A module-based analytical strategy to identify novel disease-associated genes shows an inhibitory role for interleukin 7 Receptor in allergic inflammation

Identification of Effective Diagnostic Biomarkers and Immune Cell Infiltration in Atopic Dermatitis by Comprehensive Bioinformatics Analysis

Background: Atopic dermatitis (AD) is a dermatological disorder characterized by symptoms such as chronically inflamed skin and frequently intolerable itching. The mechanism underlying AD development is still unclear. Our study aims to identify the diagnostic and therapeutic biomarkers for AD and provide insight into immune mechanisms at the molecular level through bioinformatics analysis.

Methods: The GSE6012, GSE32924, and GSE36842 gene expression profiles were obtained for analysis from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were segregated using the “Batch correction” and “RobustRankAggreg” methods. Weighted gene co-expression network analysis (WGCNA) was performed to screen for module genes with AD traits. Then, common DEGs (co-DEGs) were screened out via combined differential expression analysis and WGCNA. Functional enrichment analysis was performed for these co-DEGs using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), followed by protein-protein interaction network analysis. Candidate hub genes were identified using the “cytoHubba” plugin in Cytoscape, and their value for AD diagnosis was validated using receiver operating characteristic curve analysis in the external database GSE120721. Immunohistochemical staining was performed for further validation. The CIBERSORT algorithm was used to evaluate skin samples obtained from healthy controls (HCs) and lesions of AD patients, to determine the extent of immune cell infiltration. The association between the identified hub genes and significant differential immune cells was analyzed using Pearson correlation analysis.

Results: A total of 259 DEGs were acquired from the intersection of DEGs obtained by the two independent procedures, and 331 AD-trait module genes were separated out from the blue module via WGCNA analysis. Then, 169 co-DEGs arising from the intersection of the 259 DEGs and the 331 AD-trait module genes were obtained. We found that co-DEGs were significantly enhanced in the type I interferon and IL-17 signal transduction pathways. Thirteen potential hub genes were identified using Cytoscape. Five hub genes (CCR7, CXCL10, IRF7, MMP1, and RRM2) were identified after screening via external dataset validation and immunohistochemical analysis. We also identified four significant differential immune cells, i.e., activated dendritic cells, plasma cells, resting mast cells, and CD4+ naïve T cells, between AD patients and HCs. Moreover, the relationship between the identified hub genes and significant differential immune cells was analyzed. The results showed that the CCR7 expression level was positively correlated with the number of CD4+ naïve T cells (R = 0.42, p = 0.011).

Conclusion: CCR7, CXCL10, IRF7, MMP1, and RRM2 could be potential diagnostic and therapeutic biomarkers for AD. CCR7 expression level was positively correlated with the number of CD4+ naïve T cells in AD. These findings need to be corroborated in future studies.

Multi-omics-based identification of atopic dermatitis target genes and their potential associations with metabolites and miRNAs

Atopic dermatitis (AD), or atopic eczema, is one of the most common inflammatory skin diseases with up to 10% prevalence in adults, and approximately 15-20% in children in industrialized countries. As a result, there is an unmet need for faster, safer, and effective treatments for AD. AD pathogenesis represents a complex interplay between multiple factors, such as environmental factors or stimuli, genetic factors, immune dysfunctions. However, although multi-omics label studies have been very useful in understanding the pathophysiological mechanisms of AD and its clinical manifestations, there have been very few studies that integrate different labels of omics data. Here, we attempted to integrate gene expression and metabolomics datasets from multiple different publicly available AD cohort datasets and conduct an integrated systems-level AD analysis. We used four different GEO transcriptome data sets and, by applying an elastic net machine learning algorithm, identified robust hub genes that can be used as signatures, for example, H2AFX, MCM7, ESR1 and SF3A2. Moreover, we investigated potential associations of those genes by applying a pathway-based approach over metabolomics and miRNA datasets. Our results revealed potential novel associations between fatty acids and peroxisomal lipid metabolism pathways, as well as with several microRNAs.

Transcriptomic insight into the translational value of two murine models in human atopic dermatitis

This study sought to develop a novel diagnostic tool for atopic dermatitis (AD). Mouse transcriptome data were obtained via RNA-sequencing of dorsal skin tissues of CBA/J mice affected with contact hypersensitivity (induced by treatment with 1-chloro-2,4-dinitrobenzene) or brush stimulation-induced AD-like skin condition. Human transcriptome data were collected from German, Swedish, and American cohorts of AD patients from the Gene Expression Omnibus database. edgeR and SAM algorithms were used to analyze differentially expressed murine and human genes, respectively. The FAIME algorithm was then employed to assign pathway scores based on KEGG pathway database annotations. Numerous genes and pathways demonstrated similar dysregulation patterns in both the murine models and human AD. Upon integrating transcriptome information from both murine and human data, we identified 36 commonly dysregulated differentially expressed genes, which were designated as a 36-gene signature. A severity score (AD index) was applied to each human sample to assess the predictive power of the 36-gene AD signature. The diagnostic power and predictive accuracy of this signature were demonstrated for both AD severity and treatment outcomes in patients with AD. This genetic signature is expected to improve both AD diagnosis and targeted preclinical research.

Perphenazine Attenuates the Pro-Inflammatory Responses in Mouse Models of Th2-Type Allergic Dermatitis

Developing dermatitis therapeutics has been faced with challenges including adverse effects of topical steroid and high cost of new developing drugs. Here, we found the expression levels of dopamine receptor D2 is higher in skin biopsies of dermatitis patients and an oxazolone-induced animal model of dermatitis. We used perphenazine, an FDA-approved dopamine receptor antagonist to determine the therapeutic effect. Two different animal models including 12-o-tetradecanoylphorbol-13-acetate (TPA) and oxazolone (OXA)-induced dermatitis were employed. TPA and OXA-mediated ear swelling was attenuated by perphenazine. Moreover, perphenazine inhibited infiltrated mast cells into lesion area. We found levels of serum IgE, histamine and cytokines are decreased in mice cotreated with perphenazine and OXA compared to OXA-treated mice. Overall, this is a first study showing that the FDA-approved, anti-psychotic drug, perphenazine, alleviates animal models of dermatitis.

Bleomycin hydrolase regulates the release of chemokines important for inflammation and wound healing by keratinocytes

Bleomycin hydrolase (BLMH) is a well-conserved cysteine protease widely expressed in several mammalian tissues. In skin, which contains high levels of BLMH, this protease is involved in the degradation of citrullinated filaggrin monomers into free amino acids important for skin hydration. Interestingly, the expression and activity of BLMH is reduced in patients with atopic dermatitis (AD) and psoriasis, and BLMH knockout mice acquire tail dermatitis. Apart from its already known function, we have discovered a novel role of BLMH in the regulation of inflammatory chemokines and wound healing. We show that lowered BLMH levels in keratinocytes result in increased release of the pro-inflammatory chemokines CXCL8 and GROα, which are upregulated in skin from AD patients compared to healthy individuals. Conditioned media from keratinocytes expressing low levels of BLMH increased chemotaxis by neutrophils and caused a delayed wound healing in the presence of low-level TNFα. This defective wound healing was improved by blocking the shared receptor of CXCL8 and GROα, namely CXCR2, using a specific receptor antagonist. Collectively, our results present a novel function of BLMH in regulating the secretion of chemokines involved in inflammation and wound healing in human keratinocytes.

Regulation of interferon stimulated gene expression levels at homeostasis

Interferon stimulated genes (ISGs), a collection of genes important in the early innate immune response, are upregulated in response to stimulation by extracellular type I interferons. The regulation of ISGs has been extensively studied in cells exposed to significant interferon stimulation, but less is known about ISG regulation in homeostatic regimes in which extracellular interferon levels are low. Using a collection of pre-existing, publicly available microarray datasets, we investigated ISG regulation at homeostasis in CD4, pulmonary epithelial, fibroblast and macrophage cells. We used a linear regression model to predict ISG expression levels from regulator expression levels. Our results suggest significant regulation of ISG expression at homeostasis, both through the ISGF3 molecule and through IRF7 and IRF8 associated pathways. We find that roughly 50% of ISGs have expression levels significantly correlated with ISGF3 expression levels at homeostasis, supporting previous results suggesting that homeostatic IFN levels have broad functional consequences. We find that ISG expression levels varied in their correlation with ISGF3, with epithelial and macrophage cells showing more correlation than CD4 and fibroblast cells. Our analysis provides a novel approach for decomposing and quantifying ISG regulation.

An admixture mapping meta-analysis implicates genetic variation at 18q21 with asthma susceptibility in Latinos

BACKGROUND

Asthma is a common but complex disease with racial/ethnic differences in prevalence, morbidity, and response to therapies.

OBJECTIVE

We sought to perform an analysis of genetic ancestry to identify new loci that contribute to asthma susceptibility.

METHODS

We leveraged the mixed ancestry of 3902 Latinos and performed an admixture mapping meta-analysis for asthma susceptibility. We replicated associations in an independent study of 3774 Latinos, performed targeted sequencing for fine mapping, and tested for disease correlations with gene expression in the whole blood of more than 500 subjects from 3 racial/ethnic groups.

RESULTS

We identified a genome-wide significant admixture mapping peak at 18q21 in Latinos (P = 6.8 × 10-6), where Native American ancestry was associated with increased risk of asthma (odds ratio [OR], 1.20; 95% CI, 1.07-1.34; P = .002) and European ancestry was associated with protection (OR, 0.86; 95% CI, 0.77-0.96; P = .008). Our findings were replicated in an independent childhood asthma study in Latinos (P = 5.3 × 10-3, combined P = 2.6 × 10-7). Fine mapping of 18q21 in 1978 Latinos identified a significant association with multiple variants 5' of SMAD family member 2 (SMAD2) in Mexicans, whereas a single rare variant in the same window was the top association in Puerto Ricans. Low versus high SMAD2 blood expression was correlated with case status (13.4% lower expression; OR, 3.93; 95% CI, 2.12-7.28; P < .001). In addition, lower expression of SMAD2 was associated with more frequent exacerbations among Puerto Ricans with asthma.

CONCLUSION

Ancestry at 18q21 was significantly associated with asthma in Latinos and implicated multiple ancestry-informative noncoding variants upstream of SMAD2 with asthma susceptibility. Furthermore, decreased SMAD2 expression in blood was strongly associated with increased asthma risk and increased exacerbations.

Potential Involvement of Type I Interferon Signaling in Immunotherapy in Seasonal Allergic Rhinitis

Specific immunotherapy (SIT) reverses the symptoms of seasonal allergic rhinitis (SAR) in most patients. Recent studies report type I interferons shifting the balance between type I T helper cell (Th1) and type II T helper cells (Th2) towards Th2 dominance by inhibiting the differentiation of naive T cells into Th1 cells. As SIT is thought to cause a shift towards Th1 dominance, we hypothesized that SIT would alter interferon type I signaling. To test this, allergen and diluent challenged CD4⁺ T cells from healthy controls and patients from different time points were analyzed. The initial experiments focused on signature genes of the pathway and found complex changes following immunotherapy, which were consistent with our hypothesis. As interferon signaling involves multiple genes, expression profiling studies were performed, showing altered expression of the pathway. These findings require validation in a larger group of patients in further studies.

Gene expression profiling of asthma phenotypes demonstrates molecular signatures of atopy and asthma control

BACKGROUND

Recent studies have used cluster analysis to identify phenotypic clusters of asthma with differences in clinical traits, as well as differences in response to therapy with anti-inflammatory medications. However, the correspondence between different phenotypic clusters and differences in the underlying molecular mechanisms of asthma pathogenesis remains unclear.

OBJECTIVE

We sought to determine whether clinical differences among children with asthma in different phenotypic clusters corresponded to differences in levels of gene expression.

METHODS

We explored differences in gene expression profiles of CD4(+) lymphocytes isolated from the peripheral blood of 299 young adult participants in the Childhood Asthma Management Program study. We obtained gene expression profiles from study subjects between 9 and 14 years of age after they participated in a randomized, controlled longitudinal study examining the effects of inhaled anti-inflammatory medications over a 48-month study period, and we evaluated the correspondence between our earlier phenotypic cluster analysis and subsequent follow-up clinical and molecular profiles.

RESULTS

We found that differences in clinical characteristics observed between subjects assigned to different phenotypic clusters persisted into young adulthood and that these clinical differences were associated with differences in gene expression patterns between subjects in different clusters. We identified a subset of genes associated with atopic status, validated the presence of an atopic signature among these genes in an independent cohort of asthmatic subjects, and identified the presence of common transcription factor binding sites corresponding to glucocorticoid receptor binding.

CONCLUSION

These findings suggest that phenotypic clusters are associated with differences in the underlying pathobiology of asthma. Further experiments are necessary to confirm these findings.

A Comprehensive Analysis of the Transcriptomes of Marssonina brunnea and Infected Poplar Leaves to Capture Vital Events in Host-Pathogen Interactions

Background

Understanding host-pathogen interaction mechanisms helps to elucidate the entire infection process and focus on important events, and it is a promising approach for improvement of disease control and selection of treatment strategy. Time-course host-pathogen transcriptome analyses and network inference have been applied to unravel the direct or indirect relationships of gene expression alterations. However, time series analyses can suffer from absent time points due to technical problems such as RNA degradation, which limits the application of algorithms that require strict sequential sampling. Here, we introduce an efficient method using independence test to infer an independent network that is exclusively concerned with the frequency of gene expression changes.

Results

Highly resistant NL895 poplar leaves and weakly resistant NL214 leaves were infected with highly active and weakly active Marssonina brunnea, respectively, and were harvested at different time points. The independent network inference illustrated the top 1,000 vital fungus-poplar relationships, which contained 768 fungal genes and 54 poplar genes. These genes could be classified into three categories: a fungal gene surrounded by many poplar genes; a poplar gene connected to many fungal genes; and other genes (possessing low degrees of connectivity). Notably, the fungal gene M6_08342 (a metalloprotease) was connected to 10 poplar genes, particularly including two disease-resistance genes. These core genes, which are surrounded by other genes, may be of particular importance in complicated infection processes and worthy of further investigation.

Conclusions

We provide a clear framework of the interaction network and identify a number of candidate key effectors in this process, which might assist in functional tests, resistant clone selection, and disease control in the future.

MicroRNAs act complementarily to regulate disease-related mRNA modules in human diseases

MicroRNAs (miRNAs) play a key role in regulating mRNA expression, and individual miRNAs have been proposed as diagnostic and therapeutic candidates. The identification of such candidates is complicated by the involvement of multiple miRNAs and mRNAs as well as unknown disease topology of the miRNAs. Here, we investigated if disease-associated miRNAs regulate modules of disease-associated mRNAs, if those miRNAs act complementarily or synergistically, and if single or combinations of miRNAs can be targeted to alter module functions. We first analyzed publicly available miRNA and mRNA expression data for five different diseases. Integrated target prediction and network-based analysis showed that the miRNAs regulated modules of disease-relevant genes. Most of the miRNAs acted complementarily to regulate multiple mRNAs. To functionally test these findings, we repeated the analysis using our own miRNA and mRNA expression data from CD4+ T cells from patients with seasonal allergic rhinitis. This is a good model of complex diseases because of its well-defined phenotype and pathogenesis. Combined computational and functional studies confirmed that miRNAs mainly acted complementarily and that a combination of two complementary miRNAs, miR-223 and miR-139-3p, could be targeted to alter disease-relevant module functions, namely, the release of type 2 helper T-cell (Th2) cytokines. Taken together, our findings indicate that miRNAs act complementarily to regulate modules of disease-related mRNAs and can be targeted to alter disease-relevant functions.

Modules in Biological Networks

One of the most prominent properties of networks representing complex systems is modularity. Network-based module identification has captured the attention of a diverse group of scientists from various domains and a variety of methods have been developed. The ability to decompose complex biological systems into modules allows the use of modules rather than individual genes as units in biological studies. A modular view is shaping research methods in biology. Module-based approaches have found broad applications in protein complex identification, protein function prediction, protein expression prediction, as well as disease studies. Compared to single gene-level analyses, module-level analyses offer higher robustness and sensitivity. More importantly, module-level analyses can lead to a better understanding of the design and organization of complex biological systems.

Limitations and possibilities of low cell number ChIP-seq

Background

Chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-seq) offers high resolution, genome-wide analysis of DNA-protein interactions. However, current standard methods require abundant starting material in the range of 1–20 million cells per immunoprecipitation, and remain a bottleneck to the acquisition of biologically relevant epigenetic data. Using a ChIP-seq protocol optimised for low cell numbers (down to 100,000 cells / IP), we examined the performance of the ChIP-seq technique on a series of decreasing cell numbers.

Results

We present an enhanced native ChIP-seq method tailored to low cell numbers that represents a 200-fold reduction in input requirements over existing protocols. The protocol was tested over a range of starting cell numbers covering three orders of magnitude, enabling determination of the lower limit of the technique. At low input cell numbers, increased levels of unmapped and duplicate reads reduce the number of unique reads generated, and can drive up sequencing costs and affect sensitivity if ChIP is attempted from too few cells.

Conclusions

The optimised method presented here considerably reduces the input requirements for performing native ChIP-seq. It extends the applicability of the technique to isolated primary cells and rare cell populations (e.g. biobank samples, stem cells), and in many cases will alleviate the need for cell culture and any associated alteration of epigenetic marks. However, this study highlights a challenge inherent to ChIP-seq from low cell numbers: as cell input numbers fall, levels of unmapped sequence reads and PCR-generated duplicate reads rise. We discuss a number of solutions to overcome the effects of reducing cell number that may aid further improvements to ChIP performance.

Increased expression of IRF4 and ETS1 in CD4+ cells from patients with intermittent allergic rhinitis

BACKGROUND

  The transcription factor (TF) IRF4 is involved in the regulation of Th1, Th2, Th9, and Th17 cells, and animal studies have indicated an important role in allergy. However, IRF4 and its target genes have not been examined in human allergy.

METHODS

  IRF4 and its target genes were examined in allergen-challenged CD4(+) cells from patients with IAR, using combined gene expression microarrays and chromatin immunoprecipitation chips (ChIP-chips), computational target prediction, and RNAi knockdowns.

RESULTS

  IRF4 increased in allergen-challenged CD4(+) cells from patients with IAR, and functional studies supported its role in Th2 cell activation. IRF4 ChIP-chip showed that IRF4 regulated a large number of genes relevant to Th cell differentiation. However, neither Th1 nor Th2 cytokines were the direct targets of IRF4. To examine whether IRF4 induced Th2 cytokines via one or more downstream TFs, we combined gene expression microarrays, ChIP-chips, and computational target prediction and found a putative intermediary TF, namely ETS1 in allergen-challenged CD4(+) cells from allergic patients. ETS1 increased significantly in allergen-challenged CD4(+) cells from patients compared to controls. Gene expression microarrays before and after ETS1 RNAi knockdown showed that ETS1 induced Th2 cytokines as well as disease-related pathways.

CONCLUSIONS

  Increased expression of IRF4 in allergen-challenged CD4(+) cells from patients with intermittent allergic rhinitis leads to activation of a complex transcriptional program, including Th2 cytokines.

Interferon regulatory factor 7 is a major hub connecting interferon-mediated responses in virus-induced asthma exacerbations in vivo

BACKGROUND

Exacerbations are responsible for a substantial burden of morbidity and health care use in children with asthma. Most asthma exacerbations are triggered by viral infections; however, the underlying mechanisms have not been systematically investigated.

OBJECTIVE

The objective of this study was to elucidate the molecular networks that underpin virus-induced exacerbations in asthmatic children in vivo.

METHODS

We followed exacerbation-prone asthmatic children prospectively and profiled global patterns of gene expression in nasal lavage samples obtained during an acute, moderate, picornavirus-induced exacerbation and 7 to 14 days later. Coexpression network analysis and prior knowledge was used to reconstruct the underlying gene networks.

RESULTS

The data showed that an intricate modular program consisting of more than 1000 genes was upregulated during acute exacerbations in comparison with 7 to 14 days later. The modules were enriched for coherent cellular processes, including interferon-induced antiviral responses, innate pathogen sensing, response to wounding, small nucleolar RNAs, and the ubiquitin-proteosome and lysosome degradation pathways. Reconstruction of the wiring diagram of the modules revealed the presence of hyperconnected hub nodes, most notably interferon regulatory factor 7, which was identified as a major hub linking interferon-mediated antiviral responses.

CONCLUSIONS

This study provides an integrated view of the inflammatory networks that are upregulated during virus-induced asthma exacerbations in vivo. A series of innate signaling hubs were identified that could be novel therapeutic targets for asthma attacks.

Interleukin 7 receptor gene polymorphisms and haplotypes are associated with susceptibility to IgA nephropathy in Korean children

An abnormal T-cell response is involved in the pathogenesis of various renal diseases. Survival of naïve T cells is dependent on interleukin 7 (IL7) and its receptor (IL7R). Thus, we investigated the association between IL7R single nucleotide polymorphisms (SNPs) and childhood IgA nephropathy (IgAN). We analyzed the genotypic distributions of two missense SNPs of IL7R, rs1494558 (Ile66Thr) and rs1494555 (Val138Ile), among 198 pediatric IgAN patients and 288 healthy controls. Haplotype analysis and measurement of pair-wise linkage disequilibrium were performed. In addition, the genotypes of patient subgroups, determined by the presence of nephrotic range proteinuria (>40 mg/m(2)/h) and pathological advancement, were analyzed. The genotyping data of IgAN patients and controls showed significant differences in rs1494558 (codominant, P=0.0003; dominant, P=0.0003) and rs1494555 (codominant, P=0.0038; dominant, P=0.0099). In the haplotype analysis, AC (codominant, P=0.0066) and GT (codominant, P=0.0005; dominant, P=0.0006) were significantly associated with susceptibility to IgAN. Furthermore, in the analysis of clinical subgroups of IgAN patients, rs1494558 was associated with nephrotic range proteinuria (codominant, P=0.027; recessive, P=0.023). Our results suggest that IL7R may be associated with disease susceptibility and proteinuria in childhood IgAN.

A pathway-based approach to find novel markers of local glucocorticoid treatment in intermittent allergic rhinitis

BACKGROUND

Glucocorticoids (GCs) may affect the expression of hundreds of genes in different cells and tissues from patients with intermittent allergic rhinitis (IAR). It is a formidable challenge to understand these complex changes by studying individual genes. In this study, we aimed to identify (i) pathways affected by local GC treatment and (ii) examine if those pathways could be used to find novel markers of local GC treatment in nasal fluids from patients with IAR.

METHODS

Gene expression microarray- and iTRAQ-based proteomic analyses of nasal fluids, nasal fluid cells and nasal mucosa from patients with IAR were performed to find pathways enriched for differentially expressed genes and proteins. Proteins representing those pathways were analyzed with ELISA in an independent material of nasal fluids from 23 patients with IAR before and after treatment with a local GC.

RESULTS

Transcriptomal and proteomic high-throughput analyses of nasal fluids, nasal fluid cells and nasal mucosal showed that local GC treatment affected a wide variety of pathways in IAR such as the glucocorticoid receptor pathway and the acute phase response pathway. Extracellular proteins encoded by genes in those pathways were analyzed in an independent material of nasal fluids from patients. Proteins that changed significantly in expression included known biomarkers such as eosinophil cationic protein but also proteins that had not been previously described in IAR, namely CCL2, M-CSF, CXCL6 and apoH.

CONCLUSION

Pathway-based analyses of genomic and proteomic high-throughput data can be used as a complementary approach to identify novel potential markers of GC treatment in IAR.

Allergen challenge of peripheral blood mononuclear cells from patients with seasonal allergic rhinitis increases IL-17RB, which regulates basophil apoptosis and degranulation

BACKGROUND

Previously, expression profiling has been used to analyse allergen-challenged T-helper type 2 cells, nasal biopsies and nasal fluid cells from patients with seasonal allergic rhinitis (SAR). Allergen-challenged peripheral blood mononuclear cells (PBMCs) provide a human in vitro model of how antigen-presenting cells, CD4+ T cells and effector cells such as basophils interact in allergic inflammation.

OBJECTIVE

To identify novel genes and pathways in allergen-challenged PBMCs from patients with SAR using gene expression profiling and functional studies.

METHODS

PBMCs from 11 patients with SAR and 23 healthy controls were analysed with gene expression profiling. mRNA expression of IL17RB in basophils was evaluated using quantitative real-time PCR. Membrane protein expression and apoptosis of basophils were examined by flow cytometry. Degranulation of basophils was assessed by measuring beta-hexosaminidase release. Cytokine release was measured using ELISA.

RESULTS

Gene expression microarray analysis of allergen-challenged PBMCs showed that 209 out of 44000 genes were differentially expressed in patients compared with controls. IL17RB was the gene whose expression increased most in patients (P<0.0001). FACS analysis of PBMCs showed, for the first time, that basophils express IL-17RB. Following allergen challenge, IL-17RB protein increased significantly on basophils from patients compared with controls (P<0.05). IL-3 significantly increased both mRNA and protein expressions of IL17RB. Activation of IL-17RB by its ligand, IL-25, inhibited apoptosis of basophils. Moreover, IgE-mediated degranulation was enhanced by IL-25.

CONCLUSION

Increased expression of IL-17RB on allergen-challenged basophil is regulated by IL-3, inhibits apoptosis and promotes IgE-mediated degranulation of basophils.

Pathogenic mechanisms of allergic inflammation: atopic asthma as a paradigm

Prospective studies tracking birth cohorts over periods of years indicate that the seeds for atopic asthma in adulthood are sewn during early life. The key events involve programming of functional phenotypes within the immune and respiratory systems which determine long-term responsiveness to ubiquitous environmental stimuli, particularly respiratory viruses and aeroallergens. A crucial component of asthma pathogenesis is early sensitization to aeroallergens stemming from a failure of mucosal tolerance mechanisms during the preschool years, which is associated with delayed postnatal maturation of a range of adaptive and innate immune functions. These maturational defects also increase risk for severe respiratory infections, and the combination of sensitization and infections maximizes risk for early development of the persistent asthma phenotype. Interactions between immunoinflammatory pathways stimulated by these agents also sustain the disease in later life as major triggers of asthma exacerbations. Recent studies on the nature of these interactions suggest the operation of an infection-associated lung:bone marrow axis involving upregulation of FcERlalpha on myeloid precursor populations prior to their migration to the airways, thus amplifying local inflammation via IgE-mediated recruitment of bystander atopic effector mechanisms. The key participants in the disease process are airway mucosal dendritic cells and adjacent epithelial cells, and transiting CD4(+) effector and regulatory T-cell populations, and increasingly detailed characterization of their roles at different stages of pathogenesis is opening up novel possibilities for therapeutic control of asthma. Of particular interest is the application of genomics-based approaches to drug target identification in cell populations of interest, exemplified by recent findings discussed below relating to the gene network(s) triggered by activation of Th2-memory cells from atopics.

Network properties of complex human disease genes identified through genome-wide association studies

Background

Previous studies of network properties of human disease genes have mainly focused on monogenic diseases or cancers and have suffered from discovery bias. Here we investigated the network properties of complex disease genes identified by genome-wide association studies (GWAs), thereby eliminating discovery bias.

Principal findings

We derived a network of complex diseases (n = 54) and complex disease genes (n = 349) to explore the shared genetic architecture of complex diseases. We evaluated the centrality measures of complex disease genes in comparison with essential and monogenic disease genes in the human interactome. The complex disease network showed that diseases belonging to the same disease class do not always share common disease genes. A possible explanation could be that the variants with higher minor allele frequency and larger effect size identified using GWAs constitute disjoint parts of the allelic spectra of similar complex diseases. The complex disease gene network showed high modularity with the size of the largest component being smaller than expected from a randomized null-model. This is consistent with limited sharing of genes between diseases. Complex disease genes are less central than the essential and monogenic disease genes in the human interactome. Genes associated with the same disease, compared to genes associated with different diseases, more often tend to share a protein-protein interaction and a Gene Ontology Biological Process.

Conclusions

This indicates that network neighbors of known disease genes form an important class of candidates for identifying novel genes for the same disease.

Bridging the gap between systems biology and medicine

Systems biology has matured considerably as a discipline over the last decade, yet some of the key challenges separating current research efforts in systems biology and clinically useful results are only now becoming apparent. As these gaps are better defined, the new discipline of systems medicine is emerging as a translational extension of systems biology. How is systems medicine defined? What are relevant ontologies for systems medicine? What are the key theoretic and methodologic challenges facing computational disease modeling? How are inaccurate and incomplete data, and uncertain biologic knowledge best synthesized in useful computational models? Does network analysis provide clinically useful insight? We discuss the outstanding difficulties in translating a rapidly growing body of data into knowledge usable at the bedside. Although core-specific challenges are best met by specialized groups, it appears fundamental that such efforts should be guided by a roadmap for systems medicine drafted by a coalition of scientists from the clinical, experimental, computational, and theoretic domains.

Computational methods for discovering gene networks from expression data

Designing and conducting experiments are routine practices for modern biologists. The real challenge, especially in the post-genome era, usually comes not from acquiring data, but from subsequent activities such as data processing, analysis, knowledge generation and gaining insight into the research question of interest. The approach of inferring gene regulatory networks (GRNs) has been flourishing for many years, and new methods from mathematics, information science, engineering and social sciences have been applied. We review different kinds of computational methods biologists use to infer networks of varying levels of accuracy and complexity. The primary concern of biologists is how to translate the inferred network into hypotheses that can be tested with real-life experiments. Taking the biologists' viewpoint, we scrutinized several methods for predicting GRNs in mammalian cells, and more importantly show how the power of different knowledge databases of different types can be used to identify modules and subnetworks, thereby reducing complexity and facilitating the generation of testable hypotheses.

Can modular analysis identify disease-associated candidate genes for therapeutics?

Complex diseases such as allergy change gene expression in several cell types and tissues. Benson and colleagues have now shown, in a paper in BMC Systems Biology, that this complexity can be studied effectively using an integrated experimental and computational modular analysis. Their strategy revealed a core of allergy-associated genes of potential therapeutic value.