Human V α 7.2-J α 33 mucosal-associated invariant T cells in endometrial ectopic tissues tend to produce interferon-gamma: A new player in endometriosis etiology: A case-control study.

Background: Endometriosis is a chronic estrogen-related inflammatory disorder that is known by proliferating endometrial cells in a place outside the uterus. The high presence of immune cells in the peritoneal fluid of women with endometriosis confirms the involvement of the immune system in the pathogenesis of the disease. Mucosal-associated invariant T (MAIT) cells play an undeniable impact on mucosal immunity by the production of interleukin-17, interferon-gamma (IFN-γ), and tumor necrosis factor-alpha. The function of the cells in the pathogenesis of endometriosis is less investigated. Objective: This study aims to investigate the infiltration of MAIT cells by using the determination levels of V α 7.2-J α 33 gene expression in eutopic and ectopic tissue of endometriosis lesions. Materials and Methods: In this case-control study, the tested samples include 20 eutopic and 20 ectopic tissues of women with endometriosis and 20 uterine endometrial tissues of women in the control group. Expressions of the V α 7.2-J α 33 tumor necrosis factor-alpha, interleukin-17A, and IFN-γ genes were analyzed by quantitative reverse transcriptase-polymerase chain reaction. Results: According to the results, V α 7.2-J α 33 gene expression did not show substantial elevation in the uterine and eutopic endometrial tissues compared to internal gene control as well as in ectopic tissues. Correlation analysis approved a positive relationship between V α 7.2-J α 33 expression genes and IFN-γ levels in ectopic tissues. Conclusion: Considering the low-expression specific gene of MAIT cells in ectopic tissue, it can be concluded that these cells are present in the endometriotic environment to a certain extent, and there is a possibility of their role in the progression of endometriosis by secreting IFN- γ .

Comparison of Common Monogenic Defects in a Large Predominantly Antibody Deficiency Cohort.

BACKGROUND: Predominantly antibody deficiencies (PADs) are the most common primary immunodeficiencies, characterized by hypogammaglobulinemia and inability to generate effective antibody responses. OBJECTIVE: We intended to report most common monogenic PADs and to investigate how patients with PAD who were primarily diagnosed as suffering from agammaglobulinemia, hyper-IgM (HIgM) syndrome, and common variable immunodeficiency (CVID) have different clinical and immunological findings. METHODS: Stepwise next-generation sequencing and Sanger sequencing were performed for confirmation of the mutations in the patients clinically diagnosed as suffering from agammaglobulinemia, HIgM syndrome, and CVID. RESULTS: Among 550 registered patients, the predominant genetic defects associated with agammaglobulinemia (48 Bruton's tyrosine kinase [BTK] and 6 µ heavy chain deficiencies), HIgM syndrome (21 CD40 ligand and 7 activation-induced cytidine deaminase deficiencies), and CVID (17 lipopolysaccharides-responsive beige-like anchor deficiency and 12 atypical Immunodeficiency, Centromeric instability, and Facial dysmorphism syndromes) were identified. Clinical disease severity was significantly higher in patients with µ heavy chain and CD40 ligand mutations compared with patients with BTK (P = .003) and activation-induced cytidine deaminase (P = .009) mutations. Paralysis following live polio vaccination was considerably higher in patients with µ heavy chain deficiency compared with BTK deficiency (P < .001). We found a genotype-phenotype correlation among patients with BTK mutations regarding clinical manifestation of meningitis and chronic diarrhea. Surprisingly, we noticed that first presentations in most patients with Immunodeficiency, Centromeric instability, and Facial dysmorphism were respiratory complications (P = .008), whereas first presentations in patients with lipopolysaccharides-responsive beige-like anchor deficiency were nonrespiratory complications (P = .008). CONCLUSIONS: This study highlights similarities and differences in the clinical and genetic spectrum of the most common PAD-associated gene defects. This comprehensive comparison will facilitate clinical decision making, and improve prognosis and targeted treatment.

Long-term evaluation of a historical cohort of Iranian common variable immunodeficiency patients.

OBJECTIVES: Common variable immune deficiency (CVID) is the most frequent form of symptomatic primary immunodeficiency disease, characterized by hypogammaglobulinemia, recurrent infections and increased predisposition to autoimmunity and malignancies. The aim of this study was to reconsider important points of previously performed studies on Iranian CVID patients diagnosed and followed from 1984 to 2013. METHODS: Diagnosis was made using approved criteria including reductions of serum levels of immunoglobulins and exclusion of well-known single gene defects in individuals with an age >4 years and evidence of specific antibody deficiency. RESULTS: Detailed information on demographic data, survival rates, clinical phenotypes, immunologic and genetic data and treatment of 173 patients are provided. The early onset presentation (74.5%) and rate of consanguineous marriage (61.2%) were considerably higher in our cohort. Our study revealed clinically related correlations regarding consanguinity, the population of naïve CD4(+) T cells and switched-memory B cells, cytokine levels and special genetic factors (including HLA and AID genes). CONCLUSION: Despite current efforts, more comprehensive studies are needed, especially for classification and investigation of the genetic background and prognostic factors for patients with CVID in order to better managment and followup of patinets.

Regularization in finite mixture of regression models with diverging number of parameters.

Feature (variable) selection has become a fundamentally important problem in recent statistical literature. Sometimes, in applications, many variables are introduced to reduce possible modeling biases, but the number of variables a model can accommodate is often limited by the amount of data available. In other words, the number of variables considered depends on the sample size, which reflects the estimability of the parametric model. In this article, we consider the problem of feature selection in finite mixture of regression models when the number of parameters in the model can increase with the sample size. We propose a penalized likelihood approach for feature selection in these models. Under certain regularity conditions, our approach leads to consistent variable selection. We carry out extensive simulation studies to evaluate the performance of the proposed approach under controlled settings. We also applied the proposed method to two real data. The first is on telemonitoring of Parkinson's disease (PD), where the problem concerns whether dysphonic features extracted from the patients' speech signals recorded at home can be used as surrogates to study PD severity and progression. The second is on breast cancer prognosis, in which one is interested in assessing whether cell nuclear features may offer prognostic values on long-term survival of breast cancer patients. Our analysis in each of the application revealed a mixture structure in the study population and uncovered a unique relationship between the features and the response variable in each of the mixture component.

Pairwise network mechanisms in the host signaling response to coxsackievirus B3 infection.

Signal transduction networks can be perturbed biochemically, genetically, and pharmacologically to unravel their functions. But at the systems level, it is not clear how such perturbations are best implemented to extract molecular mechanisms that underlie network function. Here, we combined pairwise perturbations with multiparameter phosphorylation measurements to reveal causal mechanisms within the signaling network response of cardiomyocytes to coxsackievirus B3 (CVB3) infection. Using all possible pairs of six kinase inhibitors, we assembled a dynamic nine-protein phosphorylation signature of perturbed CVB3 infectivity. Cluster analysis of the resulting dataset showed repeatedly that paired inhibitor data were required for accurate data-driven predictions of kinase substrate links in the host network. With pairwise data, we also derived a high-confidence network based on partial correlations, which identified phospho-IκBα as a central "hub" in the measured phosphorylation signature. The reconstructed network helped to connect phospho-IκBα with an autocrine feedback circuit in host cells involving the proinflammatory cytokines, TNF and IL-1. Autocrine blockade substantially inhibited CVB3 progeny release and improved host cell viability, implicating TNF and IL-1 as cell autonomous components of CVB3-induced myocardial damage. We conclude that pairwise perturbations, when combined with network-level intracellular measurements, enrich for mechanisms that would be overlooked by single perturbants.

A Robust Unified Approach to Analyzing Methylation and Gene Expression Data.

Microarray technology has made it possible to investigate expression levels, and more recently methylation signatures, of thousands of genes simultaneously, in a biological sample. Since more and more data from different biological systems or technological platforms are being generated at an incredible rate, there is an increasing need to develop statistical methods that are applicable to multiple data types and platforms. Motivated by such a need, a flexible finite mixture model that is applicable to methylation, gene expression, and potentially data from other biological systems, is proposed. Two major thrusts of this approach are to allow for a variable number of components in the mixture to capture non-biological variation and small biases, and to use a robust procedure for parameter estimation and probe classification. The method was applied to the analysis of methylation signatures of three breast cancer cell lines. It was also tested on three sets of expression microarray data to study its power and type I error rates. Comparison with a number of existing methods in the literature yielded very encouraging results; lower type I error rates and comparable/better power were achieved based on the limited study. Furthermore, the method also leads to more biologically interpretable results for the three breast cancer cell lines.

Gamma-Normal-Gamma mixture model for detecting differentially methylated loci in three breast cancer cell lines.

With state-of-the-art microarray technologies now available for whole genome CpG island (CGI) methylation profiling, there is a need to develop statistical models that are specifically geared toward the analysis of such data. In this article, we propose a Gamma-Normal-Gamma (GNG) mixture model for describing three groups of CGI loci: hypomethylated, undifferentiated, and hypermethylated, from a single methylation microarray. This model was applied to study the methylation signatures of three breast cancer cell lines: MCF7, T47D, and MDAMB361. Biologically interesting and interpretable results are obtained, which highlights the heterogeneity nature of the three cell lines. This underlies the premise for the need of analyzing each of the microarray slides individually as opposed to pooling them together for a single analysis. Our comparisons with the fitted densities from the Normal-Uniform (NU) mixture model in the literature proposed for gene expression analysis show an improved goodness of fit of the GNG model over the NU model. Although the GNG model was proposed in the context of single-slide methylation analysis, it can be readily adapted to analyze multi-slide methylation data as well as other types of microarray data.