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Background and aims:Infliximab is an effective treatment for ulcerative colitis with over 60% of patients responding to treatment and up to 30% reaching remission. The mechanism of resistance to anti-tumour necrosis factor α (anti-TNFα) is unknown. This study used colonic mucosal gene expression to provide a predictive response signature for infliximab treatment in ulcerative colitis.Methods:Two cohorts of patients who received their first treatment with infliximab for refractory ulcerative colitis were studied. Response to infliximab was defined as endoscopic and histological healing. Total RNA from pre-treatment colonic mucosal biopsies was analysed with Affymetrix Human Genome U133 Plus 2.0 Arrays. Quantitative RT-PCR was used to confirm microarray data.Results:For predicting response to infliximab treatment, pre-treatment colonic mucosal expression profiles were compared for responders and non-responders. Comparative analysis identified 179 differentially expressed probe sets in cohort A and 361 in cohort B with an overlap of 74 probe sets, representing 53 known genes, between both analyses. Comparative analysis of both cohorts combined, yielded 212 differentially expressed probe sets. The top five differentially expressed genes in a combined analysis of both cohorts were osteoprotegerin, stanniocalcin-1, prostaglandin-endoperoxide synthase 2, interleukin 13 receptor alpha 2 and interleukin 11. All proteins encoded by these genes are involved in the adaptive immune response. These markers separated responders from non-responders with 95% sensitivity and 85% specificity.Conclusion:Gene array studies of ulcerative colitis mucosal biopsies identified predictive panels of genes for (non-)response to infliximab. Further study of the pathways involved should allow a better understanding of the mechanisms of resistance to infliximab therapy in ulcerative colitis.ClinicalTrials.gov number, NCT00639821.

Spatial transcriptomics (ST) technology through in situ capturing has enabled topographical gene expression profiling of tumor tissues. However, each capturing spot may contain diverse immune and malignant cells, with different cell densities across tissue regions. Cell type deconvolution in tumor ST data remains challenging for existing methods designed to decompose general ST or bulk tumor data. We develop the Spatial Cellular Estimator for Tumors (SpaCET) to infer cell identities from tumor ST data. SpaCET first estimates cancer cell abundance by integrating a gene pattern dictionary of copy number alterations and expression changes in common malignancies. A constrained regression model then calibrates local cell densities and determines immune and stromal cell lineage fractions. SpaCET provides higher accuracy than existing methods based on simulation and real ST data with matched double-blind histopathology annotations as ground truth. Further, coupling cell fractions with ligand-receptor coexpression analysis, SpaCET reveals how intercellular interactions at the tumor-immune interface promote cancer progression. Cell type deconvolution in tumor spatial transcriptomics (ST) data remains challenging. Here, the authors develop Spatial Cellular Estimator for Tumors (SpaCET) to infer cell types and intercellular interactions from ST data in cancer across different platforms, with improved performance over similar methods.

Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org .

Stratification of asthma at the molecular level, especially using accessible biospecimens, could greatly enable patient selection for targeted therapy. To determine the value of blood analysis to identify transcriptional differences between clinically defined asthma and nonasthma groups, identify potential patient subgroups based on gene expression, and explore biological pathways associated with identified differences. Transcriptomic profiles were generated by microarray analysis of blood from 610 patients with asthma and control participants in the U-BIOPRED (Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes) study. Differentially expressed genes (DEGs) were identified by analysis of variance, including covariates for RNA quality, sex, and clinical site, and Ingenuity Pathway Analysis was applied. Patient subgroups based on DEGs were created by hierarchical clustering and topological data analysis. A total of 1,693 genes were differentially expressed between patients with severe asthma and participants without asthma. The differences from participants without asthma in the nonsmoking severe asthma and mild/moderate asthma subgroups were significantly related (r = 0.76), with a larger effect size in the severe asthma group. The majority of, but not all, differences were explained by differences in circulating immune cell populations. Pathway analysis showed an increase in chemotaxis, migration, and myeloid cell trafficking in patients with severe asthma, decreased B-lymphocyte development and hematopoietic progenitor cells, and lymphoid organ hypoplasia. Cluster analysis of DEGs led to the creation of subgroups among the patients with severe asthma who differed in molecular responses to oral corticosteroids. Blood gene expression differences between clinically defined subgroups of patients with asthma and individuals without asthma, as well as subgroups of patients with severe asthma defined by transcript profiles, show the value of blood analysis in stratifying patients with asthma and identifying molecular pathways for further study. Clinical trial registered with www.clinicaltrials.gov (NCT01982162).

Purpose Multi-gene signatures provide biological insight and risk stratification in breast cancer. Intrinsic molecular subtypes defined by mRNA expression of 50 genes (PAM50) are prognostic in hormone-receptor positive postmenopausal breast cancer. Yet, for 25–40% in the PAM50 intermediate risk group, long-term risk remains uncertain. Our study aimed to (i) test the long-term prognostic value of the PAM50 signature in pre- and post-menopausal breast cancer; (ii) investigate if the PAM50 model could be improved by addition of other mRNAs implicated in oncogenesis. Methods We used archived FFPE samples from 1723 breast cancer survivors; high quality reads were obtained on 1253 samples. Transcript expression was quantified using a custom codeset with probes for > 100 targets. Cox models assessed gene signatures for breast cancer relapse and survival. Results Over 15 + years of follow-up, PAM50 subtypes were ( P  < 0.01) associated with breast cancer outcomes after accounting for tumor stage, grade and age at diagnosis. Results did not differ by menopausal status at diagnosis. Women with Luminal B (versus Luminal A) subtype had a > 60% higher hazard. Addition of a 13-gene hypoxia signature improved prognostication with > 40% higher hazard in the highest vs lowest hypoxia tertiles. Conclusions PAM50 intrinsic subtypes were independently prognostic for long-term breast cancer survival, irrespective of menopausal status. Addition of hypoxia signatures improved risk prediction. If replicated, incorporating the 13-gene hypoxia signature into the existing PAM50 risk assessment tool, may refine risk stratification and further clarify treatment for breast cancer.

While triple-negative breast cancer (TNBC) is known to be heterogeneous at the genomic and transcriptomic levels, spatial information on tumor organization and cell composition is still lacking. Here, we investigate TNBC tumor architecture including its microenvironment using spatial transcriptomics on a series of 92 patients. We perform an in-depth characterization of tumor and stroma organization and composition using an integrative approach combining histomorphological and spatial transcriptomics. Furthermore, a detailed molecular characterization of tertiary lymphoid structures leads to identify a gene signature strongly associated to disease outcome and response to immunotherapy in several tumor types beyond TNBC. A stepwise clustering analysis identifies nine TNBC spatial archetypes, further validated in external datasets. Several spatial archetypes are associated with disease outcome and characterized by potentially actionable features. In this work, we provide a comprehensive insight into the complexity of TNBC ecosystem with potential clinical relevance, opening avenues for treatment tailoring including immunotherapy. Triple-negative breast cancer (TNBC) is a heterogenous disease with several molecular subtypes previously described. Here the authors perform a spatial transcriptomics analysis on a series of 92 patients, providing additional insights into the heterogeneity of TNBC, with implications for clinical outcomes and therapy.

Objectives To assess the efficacy of the interleukin 1 receptor antagonist anakinra in systemic-onset juvenile idiopathic arthritis (SJIA). Methods A multicentre, randomised, double-blind, placebo-controlled trial was conducted. The primary objective was to compare the efficacy of a 1-month treatment with anakinra (2 mg/kg subcutaneous daily, maximum 100 mg) with a placebo between two groups each with 12 patients with SJIA. Response was defined by a 30% improvement of the paediatric American College of Rheumatology criteria for JIA, resolution of systemic symptoms and a decrease of at least 50% of both C-reactive protein and erythrocyte sedimentation rate compared with baseline. After month 1 (M1), patients taking placebo were switched to anakinra. Secondary objectives included tolerance and efficacy assessment for 12 months, and analyses of treatment effect on blood gene expression profiling. Results At M1, 8/12 responders were receiving anakinra and 1 responder receiving placebo (p=0.003). Ten patients from the placebo group switched to anakinra; nine were responders at M2. Between M1 and M12, six patients stopped treatment owing to an adverse event (n=2), lack of efficacy (n=2) or a disease flare (n=2). Blood gene expression profiling at enrolment and at 6 months' follow-up showed one set of dysregulated genes that reverted to normal values in the clinical responders and a different set, including interferon (IFN)-inducible genes, that was induced by anakinra. Conclusions Anakinra treatment is effective in SJIA, at least in the short term. It is associated with normalisation of blood gene expression profiles in clinical responders and induces a de novo IFN signature. Trial Registration Number: NCT00339157.

Productivity of Indian mustard ( B. juncea ), a major oil yielding crop in rapeseed-mustard group is heavily inflicted by mustard aphid, L. erysimi . Mustard aphid, a specialist aphid species on rapeseed-mustard crops, rapidly multiplies and colonizes the plants leading to successful infestation. In contrary, legume specific cowpea aphid, A. craccivora when released on B. juncea plants fails to build up population and thus remains unsuccessful in infestation. In the present study, differential host response of B. juncea to the two aphid species, one being successful insect-pest and the other being unsuccessful on it has been studied based on transcriptome analysis. Differential feeding efficiency of the two aphid species on mustard plants was evident from the amount of secreted honeydews. Leaf-transcriptomes of healthy and infested plants, treated with the two aphid species, were generated by RNA sequencing on Illumina platform and de novo assembly of the quality reads. A comparative assessment of the differentially expressed genes due to treatments revealed a large extent of overlaps as well as distinctness with respect to the set of genes and their direction of regulation. With respect to host-genes related to transcription factors, oxidative homeostasis, defense hormones and secondary metabolites, L. erysimi led to either suppression or limited activation of the transcript levels compared to A . craccivora . Further, a comprehensive view of the DEGs suggested more potential of successful insect-pests towards transcriptional reprogramming of the host. qRT-PCR based validation of randomly selected up- and down-regulated transcripts authenticated the transcriptome data.

Studies of the singlet oxygen (¹O₂)-overproducing flu and chlorina1 (ch1) mutants of Arabidopsis (Arabidopsis thaliana) have shown that ¹O₂-induced changes in gene expression can lead to either programmed cell death (PCD) or acclimation. A transcriptomic analysis of the ch1 mutant has allowed the identification of genes whose expression is specifically affected by each phenomenon. One such gene is OXIDATIVE SIGNAL INDUCIBLE1 (OXI1) encoding an AGC kinase that was noticeably induced by excess light energy and ¹O₂ stress conditions leading to cell death. Photo-induced oxidative damage and cell death were drastically reduced in the OXI1 null mutant (oxi1) and in the double mutant ch1*oxi1 compared with the wild type and the ch1 single mutant, respectively. This occurred without any changes in the production rate of ¹O₂ but was cancelled by exogenous applications of the phytohormone jasmonate. OXI1-mediated ¹O₂ signaling appeared to operate through a different pathway from the previously characterized OXI1-dependent response to pathogens and H₂O₂ and was found to be independent of the EXECUTER proteins. In high-light-stressed plants, the oxi1 mutation was associated with reduced jasmonate levels and with the up-regulation of genes encoding negative regulators of jasmonate signaling and PCD. Our results show that OXI1 is a new regulator of ¹O₂-induced PCD, likely acting upstream of jasmonate.

Background Circulating microRNAs have shown promise as non-invasive biomarkers and predictors of disease activity. Prior asthma studies using clinical, biochemical and genomic data have not shown excellent prediction of exacerbation. We hypothesized that a panel of circulating microRNAs in a pediatric asthma cohort combined with an exacerbation clinical score might predict exacerbation better than the latter alone. Methods Serum samples from 153 children at randomization in the Childhood Asthma Management Program were profiled for 754 microRNAs. Data dichotomized for asthma exacerbation one year after randomization to inhaled corticosteroid treatment were used for binary logistic regression with miRNA expressions and exacerbation clinical score. Results 12 of 125 well-detected circulating microRNAs had significant odd ratios for exacerbation with miR-206 being most significant. Each doubling of expression of the 12 microRNA corresponded to a 25–67% increase in exacerbation risk. Stepwise logistic regression yielded a 3-microRNA model (miR-146b, miR-206 and miR-720) that, combined with the exacerbation clinical score, had excellent predictive power with a 0.81 AUROC. These 3 microRNAs were involved in NF-kβ and GSK3/AKT pathways. Conclusions This combined circulating microRNA-clinical score model predicted exacerbation in asthmatic subjects on inhaled corticosteroids better than each constituent feature alone. Trial registration ClinicalTrials.gov Identifier: NCT00000575 .