights into mechanisms underlying abnormal cardiogenesis in DS, ultimately aiding the development of therapeutic strategies.

Phase II trial of fulvestrant plus enzalutamide in ER+/HER2- advanced breast cancer.

This clinical trial combined fulvestrant with the anti-androgen enzalutamide in women with metastatic ER+/HER2- breast cancer (BC). Eligible patients were women with ECOG 0-2, ER+/HER2- measurable or evaluable metastatic BC. Prior fulvestrant was allowed. Fulvestrant was administered at 500 mg IM on days 1, 15, 29, and every 4 weeks thereafter. Enzalutamide was given at 160 mg po daily. Fresh tumor biopsies were required at study entry and after 4 weeks of treatment. The primary efficacy endpoint of the trial was the clinical benefit rate at 24 weeks (CBR24). The median age was 61 years (46-87); PS 1 (0-1); median of 4 prior non-hormonal and 3 prior hormonal therapies for metastatic disease. Twelve had prior fulvestrant, and 91% had visceral disease. CBR24 was 25% (7/28 evaluable). Median progression-free survival (PFS) was 8 weeks (95% CI: 2-52). Adverse events were as expected for hormonal therapy. Significant (p < 0.1) univariate relationships existed between PFS and ER%, AR%, and PIK3CA and/or PTEN mutations. Baseline levels of phospho-proteins in the mTOR pathway were more highly expressed in biopsies of patients with shorter PFS. Fulvestrant plus enzalutamide had manageable side effects. The primary endpoint of CBR24 was 25% in heavily pretreated metastatic ER+/HER2- BC. Short PFS was associated with activation of the mTOR pathway, and PIK3CA and/or PTEN mutations were associated with an increased hazard of progression. Thus, a combination of fulvestrant or other SERD plus AKT/PI3K/mTOR inhibitor with or without AR inhibition warrants investigation in second-line endocrine therapy of metastatic ER+ BC.

Significant increase of high-risk chromosome 1q gain and 6q loss at recurrence in posterior fossa group A ependymoma: A multicenter study.

BACKGROUND: Ependymoma (EPN) posterior fossa group A (PFA) has the highest rate of recurrence and the worst prognosis of all EPN molecular groups. At relapse, it is typically incurable even with re-resection and re-irradiation. The biology of recurrent PFA remains largely unknown; however, the increasing use of surgery at first recurrence has now provided access to clinical samples to facilitate a better understanding of this. METHODS: In this large longitudinal international multicenter study, we examined matched samples of primary and recurrent disease from PFA patients to investigate the biology of recurrence. RESULTS: DNA methylome derived copy number variants (CNVs) revealed large-scale chromosome gains and losses at recurrence in PFA. CNV changes were dominated by chromosome 1q gain and/or 6q loss, both previously identified as high-risk factors in PFA, which were present in 23% at presentation but increased to 61% at first recurrence. Multivariate survival analyses of this cohort showed that cases with 1q gain or 6q loss at first recurrence were significantly more likely to recur again. Predisposition to 1q+/6q- CNV changes at recurrence correlated with hypomethylation of heterochromatin-associated DNA at presentation. Cellular and molecular analyses revealed that 1q+/6q- PFA had significantly higher proportions of proliferative neuroepithelial undifferentiated progenitors and decreased differentiated neoplastic subpopulations. CONCLUSIONS: This study provides clinically and preclinically actionable insights into the biology of PFA recurrence. The hypomethylation predisposition signature in PFA is a potential risk-classifier for trial stratification. We show that the cellular heterogeneity of PFAs evolves largely because of genetic evolution of neoplastic cells.

Deep learning on graphs for multi-omics classification of COPD.

Network approaches have successfully been used to help reveal complex mechanisms of diseases including Chronic Obstructive Pulmonary Disease (COPD). However despite recent advances, we remain limited in our ability to incorporate protein-protein interaction (PPI) network information with omics data for disease prediction. New deep learning methods including convolution Graph Neural Network (ConvGNN) has shown great potential for disease classification using transcriptomics data and known PPI networks from existing databases. In this study, we first reconstructed the COPD-associated PPI network through the AhGlasso (Augmented High-Dimensional Graphical Lasso Method) algorithm based on one independent transcriptomics dataset including COPD cases and controls. Then we extended the existing ConvGNN methods to successfully integrate COPD-associated PPI, proteomics, and transcriptomics data and developed a prediction model for COPD classification. This approach improves accuracy over several conventional classification methods and neural networks that do not incorporate network information. We also demonstrated that the updated COPD-associated network developed using AhGlasso further improves prediction accuracy. Although deep neural networks often achieve superior statistical power in classification compared to other methods, it can be very difficult to explain how the model, especially graph neural network(s), makes decisions on the given features and identifies the features that contribute the most to prediction generally and individually. To better explain how the spectral-based Graph Neural Network model(s) works, we applied one unified explainable machine learning method, SHapley Additive exPlanations (SHAP), and identified CXCL11, IL-2, CD48, KIR3DL2, TLR2, BMP10 and several other relevant COPD genes in subnetworks of the ConvGNN model for COPD prediction. Finally, Gene Ontology (GO) enrichment analysis identified glycosaminoglycan, heparin signaling, and carbohydrate derivative signaling pathways significantly enriched in the top important gene/proteins for COPD classifications.

Single-Cell Analysis Reveals Distinct Immune and Smooth Muscle Cell Populations that Contribute to Chronic Thromboembolic Pulmonary Hypertension.

Rationale: Chronic thromboembolic pulmonary hypertension (CTEPH) is a sequela of acute pulmonary embolism (PE) in which the PE remodels into a chronic scar in the pulmonary arteries. This results in vascular obstruction, pulmonary microvasculopathy, and pulmonary hypertension. Objectives: Our current understanding of CTEPH pathobiology is primarily derived from cell-based studies limited by the use of specific cell markers or phenotypic modulation in cell culture. Therefore, our main objective was to identify the multiple cell types that constitute CTEPH thrombusy and to study their dysfunction. Methods: Here we used single-cell RNA sequencing of tissue removed at the time of pulmonary endarterectomy surgery from five patients to identify the multiple cell types. Using in vitro assays, we analyzed differences in phenotype between CTEPH thrombus and healthy pulmonary vascular cells. We studied potential therapeutic targets in cells isolated from CTEPH thrombus. Measurements and Main Results: Single-cell RNA sequencing identified multiple cell types, including macrophages, T cells, and smooth muscle cells (SMCs), that constitute CTEPH thrombus. Notably, multiple macrophage subclusters were identified but broadly split into two categories, with the larger group characterized by an upregulation of inflammatory signaling predicted to promote pulmonary vascular remodeling. CD4+ and CD8+ T cells were identified and likely contribute to chronic inflammation in CTEPH. SMCs were a heterogeneous population, with a cluster of myofibroblasts that express markers of fibrosis and are predicted to arise from other SMC clusters based on pseudotime analysis. Additionally, cultured endothelial, smooth muscle, and myofibroblast cells isolated from CTEPH fibrothrombotic material have distinct phenotypes from control cells with regard to angiogenic potential and rates of proliferation and apoptosis. Last, our analysis identified PAR1 (protease-activated receptor 1) as a potential therapeutic target that links thrombosis to chronic PE in CTEPH, with PAR1 inhibition decreasing SMC and myofibroblast proliferation and migration. Conclusions: These findings suggest a model for CTEPH similar to atherosclerosis, with chronic inflammation promoted by macrophages and T cells driving vascular remodeling through SMC modulation, and suggest new approaches for pharmacologically targeting this disease.

Elective nodal irradiation mitigates local and systemic immunity generated by combination radiation and immunotherapy in head and neck tumors.

In the setting of conventional radiation therapy, even when combined with immunotherapy, head and neck cancer often recurs locally and regionally. Elective nodal irradiation (ENI) is commonly employed to decrease regional recurrence. Given our developing understanding that immune cells are radio-sensitive, and that T cell priming occurs in the draining lymph nodes (DLNs), we hypothesize that radiation therapy directed at the primary tumor only will increase the effectiveness of immunotherapies. We find that ENI increases local, distant, and metastatic tumor growth. Multi-compartmental analysis of the primary/distant tumor, the DLNs, and the blood shows that ENI decreases the immune response systemically. Additionally, we find that ENI decreases antigen-specific T cells and epitope spreading. Treating the primary tumor with radiation and immunotherapy, however, fails to reduce regional recurrence, but this is reversed by either concurrent sentinel lymph node resection or irradiation. Our data support using lymphatic sparing radiation therapy for head and neck cancer.

Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death in the United States. COPD represents one of many areas of research where identifying complex pathways and networks of interacting biomarkers is an important avenue toward studying disease progression and potentially discovering cures. Recently, sparse multiple canonical correlation network analysis (SmCCNet) was developed to identify complex relationships between omics associated with a disease phenotype, such as lung function. SmCCNet uses two sets of omics datasets and an associated output phenotypes to generate a multi-omics graph, which can then be used to explore relationships between omics in the context of a disease. Detecting significant subgraphs within this multi-omics network, i.e., subgraphs which exhibit high correlation to a disease phenotype and high inter-connectivity, can help clinicians identify complex biological relationships involved in disease progression. The current approach to identifying significant subgraphs relies on hierarchical clustering, which can be used to inform clinicians about important pathways involved in the disease or phenotype of interest. The reliance on a hierarchical clustering approach can hinder subgraph quality by biasing toward finding more compact subgraphs and removing larger significant subgraphs. This study aims to introduce new significant subgraph detection techniques. In particular, we introduce two subgraph detection methods, dubbed Correlated PageRank and Correlated Louvain, by extending the Personalized PageRank Clustering and Louvain algorithms, as well as a hybrid approach combining the two proposed methods, and compare them to the hierarchical method currently in use. The proposed methods show significant improvement in the quality of the subgraphs produced when compared to the current state of the art.

Identifying miRNA-mRNA Networks Associated With COPD Phenotypes.

Chronic obstructive pulmonary disease (COPD) is characterized by expiratory airflow limitation and symptoms such as shortness of breath. Although many studies have demonstrated dysregulated microRNA (miRNA) and gene (mRNA) expression in the pathogenesis of COPD, how miRNAs and mRNAs systematically interact and contribute to COPD development is still not clear. To gain a deeper understanding of the gene regulatory network underlying COPD pathogenesis, we used Sparse Multiple Canonical Correlation Network (SmCCNet) to integrate whole blood miRNA and RNA-sequencing data from 404 participants in the COPDGene study to identify novel miRNA-mRNA networks associated with COPD-related phenotypes including lung function and emphysema. We hypothesized that phenotype-directed interpretable miRNA-mRNA networks from SmCCNet would assist in the discovery of novel biomarkers that traditional single biomarker discovery methods (such as differential expression) might fail to discover. Additionally, we investigated whether adjusting -omics and clinical phenotypes data for covariates prior to integration would increase the statistical power for network identification. Our study demonstrated that partial covariate adjustment for age, sex, race, and CT scanner model (in the quantitative emphysema networks) improved network identification when compared with no covariate adjustment. However, further adjustment for current smoking status and relative white blood cell (WBC) proportions sometimes weakened the power for identifying lung function and emphysema networks, a phenomenon which may be due to the correlation of smoking status and WBC counts with the COPD-related phenotypes. With partial covariate adjustment, we found six miRNA-mRNA networks associated with COPD-related phenotypes. One network consists of 2 miRNAs and 28 mRNAs which had a 0.33 correlation (p = 5.40E-12) to forced expiratory volume in 1 s (FEV1) percent predicted. We also found a network of 5 miRNAs and 81 mRNAs that had a 0.45 correlation (p = 8.80E-22) to percent emphysema. The miRNA-mRNA networks associated with COPD traits provide a systems view of COPD pathogenesis and complements biomarker identification with individual miRNA or mRNA expression data.

Multi-omics subtyping pipeline for chronic obstructive pulmonary disease.

Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects). 489 subjects had all three contemporaneous -omics profiles. Autoencoder embeddings were performed individually for each -omics dataset. Embeddings underwent subspace clustering using MineClus, either individually by -omics or combined, followed by recursive feature selection based on Support Vector Machines. Clusters were tested for associations with clinical variables. Optimal single -omics clustering typically resulted in two clusters. Although there was overlap for individual -omics cluster membership, each -omics cluster tended to be defined by unique molecular pathways. For example, prominent molecular features of the metabolome-based clustering included sphingomyelin, while key molecular features of the transcriptome-based clusters were related to immune and bacterial responses. We also found that when we integrated the -omics data at a later stage, we identified subtypes that varied based on age, severity of disease, in addition to diffusing capacity of the lungs for carbon monoxide, and precent on atrial fibrillation. In contrast, when we integrated the -omics data at an earlier stage by treating all data sets equally, there were no clinical differences between subtypes. Similar to clinical clustering, which has revealed multiple heterogenous clinical phenotypes, we show that transcriptomics, proteomics, and metabolomics tend to define clusters of COPD patients with different clinical characteristics. Thus, integrating these different -omics data sets affords additional insight into the molecular nature of COPD and its heterogeneity.

An Augmented High-Dimensional Graphical Lasso Method to Incorporate Prior Biological Knowledge for Global Network Learning.

Biological networks are often inferred through Gaussian graphical models (GGMs) using gene or protein expression data only. GGMs identify conditional dependence by estimating a precision matrix between genes or proteins. However, conventional GGM approaches often ignore prior knowledge about protein-protein interactions (PPI). Recently, several groups have extended GGM to weighted graphical Lasso (wGlasso) and network-based gene set analysis (Netgsa) and have demonstrated the advantages of incorporating PPI information. However, these methods are either computationally intractable for large-scale data, or disregard weights in the PPI networks. To address these shortcomings, we extended the Netgsa approach and developed an augmented high-dimensional graphical Lasso (AhGlasso) method to incorporate edge weights in known PPI with omics data for global network learning. This new method outperforms weighted graphical Lasso-based algorithms with respect to computational time in simulated large-scale data settings while achieving better or comparable prediction accuracy of node connections. The total runtime of AhGlasso is approximately five times faster than weighted Glasso methods when the graph size ranges from 1,000 to 3,000 with a fixed sample size (n = 300). The runtime difference between AhGlasso and weighted Glasso increases when the graph size increases. Using proteomic data from a study on chronic obstructive pulmonary disease, we demonstrate that AhGlasso improves protein network inference compared to the Netgsa approach by incorporating PPI information.

Identifying Protein-metabolite Networks Associated with COPD Phenotypes.

Chronic obstructive pulmonary disease (COPD) is a disease in which airflow obstruction in the lung makes it difficult for patients to breathe. Although COPD occurs predominantly in smokers, there are still deficits in our understanding of the additional risk factors in smokers. To gain a deeper understanding of the COPD molecular signatures, we used Sparse Multiple Canonical Correlation Network (SmCCNet), a recently developed tool that uses sparse multiple canonical correlation analysis, to integrate proteomic and metabolomic data from the blood of 1008 participants of the COPDGene study to identify novel protein-metabolite networks associated with lung function and emphysema. Our aim was to integrate -omic data through SmCCNet to build interpretable networks that could assist in the discovery of novel biomarkers that may have been overlooked in alternative biomarker discovery methods. We found a protein-metabolite network consisting of 13 proteins and 7 metabolites which had a -0.34 correlation (p-value = 2.5 × 10-28) to lung function. We also found a network of 13 proteins and 10 metabolites that had a -0.27 correlation (p-value = 2.6 × 10-17) to percent emphysema. Protein-metabolite networks can provide additional information on the progression of COPD that complements single biomarker or single -omic analyses.

Plasma Metabolomic Signatures of Chronic Obstructive Pulmonary Disease and the Impact of Genetic Variants on Phenotype-Driven Modules.

Background: Small studies have recently suggested that there are specific plasma metabolic signatures in chronic obstructive pulmonary disease (COPD), but there have been no large comprehensive study of metabolomic signatures in COPD that also integrate genetic variants. Materials and Methods: Fresh frozen plasma from 957 non-Hispanic white subjects in COPDGene was used to quantify 995 metabolites with Metabolon's global metabolomics platform. Metabolite associations with five COPD phenotypes (chronic bronchitis, exacerbation frequency, percent emphysema, post-bronchodilator forced expiratory volume at one second [FEV1]/forced vital capacity [FVC], and FEV1 percent predicted) were assessed. A metabolome-wide association study was performed to find genetic associations with metabolite levels. Significantly associated single-nucleotide polymorphisms were tested for replication with independent metabolomic platforms and independent cohorts. COPD phenotype-driven modules were identified in network analysis integrated with genetic associations to assess gene-metabolite-phenotype interactions. Results: Of metabolites tested, 147 (14.8%) were significantly associated with at least 1 COPD phenotype. Associations with airflow obstruction were enriched for diacylglycerols and branched chain amino acids. Genetic associations were observed with 109 (11%) metabolites, 72 (66%) of which replicated in an independent cohort. For 20 metabolites, more than 20% of variance was explained by genetics. A sparse network of COPD phenotype-driven modules was identified, often containing metabolites missed in previous testing. Of the 26 COPD phenotype-driven modules, 6 contained metabolites with significant met-QTLs, although little module variance was explained by genetics. Conclusion: A dysregulation of systemic metabolism was predominantly found in COPD phenotypes characterized by airflow obstruction, where we identified robust heritable effects on individual metabolite abundances. However, network analysis, which increased the statistical power to detect associations missed previously in classic regression analyses, revealed that the genetic influence on COPD phenotype-driven metabolomic modules was modest when compared with clinical and environmental factors.

Bronchoalveolar Lavage Fluid from COPD Patients Reveals More Compounds Associated with Disease than Matched Plasma.

Smoking causes chronic obstructive pulmonary disease (COPD). Though recent studies identified a COPD metabolomic signature in blood, no large studies examine the metabolome in bronchoalveolar lavage (BAL) fluid, a more direct representation of lung cell metabolism. We performed untargeted liquid chromatography-mass spectrometry (LC-MS) on BAL and matched plasma from 115 subjects from the SPIROMICS cohort. Regression was performed with COPD phenotypes as the outcome and metabolites as the predictor, adjusted for clinical covariates and false discovery rate. Weighted gene co-expression network analysis (WGCNA) grouped metabolites into modules which were then associated with phenotypes. K-means clustering grouped similar subjects. We detected 7939 and 10,561 compounds in BAL and paired plasma samples, respectively. FEV1/FVC (Forced Expiratory Volume in One Second/Forced Vital Capacity) ratio, emphysema, FEV1 % predicted, and COPD exacerbations associated with 1230, 792, eight, and one BAL compounds, respectively. Only two plasma compounds associated with a COPD phenotype (emphysema). Three BAL co-expression modules associated with FEV1/FVC and emphysema. K-means BAL metabolomic signature clustering identified two groups, one with more airway obstruction (34% of subjects, median FEV1/FVC 0.67), one with less (66% of subjects, median FEV1/FVC 0.77; p < 2 × 10-4). Associations between metabolites and COPD phenotypes are more robustly represented in BAL compared to plasma.

Unsupervised discovery of phenotype-specific multi-omics networks.

MOTIVATION: Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits. RESULTS: We propose a novel technique, sparse multiple canonical correlation network analysis (SmCCNet), for integrating multiple omics data types along with a quantitative phenotype of interest, and for constructing multi-omics networks that are specific to the phenotype. As a case study, we focus on miRNA-mRNA networks. Through simulations, we demonstrate that SmCCNet has better overall prediction performance compared to popular gene expression network construction and integration approaches under realistic settings. Applying SmCCNet to studies on chronic obstructive pulmonary disease (COPD) and breast cancer, we found enrichment of known relevant pathways (e.g. the Cadherin pathway for COPD and the interferon-gamma signaling pathway for breast cancer) as well as less known omics features that may be important to the diseases. Although those applications focus on miRNA-mRNA co-expression networks, SmCCNet is applicable to a variety of omics and other data types. It can also be easily generalized to incorporate multiple quantitative phenotype simultaneously. The versatility of SmCCNet suggests great potential of the approach in many areas. AVAILABILITY AND IMPLEMENTATION: The SmCCNet algorithm is written in R, and is freely available on the web at https://cran.r-project.org/web/packages/SmCCNet/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Interferon Beta Contributes to Astrocyte Activation in the Brain following Reovirus Infection.

Reovirus encephalitis in mice was used as a model system to investigate astrocyte activation (astrogliosis) following viral infection of the brain. Reovirus infection resulted in astrogliosis, as evidenced by increased expression of glial fibrillary acidic protein (GFAP), and the upregulation of genes that have been previously associated with astrocyte activation. Astrocyte activation occurred in regions of the brain that are targeted by reovirus but extended beyond areas of active infection. Astrogliosis also occurred following reovirus infection of ex vivo brain slice cultures (BSCs), demonstrating that factors intrinsic to the brain are sufficient to activate astrocytes and that this process can occur in the absence of any contribution from the peripheral immune response. In agreement with previous reports, reovirus antigen did not colocalize with GFAP in infected brains, suggesting that reovirus does not infect astrocytes. Reovirus-infected neurons produce interferon beta (IFN-ß). IFN-ß treatment of primary astrocytes resulted in both the upregulation of GFAP and cytokines that are associated with astrocyte activation. In addition, the ability of media from reovirus-infected BSCs to activate primary astrocytes was blocked by anti-IFN-ß antibodies. These results suggest that IFN-ß, likely released from reovirus-infected neurons, results in the activation of astrocytes during reovirus encephalitis. In areas where infection and injury were pronounced, an absence of GFAP staining was consistent with activation-induced cell death as a mechanism of inflammation control. In support of this, activated Bak and cleaved caspase 3 were detected in astrocytes within reovirus-infected brains, indicating that activated astrocytes undergo apoptosis.IMPORTANCE Viral encephalitis is a significant cause of worldwide morbidity and mortality, and specific treatments are extremely limited. Virus infection of the brain triggers neuroinflammation; however, the role of neuroinflammation in the pathogenesis of viral encephalitis is unclear. Initial neuroinflammatory responses likely contribute to viral clearance, but prolonged exposure to proinflammatory cytokines released during neuroinflammation may be deleterious and contribute to neuronal death and tissue injury. Activation of astrocytes is a hallmark of neuroinflammation. Here, we show that reovirus infection of the brain results in the activation of astrocytes via an IFN-ß-mediated process and that these astrocytes later die by Bak-mediated apoptosis. A better understanding of neuroinflammatory responses during viral encephalitis may facilitate the development of new treatment strategies for these diseases.

Mitochondrial p53 Contributes to Reovirus-Induced Neuronal Apoptosis and Central Nervous System Injury in a Mouse Model of Viral Encephalitis.

UNLABELLED: The tumor suppressor p53 plays a critical part in determining cell fate both as a regulator of the transcription of several proapoptotic genes and through its binding interactions with Bcl-2 family proteins at mitochondria. We now demonstrate that p53 protein levels are increased in infected brains during reovirus encephalitis. This increase occurs in the cytoplasm of reovirus-infected neurons and is associated with the activation of caspase 3. Increased levels of p53 in reovirus-infected brains are not associated with increased expression levels of p53 mRNA, suggesting that p53 regulation occurs at the protein level. Increased levels of p53 are also not associated with the increased expression levels of p53-regulated, proapoptotic genes. In contrast, upregulated p53 accumulates in mitochondria. Previous reports demonstrated that the binding of p53 to Bak at mitochondria causes Bak activation and results in apoptosis. We now show that Bak is activated and that activated Bak is bound to p53 during reovirus encephalitis. In addition, survival is enhanced in reovirus-infected Bak(-/-) mice compared to controls, demonstrating a role for Bak in reovirus pathogenesis. Inhibition of the mitochondrial translocation of p53 with pifithrin µ prevents the formation of p53/Bak complexes following reovirus infection of ex vivo brain slice cultures and results in decreased apoptosis and tissue injury. These results suggest that the mitochondrial localization of p53 regulates reovirus-induced pathogenesis in the central nervous system (CNS) through its interactions with Bak. IMPORTANCE: There are virtually no specific treatments of proven efficacy for virus-induced neuroinvasive diseases. A better understanding of the pathogenesis of virus-induced CNS injury is crucial for the rational development of novel therapies. Our studies demonstrate that p53 is activated in the brain following reovirus infection and may provide a therapeutic target for virus-induced CNS disease.

IL-4 confers resistance to IL-27-mediated suppression on CD4+ T cells by impairing signal transducer and activator of transcription 1 signaling.

BACKGROUND: TH2 cells play a critical role in the pathogenesis of allergic asthma. Established TH2 cells have been shown to resist reprogramming into TH1 cells. The inherent stability of TH2 cells poses a significant barrier to treating allergic diseases. OBJECTIVE: We sought to understand the mechanisms by which CD4(+) T cells from asthmatic patients resist the IL-27-mediated inhibition. METHODS: We isolated and cultured CD4(+) T cells from both healthy subjects and allergic asthmatic patients to test whether IL-27 can inhibit IL-4 production by the cultured CD4(+) T cells using ELISA. Culturing conditions that resulted in resistance to IL-27 were determined by using both murine and human CD4(+) T-cell culture systems. Signal transducer and activator of transcription (STAT) 1 phosphorylation was analyzed by means of Western blotting and flow cytometry. Suppressor of cytokine signaling (Socs) mRNA expression was measured by using quantitative PCR. The small interfering RNA method was used to knockdown the expression of Socs3 mRNA. RESULTS: We demonstrated that CD4(+) T cells from asthmatic patients resisted the suppression of IL-4 production mediated by IL-27. We observed that repeated exposure to TH2-inducing conditions rendered healthy human CD4(+) T cells resistant to IL-27-mediated inhibition. Using an in vitro murine culture system, we further demonstrated that repeated or higher doses of IL-4 stimulation, but not IL-2 stimulation, upregulated Socs3 mRNA expression and impaired IL-27-induced STAT1 phosphorylation. The knockdown of Socs3 mRNA expression restored IL-27-induced STAT1 phosphorylation and IL-27-mediated inhibition of IL-4 production. CONCLUSIONS: Our findings demonstrate that differentiated TH2 cells can resist IL-27-induced reprogramming toward TH1 cells by downregulating STAT1 phosphorylation and likely explain why the CD4(+) T cells of asthmatic patients are resistant to IL-27-mediated inhibition.

Antagonistic regulation by the transcription factors C/EBPα and MITF specifies basophil and mast cell fates.

It remains unclear whether basophils and mast cells are derived from a common progenitor. Furthermore, how basophil versus mast cell fate is specified has not been investigated. Here, we have identified a population of granulocyte-macrophage progenitors (GMPs) that were highly enriched in the capacity to differentiate into basophils and mast cells while retaining a limited capacity to differentiate into myeloid cells. We have designated these progenitor cells "pre-basophil and mast cell progenitors" (pre-BMPs). STAT5 signaling was required for the differentiation of pre-BMPs into both basophils and mast cells and was critical for inducing two downstream molecules: C/EBPα and MITF. We have identified C/EBPα as the critical basophil transcription factor for specifying basophil cell fate and MITF as the crucial transcription factor for specifying mast cell fate. C/EBPα and MITF silenced each other's transcription in a directly antagonistic fashion. Our study reveals how basophil and mast cell fate is specified.