Regulatory T Cell-Derived TGF-ß1 Controls Multiple Checkpoints Governing Allergy and Autoimmunity.

The mechanisms by which regulatory T (Treg) cells differentially control allergic and autoimmune responses remain unclear. We show that Treg cells in food allergy (FA) had decreased expression of transforming growth factor beta 1 (TGF-ß1) because of interleukin-4 (IL-4)- and signal transducer and activator of transciription-6 (STAT6)-dependent inhibition of Tgfb1 transcription. These changes were modeled by Treg cell-specific Tgfb1 monoallelic inactivation, which induced allergic dysregulation by impairing microbiota-dependent retinoic acid receptor-related orphan receptor gamma t (ROR-γt)+ Treg cell differentiation. This dysregulation was rescued by treatment with Clostridiales species, which upregulated Tgfb1 expression in Treg cells. Biallelic deficiency precipitated fatal autoimmunity with intense autoantibody production and dysregulated T follicular helper and B cell responses. These results identify a privileged role of Treg cell-derived TGF-ß1 in regulating allergy and autoimmunity at distinct checkpoints in a Tgfb1 gene dose- and microbiota-dependent manner.

Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing.

MOTIVATION: Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures. RESULTS: The novel approach Dr Insight implements a frame-breaking statistical model for the 'hand-shake' between disease and drug data. The genome-wide screening of concordantly expressed genes (CEGs) eliminates the need for subjective selection of query signatures, added to eliciting better proxy for potential disease-specific drug targets. Extensive comparisons on simulated and real cancer datasets have validated the superior performance of Dr Insight over several popular drug-repurposing methods to detect known cancer drugs and drug-target interactions. A proof-of-concept trial using the TCGA breast cancer dataset demonstrates the application of Dr Insight for a comprehensive analysis, from redirection of drug therapies, to a systematic construction of disease-specific drug-target networks. AVAILABILITY AND IMPLEMENTATION: Dr Insight R package is available at https://cran.r-project.org/web/packages/DrInsight/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Phantom: investigating heterogeneous gene sets in time-course data.

MOTIVATION: Gene set analysis is a powerful tool to study the coordinative change of time-course data. However, most existing methods only model the overall change of a gene set, yet completely overlooked heterogeneous time-dependent changes within sub-sets of genes. RESULTS: We have developed a novel statistical method, Phantom, to investigate gene set heterogeneity. Phantom employs the principle of multi-objective optimization to assess the heterogeneity inside a gene set, which also accounts for the temporal dependency in time-course data. Phantom improves the performance of gene set based methods to detect biological changes across time. AVAILABILITY AND IMPLEMENTATION: Phantom webpage can be accessed at: http://www.baylorhealth.edu/Phantom . R package of Phantom is available at https://cran.r-project.org/web/packages/phantom/index.html . CONTACT: jinghua.gu@bswhealth.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quantitative gene set analysis generalized for repeated measures, confounder adjustment, and continuous covariates.

BACKGROUND: Gene set analysis (GSA) of gene expression data can be highly powerful when the biological signal is weak compared to other sources of variability in the data. However, many gene set analysis approaches utilize permutation tests which are not appropriate for complex study designs. For example, the correlation of subjects is broken when comparing time points within a longitudinal study. Linear mixed models provide a method to analyze longitudinal studies as well as adjust for potential confounding factors and account for sources of variability that are not of primary interest. Currently, there are no known gene set analysis approaches that fully account for these study design and analysis aspects. In order to do so, we generalize the QuSAGE gene set analysis algorithm, denoted Q-Gen, and provide the necessary estimation adjustments to incorporate linear mixed model analyses. RESULTS: We assessed the performance of our generalized method in comparison to the original QuSAGE method in settings such as longitudinal repeated measures analysis and accounting for potential confounders. We demonstrate that the original QuSAGE method can not control for type-I error when these complexities exist. In addition to statistical appropriateness, analysis of a longitudinal influenza study suggests Q-Gen can allow for greater sensitivity when exploring a large number of gene sets. CONCLUSIONS: Q-Gen is an extension to the gene set analysis method of QuSAGE, and allows for linear mixed models to be applied appropriately within a gene set analysis framework. It provides GSA an added layer of flexibility that was not currently available. This flexibility allows for more appropriate statistical modeling of complex data structures that are inherent to many microarray study designs and can provide more sensitivity.

Microbial symbionts regulate the primary Ig repertoire.

The ability of immunoglobulin (Ig) to recognize pathogens is critical for optimal immune fitness. Early events that shape preimmune Ig repertoires, expressed on IgM+ IgD+ B cells as B cell receptors (BCRs), are poorly defined. Here, we studied germ-free mice and conventionalized littermates to explore the hypothesis that symbiotic microbes help shape the preimmune Ig repertoire. Ig-binding assays showed that exposure to conventional microbial symbionts enriched frequencies of antibacterial IgM+ IgD+ B cells in intestine and spleen. This enrichment affected follicular B cells, involving a diverse set of Ig-variable region gene segments, and was T cell-independent. Functionally, enrichment of microbe reactivity primed basal levels of small intestinal T cell-independent, symbiont-reactive IgA and enhanced systemic IgG responses to bacterial immunization. These results demonstrate that microbial symbionts influence host immunity by enriching frequencies of antibacterial specificities within preimmune B cell repertoires and that this may have consequences for mucosal and systemic immunity.

Transnuclear TRP1-specific CD8 T cells with high or low affinity TCRs show equivalent antitumor activity.

We have generated, via somatic cell nuclear transfer, two independent lines of transnuclear (TN) mice, using as nuclear donors CD8 T cells, sorted by tetramer staining, that recognize the endogenous melanoma antigen TRP1. These two lines of nominally identical specificity differ greatly in their affinity for antigen (TRP1(high) or TRP1(low)) as inferred from tetramer dissociation and peptide responsiveness. Ex vivo-activated CD8 T cells from either TRP1(high) or TRP1(low) mice show cytolytic activity in 3D tissue culture and in vivo, and slow the progression of subcutaneous B16 melanoma. Although naïve TRP1(low) CD8 T cells do not affect tumor growth, upon activation these cells function indistinguishably from TRP1(high) cells in vivo, limiting tumor cell growth and increasing mouse survival. The anti-tumor effect of both TRP1(high) and TRP1(low) CD8 T cells is enhanced in RAG-deficient hosts. However, tumor outgrowth eventually occurs, likely due to T cell exhaustion. The TRP1 TN mice are an excellent model for examining the functional attributes of T cells conferred by TCR affinity, and they may serve as a platform for screening immunomodulatory cancer therapies.