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Contains fulltext : 237768.pdf (Publisher’s version ) (Open Access) The two most common chronic inflammatory skin diseases are atopic dermatitis (AD) and psoriasis. The underpinnings of the remarkable degree of clinical heterogeneity of AD and psoriasis are poorly understood and, as a consequence, disease onset and progression are unpredictable and the optimal type and time point for intervention are as yet unknown. The BIOMAP project is the first IMI (Innovative Medicines Initiative) project dedicated to investigating the causes and mechanisms of AD and psoriasis and to identify potential biomarkers responsible for the variation in disease outcome. The consortium includes 7 large pharmaceutical companies and 25 non-industry partners including academia. Since there is mounting evidence supporting an important role for microbial exposures and our microbiota as factors mediating immune polarization and AD and psoriasis pathogenesis, an entire work package is dedicated to the investigation of skin and gut microbiome linked to AD or psoriasis. The large collaborative BIOMAP project will enable the integration of patient cohorts, data and knowledge in unprecedented proportions. The project has a unique opportunity with a potential to bridge and fill the gaps between current problems and solutions. This review highlights the power and potential of the BIOMAP project in the investigation of microbe-host interplay in AD and psoriasis.

Summary Quantitative structure–activity relationship (QSAR) modelling is currently used in multiple fields to relate structural properties of compounds to their biological activities. This technique is also used for drug design purposes with the aim of predicting parameters that determine drug behaviour. To this end, a sophisticated process, involving various analytical steps concatenated in series, is employed to identify and fine-tune the optimal set of predictors from a large dataset of molecular descriptors (MDs). The search of the optimal model requires to optimize multiple objectives at the same time, as the aim is to obtain the minimal set of features that maximizes the goodness of fit and the applicability domain (AD). Hence, a multi-objective optimization strategy, improving multiple parameters in parallel, can be applied. Here we propose a new multi-niche multi-objective genetic algorithm that simultaneously enables stable feature selection as well as obtaining robust and validated regression models with maximized AD. We benchmarked our method on two simulated datasets. Moreover, we analyzed an aquatic acute toxicity dataset and compared the performances of single- and multi-objective fitness functions on different regression models. Our results show that our multi-objective algorithm is a valid alternative to classical QSAR modelling strategy, for continuous response values, since it automatically finds the model with the best compromise between statistical robustness, predictive performance, widest AD, and the smallest number of MDs. Availability and implementation The python implementation of MaNGA is available at https://github.com/Greco-Lab/MaNGA. Supplementary information Supplementary data are available at Bioinformatics online.

Typical clustering analysis for large-scale genomics data combines two unsupervised learning techniques: dimensionality reduction and clustering (DR-CL) methods. It has been demonstrated that transforming gene expression to pathway-level information can improve the robustness and interpretability of disease grouping results. This approach, referred to as biological knowledge-driven clustering (BK-CL) approach, is often neglected, due to a lack of tools enabling systematic comparisons with more established DR-based methods. Moreover, classic clustering metrics based on group separability tend to favor the DR-CL paradigm, which may increase the risk of identifying less actionable disease subtypes that have ambiguous biological and clinical explanations. Hence, there is a need for developing metrics that assess biological and clinical relevance. To facilitate the systematic analysis of BK-CL methods, we propose a computational protocol for quantitative analysis of clustering results derived from both DR-CL and BK-CL methods. Moreover, we propose a new BK-CL method that combines prior knowledge of disease relevant genes, network diffusion algorithms and gene set enrichment analysis to generate robust pathway-level information. Benchmarking studies were conducted to compare the grouping results from different DR-CL and BK-CL approaches with respect to standard clustering evaluation metrics, concordance with known subtypes, association with clinical outcomes and disease modules in co-expression networks of genes. No single approach dominated every metric, showing the importance multi-objective evaluation in clustering analysis. However, we demonstrated that, on gene expression data sets derived from TCGA samples, the BK-CL approach can find groupings that provide significant prognostic value in both breast and prostate cancers.

The COVID-19 disease led to an unprecedented health emergency, still ongoing worldwide. Given the lack of a vaccine or a clear therapeutic strategy to counteract the infection as well as its secondary effects, there is currently a pressing need to generate new insights into the SARS-CoV-2 induced host response. Biomedical data can help to investigate new aspects of the COVID-19 pathogenesis, but source heterogeneity represents a major drawback and limitation. In this work, we applied data integration methods to develop a Unified Knowledge Space (UKS) and used it to identify a new set of genes associated with SARS-CoV-2 host response, both in vitro and in vivo. Functional analysis of these genes reveals possible long-term systemic effects of the infection, such as vascular remodelling and fibrosis. Finally, we identified a set of potentially relevant drugs targeting proteins involved in multiple steps of the host response to the virus.

Motivation Network analysis is a powerful approach to investigate biological systems. It is often applied to study gene co-expression patterns derived from transcriptomics experiments. Even though co-expression analysis is widely used, there is still a lack of tools that are open and customizable on the basis of different network types and analysis scenarios (e.g. through function accessibility), but are also suitable for novice users by providing complete analysis pipelines. Results We developed VOLTA, a Python package suited for complex co-expression network analysis. VOLTA is designed to allow users direct access to the individual functions, while they are also provided with complete analysis pipelines. Moreover, VOLTA offers when possible multiple algorithms applicable to each analytical step (e.g. multiple community detection or clustering algorithms are provided), hence providing the user with the possibility to perform analysis tailored to their needs. This makes VOLTA highly suitable for experienced users who wish to build their own analysis pipelines for a wide range of networks as well as for novice users for which a ‘plug and play’ system is provided. Availability and implementation The package and used data are available at GitHub: https://github.com/fhaive/VOLTA and 10.5281/zenodo.5171719. Supplementary information Supplementary data are available at Bioinformatics online.

MotivationDeep immune receptor sequencing, Repseq, provides unprecedented opportunities to identify condition-associated T-cell clones, represented by T-cell receptor (TCR) CDR3 sequences. TCR profiling has potential value for increasing immunopathological understanding of various diseases, and holds considerable clinical relevance. However, due to the immense diversity of the immune repertoire, identification of condition relevant TCR CDR3s from total repertoires has so far been limited either to mostly “public” CDR3 sequences, which are shared across unrelated individuals, or to comparisons of CDR3 frequencies from multiple samples from the same individual. A methodology for the identification of condition-associated TCR CDR3s by population level comparison of groups of Repseq samples is currently lacking.ResultsWe implemented a computational pipeline that allows population level comparison of Repseq sample groups at the level of the immune repertoire sub-units that are shared across individuals. These sub-units (or sub-repertoires) represent shared immuno-genomic features across individuals that potentially encode common signatures in the immune response to antigens. The method first performs unsupervised clustering of CDR3 sequences within each sample based on their similarity in nucleotide or amino acid subsequence frequency. Next, it finds matching clusters across samples, the immune sub-repertoires, and performs statistical differential abundance testing at the level of the identified sub-repertoires. We applied the method on total TCR CDR3β Repseq datasets of celiac disease patients in gluten exposed and unexposed conditions, as well as on public dataset of yellow fever vaccination volunteers before and after immunization. The method successfully identified condition-associated CDR3β sequences, as evidenced by considerable agreement of TRBV-gene and positional amino acid usage patterns in the detected CDR3β sequences with previously known CDR3β species relevant to celiac disease. The method also recovered significantly high numbers of previously known CDR3β sequences, relevant to each condition than would be expected by chance. We conclude that immune sub-repertoires of similar immuno-genomic features, shared across unrelated individuals, encode common immunological information. Moreover, they can serve as viable units of population level immune repertoire comparison, serving as proxy for identification of condition-associated CDR3 sequences.

Toxicogenomics (TGx) approaches are increasingly applied to gain insight into the possible toxicity mechanisms of engineered nanomaterials (ENMs). Omics data can be valuable to elucidate the mechanism of action of chemicals and to develop predictive models in toxicology. While vast amounts of transcriptomics data from ENM exposures have already been accumulated, a unified, easily accessible and reusable collection of transcriptomics data for ENMs is currently lacking. In an attempt to improve the FAIRness of already existing transcriptomics data for ENMs, we curated a collection of homogenized transcriptomics data from human, mouse and rat ENM exposures in vitro and in vivo including the physicochemical characteristics of the ENMs used in each study., Measurement(s) microarray data • transcriptome • RNA • Toxicogenomics Technology Type(s) digital curation Factor Type(s) exposure to engineered nanomaterials Sample Characteristic - Organism Homo sapiens • Mus musculus • Rattus Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13154381

Publisher Copyright: © 2021 The Authors. Published by American Chemical Society. Identification of HLA class I ligands from the tumor surface (ligandome or immunopeptidome) is essential for designing T-cell mediated cancer therapeutic approaches. However, the sensitivity of the process for isolating MHC-I restricted tumor-specific peptides has been the major limiting factor for reliable tumor antigen characterization, making clear the need for technical improvement. Here, we describe our work from the fabrication and development of a microfluidic-based chip (PeptiCHIP) and its use to identify and characterize tumor-specific ligands on clinically relevant human samples. Specifically, we assessed the potential of immobilizing a pan-HLA antibody on solid surfaces via well-characterized streptavidin-biotin chemistry, overcoming the limitations of the cross-linking chemistry used to prepare the affinity matrix with the desired antibodies in the immunopeptidomics workflow. Furthermore, to address the restrictions related to the handling and the limited availability of tumor samples, we further developed the concept toward the implementation of a microfluidic through-flow system. Thus, the biotinylated pan-HLA antibody was immobilized on streptavidin-functionalized surfaces, and immune-affinity purification (IP) was carried out on customized microfluidic pillar arrays made of thiol-ene polymer. Compared to the standard methods reported in the field, our methodology reduces the amount of antibody and the time required for peptide isolation. In this work, we carefully examined the specificity and robustness of our customized technology for immunopeptidomics workflows. We tested this platform by immunopurifying HLA-I complexes from 1 × 106 cells both in a widely studied B-cell line and in patients-derived ex vivo cell cultures, instead of 5 × 108 cells as required in the current technology. After the final elution in mild acid, HLA-I-presented peptides were identified by tandem mass spectrometry and further investigated by in vitro methods. These results highlight the potential to exploit microfluidics-based strategies in immunopeptidomics platforms and in personalized immunopeptidome analysis from cells isolated from individual tumor biopsies to design tailored cancer therapeutic vaccines. Moreover, the possibility to integrate multiple identical units on a single chip further improves the throughput and multiplexing of these assays with a view to clinical needs.

Motivation The analysis of dose-dependent effects on the gene expression is gaining attention in the field of toxicogenomics. Currently available computational methods are usually limited to specific omics platforms or biological annotations and are able to analyse only one experiment at a time. Results We developed the software BMDx with a graphical user interface for the Benchmark Dose (BMD) analysis of transcriptomics data. We implemented an approach based on the fitting of multiple models and the selection of the optimal model based on the Akaike Information Criterion. The BMDx tool takes as an input a gene expression matrix and a phenotype table, computes the BMD, its related values, and IC50/EC50 estimations. It reports interactive tables and plots that the user can investigate for further details of the fitting, dose effects and functional enrichment. BMDx allows a fast and convenient comparison of the BMD values of a transcriptomics experiment at different time points and an effortless way to interpret the results. Furthermore, BMDx allows to analyse and to compare multiple experiments at once. Availability and implementation BMDx is implemented as an R/Shiny software and is available at https://github.com/Greco-Lab/BMDx/. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Omics technologies have the potential to facilitate the discovery of new biomarkers. However, only few omics-derived biomarkers have been successfully translated into clinical applications to date. Feature selection is a crucial step in this process that identifies small sets of features with high predictive power. Models consisting of a limited number of features are not only more robust in analytical terms, but also ensure cost effectiveness and clinical translatability of new biomarker panels. Here we introduce GARBO, a novel multi-island adaptive genetic algorithm to simultaneously optimize accuracy and set size in omics-driven biomarker discovery problems. Results Compared to existing methods, GARBO enables the identification of biomarker sets that best optimize the trade-off between classification accuracy and number of biomarkers. We tested GARBO and six alternative selection methods with two high relevant topics in precision medicine: cancer patient stratification and drug sensitivity prediction. We found multivariate biomarker models from different omics data types such as mRNA, miRNA, copy number variation, mutation and DNA methylation. The top performing models were evaluated by using two different strategies: the Pareto-based selection, and the weighted sum between accuracy and set size (w = 0.5). Pareto-based preferences show the ability of the proposed algorithm to search minimal subsets of relevant features that can be used to model accurate random forest-based classification systems. Moreover, GARBO systematically identified, on larger omics data types, such as gene expression and DNA methylation, biomarker panels exhibiting higher classification accuracy or employing a number of features much lower than those discovered with other methods. These results were confirmed on independent datasets. Availability and implementation github.com/Greco-Lab/GARBO. Contact dario.greco@tuni.fi Supplementary information Supplementary data are available at Bioinformatics online.

Background Copper oxide (CuO) nanomaterials are used in a wide range of industrial and commercial applications. These materials can be hazardous, especially if they are inhaled. As a result, the pulmonary effects of CuO nanomaterials have been studied in healthy subjects but limited knowledge exists today about their effects on lungs with allergic airway inflammation (AAI). The objective of this study was to investigate how pristine CuO modulates allergic lung inflammation and whether surface modifications can influence its reactivity. CuO and its carboxylated (CuO COOH), methylaminated (CuO NH3) and PEGylated (CuO PEG) derivatives were administered here on four consecutive days via oropharyngeal aspiration in a mouse model of AAI. Standard genome-wide gene expression profiling as well as conventional histopathological and immunological methods were used to investigate the modulatory effects of the nanomaterials on both healthy and compromised immune system. Results Our data demonstrates that although CuO materials did not considerably influence hallmarks of allergic airway inflammation, the materials exacerbated the existing lung inflammation by eliciting dramatic pulmonary neutrophilia. Transcriptomic analysis showed that CuO, CuO COOH and CuO NH3 commonly enriched neutrophil-related biological processes, especially in healthy mice. In sharp contrast, CuO PEG had a significantly lower potential in triggering changes in lungs of healthy and allergic mice revealing that surface PEGylation suppresses the effects triggered by the pristine material. Conclusions CuO as well as its functionalized forms worsen allergic airway inflammation by causing neutrophilia in the lungs, however, our results also show that surface PEGylation can be a promising approach for inhibiting the effects of pristine CuO. Our study provides information for health and safety assessment of modified CuO materials, and it can be useful in the development of nanomedical applications. Electronic supplementary material The online version of this article (10.1186/s12989-019-0309-1) contains supplementary material, which is available to authorized users.

Application of microarrays in omics technologies enables quantification of many biomolecules simultaneously. It is widely applied to observe the positive or negative effect on biomolecule activity in perturbed versus the steady state by quantitative comparison. Community resources, such as Bioconductor and CRAN, host tools based on R that have become standard for high-throughput analytics. However, there is a need for intuitive and easy-to-use platform to process omics data, visualize, and interpret results, which is computational skill neutral. We propose an integrated software solution, eUTOPIA, that implements a set of essential processing steps as a guided workflow presented to the user as an R Shiny application. eUTOPIA allows researchers to perform preprocessing and analysis of microarray data via a simple and intuitive graphical interface while using state of the art methods. eUTOPIA is free for academic use and can be obtained from the GitHub repository https://github.com/Greco-Lab/eUTOPIA.

Single-nucleotide polymorphisms (SNPs) in GSDMB (Gasdermin B) and ORMDL3 (ORMDL sphingolipid biosynthesis regulator 3) are strongly associated with childhood asthma, but the molecular alterations contributing to disease remain unknown. We investigated the effects of asthma-associated SNPs on DNA methylation and mRNA levels of GSDMB and ORMDL3. Genetic association between GSDMB/ORMDL3 and physician-diagnosed childhood asthma was confirmed in the Swedish birth-cohort BAMSE. CpG-site SNPs (rs7216389 and rs4065275) showed differences in DNA methylation depending on carrier status of the risk alleles, and were significantly associated with methylation levels in two CpG sites in the 5' UTR (untranslated region) of ORMDL3. In the Swedish Search study, we found significant differences in DNA methylation between asthmatics and controls in five CpG sites; after adjusting for lymphocyte and neutrophil cell counts, three remained significant: one in IKZF3 [IKAROS family zinc finger 3 (Aiolos); cg16293631] and two in the CpG island (CGI) of ORMDL3 (cg02305874 and cg16638648). Also, cg16293631 and cg02305874 correlated with mRNA levels of ORMDL3. The association between methylation and asthma was independent of the genotype in rs7216389, rs4065275 and rs12603332. Both SNPs and CpG sites showed significant associations with ORMDL3 mRNA levels. SNPs influenced expression independently of methylation, and the residual association between methylation and expression was not mediated by these SNPs. We found a differentially methylated region in the CGI shore of ORMDL3 with six CpG sites less methylated in CD8(+) T-cells. In summary, this study supports that there are differences in DNA methylation at this locus between asthmatics and controls; and both SNPs and CpG sites are independently associated with ORMDL3 expression.

The explosion of omics data availability in cancer research has boosted the knowledge of the molecular basis of cancer, although the strategies for its definitive resolution are still not well established. The complexity of cancer biology, given by the high heterogeneity of cancer cells, leads to the development of pharmacoresistance for many patients, hampering the efficacy of therapeutic approaches. Machine learning techniques have been implemented to extract knowledge from cancer omics data in order to address fundamental issues in cancer research, as well as the classification of clinically relevant sub-groups of patients and for the identification of biomarkers for disease risk and prognosis. Rule induction algorithms are a group of pattern discovery approaches that represents discovered relationships in the form of human readable associative rules. The application of such techniques to the modern plethora of collected cancer omics data can effectively boost our understanding of cancer-related mechanisms. In fact, the capability of these methods to extract a huge amount of human readable knowledge will eventually help to uncover unknown relationships between molecular attributes and the malignant phenotype. In this review, we describe applications and strategies for the usage of rule induction approaches in cancer omics data analysis. In particular, we explore the canonical applications and the future challenges and opportunities posed by multi-omics integration problems.

SummaryProliferating cells require coordinated gene expression between the nucleus and mitochondria in order to divide, ensuring sufficient organelle number in daughter cells [1]. However, the machinery and mechanisms whereby proliferating cells monitor mitochondria and coordinate organelle biosynthesis remain poorly understood. Antibiotics inhibiting mitochondrial translation have emerged as therapeutics for human cancers because they block cell proliferation [2, 3]. These proliferative defects were attributable to modest decreases in mitochondrial respiration [3, 4], even though tumors are mainly glycolytic [5] and mitochondrial respiratory chain function appears to play a minor role in cell proliferation in vivo [6]. Here we challenge this interpretation by demonstrating that one class of antiproliferative antibiotic induces stalled mitochondrial ribosomes, which triggers a mitochondrial ribosome and RNA decay pathway. Rescue of the stalled mitochondrial ribosomes initiates a retrograde signaling response to block cell proliferation and occurs prior to any loss of mitochondrial respiration. The loss of respiratory chain function is simply a downstream effect of impaired mitochondrial translation and not the antiproliferative signal. This mitochondrial ribosome quality-control pathway is actively monitored in cells and constitutes an important organelle checkpoint for cell division.

Human mesenchymal stem/stromal cells (hMSC) are increasingly used in advanced cellular therapies. The clinical use of hMSCs demands sequential cell expansions. As it is well established that membrane glycerophospholipids (GPL) provide precursors for signaling lipids that modulate cellular functions, we studied the effect of the donor's age and cell doublings on the GPL profile of human bone marrow MSC (hBMSC). The hBMSCs, which were harvested from five young and five old adults, showed clear compositional changes during expansion seen at the level of lipid classes, lipid species, and acyl chains. The ratio of phosphatidylinositol to phosphatidylserine increased toward the late-passage samples. Furthermore, 20:4n-6-containing species of phosphatidylcholine and phosphatidylethanolamine accumulated while the species containing monounsaturated fatty acids (FA) decreased during passaging. Additionally, in the total FA pool of the cells, 20:4n-6 increased, which happened at the expense of n-3 polyunsaturated FAs, especially 22:6n-3. The GPL and FA correlated with the decreased immunosuppressive capacity of hBMSCs during expansion. Our observations were further supported by alterations in the gene expression levels of several enzymes involved in lipid metabolism and immunomodulation. The results show that extensive expansion of hBMSCs harmfully modulates membrane GPLs, affecting lipid signaling and eventually impairing functionality.

Estrogen receptor (ER)-negative breast cancer shows a higher incidence in women of African ancestry compared to women of European ancestry. In search of common risk alleles for ER-negative breast cancer, we combined genome-wide association study (GWAS) data from women of African ancestry (1,004 ER-negative cases and 2,745 controls) and European ancestry (1,718 ER-negative cases and 3,670 controls), with replication testing conducted in an additional 2,292 ER-negative cases and 16,901 controls of European ancestry. We identified a common risk variant for ER-negative breast cancer at the TERT-CLPTM1L locus on chromosome 5p15 (rs10069690: per-allele odds ratio (OR) = 1.18 per allele, P = 1.0 × 10(-10)). The variant was also significantly associated with triple-negative (ER-negative, progesterone receptor (PR)-negative and human epidermal growth factor-2 (HER2)-negative) breast cancer (OR = 1.25, P = 1.1 × 10(-9)), particularly in younger women (

We present miRSeqNovel, an R based workflow for miRNA sequencing data analysis. miRSeqNovel can process both colorspace (SOLiD) and basespace (Illumina/Solexa) data by different mapping algorithms. It finds differentially expressed miRNAs and gives conservative prediction of novel miRNA candidates with customized parameters. miRSeqNovel is freely available at http://sourceforge.net/projects/mirseq/files.

There is evidence that developmental-like plasticity can be reactivated in the adult visual cortex. Although activity-dependent transcription factors underlying the process of plasticity reactivation are currently unknown, recent studies point towards NPAS4 as a candidate gene for the occurrence of plasticity in the adult visual system. Here, we addressed whether NPAS4 is involved in the reinstatement of plasticity by using the monocular deprivation protocol and long-term fluoxetine treatment as a pharmacological strategy that restores plasticity in adulthood. A combination of molecular assays for gene expression and epigenetic analysis, gene delivery by lentiviral infection, shRNA interference and electrophysiology as a functional read-out, revealed a previously unknown role for the transcription factor NPAS4 in the regulation of adult visual cortical plasticity. We found that NPAS4 overexpression restores ocular dominance plasticity in adult naive animals whereas NPAS4 down-regulation prevents the plastic outcome caused by fluoxetine in adulthood. Our findings lead the way to the identification of novel therapeutic targets for pathological conditions where reorganization of neuronal networks would be beneficial in adult life.

Genome-wide association studies (GWAS) of breast cancer defined by hormone receptor status have revealed loci contributing to susceptibility of estrogen receptor (ER)-negative subtypes. To identify additional genetic variants for ER-negative breast cancer, we conducted the largest meta-analysis of ER-negative disease to date, comprising 4754 ER-negative cases and 31 663 controls from three GWAS: NCI Breast and Prostate Cancer Cohort Consortium (BPC3) (2188 ER-negative cases; 25 519 controls of European ancestry), Triple Negative Breast Cancer Consortium (TNBCC) (1562 triple negative cases; 3399 controls of European ancestry) and African American Breast Cancer Consortium (AABC) (1004 ER-negative cases; 2745 controls). We performed in silico replication of 86 SNPs at P ≤ 1 × 10(-5) in an additional 11 209 breast cancer cases (946 with ER-negative disease) and 16 057 controls of Japanese, Latino and European ancestry. We identified two novel loci for breast cancer at 20q11 and 6q14. SNP rs2284378 at 20q11 was associated with ER-negative breast cancer (combined two-stage OR = 1.16; P = 1.1 × 10(-8)) but showed a weaker association with overall breast cancer (OR = 1.08, P = 1.3 × 10(-6)) based on 17 869 cases and 43 745 controls and no association with ER-positive disease (OR = 1.01, P = 0.67) based on 9965 cases and 22 902 controls. Similarly, rs17530068 at 6q14 was associated with breast cancer (OR = 1.12; P = 1.1 × 10(-9)), and with both ER-positive (OR = 1.09; P = 1.5 × 10(-5)) and ER-negative (OR = 1.16, P = 2.5 × 10(-7)) disease. We also confirmed three known loci associated with ER-negative (19p13) and both ER-negative and ER-positive breast cancer (6q25 and 12p11). Our results highlight the value of large-scale collaborative studies to identify novel breast cancer risk loci.

The 19p13.1 breast cancer susceptibility locus is a modifier of breast cancer risk in BRCA1 mutation carriers and is also associated with the risk of ovarian cancer. Here, we investigated 19p13.1 variation and risk of breast cancer subtypes, defined by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) status, using 48,869 breast cancer cases and 49,787 controls from the Breast Cancer Association Consortium (BCAC). Variants from 19p13.1 were not associated with breast cancer overall or with ER-positive breast cancer but were significantly associated with ER-negative breast cancer risk [rs8170 OR, 1.10; 95% confidence interval (CI), 1.05–1.15; P = 3.49 × 10−5] and triple-negative (ER-, PR-, and HER2-negative) breast cancer (rs8170: OR, 1.22; 95% CI, 1.13–1.31; P = 2.22 × 10−7). However, rs8170 was no longer associated with ER-negative breast cancer risk when triple-negative cases were excluded (OR, 0.98; 95% CI, 0.89–1.07; P = 0.62). In addition, a combined analysis of triple-negative cases from BCAC and the Triple Negative Breast Cancer Consortium (TNBCC; N = 3,566) identified a genome-wide significant association between rs8170 and triple-negative breast cancer risk (OR, 1.25; 95% CI, 1.18–1.33; P = 3.31 × 10−13]. Thus, 19p13.1 is the first triple-negative–specific breast cancer risk locus and the first locus specific to a histologic subtype defined by ER, PR, and HER2 to be identified. These findings provide convincing evidence that genetic susceptibility to breast cancer varies by tumor subtype and that triple-negative tumors and other subtypes likely arise through distinct etiologic pathways. Cancer Res; 72(7); 1795–803. ©2012 AACR.

NQO1 participates in cellular defense against oxidative stress and regulates apoptosis via p53- and NFκB-mediated pathways. We have previously found that homozygous missense variant NQO1*2 (rs1800566) predicts poor survival among breast cancer patients, particularly after anthracycline-based adjuvant chemotherapy. Here, we investigated NQO1 and NFκB protein expression and global gene expression profiles in breast tumors with correlation to tumor characteristics and survival after adjuvant chemotherapy. We used immunohistochemical analysis of tissue microarrays to study NQO1 and NFκB expression in two series of tumors: 1000 breast tumors unselected for treatment and 113 from a clinical trial comparing chemotherapy regimens after anthracycline treatment in advanced breast cancer. We used gene expression arrays to define genes co-expressed with NQO1 and NFκB. NQO1 and nuclear NFκB were expressed in 83% and 11% of breast tumors, and correlated inversely (P = 0.012). NQO1 protein expression was associated with estrogen receptor (ER) expression (P = 0.011), whereas 34.5% of NFκB-nuclear/activated tumors were ER negative (P = 0.001). NQO1 protein expression and NFκB activation showed only trends, but no statistical significance for patient survival or outcome after anthracycline treatment. Gene expression analysis highlighted 193 genes that significantly correlated with both NQO1 and NFκB in opposite directions, consistent with the expression patterns of the two proteins. Inverse correlation was found with genes related to oxidation/reduction, lipid biosynthesis and steroid metabolism, immune response, lymphocyte activation, Jak-STAT signaling and apoptosis. The inverse relationship between NQO1 protein expression and NFκB activation, underlined also by inverse patterns of association with ER and gene expression profiles of tumors, suggests that NQO1-NFκB interaction in breast cancer is different from several other tissue types, possibly due to estrogen receptor signaling in breast cancer. Neither NQO1 nor NFκB protein expression appear as significant prognostic or predictive markers in breast cancer.

Visual Abstract, Anxiety disorders often manifest in genetically susceptible individuals after psychosocial stress, but the mechanisms underlying these gene-environment interactions are largely unknown. We used the chronic social defeat stress (CSDS) mouse model to study resilience and susceptibility to chronic psychosocial stress. We identified a strong genetic background effect in CSDS-induced social avoidance (SA) using four inbred mouse strains: 69% of C57BL/6NCrl (B6), 23% of BALB/cAnNCrl, 19% of 129S2/SvPasCrl, and 5% of DBA/2NCrl (D2) mice were stress resilient. Furthermore, different inbred mouse strains responded differently to stress, suggesting they use distinct coping strategies. To identify biological pathways affected by CSDS, we used RNA-sequencing (RNA-seq) of three brain regions of two strains, B6 and D2: medial prefrontal cortex (mPFC), ventral hippocampus (vHPC), and bed nucleus of the stria terminalis (BNST). We discovered overrepresentation of oligodendrocyte (OLG)-related genes in the differentially expressed gene population. Because OLGs myelinate axons, we measured myelin thickness and found significant region and strain-specific differences. For example, in resilient D2 mice, mPFC axons had thinner myelin than controls, whereas susceptible B6 mice had thinner myelin than controls in the vHPC. Neither myelin-related gene expression in several other regions nor corpus callosum thickness differed between stressed and control animals. Our unbiased gene expression experiment suggests that myelin plasticity is a substantial response to chronic psychosocial stress, varies across brain regions, and is genetically controlled. Identification of genetic regulators of the myelin response will provide mechanistic insight into the molecular basis of stress-related diseases, such as anxiety disorders, a critical step in developing targeted therapy.

Chlamydia pneumoniae, a gram-negative obligate intracellular bacterium, is a common cause of upper and lower respiratory tract infections worldwide. Persistent C. pneumoniae infections have been linked to chronic disease processes, such as atherosclerosis. In the present study, we examined gene expression changes in the human epithelial cell line at different stages of acute C. pneumoniae infection and used gene ontology annotation, along with single-gene analysis, to select a small group of target genes that could possibly play a key role in C. pneumoniae infection. Selected genes were silenced using small interfering RNA, and the effect of silencing on the number of C. pneumoniae inclusions was measured by time-resolved fluorometric immunoassay. The greatest reduction in the number of C. pneumoniae inclusions was due to the silencing of the gene coding for the transcription factor early growth response 1, which decreased the number of inclusions by 38.6%.

We investigated the effects of the human papillomavirus type 16 E5 oncogene on cellular gene expression in human epithelial cells using cDNA microarray. In a genome-wide microarray assay, the expression of 179 genes was found to be significantly altered due to E5 expression. The expression of lamin A/C was downregulated at protein level. The expression of protein kinase C-delta and phosphoinositide-3-kinase proteins was found to be upregulated. We also observed increased motility of E5-expressing cells. We conclude that the E5 protein affects several cellular pathways involved in cell adhesion, cell motility and mitogenic signaling. These alterations may together lead to inhibition of apoptosis and facilitate the establishment of persistent infection in the epithelium.

We have utilized a two-stage study design to search for SNPs associated with the survival of breast cancer patients treated with adjuvant chemotherapy. Our initial GWS data set consisted of 805 Finnish breast cancer cases (360 treated with adjuvant chemotherapy). The top 39 SNPs from this stage were analyzed in three independent data sets: iCOGS (n=6720 chemotherapy-treated cases), SUCCESS-A (n=3596), and POSH (n=518). Two SNPs were successfully validated: rs6500843 (any chemotherapy; per-allele HR 1.16, 95% C.I. 1.08-1.26, p=0.0001, p(adjusted)=0.0091), and rs11155012 (anthracycline therapy; per-allele HR 1.21, 95% C.I. 1.08-1.35, p=0.0010, p(adjusted)=0.0270). The SNP rs6500843 was found to specifically interact with adjuvant chemotherapy, independently of standard prognostic markers (p(interaction)=0.0009), with the rs6500843-GG genotype corresponding to the highest hazard among chemotherapy-treated cases (HR 1.47, 95% C.I. 1.20-1.80). Upon trans-eQTL analysis of public microarray data, the rs6500843 locus was found to associate with the expression of a group of genes involved in cell cycle control, notably AURKA, the expression of which also exhibited differential prognostic value between chemotherapy-treated and untreated cases in our analysis of microarray data. Based on previously published information, we propose that the eQTL genes may be connected to the rs6500843 locus via a RBFOX1-FOXM1 -mediated regulatory pathway. ispartof: Oncotarget vol:6 issue:10 pages:7390-407 ispartof: location:United States status: published

Background OMICs technologies allow to assay the state of a large number of different features (e.g., mRNA expression, miRNA expression, copy number variation, DNA methylation, etc.) from the same samples. The objective of these experiments is usually to find a reduced set of significant features, which can be used to differentiate the conditions assayed. In terms of development of novel feature selection computational methods, this task is challenging for the lack of fully annotated biological datasets to be used for benchmarking. A possible way to tackle this problem is generating appropriate synthetic datasets, whose composition and behaviour are fully controlled and known a priori. Results Here we propose a novel method centred on the generation of networks of interactions among different biological molecules, especially involved in regulating gene expression. Synthetic datasets are obtained from ordinary differential equations based models with known parameters. Our results show that the generated datasets are well mimicking the behaviour of real data, for popular data analysis methods are able to selectively identify existing interactions. Conclusions The proposed method can be used in conjunction to real biological datasets in the assessment of data mining techniques. The main strength of this method consists in the full control on the simulated data while retaining coherence with the real biological processes. The R package MVBioDataSim is freely available to the scientific community at http://neuronelab.unisa.it/?p=1722. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0577-1) contains supplementary material, which is available to authorized users.

Background Age-related changes in DNA methylation occurring in blood leukocytes during early childhood may reflect epigenetic maturation. We hypothesized that some of these changes involve gene networks of critical relevance in leukocyte biology and conducted a prospective study to elucidate the dynamics of DNA methylation. Serial blood samples were collected at 3, 6, 12, 24, 36, 48 and 60 months after birth in ten healthy girls born in Finland and participating in the Type 1 Diabetes Prediction and Prevention Study. DNA methylation was measured using the HumanMethylation450 BeadChip. Results After filtering for the presence of polymorphisms and cell-lineage-specific signatures, 794 CpG sites showed significant DNA methylation differences as a function of age in all children (41.6% age-methylated and 58.4% age-demethylated, Bonferroni-corrected P value

We read with interest the paper by Rehman et al 1 reporting the contribution of Nod2 genotype to the composition of gut microbiota in mice and Crohn's disease (CD) patients. This was followed by a similar description for another CD-predisposing gene, FUT2.2 To date, 163 CD- and ulcerative colitis-risk loci have been identified, and while most of the known causative genes are involved in immune functions and response to infections, their effects on the composition of the gut microbiota are mostly unknown. Studies like those mentioned above are therefore very important, since the relative abundance of specific enteric bacteria has been clearly shown to be of pathogenetic relevance in mouse models of colitis.3 By studying genotype–microbiota correlations in healthy individuals, key information could also be sought for devoid of potentially confounding effects from disease status and therapeutic treatment. We studied the impact of 30 unequivocal CD-risk loci, each tagged by a single nucleotide polymorphism (SNP), on the mucosa-associated …

In the mammalian central nervous system (CNS) an important contingent of dopaminergic neurons are localized in the substantia nigra and in the ventral tegmental area of the ventral midbrain. They constitute an anatomically and functionally heterogeneous group of cells involved in a variety of regulatory mechanisms, from locomotion to emotional/motivational behavior. Midbrain dopaminergic neuron (mDA) primary cultures represent a useful tool to study molecular mechanisms involved in their development and maintenance. Considerable information has been gathered on the mDA neurons development and maturation in vivo, as well as on the molecular features of mDA primary cultures. Here we investigated in detail the gene expression differences between the tissue of origin and ventral midbrain primary cultures enriched in mDA neurons, using microarray technique. We integrated the results based on different re-annotations of the microarray probes. By using knowledge-based gene network techniques and promoter sequence analysis, we also uncovered mechanisms that might regulate the expression of CNS genes involved in the definition of the identity of specific cell types in the ventral midbrain. We integrate bioinformatics and functional genomics, together with developmental neurobiology. Moreover, we propose guidelines for the computational analysis of microarray gene expression data. Our findings help to clarify some molecular aspects of the development and differentiation of DA neurons within the midbrain.

Background Carbon nanotubes (CNT) represent a great promise for technological and industrial development but serious concerns on their health effects have also emerged. Rod-shaped CNT are, in fact, able to induce asbestos-like pathogenicity in mice including granuloma formation in abdominal cavity and sub-pleural fibrosis. Exposure to CNT, especially in the occupational context, happens mainly by inhalation. However, little is known about the possible effects of CNT on pulmonary allergic diseases, such as asthma. Methods We exposed mice by inhalation to two types of multi-walled CNT, rigid rod-like and flexible tangled CNT, for four hours a day once or on four consecutive days. Early events were monitored immediately and 24 hours after the single inhalation exposure and the four day exposure mimicked an occupational work week. Mast cell deficient mice were used to evaluate the role of mast cells in the occurring inflammation. Results Here we show that even a short-term inhalation of the rod-like CNT induces novel innate immunity-mediated allergic-like airway inflammation in healthy mice. Marked eosinophilia was accompanied by mucus hypersecretion, AHR and the expression of Th2-type cytokines. Exploration of the early events by transcriptomics analysis reveals that a single 4-h exposure to rod-shaped CNT, but not to tangled CNT, causes a radical up-regulation of genes involved in innate immunity and cytokine/chemokine pathways. Mast cells were found to partially regulate the inflammation caused by rod-like CNT, but also alveaolar macrophages play an important role in the early stages. Conclusions These observations emphasize the diverse abilities of CNT to impact the immune system, and they should be taken into account for hazard assessment. Electronic supplementary material The online version of this article (doi:10.1186/s12989-014-0048-2) contains supplementary material, which is available to authorized users.

BRCA1 is a multifunctional protein best known for its role in DNA repair and association with breast and ovarian cancers. To uncover novel biologically significant molecular functions of BRCA1, we tested a panel of 198 approved and experimental drugs to inhibit growth of MDA-MB-231 breast cancer cells depleted for BRCA1 by siRNA. 26S proteasome inhibitors bortezomib and carfilzomib emerged as a new class of selective BRCA1-targeting agents. The effect was confirmed in HeLa and U2OS cancer cell lines using two independent siRNAs, and in mouse embryonic stem (ES) cells with inducible deletion of Brca1. Bortezomib treatment did not cause any increase in nuclear foci containing phosphorylated histone H2AX, and knockdown of BRCA2 did not entail sensitivity to bortezomib, suggesting that the DNA repair function of BRCA1 may not be directly involved. We found that a toxic effect of bortezomib on BRCA1-depleted cells is mostly due to deregulated cell cycle checkpoints mediated by RB1-E2F pathway and 53BP1. Similar to BRCA1, depletion of RB1 also conferred sensitivity to bortezomib, whereas suppression of E2F1 or 53BP1 together with BRCA1 reduced induction of apoptosis after bortezomib treatment. A gene expression microarray study identified additional genes activated by bortezomib treatment only in the context of inactivation of BRCA1 including a critical involvement of the ERN1-mediated unfolded protein response. Our data indicate that BRCA1 has a novel molecular function affecting cell cycle checkpoints in a manner dependent on the 26S proteasome activity.

Neuroendocrine tumors (NETs) arise from disseminated neuroendocrine cells and express general and specific neuroendocrine markers. Neuropeptide S receptor 1 (NPSR1) is expressed in neuroendocrine cells and its ligand neuropeptide S (NPS) affects cell proliferation. Our aim was to study whether NPS/NPSR1 could be used as a biomarker for neuroendocrine neoplasms and to identify the gene pathways affected by NPS/NPSR1. We collected a cohort of NETs comprised of 91 samples from endocrine glands, digestive tract, skin, and lung. Tumor type was validated by immunostaining of chromogranin-A and synaptophysin expression and tumor grade was analyzed by Ki-67 proliferation index. NPS and NPSR1 expression was quantified by immunohistochemistry using polyclonal antibodies against NPS and monoclonal antibodies against the amino-terminus and carboxy-terminus of NPSR1 isoform A (NPSR1-A). The effects of NPS on downstream signaling were studied in a human SH-SY5Y neuroblastoma cell line which overexpresses NPSR1-A and is of neuroendocrine origin. NPSR1 and NPS were expressed in most NET tissues, with the exception of adrenal pheochromocytomas in which NPS/NPSR1 immunoreactivity was very low. Transcriptome analysis of NPSR1-A overexpressing cells revealed that mitogen-activated protein kinase (MAPK) pathways, circadian activity, focal adhesion, transforming growth factor beta, and cytokine–cytokine interactions were the most altered gene pathways after NPS stimulation. Our results show that NETs are a source of NPS and NPSR1, and that NPS affects cancer-related pathways. Electronic supplementary material The online version of this article (doi:10.1007/s00428-014-1602-x) contains supplementary material, which is available to authorized users.

Selecting relevant features is a common task in most OMICs data analysis, where the aim is to identify a small set of key features to be used as biomarkers. To this end, two alternative but equally valid methods are mainly available, namely the univariate (filter) or the multivariate (wrapper) approach. The stability of the selected lists of features is an often neglected but very important requirement. If the same features are selected in multiple independent iterations, they more likely are reliable biomarkers. In this study, we developed and evaluated the performance of a novel method for feature selection and prioritization, aiming at generating robust and stable sets of features with high predictive power. The proposed method uses the fuzzy logic for a first unbiased feature selection and a Random Forest built from conditional inference trees to prioritize the candidate discriminant features. Analyzing several multi-class gene expression microarray data sets, we demonstrate that our technique provides equal or better classification performance and a greater stability as compared to other Random Forest-based feature selection methods.

Azacitidine induces profound genome-wide hypomethylation in primary myelodysplastic bone marrow cultures but may also reduce histone acetylation

Gliadin triggers T-cell mediated immunity in celiac disease, and has cytotoxic effects on enterocytes mediated through obscure mechanisms. In addition, gliadin transport mechanisms, potential cell surface receptors and gliadin-activated downstream signaling pathways are not completely understood. In order to screen for novel downstream gliadin target genes we performed a systematic whole genome expression study on intestinal epithelial cells. Undifferentiated Caco-2 cells were exposed to pepsin- and trypsin- digested gliadin (PT-G), a blank pepsin-trypsin control (PT) and to a synthetic peptide corresponding to gliadin p31-43 peptide for six hours. RNA from four different experiments was used for hybridization on Agilent one color human whole genome DNA microarray chips. The microarray data were analyzed using the Bioconductor package LIMMA. Genes with nominal p

Leptin is an adipocyte-derived hormone that controls food intake and reproductive and immune functions in rodents. In uncontrolled human studies, low leptin levels are associated with impaired immune responses and reduced T-cell counts; however, the effects of leptin replacement on the adaptive immune system have not yet been reported in the context of randomized, controlled studies and/or in conditions of chronic acquired leptin deficiency. To address these questions, we performed a randomized, double-blinded, placebo-controlled trial of recombinant methionyl-human leptin (metreleptin) administration in replacement doses in women experiencing the female triad (hypothalamic amenorrhea) with acquired chronic hypoleptinemia induced by negative energy balance. Metreleptin restored both CD4 + T-cell counts and their in vitro proliferative responses in these women. These changes were accompanied by a transcriptional signature in which genes relevant to cell survival and hormonal response were up-regulated, and apoptosis genes were down-regulated in circulating immune cells. We also observed that signaling pathways involved in cell growth/survival/proliferation, such as the STAT3, AMPK, mTOR, ERK1/2, and Akt pathways, were activated directly by acute in vivo metreleptin administration in peripheral blood mononuclear cells and CD4 + T-cells both from subjects with chronic hypoleptinemia and from normoleptinemic, lean female subjects. Our data show that metreleptin administration, in doses that normalize circulating leptin levels, induces transcriptional changes, activates intracellular signaling pathways, and restores CD4 + T-cell counts. Thus, metreleptin may prove to be a safe and effective therapy for selective CD4 + T-cell immune reconstitution in hypoleptinemic states such as tuberculosis and HIV infection in which CD4 + T cells are reduced.

Existing computational methods for drug repositioning either rely only on the gene expression response of cell lines after treatment, or on drug-to-disease relationships, merging several information levels. However, the noisy nature of the gene expression and the scarcity of genomic data for many diseases are important limitations to such approaches. Here we focused on a drug-centered approach by predicting the therapeutic class of FDA-approved compounds, not considering data concerning the diseases. We propose a novel computational approach to predict drug repositioning based on state-of-the-art machine-learning algorithms. We have integrated multiple layers of information: i) on the distances of the drugs based on how similar are their chemical structures, ii) on how close are their targets within the protein-protein interaction network, and iii) on how correlated are the gene expression patterns after treatment. Our classifier reaches high accuracy levels (78%), allowing us to re-interpret the top misclassifications as re-classifications, after rigorous statistical evaluation. Efficient drug repurposing has the potential to significantly impact the whole field of drug development. The results presented here can significantly accelerate the translation into the clinics of known compounds for novel therapeutic uses.

Methylation of cytosines at CpG sites is a common epigenetic DNA modification that can be measured by a large number of methods, now even in a genome-wide manner for hundreds of thousands of sites. The application of DNA methylation analysis is becoming widely popular in complex disorders, for example, to understand part of the "missing heritability". The DNA samples most readily available for methylation studies are derived from whole blood. However, blood consists of many functionally and developmentally distinct cell populations in varying proportions. We studied whether such variation might affect the interpretation of methylation studies based on whole blood DNA. We found in healthy male blood donors there is important variation in the methylation profiles of whole blood, mononuclear cells, granulocytes, and cells from seven selected purified lineages. CpG methylation between mononuclear cells and granulocytes differed for 22% of the 8252 probes covering the selected 343 genes implicated in immune-related disorders by genome-wide association studies, and at least one probe was differentially methylated for 85% of the genes, indicating that whole blood methylation results might be unintelligible. For individual genes, even if the overall methylation patterns might appear similar, a few CpG sites in the regulatory regions may have opposite methylation patterns (i.e., hypo/hyper) in the main blood cell types. We conclude that interpretation of whole blood methylation profiles should be performed with great caution and for any differences implicated in a disorder, the differences resulting from varying proportions of white blood cell types should be considered.

Tyrosine kinase inhibitors have greatly improved the prognosis of chronic myeloid leukemia (CML). In addition to direct kinase inhibition, their effects can also be mediated through immune modulation, such as expansion of cytotoxic T and natural-killer cells observed during dasatinib therapy. As natural-killer cell and partially CD8 + T-cell function are regulated by killer immunoglobulin-like receptors (KIRs), we studied whether the KIR gene profile is associated with clinical therapy response in dasatinib-treated CML patients (n = 191). In first-line patients, the absence of the inhibitory KIR2DL5A ( p = 0.0489), 2DL5B ( p = 0.030), and 2DL5all ( p = 0.0272) genes were associated with improved molecular response at the 12-month time point. In addition, the same trend was seen with two activating KIR genes, 2DS1 ( p = 0.061) and 2DS2 ( p = 0.071). Furthermore, when patients were clustered into two groups by their KIR gene profile, the BCR-ABL1 transcript levels differed significantly between the groups ( p = 0.047), showing that patients who lacked several KIR genes had better response. The comparison of first-line and second-line patients did not show any significant differences in either KIR or human leukocyte antigen genotypes. Our results show that immunogenetic factors, such as the KIR gene profile, can play a role in tyrosine kinase inhibitor therapy response. Additional studies are warranted to elucidate the functional significance of KIR genes associated with treatment outcomes.

The brain is the most cholesterol-enriched tissue in the body. During brain development, desmosterol, an immediate precursor of cholesterol, transiently accumulates up to 30% of total brain sterols. This massive desmosterol deposition appears to be present in all mammalian species reported so far, including humans, but how it is achieved is not well understood. Here, we propose that desmosterol accumulation in the developing brain may be primarily caused by post-transcriptional repression of 3?-hydroxysterol 24-reductase (DHCR24) by progesterone. Furthermore, distinct properties of desmosterol may serve to increase the membrane active pool of sterols in the brain: desmosterol cannot be hydroxylated to generate 24S-hydroxycholesterol, a brain derived secretory sterol, desmosterol has a reduced propensity to be esterified as compared to cholesterol, and desmosterol may activate LXR to stimulate astrocyte sterol secretion. This regulated accumulation of desmosterol by progesterone- induced suppression of DHCR24 may facilitate the rapid enrichment and distribution of membrane sterols in the developing brain. © FASEB.

The sensing by T cells of metabolic and energetic changes in the microenvironment can determine the differentiation, maturation, and activation of these cells. Although it is known that mammalian target of rapamycin (mTOR) gauges nutritonal and energetic signals in the extracellular milieu, it is not known how mTOR and metabolism influence CD4+CD25−FOXP3− effector T cell (Teff) responses. In this article, we show that leptin-induced activation of mTOR, which, in turn, controls leptin production and signaling, causes a defined cellular, biochemical, and transcriptional signature that determine the outcome of Teff responses, both in vitro and in vivo. The blockade of leptin/leptin receptor signaling, induced by genetic means or by starvation, leads to impaired mTOR activity that inhibits the proliferation of Teffs in vivo. Notably, the transcriptional signature of Teffs in the presence of leptin blockade appears similar to that observed in rapamycin-treated Teffs. These results identify a novel link between nutritional status and Teff responses through the leptin–mTOR axis and define a potential target for Teff modulation in normal and pathologic conditions.

The differential diagnosis of schizophrenia (SZ) and bipolar disorder (BD) is based solely on clinical features and upon a subset of overlapping symptoms. Within the last years, an increasing amount of clinical, epidemiological and genetic data suggested inconsistent with the Kraepelinian dichotomy. We performed re-analysis of genome-wide gene expression data obtained from postmortem prefrontal cortex (PEC) of both BD and SZ patients with matched controls from four independent microarray experiments. We found 2,577 and 477 genes specifically altered in BD and SZ, respectively. Of these, 164 genes were shared between the syndromes. We identified genes of the transcriptional and post-transcriptional machineries altered in BD and genes of the development changed in SZ. Our results showed that the genomic expression profile of BD and SZ had some similarity but still could be well-distinguished by suitable statistical test.

Triple-negative breast cancers are an aggressive subtype of breast cancer with poor survival, but there remains little known about the etiologic factors that promote its initiation and development. Commonly inherited breast cancer risk factors identified through genome-wide association studies display heterogeneity of effect among breast cancer subtypes as defined by the status of estrogen and progesterone receptors. In the Triple Negative Breast Cancer Consortium (TNBCC), 22 common breast cancer susceptibility variants were investigated in 2,980 Caucasian women with triple-negative breast cancer and 4,978 healthy controls. We identified six single-nucleotide polymorphisms, including rs2046210 (ESR1), rs12662670 (ESR1), rs3803662 (TOX3), rs999737 (RAD51L1), rs8170 (19p13.1), and rs8100241 (19p13.1), significantly associated with the risk of triple-negative breast cancer. Together, our results provide convincing evidence of genetic susceptibility for triple-negative breast cancer. Cancer Res; 71(19); 6240–9. ©2011 AACR.

MicroRNAs (miRNAs) are small regulatory molecules that cause post-transcriptional gene silencing. Although some miRNAs are known to have region-specific expression patterns in the adult brain, the functional consequences of the region-specificity to the gene regulatory networks of the brain nuclei are not clear. Therefore, we studied miRNA expression patterns by miRNA-Seq and microarrays in two brain regions, frontal cortex (FCx) and hippocampus (HP), which have separate biological functions. We identified 354 miRNAs from FCx and 408 from HP using miRNA-Seq, and 245 from FCx and 238 from HP with microarrays. Several miRNA families and clusters were differentially expressed between FCx and HP, including the miR-8 family, miR-182|miR-96|miR-183 cluster, and miR-212|miR-312 cluster overexpressed in FCx and miR-34 family overexpressed in HP. To visualize the clusters, we developed support for viewing genomic alignments of miRNA-Seq reads in the Chipster genome browser. We carried out pathway analysis of the predicted target genes of differentially expressed miRNA families and clusters to assess their putative biological functions. Interestingly, several miRNAs from the same family/cluster were predicted to regulate specific biological pathways. We have developed a miRNA-Seq approach with a bioinformatic analysis workflow that is suitable for studying miRNA expression patterns from specific brain nuclei. FCx and HP were shown to have distinct miRNA expression patterns which were reflected in the predicted gene regulatory pathways. This methodology can be applied for the identification of brain region-specific and phenotype-specific miRNA-mRNA-regulatory networks from the adult and developing rodent brain.

OBJECTIVE Combination antiretroviral therapy (cART) is associated with lipodystrophy, i.e., loss of subcutaneous adipose tissue in the abdomen, limbs, and face and its accumulation intra-abdominally. No fat is lost dorsocervically and it can even accumulate in this region (buffalo hump). It is unknown how preserved dorsocervical fat differs from abdominal subcutaneous fat in HIV-1–infected cART-treated patients with (cART+LD+) and without (cART+LD−) lipodystrophy. RESEARCH DESIGN AND METHODS We used histology, microarray, PCR, and magnetic resonance imaging to compare dorsocervical and abdominal subcutaneous adipose tissue in cART+LD+ (n = 21) and cART+LD− (n = 11). RESULTS Albeit dorsocervical adipose tissue in cART+LD+ seems spared from lipoatrophy, its mitochondrial DNA (mtDNA; copies/cell) content was significantly lower (by 62%) than that of the corresponding tissue in cART+LD−. Expression of CD68 mRNA, a marker of macrophages, and numerous inflammatory genes in microarray were significantly lower in dorsocervical versus abdominal subcutaneous adipose tissue. Genes with the greatest difference in expression between the two depots were those involved in regulation of transcription and regionalization (homeobox genes), irrespective of lipodystrophy status. There was negligible mRNA expression of uncoupling protein 1, a gene characteristic of brown adipose tissue, in either depot. CONCLUSIONS Because mtDNA is depleted even in the nonatrophic dorsocervical adipose tissue, it is unlikely that the cause of lipoatrophy is loss of mtDNA. Dorsocervical adipose tissue is less inflamed than lipoatrophic adipose tissue. It does not resemble brown adipose tissue. The greatest difference in gene expression between dorsocervical and abdominal subcutaneous adipose tissue is in expression of homeobox genes.

Lactobacillus rhamnosus GG (GG) is a widely used and intensively studied probiotic bacterium. Although the health benefits of strain GG are well documented, the systematic exploration of mechanisms by which this strain exerts probiotic effects in the host has only recently been initiated. The ability to survive the harsh conditions of the gastrointestinal tract, including gastric juice containing bile salts, is one of the vital characteristics that enables a probiotic bacterium to transiently colonize the host. Here we used gene expression profiling at the transcriptome and proteome levels to investigate the cellular response of strain GG toward bile under defined bioreactor conditions. The analyses revealed that in response to growth of strain GG in the presence of 0.2% ox gall the transcript levels of 316 genes changed significantly (p < 0.01, t test), and 42 proteins, including both intracellular and surface-exposed proteins (i.e. surfome), were differentially abundant (p < 0.01, t test in total proteome analysis; p < 0.05, t test in surfome analysis). Protein abundance changes correlated with transcriptome level changes for 14 of these proteins. The identified proteins suggest diverse and specific changes in general stress responses as well as in cell envelope-related functions, including in pathways affecting fatty acid composition, cell surface charge, and thickness of the exopolysaccharide layer. These changes are likely to strengthen the cell envelope against bile-induced stress and signal the GG cells of gut entrance. Notably, the surfome analyses demonstrated significant reduction in the abundance of a protein catalyzing the synthesis of exopolysaccharides, whereas a protein dedicated for active removal of bile compounds from the cells was up-regulated. These findings suggest a role for these proteins in facilitating the well founded interaction of strain GG with the host mucus in the presence of sublethal doses of bile. The significance of these findings in terms of the functionality of a probiotic bacterium is discussed.

BackgroundThe U12-type spliceosome is responsible for the removal of a subset of introns from eukaryotic mRNAs. U12-type introns are spliced less efficiently than normal U2-type introns, which suggests a rate-limiting role in gene expression. The Drosophila genome contains about 20 U12-type introns, many of them in essential genes, and the U12-type spliceosome has previously been shown to be essential in the fly.Methodology/principal findingsWe have used a Drosophila line with a P-element insertion in U6atac snRNA, an essential component of the U12-type spliceosome, to investigate the impact of U12-type introns on gene expression at the organismal level during fly development. This line exhibits progressive accumulation of unspliced U12-type introns during larval development and the death of larvae at the third instar stage. Surprisingly, microarray and RT-PCR analyses revealed that most genes containing U12-type introns showed only mild perturbations in the splicing of U12-type introns. In contrast, we detected widespread downstream effects on genes that do not contain U12-type introns, with genes related to various metabolic pathways constituting the largest group.Conclusions/significanceU12-type intron-containing genes exhibited variable gene-specific responses to the splicing defect, with some genes showing up- or downregulation, while most did not change significantly. The observed residual U12-type splicing activity could be explained with the mutant U6atac allele having a low level of catalytic activity. Detailed analysis of all genes suggested that a defect in the splicing of the U12-type intron of the mitochondrial prohibitin gene may be the primary cause of the various downstream effects detected in the microarray analysis.

Background DNA microarrays provide an efficient method for measuring activity of genes in parallel and even covering all the known transcripts of an organism on a single array. This has to be balanced against that analyzing data emerging from microarrays involves several consecutive steps, and each of them is a potential source of errors. Errors tend to accumulate when moving from the lower level towards the higher level analyses because of the sequential nature. Eliminating such errors does not seem feasible without completely changing the technologies, but one should nevertheless try to meet the goal of being able to realistically assess degree of the uncertainties that are involved when drawing the final conclusions from such analyses. Results We present a Bayesian hierarchical model for finding differentially expressed genes between two experimental conditions, proposing an integrated statistical approach where correcting signal saturation, systematic array effects, dye effects, and finding differentially expressed genes, are all modeled jointly. The integration allows all these components, and also the associated errors, to be considered simultaneously. The inference is based on full posterior distribution of gene expression indices and on quantities derived from them rather than on point estimates. The model was applied and tested on two different datasets. Conclusions The method presents a way of integrating various steps of microarray analysis into a single joint analysis, and thereby enables extracting information on differential expression in a manner, which properly accounts for various sources of potential error in the process.