PRMT5-Selective Inhibitors Suppress Inflammatory T Cell Responses and Experimental Autoimmune Encephalomyelitis

In the autoimmune disease multiple sclerosis and its animal model, experimental autoimmune encephalomyelitis (EAE), expansion of pathogenic, myelin-specific Th1 cell populations drives active disease; selectively targeting this process may be the basis for a new therapeutic approach. Previous studies have hinted at a role for protein arginine methylation in immune responses, including T cell–mediated autoimmunity and EAE. However, a conclusive role for the protein arginine methyltransferase (PRMT) enzymes that catalyze these reactions has been lacking. PRMT5 is the main PRMT responsible for symmetric dimethylation of arginine residues of histones and other proteins. PRMT5 drives embryonic development and cancer, but its role in T cells, if any, has not been investigated. In this article, we show that PRMT5 is an important modulator of CD4+ T cell expansion. PRMT5 was transiently upregulated during maximal proliferation of mouse and human memory Th cells. PRMT5 expression was regulated upstream by the NF-κB pathway, and it promoted IL-2 production and proliferation. Blocking PRMT5 with novel, highly selective small molecule PRMT5 inhibitors severely blunted memory Th expansion, with preferential suppression of Th1 cells over Th2 cells. In vivo, PRMT5 blockade efficiently suppressed recall T cell responses and reduced inflammation in delayed-type hypersensitivity and clinical disease in EAE mouse models. These data implicate PRMT5 in the regulation of adaptive memory Th cell responses and suggest that PRMT5 inhibitors may be a novel therapeutic approach for T cell–mediated inflammatory disease

A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data

Background: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants. Results: In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data. Conclusions: Our study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Control of the Inflammatory Macrophage Transcriptional Signature by miR-155

<div><p>Inflammatory M1 spectrum macrophages protect from infection but can cause inflammatory disease and tissue damage, whereas alternatively activated/M2 spectrum macrophages reduce inflammation and promote tissue repair. Modulation of macrophage phenotype may be therapeutically beneficial and requires further understanding of the molecular programs that control macrophage differentiation. A potential mechanism by which macrophages differentiate may be through microRNA (miRNA), which bind to messenger RNA and post-transcriptionally modify gene expression, cell phenotype and function. We hypothesized that the inflammation-associated miRNA, miR-155, would be required for typical development of macrophage inflammatory state. miR-155 was rapidly up-regulated over 100-fold in inflammatory M1(LPS + IFN-γ), but not M2(IL-4), macrophages. Inflammatory genes <i>Inos</i>, <i>Il1b</i> and <i>Tnfa</i> and their corresponding protein or enzymatic products were reduced up to 72% in miR-155 knockout mouse M1(LPS + IFN-γ) macrophages, but miR-155 deficiency did not affect expression of the M2-associated gene <i>Arg1</i> in M2(IL-4) macrophages. Additionally, a miR-155 oligonucleotide inhibitor efficiently suppressed <i>Inos</i> and <i>Tnfa</i> gene expression in wild-type M1(LPS + IFN-γ) macrophages. Comparative transcriptional profiling of unstimulated and M1(LPS + IFN-γ) macrophages derived from wild-type (WT) and miR-155 knockout (KO) mice revealed that half (approximately 650 genes) of the signature we previously identified in WT M1(LPS + IFN-γ) macrophages was dependent on miR-155. Real-Time PCR of independent datasets confirmed that miR-155 contributed to suppression of its validated mRNA targets <i>Inpp5d</i>, <i>Tspan14</i>, <i>Ptprj</i> and <i>Mafb</i> and induction of <i>Inos</i>, <i>Il1b</i>, <i>Tnfa</i>, <i>Il6</i> and <i>Il12</i>. Overall, these data indicate that miR-155 plays an essential role in driving the inflammatory phenotype of M1(LPS+ IFN-γ) macrophages.</p></div

miR-155 is associated with the classically activated macrophage phenotype.

<p>Expression of miRNAs was determined by Taqman Real-Time PCR and expressed as mean relative expression (+ SEM) in <b>(A)</b> macrophages stimulated <i>in vitro</i> for 24 hours in M0, M1, and M2 (n = 3) conditions; expression relative to M0 condition; Post-hoc ANOVA, **p<0.005. <b>(B)</b> M1 and M2 macrophages activated <i>in vitro</i> over a 48 hour period; expression relative to 0 hour pre-stimulation time-point. Unpaired t-test, ***p<0.001. Results reproduced in two independent experiments.</p

Classically activated macrophage signature in wild-type and miR-155 knockout macrophages.

<p>Fold Change (FC) vs. Mean Expression Value (MEV) plot of microarray data highlighting 2 FC or higher up-regulated genes (red, p≤0.05) or down-regulated genes (blue, p≤0.05) in <b>(A)</b> knockout <b>(</b>KO) M1 to M0 macrophages comparison (n = 3). (<b>B, C</b>) FC vs. MEV plot with previously described classical M1 markers highlighted (in red) and the top 15 M1-exclusive genes (identified in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159724#pone.0159724.ref017" target="_blank">17</a>]) most decreased in KO M1 macrophages (in green) in the WT M1 vs. M0 comparison (<b>B</b>) and the KO M1 vs. M0 comparison (<b>C</b>). Red lines represent a +/- 2FC cut-off.</p

miR-155 targets up-regulated more than 2 fold-change in KOM1(LPS + IFN-γ) vs. WTM1(LPS + IFN-γ) macrophages.

<p>miR-155 targets up-regulated more than 2 fold-change in KOM1(LPS + IFN-γ) vs. WTM1(LPS + IFN-γ) macrophages.</p

Reduced classically activated M1 marker expression in miR-155 knock-out (KO) macrophages.

<p><b>(A)</b> Inducible nitric oxide synthase (<i>Nos2</i>), <b>(B)</b> <i>IL1b</i>, (<b>C</b>) Tumor Necrosis Factor-α (<i>Tnfa</i>) <b>(H</b>) and Arginase-1 (<i>Arg1</i>) expression was determined by Real-Time PCR in wild-type (WT, n = 8–11) and miR-155 knockout (KO n = 8–12) bone marrow-derived macrophages <i>in vitro</i> activated in M1 or M2 conditions for 24 hours in three independent experiments. Gene expression is expressed as a percentage +/- SEM of the WT M1 condition. Unpaired t-test, *p<0.05, **p<0.005. Relative concentration of <b>(D)</b> nitric oxide (NO), <b>(E)</b> IL-1β protein and <b>(F)</b> TNF-α protein was determined using Griess assay (for NO) or Bio-Plex Suspension Array (For IL-1β and TNF-α) in cell lysates from WT (n = 5) and miR-155 KO (n = 5) bone marrow-derived macrophages <i>in vitro</i> activated in M1 or M2 conditions for 24 hours. Individual protein concentrations expressed as fraction of total protein concentration in either M0 or M1 condition. (<b>G</b>) <i>IL1b</i> expression was determined by Real-Time PCR in WT and KO bone marrow-derived macrophages transfected with a scrambled miR control (n = 5) or a miR-155 oligonucleotide mimic (n = 5) and activated in M0 (untreated), M1 (LPS+IFN-γ) or M2(IL-4) conditions for 24 hours. Gene expression is expressed as a percentage +/- SEM of the scrambled M1 condition. Unpaired t-test, ***p<0.0005, ****p<0.00005. (A-G) Data from 2–3 independent experiments.</p

miR-155 dependent M1 signature transcriptional networks.

<p>(A). Model of molecules involved in miR-155 dependent M1 activated transcriptional networks, identified by Ingenuity Pathway Analysis. miR-155-dependent M1 genes up-regulated more than 2 Fold Change (FC) in wild-type (WT) M1vs. WT M0 macrophages that were up-regulated to a lesser extent in knockout (KO) M1 vs. KO M0 macrophages are highlighted in red.</p