Blockade of dendritic cell glutaminolysis induces allergic asthma desensitization via suppression of Tfh13 polarization

Allergic asthma is a pulmonary disease characterized by airway hyperresponsiveness (AhR) in response to inhaled allergens. AhR is largely mediated by allergen-specific (Ag-specific) IgE induced mast cell (MC) degranulation which drives inflammation of the airways following allergen exposure. Ag-specific IgE production requires T follicular helper 13 cells (Tfh13s) which must first be polarized by dendritic cells (DCs). The mechanisms by which DCs polarize Tfh13s, however, are unclear. We hypothesized that the Tfh13 inducing allergen Alternaria would induce a unique metabolic response compared to non-Tfh13 inducing stimuli. We found, by untargeted metabolomic analysis, that Alternaria induced accumulation of the glutamine-derived TCA cycle metabolite α-ketoglutarate compared to LPS stimulated or unstimulated DCs. Glutamine is converted to α-ketoglutarate by glutaminase (Gls), so we predicted that inhibition of Gls may suppress Tfh13 polarization by DCs. Indeed, we found that treatment with the Gls inhibitor, CB-839, reduced Tfh13 polarization and increased T follicular regulatory cells following Alternaria exposure. This finding was recapitulated in a DC adoptive transfer model, indicating that this mechanism is DC intrinsic. We next tested if CB-839 causes allergen desensitization in a model of allergic asthma. Mice were intranasally sensitized and challenged with Alternaria and intranasally treated with CB-839 or vehicle control prior to challenge. CB-839 treated mice exhibited reduced AhR, Ag-specific IgE, and airway MC degranulation. These data demonstrate that DC glutaminolysis is required for Tfh13 polarization, and that Gls inhibitors may promote allergen desensitization in allergic asthma.

This work was funded by VCU's National Center for Advancing Translational Sciences (UL1TR002649) and NIAID (R56AI139658).

Valproate Induces the Unfolded Protein Response by Increasing Ceramide Levels

Bipolar disorder (BD), which is characterized by depression and mania, affects 1-2% of the world population. Current treatments are effective in only 40-60% of cases and cause severe side effects. Valproate (VPA) is one of the most widely used drugs for the treatment of BD, but the therapeutic mechanism of action of this drug is not understood. This knowledge gap has hampered the development of effective treatments. To identify candidate pathways affected by VPA, we performed a genome-wide expression analysis in yeast cells grown in the presence or absence of the drug. VPA caused up-regulation of FEN1 and SUR4, encoding fatty acid elongases that catalyze the synthesis of very long chain fatty acids (C24 to C26) required for ceramide synthesis. Interestingly, fen1Δ and sur4Δ mutants exhibited VPA sensitivity. In agreement with increased fatty acid elongase gene expression, VPA increased levels of phytoceramide, especially those containing C24-C26 fatty acids. Consistent with an increase in ceramide, VPA decreased the expression of amino acid transporters, increased the expression of ER chaperones, and activated the unfolded protein response element (UPRE), suggesting that VPA induces the UPR pathway. These effects were rescued by supplementation of inositol and similarly observed in inositol-starved ino1Δ cells. Starvation of ino1Δ cells increased expression of FEN1 and SUR4, increased ceramide levels, decreased expression of nutrient transporters, and induced the UPR. These findings suggest that VPA-mediated inositol depletion induces the UPR by increasing the de novo synthesis of ceramide.

An iterative approach to region growing using associative memories

The postulate is made that ``any computation which can be performed recursively can be performed easily and efficiently by iteration coupled with association.'' The ``easily and efficiently'' part of that postulate is nontrivial to prove, and is shown by examples in this paper. The use of association leads directly to potential implementation by content-addressable memories. The example addressed is region growing, often given as a classical example of the use of recursive control structures in image processing. Recursive control structures, however, are somewhat awkward to build in hardware, where the intent is to segment an image at raster scan rates. This paper describes an algorithm and hardware structure capable of per-forming region labeling iteratively at scan rates. Every pixel is individually labeled with an identifier signifying to which region it belongs. The difficulties which often justify recursion (``U''- and ``N''-shaped regions, etc.) are handled by maintaining an equivalence table in hardware, transparent to the computer, which reads the labeled pixels. The mechanism for updating the region map is explained in detail. Furthermore, simulation of the associative memory has been demon-strated to be an effective implementation of region growing in a serial computer.