Transcriptomic analysis identifies dysregulated pathways and therapeutic targets in PMM2-CDG.

PMM2-CDG (MIM # 212065), the most common congenital disorder of glycosylation, is caused by the deficiency of phosphomannomutase 2 (PMM2). It is a multisystemic disease of variable severity that particularly affects the nervous system; however, its molecular pathophysiology remains poorly understood. Currently, there is no effective treatment. We performed an RNA-seq based transcriptomic study using patient-derived fibroblasts to gain insight into the mechanisms underlying the clinical symptomatology and to identify druggable targets. Systems biology methods were used to identify cellular pathways potentially affected by PMM2 deficiency, including Senescence, Bone regulation, Cell adhesion and Extracellular Matrix (ECM) and Response to cytokines. Functional validation assays using patients' fibroblasts revealed defects related to cell proliferation, cell cycle, the composition of the ECM and cell migration, and showed a potential role of the inflammatory response in the pathophysiology of the disease. Furthermore, treatment with a previously described pharmacological chaperone reverted the differential expression of some of the dysregulated genes. The results presented from transcriptomic data might serve as a platform for identifying therapeutic targets for PMM2-CDG, as well as for monitoring the effectiveness of therapeutic strategies, including pharmacological candidates and mannose-1-P, drug repurposing.

Human Neutrophils Couple Nitric Oxide Production and Extracellular Traps Formation in Allergic Asthma.

RATIONALE: Nitric oxide (NO) is elevated in the airways and serum of allergic asthmatic patients, suggesting an important role in asthma. NO production has been widely attributed to the canonical inducible nitric oxide synthase (iNOS). Much effort has been made to inhibit this enzyme with two outcomes: no asthma improvement; and partial NO reduction, suggesting the involvement of an iNOS-independent source. OBJECTIVES: Neutrophils produce NO under inflammatory conditions and their role in asthma has been overlooked. The present study analyzes their possible role as source of NO. METHODS: Our hypothesis was tested in 99 allergic patients with intermittent bronchial asthma and 26 healthy donors. NO production by blood and sputum neutrophils in response to allergens, anti-IgE, and anti-IgE receptors Abs was assessed by Griess, flow cytometry and confocal microscopy. Extracellular traps (ETs) formation, as a possible consequence of NO production, was quantified by western blot and confocal microscopy, and reactive oxygen species by luminol-enhanced chemiluminescence. RESULTS: Among blood and sputum granulocytes from allergic asthmatic patients, only neutrophils, produce NO by an IgE-dependent mechanism. This production is independent of NOS, but dependent on a reaction between L-arginine and reactive oxygen species from NOX2. NO and ETosis are induced in parallel, and NO amplifies ETs formation, which is a key mediator in asthma. CONCLUSIONS: Our findings reveal a novel role of neutrophils as the unique allergen/IgE-dependent NO source in allergic asthma enhancing ETs formation. These results suggest that NO produced by neutrophils needs further consideration in the treatment of allergic asthma.

Exploring miRNA-target gene pair detection in disease with coRmiT.

A wide range of approaches can be used to detect micro RNA (miRNA)-target gene pairs (mTPs) from expression data, differing in the ways the gene and miRNA expression profiles are calculated, combined and correlated. However, there is no clear consensus on which is the best approach across all datasets. Here, we have implemented multiple strategies and applied them to three distinct rare disease datasets that comprise smallRNA-Seq and RNA-Seq data obtained from the same samples, obtaining mTPs related to the disease pathology. All datasets were preprocessed using a standardized, freely available computational workflow, DEG_workflow. This workflow includes coRmiT, a method to compare multiple strategies for mTP detection. We used it to investigate the overlap of the detected mTPs with predicted and validated mTPs from 11 different databases. Results show that there is no clear best strategy for mTP detection applicable to all situations. We therefore propose the integration of the results of the different strategies by selecting the one with the highest odds ratio for each miRNA, as the optimal way to integrate the results. We applied this selection-integration method to the datasets and showed it to be robust to changes in the predicted and validated mTP databases. Our findings have important implications for miRNA analysis. coRmiT is implemented as part of the ExpHunterSuite Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite.

Genetic signature detected in T cell receptors from patients with severe COVID-19.

Characterization of host genetic factors contributing to COVID-19 severity promises advances on drug discovery to fight the disease. Most genetic analyses to date have identified genome-wide significant associations involving loss-of-function variants for immune response pathways. Despite accumulating evidence supporting a role for T cells in COVID-19 severity, no definitive genetic markers have been found to support an involvement of T cell responses. We analyzed 205 whole exomes from both a well-characterized cohort of hospitalized severe COVID-19 patients and controls. Significantly enriched high impact alleles were found for 25 variants within the T cell receptor beta (TRB) locus on chromosome 7. Although most of these alleles were found in heterozygosis, at least three or more in TRBV6-5, TRBV7-3, TRBV7-6, TRBV7-7, and TRBV10-1 suggested a possible TRB loss of function via compound heterozygosis. This loss-of-function in TRB genes supports suboptimal or dysfunctional T cell responses as a major contributor to severe COVID-19 pathogenesis.

Integrating differential expression, co-expression and gene network analysis for the identification of common genes associated with tumor angiogenesis deregulation.

Angiogenesis is essential for tumor growth and cancer metastasis. Identifying the molecular pathways involved in this process is the first step in the rational design of new therapeutic strategies to improve cancer treatment. In recent years, RNA-seq data analysis has helped to determine the genetic and molecular factors associated with different types of cancer. In this work we performed integrative analysis using RNA-seq data from human umbilical vein endothelial cells (HUVEC) and patients with angiogenesis-dependent diseases to find genes that serve as potential candidates to improve the prognosis of tumor angiogenesis deregulation and understand how this process is orchestrated at the genetic and molecular level. We downloaded four RNA-seq datasets (including cellular models of tumor angiogenesis and ischaemic heart disease) from the Sequence Read Archive. Our integrative analysis includes a first step to determine differentially and co-expressed genes. For this, we used the ExpHunter Suite, an R package that performs differential expression, co-expression and functional analysis of RNA-seq data. We used both differentially and co-expressed genes to explore the human gene interaction network and determine which genes were found in the different datasets that may be key for the angiogenesis deregulation. Finally, we performed drug repositioning analysis to find potential targets related to angiogenesis inhibition. We found that that among the transcriptional alterations identified, SEMA3D and IL33 genes are deregulated in all datasets. Microenvironment remodeling, cell cycle, lipid metabolism and vesicular transport are the main molecular pathways affected. In addition to this, interacting genes are involved in intracellular signaling pathways, especially in immune system and semaphorins, respiratory electron transport and fatty acid metabolism. The methodology presented here can be used for finding common transcriptional alterations in other genetically-based diseases.

Regulation and directed inhibition of ECP production by human neutrophils.

Background: Neutrophils are involved in the pathophysiology of allergic asthma, where the Eosinophil Cationic Protein (ECP) is a critical inflammatory mediator. Although ECP production is attributed to eosinophils, we reported that ECP is also present in neutrophils from allergic patients where, in contrast to eosinophils, it is produced in an IgE-dependent manner. Given the key role of ECP in asthma, we investigated the molecular mechanisms involved in ECP production as well as the effects induced by agonists and widely used clinical approaches. We also analyzed the correlation between ECP production and lung function. Methods: Neutrophils from allergic asthmatic patients were challenged with allergens, alone or in combination with cytokines, in the presence of cell-signaling inhibitors and clinical drugs. We analyzed ECP levels by ELISA and confocal microscopy. Lung function was assessed by spirometry. Results: IgE-mediated ECP release is dependent on phosphoinositide 3-kinase, the extracellular signal-regulated kinase (ERK1/2) and the production of reactive oxygen species by NADPH-oxidase. Calcineurin phosphatase and the transcription factor NFAT are also involved. ECP release is enhanced by the cytokines interleukin (IL)-5 and granulocyte macrophage-colony stimulating factor, and inhibited by interferon-γ, IL-10, clinical drugs (formoterol, tiotropium and budesonide) and allergen-specific IT. We also found an inverse correlation between asthma severity and ECP levels. Conclusions: Our results suggest the molecular pathways involved in ECP production and potential therapeutic targets. We also provide a new method to evaluate disease severity in asthmatic patients based on the quantification of in vitro ECP production by peripheral neutrophils.

Assigning protein function from domain-function associations using DomFun.

BACKGROUND: Protein function prediction remains a key challenge. Domain composition affects protein function. Here we present DomFun, a Ruby gem that uses associations between protein domains and functions, calculated using multiple indices based on tripartite network analysis. These domain-function associations are combined at the protein level, to generate protein-function predictions. RESULTS: We analysed 16 tripartite networks connecting homologous superfamily and FunFam domains from CATH-Gene3D with functional annotations from the three Gene Ontology (GO) sub-ontologies, KEGG, and Reactome. We validated the results using the CAFA 3 benchmark platform for GO annotation, finding that out of the multiple association metrics and domain datasets tested, Simpson index for FunFam domain-function associations combined with Stouffer's method leads to the best performance in almost all scenarios. We also found that using FunFams led to better performance than superfamilies, and better results were found for GO molecular function compared to GO biological process terms. DomFun performed as well as the highest-performing method in certain CAFA 3 evaluation procedures in terms of [Formula: see text] and [Formula: see text] We also implemented our own benchmark procedure, Pathway Prediction Performance (PPP), which can be used to validate function prediction for additional annotations sources, such as KEGG and Reactome. Using PPP, we found similar results to those found with CAFA 3 for GO, moreover we found good performance for the other annotation sources. As with CAFA 3, Simpson index with Stouffer's method led to the top performance in almost all scenarios. CONCLUSIONS: DomFun shows competitive performance with other methods evaluated in CAFA 3 when predicting proteins function with GO, although results vary depending on the evaluation procedure. Through our own benchmark procedure, PPP, we have shown it can also make accurate predictions for KEGG and Reactome. It performs best when using FunFams, combining Simpson index derived domain-function associations using Stouffer's method. The tool has been implemented so that it can be easily adapted to incorporate other protein features, such as domain data from other sources, amino acid k-mers and motifs. The DomFun Ruby gem is available from https://rubygems.org/gems/DomFun . Code maintained at https://github.com/ElenaRojano/DomFun . Validation procedure scripts can be found at https://github.com/ElenaRojano/DomFun_project .

Evaluating, Filtering and Clustering Genetic Disease Cohorts Based on Human Phenotype Ontology Data with Cohort Analyzer.

Exhaustive and comprehensive analysis of pathological traits is essential to understanding genetic diseases, performing precise diagnosis and prescribing personalized treatments. It is particularly important for disease cohorts, as thoroughly detailed phenotypic profiles allow patients to be compared and contrasted. However, many disease cohorts contain patients that have been ascribed low numbers of very general and relatively uninformative phenotypes. We present Cohort Analyzer, a tool that measures the phenotyping quality of patient cohorts. It calculates multiple statistics to give a general overview of the cohort status in terms of the depth and breadth of phenotyping, allowing us to detect less well-phenotyped patients for re-examining or excluding from further analyses. In addition, it performs clustering analysis to find subgroups of patients that share similar phenotypic profiles. We used it to analyse three cohorts of genetic diseases patients with very different properties. We found that cohorts with the most specific and complete phenotypic characterization give more potential insights into the disease than those that were less deeply characterised by forming more informative clusters. For two of the cohorts, we also analysed genomic data related to the patients, and linked the genomic data to the patient-subgroups by mapping shared variants to genes and functions. The work highlights the need for improved phenotyping in this era of personalized medicine. The tool itself is freely available alongside a workflow to allow the analyses shown in this work to be applied to other datasets.

Gene expression analysis method integration and co-expression module detection applied to rare glucide metabolism disorders using ExpHunterSuite.

High-throughput gene expression analysis is widely used. However, analysis is not straightforward. Multiple approaches should be applied and methods to combine their results implemented and investigated. We present methodology for the comprehensive analysis of expression data, including co-expression module detection and result integration via data-fusion, threshold based methods, and a Naïve Bayes classifier trained on simulated data. Application to rare-disease model datasets confirms existing knowledge related to immune cell infiltration and suggest novel hypotheses including the role of calcium channels. Application to simulated and spike-in experiments shows that combining multiple methods using consensus and classifiers leads to optimal results. ExpHunter Suite is implemented as an R/Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite . It can be applied to model and non-model organisms and can be run modularly in R; it can also be run from the command line, allowing scalability with large datasets. Code and reports for the studies are available from https://github.com/fmjabato/ExpHunterSuiteExamples .

Genetic Variants in Cytosolic Phospholipase A2 Associated With Nonsteroidal Anti-Inflammatory Drug-Induced Acute Urticaria/Angioedema.

Nonsteroidal anti-inflammatory drugs (NSAIDs) are among the main triggers of drug hypersensitivity reactions, probably due to their high consumption worldwide. The most frequent type of NSAID hypersensitivity is NSAID cross-hypersensitivity, in which patients react to NSAIDs from different chemical groups in the absence of a specific immunological response. The underlying mechanism of NSAID cross-hypersensitivity has been linked to cyclooxygenase (COX)-1 inhibition causing an imbalance in the arachidonic acid pathway. Despite NSAID-induced acute urticaria/angioedema (NIUA) being the most frequent clinical phenotype, most studies have focused on NSAID-exacerbated respiratory disease. As NSAID cross-hypersensitivity reactions are idiosyncratic, only appearing in some subjects, it is believed that individual susceptibility is under the influence of genetic factors. Although associations with polymorphisms in genes from the AA pathway have been described, no previous study has evaluated the potential role of cytosolic phospholipase A2 (cPLA2) variants. This enzyme catalyzes the initial hydrolysis of membrane phospholipids to release AA, which can be subsequently metabolized into eicosanoids. Here, we analyzed for the first time the overall genetic variation in the cPLA2 gene (PLA2G4A) in NIUA patients. For this purpose, a set of tagging single nucleotide polymorphisms (tagSNPs) in PLA2G4A were selected using data from Europeans subjects in the 1,000 Genomes Project, and genotyped with the iPlex Sequenom MassArray technology. Two independent populations, each comprising NIUA patients and NSAID-tolerant controls, were recruited in Spain, for the purposes of discovery and replication, comprising a total of 1,128 individuals. Fifty-eight tagSNPs were successfully genotyped in the discovery cohort, of which four were significantly associated with NIUA after Bonferroni correction (rs2049963, rs2064471, rs12088010, and rs12746200). These polymorphisms were then genotyped in the replication cohort: rs2049963 was associated with increased risk for NIUA after Bonferroni correction under the dominant and additive models, whereas rs12088010 and rs12746200 were protective under these two inheritance models. Our results suggest a role for PLA2G4A polymorphisms in NIUA. However, further studies are required to replicate our findings, elucidate the mechanistic role, and evaluate the participation of PLA2G4A variants in other phenotypes induced by NSAID cross-hypersensitivity.

Decoding Neuromuscular Disorders Using Phenotypic Clusters Obtained From Co-Occurrence Networks.

Neuromuscular disorders (NMDs) represent an important subset of rare diseases associated with elevated morbidity and mortality whose diagnosis can take years. Here we present a novel approach using systems biology to produce functionally-coherent phenotype clusters that provide insight into the cellular functions and phenotypic patterns underlying NMDs, using the Human Phenotype Ontology as a common framework. Gene and phenotype information was obtained for 424 NMDs in OMIM and 126 NMDs in Orphanet, and 335 and 216 phenotypes were identified as typical for NMDs, respectively. 'Elevated serum creatine kinase' was the most specific to NMDs, in agreement with the clinical test of elevated serum creatinine kinase that is conducted on NMD patients. The approach to obtain co-occurring NMD phenotypes was validated based on co-mention in PubMed abstracts. A total of 231 (OMIM) and 150 (Orphanet) clusters of highly connected co-occurrent NMD phenotypes were obtained. In parallel, a tripartite network based on phenotypes, diseases and genes was used to associate NMD phenotypes with functions, an approach also validated by literature co-mention, with KEGG pathways showing proportionally higher overlap than Gene Ontology and Reactome. Phenotype-function pairs were crossed with the co-occurrent NMD phenotype clusters to obtain 40 (OMIM) and 72 (Orphanet) functionally coherent phenotype clusters. As expected, many of these overlapped with known diseases and confirmed existing knowledge. Other clusters revealed interesting new findings, indicating informative phenotypes for differential diagnosis, providing deeper knowledge of NMDs, and pointing towards specific cell dysfunction caused by pleiotropic genes. This work is an example of reproducible research that i) can help better understand NMDs and support their diagnosis by providing a new tool that exploits existing information to obtain novel clusters of functionally-related phenotypes, and ii) takes us another step towards personalised medicine for NMDs.

Systematic identification of genetic systems associated with phenotypes in patients with rare genomic copy number variations.

Copy number variation (CNV) related disorders tend to show complex phenotypic profiles that do not match known diseases. This makes it difficult to ascertain their underlying molecular basis. A potential solution is to compare the affected genomic regions for multiple patients that share a pathological phenotype, looking for commonalities. Here, we present a novel approach to associate phenotypes with functional systems, in terms of GO categories and KEGG and Reactome pathways, based on patient data. The approach uses genomic and phenomic data from the same patients, finding shared genomic regions between patients with similar phenotypes. These regions are mapped to genes to find associated functional systems. We applied the approach to analyse patients in the DECIPHER database with de novo CNVs, finding functional systems associated with most phenotypes, often due to mutations affecting related genes in the same genomic region. Manual inspection of the ten top-scoring phenotypes found multiple FunSys connections supported by the previous studies for seven of them. The workflow also produces reports focussed on the genes and FunSys connected to the different phenotypes, alongside patient-specific reports, which give details of the associated genes and FunSys for each individual in the cohort. These can be run in "confidential" mode, preserving patient confidentiality. The workflow presented here can be used to associate phenotypes with functional systems using data at the level of a whole cohort of patients, identifying important connections that could not be found when considering them individually. The full workflow is available for download, enabling it to be run on any patient cohort for which phenotypic and CNV data are available.

An improved de novo assembling and polishing of Solea senegalensis transcriptome shed light on retinoic acid signalling in larvae.

Senegalese sole is an economically important flatfish species in aquaculture and an attractive model to decipher the molecular mechanisms governing the severe transformations occurring during metamorphosis, where retinoic acid seems to play a key role in tissue remodeling. In this study, a robust sole transcriptome was envisaged by reducing the number of assembled libraries (27 out of 111 available), fine-tuning a new automated and reproducible set of workflows for de novo assembling based on several assemblers, and removing low confidence transcripts after mapping onto a sole female genome draft. From a total of 96 resulting assemblies, two "raw" transcriptomes, one containing only Illumina reads and another with Illumina and GS-FLX reads, were selected to provide SOLSEv5.0, the most informative transcriptome with low redundancy and devoid of most single-exon transcripts. It included both Illumina and GS-FLX reads and consisted of 51,348 transcripts of which 22,684 code for 17,429 different proteins described in databases, where 9527 were predicted as complete proteins. SOLSEv5.0 was used as reference for the study of retinoic acid (RA) signalling in sole larvae using drug treatments (DEAB, a RA synthesis blocker, and TTNPB, a RA-receptor agonist) for 24 and 48 h. Differential expression and functional interpretation were facilitated by an updated version of DEGenes Hunter. Acute exposure of both drugs triggered an intense, specific and transient response at 24 h but with hardly observable differences after 48 h at least in the DEAB treatments. Activation of RA signalling by TTNPB specifically increased the expression of genes in pathways related to RA degradation, retinol storage, carotenoid metabolism, homeostatic response and visual cycle, and also modified the expression of transcripts related to morphogenesis and collagen fibril organisation. In contrast, DEAB mainly decreased genes related to retinal production, impairing phototransduction signalling in the retina. A total of 755 transcripts mainly related to lipid metabolism, lipid transport and lipid homeostasis were altered in response to both treatments, indicating non-specific drug responses associated with intestinal absorption. These results indicate that a new assembling and transcript sieving were both necessary to provide a reliable transcriptome to identify the many aspects of RA action during sole development that are of relevance for sole aquaculture.

Phenotype-genotype comorbidity analysis of patients with rare disorders provides insight into their pathological and molecular bases.

Genetic and molecular analysis of rare disease is made difficult by the small numbers of affected patients. Phenotypic comorbidity analysis can help rectify this by combining information from individuals with similar phenotypes and looking for overlap in terms of shared genes and underlying functional systems. However, few studies have combined comorbidity analysis with genomic data. We present a computational approach that connects patient phenotypes based on phenotypic co-occurence and uses genomic information related to the patient mutations to assign genes to the phenotypes, which are used to detect enriched functional systems. These phenotypes are clustered using network analysis to obtain functionally coherent phenotype clusters. We applied the approach to the DECIPHER database, containing phenotypic and genomic information for thousands of patients with heterogeneous rare disorders and copy number variants. Validity was demonstrated through overlap with known diseases, co-mention within the biomedical literature, semantic similarity measures, and patient cluster membership. These connected pairs formed multiple phenotype clusters, showing functional coherence, and mapped to genes and systems involved in similar pathological processes. Examples include claudin genes from the 22q11 genomic region associated with a cluster of phenotypes related to DiGeorge syndrome and genes related to the GO term anterior/posterior pattern specification associated with abnormal development. The clusters generated can help with the diagnosis of rare diseases, by suggesting additional phenotypes for a given patient and potential underlying functional systems. Other tools to find causal genes based on phenotype were also investigated. The approach has been implemented as a workflow, named PhenCo, which can be adapted to any set of patients for which phenomic and genomic data is available. Full details of the analysis, including the clusters formed, their constituent functional systems and underlying genes are given. Code to implement the workflow is available from GitHub.

Author Correction: Glycosylated nanostructures in sublingual immunotherapy induce long-lasting tolerance in LTP allergy mouse model.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Biofilm formation displays intrinsic offensive and defensive features of Bacillus cereus.

Biofilm formation is a strategy of many bacterial species to adapt to a variety of stresses and has become a part of infections, contaminations, or beneficial interactions. In this study, we demonstrate that profound physiological changes permit Bacillus cereus to switch from a floating to a sessile lifestyle, to undergo further maturation of the biofilm and to differentiate into the offensive or defensive features. We report that floating and biofilm cells are populations that differentiate metabolically, with members of each subpopulation developing different branches of certain metabolic pathways. Secondly, biofilm populations rearrange nucleotides, sugars, amino acids, and energy metabolism. Thirdly, this metabolic rearrangement coexists with: the synthesis of the extracellular matrix, sporulation, reinforcement of the cell wall, activation of the ROS detoxification machinery and production of secondary metabolites. This strategy contributes to defend biofilm cells from competitors. However, floating cells maintain a fermentative metabolic status that ensures a higher aggressiveness against hosts, evidenced by the production of toxins. The maintenance of the two distinct subpopulations is an effective strategy to face different environmental conditions found in the life styles of B. cereus.

Glycosylated nanostructures in sublingual immunotherapy induce long-lasting tolerance in LTP allergy mouse model.

An effective specific immunotherapy should contain elements to generate specific recognition (T-cell peptides) and to modulate the immunological response towards a Th1/Treg pattern by enhancing dendritic cells (DCs). We propose a novel sublingual immunotherapy for peach allergy, using systems, that combine Prup3-T-cell peptides with mannose dendrons (D1ManPrup3 and D4ManPrup3). Peach anaphylactic mice were treated 1, 2 and 5 nM concentrations. Tolerance was assessed one/five weeks after finishing treatment by determining in vivo/in vitro parameters after challenge with Prup3. Only mice receiving D1ManPrup3 at 2 nM were protected from anaphylaxis (no temperature changes, decrease in Prup3-sIgE and -sIgG1 antibody levels, and secreting cells) compared to PBS-treated mice. Moreover, an increase of Treg-cells and regulatory cytokines (IL-10+/IFN-γ+) in CD4+-T-cells and DCs were found. These changes were maintained at least five weeks after stopping treatment. D1ManPrup3 is an effective new approach of immunotherapy inducing protection from anaphylaxis which persists after finishing treatment.

Eicosanoid mediator profiles in different phenotypes of nonsteroidal anti-inflammatory drug-induced urticaria.

BACKGROUND: The role of arachidonic acid metabolites in NSAID-induced hypersensitivity has been studied in depth for NSAID-exacerbated respiratory disease (NERD) and NSAID-exacerbated cutaneous disease (NECD). However, no information is available for NSAID-induced urticarial/angioedema (NIUA), despite it being the most frequent clinical entity induced by NSAID hypersensitivity. We evaluated changes in leukotriene and prostaglandin metabolites for NIUA patients, using patients with NECD and single-NSAID-induced urticaria/angioedema or anaphylaxis (SNIUAA) for comparison. METHODS: Urine samples were taken from patients with confirmed NSAID-induced urticaria and healthy controls, at baseline and at various time intervals after ASA administration. Eicosanoid measurement was performed using high-performance liquid chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry. RESULTS: No differences were found between groups at baseline. Following ASA administration, LTE4 and 9α,11ß-PGF2 levels were increased in both NIUA and NECD patients compared to baseline, rising initially, before decreasing toward initial levels. In addition, the levels of these metabolites were higher in NIUA and NECD when compared with the SNIUAA and control groups after ASA administration. No changes were found with respect to baseline values for SNIUAA and control groups. CONCLUSIONS: We present for the first time data regarding the role of COX-1 inhibition in NIUA. Patients with this entity show a similar pattern eicosanoid levels following ASA challenge to those with NECD. Further studies will help ascertain the cell populations involved and the underlying molecular mechanisms.

Regulatory variants: from detection to predicting impact.

Variants within non-coding genomic regions can greatly affect disease. In recent years, increasing focus has been given to these variants, and how they can alter regulatory elements, such as enhancers, transcription factor binding sites and DNA methylation regions. Such variants can be considered regulatory variants. Concurrently, much effort has been put into establishing international consortia to undertake large projects aimed at discovering regulatory elements in different tissues, cell lines and organisms, and probing the effects of genetic variants on regulation by measuring gene expression. Here, we describe methods and techniques for discovering disease-associated non-coding variants using sequencing technologies. We then explain the computational procedures that can be used for annotating these variants using the information from the aforementioned projects, and prediction of their putative effects, including potential pathogenicity, based on rule-based and machine learning approaches. We provide the details of techniques to validate these predictions, by mapping chromatin-chromatin and chromatin-protein interactions, and introduce Clustered Regularly Interspaced Short Palindromic Repeats-Associated Protein 9 (CRISPR-Cas9) technology, which has already been used in this field and is likely to have a big impact on its future evolution. We also give examples of regulatory variants associated with multiple complex diseases. This review is aimed at bioinformaticians interested in the characterization of regulatory variants, molecular biologists and geneticists interested in understanding more about the nature and potential role of such variants from a functional point of views, and clinicians who may wish to learn about variants in non-coding genomic regions associated with a given disease and find out what to do next to uncover how they impact on the underlying mechanisms.

Polymorphisms in CEP68 gene associated with risk of immediate selective reactions to non-steroidal anti-inflammatory drugs.

Non-steroidal anti-inflammatory drugs (NSAIDs) are the main triggers of drug hypersensitivity reactions. Such reactions can be pharmacologically or immunologically mediated, but in both cases individual susceptibility can be influenced by genetic factors. Polymorphisms in centrosomal protein of 68 kDa (CEP68) have been associated with pharmacologically mediated NSAIDs reactions. Here, we evaluated this gene in immunologically mediated single-NSAID-induced urticaria/angioedema or anaphylaxis (SNIUAA) by analyzing 52 single nucleotide polymorphisms in CEP68 in 176 patients and 363 NSAIDs-tolerant controls. Two intronic variants (rs2241160 and rs2241161) were significantly associated with an increased risk of SNIUAA, suggesting CEP68 to be a key player in both types of NSAIDs hypersensitivity. However, we found no overlap with genetic variants previously associated with pharmacologically mediated hypersensitivity, pointing to a complex role for this gene and its potential use in the development of biomarkers of clinical utility to diagnose patients at risk of these reactions and to differentiate entities.