45 research outputs found

    Induction of interferon-stimulated genes on the IL-4 response axis by Epstein-Barr virus infected human b cells; relevance to cellular transformation.

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    Epstein-Barr virus (EBV) is an oncogenic virus that is associated with the pathogenesis of several human lymphoid malignancies, including Hodgkin's lymphoma. Infection of normal resting B cells with EBV results in activation to lymphoblasts that are phenotypically similar to those generated by physiological stimulation with CD40L plus IL-4. One important difference is that infection leads to the establishment of permanently growing lymphoblastoid cell lines, whereas CD40L/IL-4 blasts have finite proliferation lifespans. To identify early events which might later determine why EBV infected blasts go on to establish transformed cell lines, we performed global transcriptome analyses on resting B cells and on EBV and CD40L/IL-4 blasts after 7 days culture. As anticipated there was considerable overlap in the transcriptomes of the two types of lymphoblasts when compared to the original resting B cells, reflecting common changes associated with lymphocyte activation and proliferation. Of interest to us was a subset of 255 genes that were differentially expressed between EBV and CD40L/IL-4 blasts. Genes which were more highly expressed in EBV blasts were substantially and significantly enriched for a set of interferon-stimulated genes which on further in silico analyses were found to be repressed by IL-4 in other cell contexts and to be up-regulated in micro-dissected malignant cells from Hodgkin's lymphoma biopsies when compared to their normal germinal center cell counterparts. We hypothesized that EBV and IL-4 were targeting and discordantly regulating a common set of genes. This was supported experimentally in our B cell model where IL-4 stimulation partially reversed transcriptional changes which follow EBV infection and it impaired the efficiency of EBV-induced B cell transformation. Taken together, these data suggest that the discordant regulation of interferon and IL-4 pathway genes by EBV that occurs early following infection of B cells has relevance to the development or maintenance of an EBV-associated malignancy

    Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

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    Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

    Type I Interferons Induce T Regulatory 1 Responses and Restrict Humoral Immunity during Experimental Malaria

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    We thank Christopher Hunter and Bob Axtell for critical feedback, and the Flow Cytometry Laboratory at OUHSC for technical assistance.Author Summary Humoral immunity is essential for host resistance to pathogens that trigger highly inflammatory immune responses, including Plasmodium parasites, the causative agents of malaria. Long-lived, secreted antibody responses depend on a specialized subset of CD4 T cells called T follicular helper (Tfh) cells. However, anti-Plasmodium humoral immunity is often short-lived, non-sterilizing, and immunity rapidly wanes, leaving individuals susceptible to repeated bouts of malaria. Here we explored the relationship between inflammatory type I interferons, the regulation of pathogen-specific CD4 T cell responses, and humoral immunity using models of experimental malaria and systemic virus infection. We identified that type I interferons promote the formation and accumulation of pathogen-specific CD4 T regulatory 1 cells that co-express interferon-gamma and interleukin-10. Moreover, we show that the combined activity of interferon-gamma and interleukin-10 limits the magnitude of infection-induced Tfh responses, the secretion of parasite-specific secreted antibody, and parasite control. Our study provides new insight into the regulation of T regulatory 1 responses and humoral immunity during inflammatory immune reactions against systemic infections.Yeshttp://www.plospathogens.org/static/editorial#pee

    Blood transcriptional biomarkers of acute viral infection for detection of pre-symptomatic SARS-CoV-2 infection: a nested, case-control diagnostic accuracy study

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    Background We hypothesised that host-response biomarkers of viral infections might contribute to early identification of individuals infected with SARS-CoV-2, which is critical to breaking the chains of transmission. We aimed to evaluate the diagnostic accuracy of existing candidate whole-blood transcriptomic signatures for viral infection to predict positivity of nasopharyngeal SARS-CoV-2 PCR testing.Methods We did a nested case-control diagnostic accuracy study among a prospective cohort of health-care workers (aged ≥18 years) at St Bartholomew’s Hospital (London, UK) undergoing weekly blood and nasopharyngeal swab sampling for whole-blood RNA sequencing and SARS-CoV-2 PCR testing, when fit to attend work. We identified candidate blood transcriptomic signatures for viral infection through a systematic literature search. We searched MEDLINE for articles published between database inception and Oct 12, 2020, using comprehensive MeSH and keyword terms for “viral infection”, “transcriptome”, “biomarker”, and “blood”. We reconstructed signature scores in blood RNA sequencing data and evaluated their diagnostic accuracy for contemporaneous SARS-CoV-2 infection, compared with the gold standard of SARS-CoV-2 PCR testing, by quantifying the area under the receiver operating characteristic curve (AUROC), sensitivities, and specificities at a standardised Z score of at least 2 based on the distribution of signature scores in test-negative controls. We used pairwise DeLong tests compared with the most discriminating signature to identify the subset of best performing biomarkers. We evaluated associations between signature expression, viral load (using PCR cycle thresholds), and symptom status visually and using Spearman rank correlation. The primary outcome was the AUROC for discriminating between samples from participants who tested negative throughout the study (test-negative controls) and samples from participants with PCR-confirmed SARS-CoV-2 infection (test-positive participants) during their first week of PCR positivity.Findings We identified 20 candidate blood transcriptomic signatures of viral infection from 18 studies and evaluated their accuracy among 169 blood RNA samples from 96 participants over 24 weeks. Participants were recruited between March 23 and March 31, 2020. 114 samples were from 41 participants with SARS-CoV-2 infection, and 55 samples were from 55 test-negative controls. The median age of participants was 36 years (IQR 27–47) and 69 (72%) of 96 were women. Signatures had little overlap of component genes, but were mostly correlated as components of type I interferon responses. A single blood transcript for IFI27 provided the highest accuracy for discriminating between test-negative controls and test-positive individuals at the time of their first positive SARS-CoV-2 PCR result, with AUROC of 0·95 (95% CI 0·91–0·99), sensitivity 0·84 (0·70–0·93), and specificity 0·95 (0·85–0·98) at a predefined threshold (Z score >2). The transcript performed equally well in individuals with and without symptoms. Three other candidate signatures (including two to 48 transcripts) had statistically equivalent discrimination to IFI27 (AUROCs 0·91–0·95).Interpretation Our findings support further urgent evaluation and development of blood IFI27 transcripts as a biomarker for early phase SARS-CoV-2 infection for screening individuals at high risk of infection, such as contacts of index cases, to facilitate early case isolation and early use of antiviral treatments as they emerge

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

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    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

    CD4 T cell intrinsic IFNAR signaling limits humoral immunity and parasite control via induction of Tr1 cell expression of T-bet and Blimp-1 and co-production of IFN-γ and IL-10.

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    <p>(<b>A</b>) Experimental design. <i>Tcrα</i><sup>-/-</sup> mice were seeded with equivalent numbers of CD45.1/2 WT and CD45.2/2 (<i>ifnar1</i><sup>-/-</sup>) naïve CD4 T cells and infected with 10<sup>6</sup> pRBCs one day post-transfer. Cellular reactions were analyzed 2 weeks p.i. (<b>B</b>) Representative flow plots showing the proportion of WT and <i>ifnar1</i><sup>-/-</sup> cells among recovered activated CD44<sup>hi</sup> CD4<sup>+</sup> T cells and their simultaneous expression of T-bet by Blimp-1 on day 14 p.i. (<b>C</b>) Summary graph depicting the proportion of activated WT and <i>ifnar1</i><sup>-/-</sup> CD4 T cells simultaneously expressing both T-bet and Blimp-1. (<b>D-E</b>) Summary graphs displaying the relative expression (gMFI) of Blimp-1 (<b>D</b>) and T-bet (<b>E</b>) in activated WT and <i>ifnar1</i><sup>-/-</sup> CD4 T cells. (<b>F</b>) Representative flow plots depicting the proportion of activated WT and <i>ifnar1</i><sup>-/-</sup> CD4 T cells competent to produce IFN-γ and IL-10 after ex vivo stimulation. (<b>G-I</b>) Summary data displaying the frequency of activated WT and <i>ifnar1</i><sup>-/-</sup> CD4 T cells producing either IFN-γ (<b>G</b>) or IL-10 (<b>H</b>) on day 14 p.i. after ex vivo stimulation. (<b>J-K</b>) Separate groups of <i>tcrα</i><sup>-/-</sup> mice were seeded with equivalent numbers of WT (CD45.1/2) and <i>ifnar1</i><sup>-/-</sup> (CD45.2/2) naïve CD4 T cells and infected with 10<sup>6</sup> pRBCs once day post-transfer. Parasite burdens and cellular reactions were analyzed on day 16 p.i. (<b>L</b>) Experimental design for generating mixed bone marrow chimeras in which the T cell compartment is either WT (T<sup>WT</sup>) or <i>ifnar</i>-deficient (T<sup><i>ifnar-/-</i></sup>). (<b>M</b>) Secreted parasite-specific IgG was evaluated by ELISA in chimeric mice on day 23 p.i.. (<b>N</b>) Summary data depicting the total number of Tfh cells in chimeric mice. Data (Mean +/- SD) in (<b>C-E, G-I, and M,N</b>) were analyzed using Mann-Whitney non-parametric tests and are representative of two independent experiments with 5 mice per group per experiment. Data (Mean +/- SEM) in (<b>J,K</b>) were pooled from two independent experiments with 3–4 mice/group per experiment and were analyzed using Mann-Whitney non-parametric tests.</p

    Type I IFN-mediated induction of Blimp-1 in CD4 T cells can occur independently of IL-2 signaling.

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    <p>(<b>A-B</b>) Blimp-1 reporter mice were administered either MOPC or α-IFNAR antibodies and were infected with either10<sup>6</sup> pRBCs. (<b>A</b>) Representative flow plots depicting the expression of CD25 on <i>Plasmodium</i> infection-induced effector CD4 T cells from MOPC and α-IFNAR-treated mice on day 8 p.i. (<b>B</b>) Cumulative data showing surface CD25 expression (gMFI) on <i>Plasmodium</i> infection-induced splenic CD44<sup>hi</sup>CD11a<sup>hi</sup> CD4<sup>+</sup> T cells. Data (Mean +/- SEM) in (<b>B</b>) are pooled from 2 independent experiments (3–4 mice/group). (<b>C</b>) Summary data (Mean +/- SD) showing CD25 expression (gMFI) on WT and <i>ifnar1</i><sup>-/-</sup> CD4 T cells recovered from <i>tcrα</i><sup>-/-</sup> on day 4 post-<i>Plasmodium</i> infection. Data in (B,C) were analyzed using Mann-Whitney non-parametric tests of statistical significance. (<b>D,E</b>) Naïve CD4 T cells from Blimp-1-eYFP reporter mice were stimulated with α-CD3/α-CD28 and treated with or without rIFNβ or anti-IL-2 blocking antibodies. (<b>D</b>) Summary data (Mean +/- SD) depicting surface expression of PI3K-dependent CD98 on CD44<sup>hi</sup> CD4<sup>+</sup> T cells cultured under the various treatment conditions. (<b>E</b>) Summary data (Mean +/- SEM) showing Blimp-1-eYFP expression (gMFI) in CD44<sup>hi</sup> CD4<sup>+</sup> T cells under the various treatment conditions. Data in (<b>E</b>) are normalized to Blimp-1 expression (MFI) in non-stimulated CD4 T cells and are pooled from 3 independent experiments. Data in (D,E) are representative of 3 independent experiments and were analyzed using Kruskal-Wallis non-parametric tests of statistical significance. <i>N</i>.<i>S</i>. = not statistically significant. *P<0.05, **P<0.01.</p
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