Transcript and protein signatures derived from shared molecular interactions across cancers are associated with mortality.

BACKGROUND: Characterization of shared cancer mechanisms have been proposed to improve therapy strategies and prognosis. Here, we aimed to identify shared cell-cell interactions (CCIs) within the tumor microenvironment across multiple solid cancers and assess their association with cancer mortality. METHODS: CCIs of each cancer were identified by NicheNet analysis of single-cell RNA sequencing data from breast, colon, liver, lung, and ovarian cancers. These CCIs were used to construct a shared multi-cellular tumor model (shared-MCTM) representing common CCIs across cancers. A gene signature was identified from the shared-MCTM and tested on the mRNA and protein level in two large independent cohorts: The Cancer Genome Atlas (TCGA, 9185 tumor samples and 727 controls across 22 cancers) and UK biobank (UKBB, 10,384 cancer patients and 5063 controls with proteomics data across 17 cancers). Cox proportional hazards models were used to evaluate the association of the signature with 10-year all-cause mortality, including sex-specific analysis. RESULTS: A shared-MCTM was derived from five individual cancers. A shared gene signature was extracted from this shared-MCTM and the most prominent regulatory cell type, matrix cancer-associated fibroblast (mCAF). The signature exhibited significant expression changes in multiple cancers compared to controls at both mRNA and protein levels in two independent cohorts. Importantly, it was significantly associated with mortality in cancer patients in both cohorts. The highest hazard ratios were observed for brain cancer in TCGA (HR [95%CI] = 6.90[4.64-10.25]) and ovarian cancer in UKBB (5.53[2.08-8.80]). Sex-specific analysis revealed distinct risks, with a higher mortality risk associated with the protein signature score in males (2.41[1.97-2.96]) compared to females (1.84[1.44-2.37]). CONCLUSION: We identified a gene signature from a comprehensive shared-MCTM representing common CCIs across different cancers and revealed the regulatory role of mCAF in the tumor microenvironment. The pathogenic relevance of the gene signature was supported by differential expression and association with mortality on both mRNA and protein levels in two independent cohorts.

Digital Twins for Predictive, Preventive Personalized, and Participatory Treatment of Immune-Mediated Diseases.

Digital twins are computational models of complex systems, which aim to understand and optimize those systems more effectively than would be possible in real life. Ideally, digital twins can be translated to individual patients, to characterize and computationally treat their diseases with thousands of drugs, to select the drug or drugs that cure the patients. The background problem is that many patients do not respond adequately to drug treatment. This problem reflects a wide gap between the complexity of diseases and clinical practice. Each disease may involve altered interactions between thousands of genes that vary between different cell types in different organs. To our knowledge, these altered interactions have not been characterized on a genome-, cellulome-, and organ-wide scale in any disease. Thus, clinical translation of the digital twin ideal for predictive, preventive, personalized and participatory treatment involves formidable challenges, which are close to the limits of, or beyond today's technologies. Here, I discuss recent developments and challenges in relation to that ideal focusing on immune-mediated inflammatory diseases, as well as examples from other diseases.

TET2 as a tumor suppressor and therapeutic target in T-cell acute lymphoblastic leukemia.

Pediatric T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive malignancy resulting from overproduction of immature T-cells in the thymus and is typified by widespread alterations in DNA methylation. As survival rates for relapsed T-ALL remain dismal (10 to 25%), development of targeted therapies to prevent relapse is key to improving prognosis. Whereas mutations in the DNA demethylating enzyme TET2 are frequent in adult T-cell malignancies, TET2 mutations in T-ALL are rare. Here, we analyzed RNA-sequencing data of 321 primary T-ALLs, 20 T-ALL cell lines, and 25 normal human tissues, revealing that TET2 is transcriptionally repressed or silenced in 71% and 17% of T-ALL, respectively. Furthermore, we show that TET2 silencing is often associated with hypermethylation of the TET2 promoter in primary T-ALL. Importantly, treatment with the DNA demethylating agent, 5-azacytidine (5-aza), was significantly more toxic to TET2-silenced T-ALL cells and resulted in stable re-expression of the TET2 gene. Additionally, 5-aza led to up-regulation of methylated genes and human endogenous retroviruses (HERVs), which was further enhanced by the addition of physiological levels of vitamin C, a potent enhancer of TET activity. Together, our results clearly identify 5-aza as a potential targeted therapy for TET2-silenced T-ALL.

A reassessment of DNA-immunoprecipitation-based genomic profiling.

DNA immunoprecipitation followed by sequencing (DIP-seq) is a common enrichment method for profiling DNA modifications in mammalian genomes. However, the results of independent DIP-seq studies often show considerable variation between profiles of the same genome and between profiles obtained by alternative methods. Here we show that these differences are primarily due to the intrinsic affinity of IgG for short unmodified DNA repeats. This pervasive experimental error accounts for 50-99% of regions identified as 'enriched' for DNA modifications in DIP-seq data. Correction of this error profoundly altered DNA-modification profiles for numerous cell types, including mouse embryonic stem cells, and subsequently revealed novel associations among DNA modifications, chromatin modifications and biological processes. We conclude that both matched input and IgG controls are essential in order for the results of DIP-based assays to be interpreted correctly, and that complementary, non-antibody-based techniques should be used to validate DIP-based findings to avoid further misinterpretation of genome-wide profiling data.

Bulk and single cell transcriptomic data indicate that a dichotomy between inflammatory pathways in peripheral blood and arthritic joints complicates biomarker discovery.

BACKGROUND: Unbiased studies using different genome-wide methods have identified several novel biomarkers for diagnosis and treatment response in Rheumatoid Arthritis (RA). However, clinical translation has proven difficult. Here, we hypothesized that one reason could be that inflammatory responses in peripheral blood are different from those in the arthritic joint. METHODS: We performed meta-analysis of gene expression microarray data from synovium, whole blood cells (WBC), peripheral blood mononuclear cells (PBMC), and CD4+ T cells from patients with RA and healthy controls in order to identify overlapping pathways, upstream regulators and potential biomarkers. We also analyzed single cell RNA-sequencing (scRNA-seq) data from peripheral blood and whole joints from a mouse model of antigen-induced arthritis. RESULTS: Analyses of two profiling data sets from synovium from RA patients and healthy controls all showed significant activation of pathways with known pathogenic relevance, such as the Th1 pathway, the role of NFAT in regulation of the immune response, dendritic cell maturation, iCOS-iCOSL signaling in T helper cells, Fcγ receptor-mediated phagocytosis, interferon signaling, Cdc42 signaling, and cytotoxic T lymphocyte-mediated apoptosis. The most activated upstream regulators included TNF, an important drug target, as well as IFN-gamma and CD40LG, all of which are known to play important pathogenic roles in RA. The differentially expressed genes from synovium included several potential biomarkers, such as CCL5, CCL13, CCL18, CX3CL1, CXCL6, CXCL9, CXCL10, CXCL13, IL15, IL32, IL1RN, SPP1, and TNFSF11. By contrast, microarray studies of WBC, PBMC and CD4+ T cells showed variable pathways and limited pathway overlap with synovium. Similarly, scRNA-seq data from a mouse model of arthritis did not support that inflammatory responses in peripheral blood reflect those in the arthritic joints. These data showed pathway overlap between mouse joint cells and synovium from patients with RA, but not with cells in peripheral blood. CONCLUSIONS: Our findings indicate a dichotomy between gene expression changes, pathways, upstream regulators and biomarkers in synovium and cell types in peripheral blood, which complicates identification of biomarkers in blood.

LASSIM-A network inference toolbox for genome-wide mechanistic modeling.

Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naïve Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases.

GAB2 regulates type 2 T helper cell differentiation in humans.

Th2 cell differentiation involves complex changes in expression of multiple genes, many of which have poorly characterized roles. In a gene expression microarray analysis of human primary CD4+ effector T subsets, we identified that an adaptor protein, GAB2, was preferentially expressed in human Th2 cells. The role of GAB2 in human Th2 cells is unknown. Through analysis of primary and in vitro differentiated human T effector subsets, we confirmed that human Th2 cells preferentially expressed GAB2. Further analysis of public gene expression microarray data of STAT6-knockdowned Th2 cells indicated that GAB2 expression was regulated by IL-4 and STAT6. Both siRNA knockdown and ectopic expression of GAB2 in activated T cells showed that GAB2 positively regulated IL-4 and IL-13 expression in human Th2 cells. We hence identified the adaptor protein, GAB2, as an important novel regulator of the human Th2 immune response.

DNA methylation changes separate allergic patients from healthy controls and may reflect altered CD4+ T-cell population structure.

Altered DNA methylation patterns in CD4(+) T-cells indicate the importance of epigenetic mechanisms in inflammatory diseases. However, the identification of these alterations is complicated by the heterogeneity of most inflammatory diseases. Seasonal allergic rhinitis (SAR) is an optimal disease model for the study of DNA methylation because of its well-defined phenotype and etiology. We generated genome-wide DNA methylation (N(patients) = 8, N(controls) = 8) and gene expression (N(patients) = 9, Ncontrols = 10) profiles of CD4(+) T-cells from SAR patients and healthy controls using Illumina's HumanMethylation450 and HT-12 microarrays, respectively. DNA methylation profiles clearly and robustly distinguished SAR patients from controls, during and outside the pollen season. In agreement with previously published studies, gene expression profiles of the same samples failed to separate patients and controls. Separation by methylation (N(patients) = 12, N(controls) = 12), but not by gene expression (N(patients) = 21, N(controls) = 21) was also observed in an in vitro model system in which purified PBMCs from patients and healthy controls were challenged with allergen. We observed changes in the proportions of memory T-cell populations between patients (N(patients) = 35) and controls (N(controls) = 12), which could explain the observed difference in DNA methylation. Our data highlight the potential of epigenomics in the stratification of immune disease and represents the first successful molecular classification of SAR using CD4(+) T cells.

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.

Roles of miRNAs in microcystin-LR-induced Sertoli cell toxicity.

Microcystin (MC)-LR, a cyclic heptapeptide, is a potent reproductive system toxin. To understand the molecular mechanisms of MC-induced reproductive system cytotoxicity, we evaluated global changes of miRNA and mRNA expression in mouse Sertoli cells following MC-LR treatment. Our results revealed that the exposure to MC-LR resulted in an altered miRNA expression profile that might be responsible for the modulation of mRNA expression. Bio-functional analysis indicated that the altered genes were involved in specific cellular processes, including cell death and proliferation. Target gene analysis suggested that junction injury in Sertoli cells exposed to MC-LR might be mediated by miRNAs through the regulation of the Sertoli cell-Sertoli cell pathway. Collectively, these findings may enhance our understanding on the modes of action of MC-LR on mouse Sertoli cells as well as the molecular mechanisms underlying the toxicity of MC-LR on the male reproductive system.

Efficient prediction of human protein-protein interactions at a global scale.

BACKGROUND: Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. RESULTS: On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. CONCLUSIONS: The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases.

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.

An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases.

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1,453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.

scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases.

BACKGROUND: Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. METHODS: Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. RESULTS: scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. CONCLUSIONS: We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

Multi-organ single-cell analysis reveals an on/off switch system with potential for personalized treatment of immunological diseases.

Prioritization of disease mechanisms, biomarkers, and drug targets in immune-mediated inflammatory diseases (IMIDs) is complicated by altered interactions between thousands of genes. Our multi-organ single-cell RNA sequencing of a mouse IMID model, namely collagen-induced arthritis, shows highly complex and heterogeneous expression changes in all analyzed organs, even though only joints showed signs of inflammation. We organized those into a multi-organ multicellular disease model, which shows predicted molecular interactions within and between organs. That model supports that inflammation is switched on or off by altered balance between pro- and anti-inflammatory upstream regulators (URs) and downstream pathways. Meta-analyses of human IMIDs show a similar, but graded, on/off switch system. This system has the potential to prioritize, diagnose, and treat optimal combinations of URs on the levels of IMIDs, subgroups, and individual patients. That potential is supported by UR analyses in more than 600 sera from patients with systemic lupus erythematosus.

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases.

Background: Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. Methods: Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. Results: scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. Conclusion: We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

A systematic comparison of genome-scale clustering algorithms.

BACKGROUND: A wealth of clustering algorithms has been applied to gene co-expression experiments. These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks (WGCNA) and paraclique. Comparison of these methods to evaluate their relative effectiveness provides guidance to algorithm selection, development and implementation. Most prior work on comparative clustering evaluation has focused on parametric methods. Graph theoretical methods are recent additions to the tool set for the global analysis and decomposition of microarray co-expression matrices that have not generally been included in earlier methodological comparisons. In the present study, a variety of parametric and graph theoretical clustering algorithms are compared using well-characterized transcriptomic data at a genome scale from Saccharomyces cerevisiae. METHODS: For each clustering method under study, a variety of parameters were tested. Jaccard similarity was used to measure each cluster's agreement with every GO and KEGG annotation set, and the highest Jaccard score was assigned to the cluster. Clusters were grouped into small, medium, and large bins, and the Jaccard score of the top five scoring clusters in each bin were averaged and reported as the best average top 5 (BAT5) score for the particular method. RESULTS: Clusters produced by each method were evaluated based upon the positive match to known pathways. This produces a readily interpretable ranking of the relative effectiveness of clustering on the genes. Methods were also tested to determine whether they were able to identify clusters consistent with those identified by other clustering methods. CONCLUSIONS: Validation of clusters against known gene classifications demonstrate that for this data, graph-based techniques outperform conventional clustering approaches, suggesting that further development and application of combinatorial strategies is warranted.

Combining gene expression microarray- and cluster analysis with sequence-based predictions to identify regulators of IL-13 in allergy.

The Th2 cytokine IL-13 plays a key role in allergy, by regulating IgE, airway hyper secretion, eosinophils and mast cells. In this study, we aimed to identify novel transcription factors (TFs) that potentially regulated IL-13. We analyzed Th2 polarized naïve T cells from four different blood donors with gene expression microarrays to find clusters of genes that were correlated or anti-correlated with IL13. These clusters were further filtered, by selecting genes that were functionally related. In these clusters, we identified three transcription factors (TFs) that were predicted to regulate the expression of IL13, namely CEBPB, E2F6 and AHR. siRNA mediated knockdowns of these TFs in naïve polarized T cells showed significant increases of IL13, following knockdown of CEBPB and E2F6, but not AHR. This suggested an inhibitory role of CEBPB and E2F6 in the regulation of IL13 and allergy. This was supported by analysis of E2F6, but not CEBPB, in allergen-challenged CD4+ T cells from six allergic patients and six healthy controls, which showed decreased expression of E2F6 in patients. In summary, our findings indicate an inhibitory role of E2F6 in the regulation of IL-13 and allergy. The analytical approach may be generally applicable to elucidate the complex regulatory patterns in Th2 cell polarization and allergy.

Diminished levels of nasal S100A7 (psoriasin) in seasonal allergic rhinitis: an effect mediated by Th2 cytokines.

BACKGROUND: S100A7 is an antimicrobial peptide involved in several inflammatory diseases. The aim of the present study was to explore the expression and regulation of S100A7 in seasonal allergic rhinitis (SAR). METHODS: Nasal lavage (NAL) fluid was obtained from healthy controls before and after lipopolysaccharide (LPS) provocation, from SAR patients before and after allergen challenge, and from SAR patients having completed allergen-specific immunotherapy (ASIT). Nasal biopsies, nasal epithelial cells and blood were acquired from healthy donors. The airway epithelial cell line FaDu was used for in vitro experiments. Real-time RT-PCR and immunohistochemistry were used to determine S100A7 expression in nasal tissue and cells. Release of S100A7 in NAL and culture supernatants was measured by ELISA. The function of recombinant S100A7 was explored in epithelial cells, neutrophils and peripheral blood mononuclear cells (PBMC). RESULTS: Nasal administration of LPS induced S100A7 release in healthy non-allergic subjects. The level of S100A7 was lower in NAL from SAR patients than from healthy controls, and it was further reduced in the SAR group 6 h post allergen provocation. In contrast, ASIT patients displayed higher levels after completed treatment. S100A7 was expressed in the nasal epithelium and in glands, and it was secreted by cultured epithelial cells. Stimulation with IL-4 and histamine repressed the epithelial S100A7 release. Further, recombinant S100A7 induced activation of neutrophils and PBMC. CONCLUSIONS: The present study shows an epithelial expression and excretion of S100A7 in the nose after microbial stimulation. The levels are diminished in rhinitis patients and in the presence of an allergic cytokine milieu, suggesting that the antimicrobial defense is compromised in patients with SAR.