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

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

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

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

<p>(<b>A-B</b>) Blimp-1 reporter mice were administered either MOPC or α-IFNAR antibodies and were infected with either10⁶ 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^hiCD11a^hi CD4⁺ 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>^-/- CD4 T cells recovered from <i>tcrα</i>^-/- 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^hi CD4⁺ T cells cultured under the various treatment conditions. (<b>E</b>) Summary data (Mean +/- SEM) showing Blimp-1-eYFP expression (gMFI) in CD44^hi CD4⁺ 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

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.

<p>(<b>A</b>) Experimental design. <i>Tcrα</i>^-/- mice were seeded with equivalent numbers of CD45.1/2 WT and CD45.2/2 (<i>ifnar1</i>^-/-) naïve CD4 T cells and infected with 10⁶ 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>^-/- cells among recovered activated CD44^hi CD4⁺ 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>^-/- 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>^-/- CD4 T cells. (<b>F</b>) Representative flow plots depicting the proportion of activated WT and <i>ifnar1</i>^-/- 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>^-/- 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>^-/- mice were seeded with equivalent numbers of WT (CD45.1/2) and <i>ifnar1</i>^-/- (CD45.2/2) naïve CD4 T cells and infected with 10⁶ 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^WT) or <i>ifnar</i>-deficient (T^{<i>ifnar-/-</i>}). (<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

IFN-γ and IL-10 cooperate to limit Tfh accumulation and secreted antibody responses during <i>Plasmodium</i> blood-stage infection.

<p>(<b>A</b>) Negative correlation between levels of circulating IL-10 and the total number of splenic Tfh cells in MOPC and α-IFNAR-treated mice on day 16 p.i. Data were analyzed using linear regression. (<b>B-F</b>) Groups of mice (n = 5/group) were treated with either rIgG, α-IL-10R, α-IFN-γ, or α-IFN-γ+α-IL-10R. (<b>B</b>) Parasitemia kinetics. Statistical results for comparisons between rIgG- and α-IFN-γ+α-IL-10R-treated mice are displayed. Data were analyzed using Mann-Whitney non-parametric tests. (<b>C</b>) Representative flow plots depicting the proportion of PD-1⁺CXCR5⁺ Tfh cells among CD44^hi splenic CD4⁺ T cells in rIgG-, α-IL-10R-, α-IFN-γ or α-IFN-γ+α-IL-10R-treated mice. (<b>D</b>) Summary data showing the total number of splenic Tfh cells in rIgG-, α-IL-10R-, or α-IFN-γ+α-IL-10R-treated mice on day 14 p.i. (<b>E</b>) Summary data showing ICOS expression (gMFI) on Tfh cells from rIgG-, α-IL-10R-, α-IFN-γ or α-IFN-γ+α-IL-10R-treated mice. (<b>F</b>) Summary graph displaying the relative serum titers of MSP1₁₉-specific total IgG in rIgG-, α-IL-10R-, α-IFN-γ, or α-IFN-γ+α-IL-10R-treated mice on d14 p.i. Statistical results for comparisons between rIgG- and α-IFN-γ+α-IL-10R-treated mice are displayed and data were analyzed using Mann-Whitney non-parametric tests. Summary data (Mean +/- SEM) in (<b>D-E</b>) were analyzed using Kruskal-Wallis non-parametric tests. Data in (<b>B-F</b>) are representative of two independent experiments (5 mice/group per experiment). <i>N</i>.<i>S</i>. = not statistically significant. *P<0.05, ***P<0.0001.</p

Type I interferons promote co-production of IFN-γ and IL-10 by <i>Plasmodium</i> infection-induced Tr1 cells.

<p>(<b>A-F</b>) Blimp-1-eYFP reporter mice were administered either MOPC or α-IFNAR antibodies and were infected with 10⁶ pRBCs. (<b>A</b>) Representative flow plots depicting the frequency of Blimp-1-eYFP expression and subsequent IFN-γ and IL-10 cytokine production in Blimp-1-eYFP^- and Blimp-1-eYFP⁺ splenic CD44⁺CD11a^hi CD4 T cells from MOPC and α-IFNAR-treated mice after ex vivo stimulation with PMA/Ionomycin. (<b>B</b>) Summary data displaying the proportion of Blimp-1-eYFP^- and Blimp-1-eYFP⁺ effector CD44⁺CD11a^hi CD4 T cells that are positive for either IFN-γ⁺ (left) or IL-10⁺ (right) after ex vivo stimulation. (<b>C</b>) Summary data displaying the proportion (left) and total number (right) of IFN-γ⁺ IL-10⁺ cells among Blimp-1-eYFP^- and Blimp-1-eYFP⁺ <i>Plasmodium</i> infection-induced CD4 T cells from MOPC and α-IFNAR-treated mice on day 8 p.i. (<b>D</b>) Summary data showing the gMFI of IL-10 in <i>Plasmodium</i> infection-induced CD4 T cells on day 8 p.i. (<b>E-F</b>) Summary data (Mean +/- SEM) showing the levels of circulating IL-10 (<b>E</b>) and IFN-γ (<b>F</b>) in sera from MOPC and α-IFNAR-treated mice. Data (Mean +/- SEM) in (<b>B-F</b>) are pooled from 2–3 independent experiments (3–4 mice/group per experiment) and were analyzed using Mann-Whitney non-parametric tests. Data in (<b>A-F</b>) are representative of 3 independent experiments. <i>N</i>.<i>S</i>. = not statistically significant.</p

Blockade of IFNAR signaling enhances Tfh cell accumulation and limits T regulatory 1 responses during experimental malaria.

<p>(<b>A-K</b>) Wild type (WT) mice were administered either MOPC or α-IFNAR antibodies and were infected with 10⁶ pRBCs. (<b>A</b>) Representative flow plots from day 25 p.i. depicting the percentage of CXCR5⁺PD-1⁺ Tfh cells among splenic CD44⁺ CD4 T cells from MOPC and α-IFNAR-treated mice. (<b>B</b>) Summary kinetics displaying the proportion (left) and total number (right) of Tfh cells from MOPC and α-IFNAR-treated mice. Representative flow plots (<b>C</b>) and summary data depicting total numbers of CXCR5⁺PD-1⁺Bcl-6⁺ Tfh (<b>D</b>) and ICOS (<b>E</b>) expression (geometric MFI) on Tfh cells from MOPC and α-IFNAR-treated mice on day 16 p.i. (<b>F-K</b>) Blimp-1-eYFP reporter mice were administered either MOPC or α-IFNAR antibodies and were infected with 10⁶ pRBCs. (<b>F</b>) Representative dot plots (left) depicting the proportion of splenic CD44⁺CD11a^hi CD4 T cells in MOPC and α-IFNAR-treated mice expressing either T-bet (middle) or Blimp-1-eYFP in T-bet⁺ CD4 T cells (right). (<b>G</b>) Summary kinetics depicting the relative expression of T-bet (gMFI) among <i>Plasmodium</i> infection-induced CD44⁺CD11a⁺ CD4 T cells. (<b>H</b>) Representative flow plots from MOPC and α-IFNAR-treated mice on day 8 p.i showing the proportion of splenic CD44⁺CD11a^hi CD4 T cells simultaneously expressing both T-bet and Blimp-1-eYFP. (<b>I-J</b>) Summary kinetics depicting the frequency (I) and total number (J) of T-bet⁺Blimp1-eYFP⁺ splenic CD44⁺CD11a^hi CD4 T cells in MOPC and α-IFNAR-treated mice. (<b>K</b>) Summary kinetics of Blimp-1-eYFP expression (gMFI) in splenic CD44⁺CD11a^hi CD4 T cells in MOPC and α-IFNAR-treated mice. Data (Mean +/- SEM) in (<b>A,D,G,J-K</b>) are pooled from 2–3 independent experiments per time point (3–5 mice/group for each time point) and were analyzed using Mann-Whitney (non-parametric) tests of statistical significance. <i>N</i>.<i>S</i>. = not statistically significant. *P<0.05, **P<0.01.</p

Nasopharyngeal Protein Biomarkers of Acute Respiratory Virus Infection

Infection of respiratory mucosa with viral pathogens triggers complex immunologic events in the affected host. We sought to characterize this response through proteomic analysis of nasopharyngeal lavage in human subjects experimentally challenged with influenza A/H3N2 or human rhinovirus, and to develop targeted assays measuring peptides involved in this host response allowing classification of acute respiratory virus infection. Unbiased proteomic discovery analysis identified 3285 peptides corresponding to 438 unique proteins, and revealed that infection with H3N2 induces significant alterations in protein expression. These include proteins involved in acute inflammatory response, innate immune response, and the complement cascade. These data provide insights into the nature of the biological response to viral infection of the upper respiratory tract, and the proteins that are dysregulated by viral infection form the basis of signature that accurately classifies the infected state. Verification of this signature using targeted mass spectrometry in independent cohorts of subjects challenged with influenza or rhinovirus demonstrates that it performs with high accuracy (0.8623 AUROC, 75% TPR, 97.46% TNR). With further development as a clinical diagnostic, this signature may have utility in rapid screening for emerging infections, avoidance of inappropriate antibacterial therapy, and more rapid implementation of appropriate therapeutic and public health strategies

Host-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance

Abstract Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76–0.90) with overall accuracy of 81.6% (95% CI 72.7–88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens

Sex differences in oncogenic mutational processes

Funder: Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC Canadian Network for Research and Innovation in Machining Technology); doi: https://doi.org/10.13039/501100002790Funder: Genome Canada (Génome Canada); doi: https://doi.org/10.13039/100008762Funder: Canada Foundation for Innovation (Fondation canadienne pour l'innovation); doi: https://doi.org/10.13039/501100000196Funder: Terry Fox Research Institute (Institut de Recherche Terry Fox); doi: https://doi.org/10.13039/501100004376Abstract: Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research