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.

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

Rationale: Nitric oxide (NO) is increased in the airways and serum of patients with allergic asthma, suggesting an important role in asthma. NO production has been widely attributed to the canonical inducible NO synthase. Much effort has been made to inhibit this enzyme, with two outcomes: no asthma improvement and partial NO reduction, suggesting the involvement of an inducible NO synthase-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 sources 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 receptor antibodies was assessed by Griess reagent, flow cytometry, and confocal microscopy. The formation of extracellular traps (ETs) as a possible consequence of NO production was quantified by Western blot and confocal microscopy, and reactive oxygen species were assessed with luminol-enhanced chemiluminescence. Measurements and Main Results: Among blood and sputum granulocytes from patients with allergic asthma, only neutrophils produce NO by an IgE-dependent mechanism. This production is independent of NO synthase, but dependent on a reaction between L-arginine and reactive oxygen species from NOX2 (NADPH oxidase). NO and ETosis are induced in parallel, and NO amplifies ET 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 ET formation. These results suggest that NO produced by neutrophils needs further consideration in the treatment of allergic asthma.

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.

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.

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 .

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.

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.

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.

Copy number variation in ALOX5 and PTGER1 is associated with NSAIDs-induced urticaria and/or angioedema.

OBJECTIVE: Cross-intolerance to NSAIDs is a class of drug hypersensitivity reaction, of which NSAIDs-induced urticaria and/or angioedema (NIUA) are the most frequent clinical entities. They are considered to involve dysregulation of the arachidonic acid pathway; however, this mechanism has not been confirmed for NIUA. In this work, we assessed copy number variations (CNVs) in eight of the main genes involved in the arachidonic acid pathway and their possible genetic association with NIUA. MATERIALS AND METHODS: CNVs in ALOX5, LTC4S, PTGS1, PTGS2, PTGER1, PTGER2, PTGER3, and PTGER4 were analyzed using TaqMan copy number assays. Genotyping was carried out by real-time quantitative PCR. Individual genotypes were assigned using the CopyCaller Software. Statistical analysis was carried out using GraphPad prism 5, PLINK, EPIDAT, and R version 3.1.2. RESULTS AND CONCLUSION: A total of 151 cases and 139 controls were analyzed during the discovery phase and 148 cases and 140 controls were used for replication. CNVs in open reading frames were found for ALOX5, PTGER1, PTGER3, and PTGER4. Statistically significant differences in the CNV frequency between NIUA and controls were found for ALOX5 (Pc=0.017) and PTGER1 (Pc=1.22E-04). This study represents the first analysis showing an association between CNVs in exonic regions of ALOX5 and PTGER1 and NIUA. This suggests a role of CNVs in this pathology that should be explored further.

Allergic Reactions to Metamizole: Immediate and Delayed Responses.

BACKGROUND: Pyrazolones are the most common causes of selective nonsteroidal anti-inflammatory drug (NSAID) hypersensitivity. We studied a large group of patients with immediate and delayed selective responses to metamizole. METHODS: Patients with suspicion of hypersensitivity to metamizole were evaluated. We verified acetylsalicylic acid tolerance and classified patients as immediate or delayed responders if they showed symptoms less or more than 24 h after metamizole administration. Skin tests were performed and if negative, a basophil activation test (BAT) was performed on immediate responders. If it was negative, we performed a drug provocation test (DPT) with metamizole. RESULTS: A total of 137 patients were included: 132 reacted within 24 h (single NSAID-induced urticaria/angioedema/anaphylaxis; SNIUAA) and 5 after 24 h (single NSAID-induced delayed hypersensitivity reaction; SNIDHR). Most SNIUAA patients developed anaphylaxis (60.60%); for SNIDHR, maculopapular exanthema was the most frequent entity (60%). Skin testing was positive in 62.04% of all cases and BAT in 28% of the SNIUAA patients with negative skin tests. In 5.1% of the cases, DPT with metamizole was needed to establish the diagnosis. In 22.62% of the cases, diagnosis was established by consistent and unequivocal history of repeated allergic episodes in spite of a negative skin test and BAT. CONCLUSIONS: SNIUAA to metamizole is the most frequent type of selective NSAID hypersensitivity, with anaphylaxis being the most common clinical entity. It may occur within 1 h after drug intake. SNIDHR occurs in a very low percentage of cases. The low sensitivity of diagnostic tests may be due to incomplete characterization of the chemical structures of metamizole and its metabolites.

Genetic Variants of Thymic Stromal Lymphopoietin in Nonsteroidal Anti-Inflammatory Drug-Induced Urticaria/Angioedema.

BACKGROUND: Nonsteroidal anti-inflammatory drugs (NSAIDs) are the most frequent agents involved in hypersensitivity drug reactions, with NSAID-induced urticaria and/or angioedema (NIUA) being the most common entity. Mast cells are key players in NIUA and are activated by thymic stromal lymphopoietin (TSLP). This cytokine functions through recognition by its receptor, composed of IL7Rα (interleukin-7 receptor alpha) and TSLPR (TSLP receptor). These genes have been previously associated with other inflammatory diseases. METHODS: We assessed the genetic association between single nucleotide polymorphisms (SNPs) in TSLP, IL7R and TSLPR and NIUA in Spanish individuals, using genotyped and imputed data. A total of 369 unrelated NIUA patients and 580 NSAID-tolerant control subjects were included, and 6 SNPs in TSLP, 6 in IL7R and 3 in TSLPR were genotyped. Further variants were imputed using Mach and the 1,000 Genomes Project (Phase 3) data. Association testing and statistical analyses were performed with Mach2dat and R. RESULTS: A total of 139 SNPs were tested for association following quality control. Two SNPs in TSLP (rs1816678 and rs764917) showed a nominal association (p = 0.033 and 0.024, respectively) with NIUA, although these results were not statistically significant after correcting for multiple comparisons. CONCLUSIONS: Although TSLP, IL7R and TSLPR are important genes involved in the development of the inflammatory response, we found no significant genetic association with NIUA in our population for common SNPs in these genes.

Characterisation of non-coding genetic variation in histamine receptors using AnNCR-SNP.

Almost 90 % of disease-associated genetic variants found using genome wide association studies (GWAS) are located in non-coding regions of the genome. Such variants can affect phenotype by altering important regulatory elements such as promoters, enhancers or repressors, leading to changes in gene expression and consequently disease, such as thyroid cancer and allergic diseases. A number of allergy and atopy related diseases such as asthma and atopic dermatitis are related to histamine receptors; however, these diseases are not fully characterized at the molecular level. Moreover, candidate gene based studies of common variants known as single nucleotide polymorphism (SNPs) located in the coding regions of these receptors have given mixed results. It is important to complement these approaches by identifying and characterising non-coding variants in order to further elucidate the role of these receptors in disease. Here we present an analysis of histamine receptor genes using the tool AnNCR-SNP to characterise variants in non-coding genomic regions. AnNCR-SNP combines bioinformatics and experimental data sets from various sources to predict the effects of genetic variation on gene expression regulation. We find many SNPs located in areas of open chromatin, overlapping with transcription factor binding sites and associated with changes in gene expression in expression quantitative trait loci (eQTL) experiments. Here we present the results as a catalogue of non-coding variation in histamine receptor genes to aid histamine researchers in identifying putative functional SNPs found in GWAS for further validation, and to help select variants for candidate gene studies.

Association study of genetic variants in PLA2G4A, PLCG1, LAT, SYK, and TNFRS11A genes in NSAIDs-induced urticaria and/or angioedema patients.

NSAIDs-induced urticaria and/or angioedema (NIUA) is the most frequent entity of hypersensitivity reactions to NSAIDs. The underlying cause is considered to be because of a nonspecific immunological mechanism in which mast cells are key players. We studied the association of nine single nucleotide polymorphisms in five genes involved in mast cell activation (SYK, LAT1, PLCG1, PLA2G4A, and TNFRSF11A) in 450 NIUA patients and 500 controls. We identified several statistically significant associations when stratifying patients by symptoms: PLA2G4A rs12746200 (urticaria vs. controls, Pc=0.005). PLCG1 rs2228246 (angioedema vs. controls; Pc=0.044), and TNFRS11A rs1805034 (urticaria+angioedema vs. controls; Pc=0.041). The frequency of haplotype PLCG1 rs753381-rs2228246 (C-G) in angioedema-NIUA patients was lower than that in controls (Pc=0.040). In addition, the haplotype frequency of TNFRS11A rs1805034-rs35211496 (C-T) was higher among urticaria-NIUA and urticaria+angioedema-NIUA patients than the controls (Pc=0.045 and 0.046). Our results shed light on the involvement of variants in genes related to non-immunological mast cell activation in NIUA.

A comparison of RNA-seq and exon arrays for whole genome transcription profiling of the L5 spinal nerve transection model of neuropathic pain in the rat.

BACKGROUND: The past decade has seen an abundance of transcriptional profiling studies of preclinical models of persistent pain, predominantly employing microarray technology. In this study we directly compare exon microarrays to RNA-seq and investigate the ability of both platforms to detect differentially expressed genes following nerve injury using the L5 spinal nerve transection model of neuropathic pain. We also investigate the effects of increasing RNA-seq sequencing depth. Finally we take advantage of the "agnostic" approach of RNA-seq to discover areas of expression outside of annotated exons that show marked changes in expression following nerve injury. RESULTS: RNA-seq and microarrays largely agree in terms of the genes called as differentially expressed. However, RNA-seq is able to interrogate a much larger proportion of the genome. It can also detect a greater number of differentially expressed genes than microarrays, across a wider range of fold changes and is able to assign a larger range of expression values to the genes it measures. The number of differentially expressed genes detected increases with sequencing depth. RNA-seq also allows the discovery of a number of genes displaying unusual and interesting patterns of non-exonic expression following nerve injury, an effect that cannot be detected using microarrays. CONCLUSION: We recommend the use of RNA-seq for future high-throughput transcriptomic experiments in pain studies. RNA-seq allowed the identification of a larger number of putative candidate pain genes than microarrays and can also detect a wider range of expression values in a neuropathic pain model. In addition, RNA-seq can interrogate the whole genome regardless of prior annotations, being able to detect transcription from areas of the genome not currently annotated as exons. Some of these areas are differentially expressed following nerve injury, and may represent novel genes or isoforms. We also recommend the use of a high sequencing depth in order to detect differential expression for genes with low levels of expression.

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.

Chemokine expression in peripheral tissues from the monosodium iodoacetate model of chronic joint pain.

BACKGROUND: Chronic pain arising from degenerative diseases of the joint such as osteoarthritis (OA) has a strong peripheral component which is likely to be mediator driven. Current treatments which reduce the production of such mediators i.e. non-steroidal anti-inflammatory drugs (NSAIDs), can help to lessen pain in OA patients. However, this is not always the case and complete pain relief is rarely achieved, suggesting that additional unidentified mediators play a role. Here we have investigated the notion that chemokines might act as such pain mediators in OA. RESULTS: Using the monosodium iodoacetate (MIA) model of chronic joint pain the expression of over 90 different inflammatory mediators, mainly cytokines and chemokines, were measured in tissues taken from the femorotibial joint (cartilage, subchondral bone, fat pad) using custom-made quantitative real-time polymerase chain reaction (qPCR) array cards. At both the day 3 and 14 time points, numerous inflammatory mediators were significantly up-regulated in these tissues, although it was clear that the largest transcriptional dysregulation occurred in the cartilage. Using individual qPCR to measure immune cell markers, a significant infiltration of macrophages was measured in the cartilage and fat pad at day 3. Neutrophil infiltration was also measured in the fat pad at the same time point, but no infiltration was observed at day 14. Combination of mRNA expression data from different time points and tissues identified the chemokines, CCL2, 7 and 9 as being consistently up-regulated. The overall increase in CCL2 expression was also measured at the protein level. CONCLUSION: Chemokines in general and CCL2, 7 and 9 in particular, represent promising targets for further studies into the identification of new pain mediators in chronic joint pain.

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.

ReadqPCR and NormqPCR: R packages for the reading, quality checking and normalisation of RT-qPCR quantification cycle (Cq) data.

BACKGROUND: Measuring gene transcription using real-time reverse transcription polymerase chain reaction (RT-qPCR) technology is a mainstay of molecular biology. Technologies now exist to measure the abundance of many transcripts in parallel. The selection of the optimal reference gene for the normalisation of this data is a recurring problem, and several algorithms have been developed in order to solve it. So far nothing in R exists to unite these methods, together with other functions to read in and normalise the data using the chosen reference gene(s). RESULTS: We have developed two R/Bioconductor packages, ReadqPCR and NormqPCR, intended for a user with some experience with high-throughput data analysis using R, who wishes to use R to analyse RT-qPCR data. We illustrate their potential use in a workflow analysing a generic RT-qPCR experiment, and apply this to a real dataset. Packages are available from http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.htmland http://www.bioconductor.org/packages/release/bioc/html/NormqPCR.html CONCLUSIONS: These packages increase the repetoire of RT-qPCR analysis tools available to the R user and allow them to (amongst other things) read their data into R, hold it in an ExpressionSet compatible R object, choose appropriate reference genes, normalise the data and look for differential expression between samples.

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.