B cell subset analysis of HIV+ (n = 14) and HIV- (n = 21) subjects at the time of symptomatic malaria.

<p>The B cell subsets were determined by flow cytometry: naïve cells (CD19+CD10-CD21+CD27-), activated MBCs/plasmablasts (CD19+CD10-CD21-CD27+), classical MBCs (CD19+CD10-CD21+CD27+) and atypical MBCs (CD19+CD10-CD21-CD27-). The black bar denotes median values. The frequency was determined as percent of total CD19+ B cells. The Mann Whitney rank-sum test was used to compare variables between groups.</p

Comparison of antibody breadth and magnitude between HIV+ and HIV- samples for the P. falciparum antigens displaying the greatest breadth of antibody reactivity in HIV+ samples.

<p>Frequency of detection in percent of samples by HIV status is reported. Significant differences in breadth are denoted by *, using Fisher exact test (two tailed, p value <0.05). Significant differences in antibody magnitude are denoted by **, and reported using the Empirical Bayes Moderated t-test, p<0.05, and an absolute log fold change > 1.</p><p>Comparison of antibody breadth and magnitude between HIV+ and HIV- samples for the P. falciparum antigens displaying the greatest breadth of antibody reactivity in HIV+ samples.</p

Demographics and clinical characteristics of the HIV positive (HIV+) and HIV negative (HIV-) malaria infected patients.

<p>P-values were generated using Mann-Whitney test for continuous variables and the Chi-square test for gender. Median and interquartile values are reported.</p><p>Demographics and clinical characteristics of the HIV positive (HIV+) and HIV negative (HIV-) malaria infected patients.</p

Breadth and magnitude of the IgG response to <i>P</i>. <i>falciparum</i> antigens by HIV status.

<p>(A) A microarray containing 824 <i>P</i>. <i>falciparum</i> proteins or protein fragments was probed with plasma samples from HIV+ (n = 18) and HIV- (n = 18) adults during symptomatic malaria. A. Venn diagrams showing the number of reactive antigens among HIV+ subjects (orange), HIV- subjects (blue), both HIV+ and HIV- subjects (purple) or neither (254). (B) Antibody breadth of HIV+ individuals (mean 83 antigens) and HIV- individuals (mean 208 antigens). Mean values and standard deviations are shown; Significant differences in breadth (Negative Binomial generalized linear model) (C) Magnitude of <i>P</i>. <i>falciparum</i> IgG responses by HIV status. We examined 384 antigens that were recognized in ≥ 10% of all samples and show the average IgG reactivity of each by HIV status. IgG reactivity is significantly higher in HIV- group (blue bars) compared to HIV+ group (orange bars) for 173 antigens. The red horizontal line indicates a p value of 0.05. (Empirical Bayes Moderated t-test, p<0.05, and an absolute log fold change > 1).</p

Number of reactive antibodies per sample in the HIV+ group by HIV viral load and CD4+ T cell count.

<p>The four samples with the highest antibody breadth all have CD4⁺ T cell counts >500 cells/μl and low viral loads. Dotted line denotes HIV viral load limit of detection.</p

Malaria-induced interferon-γ drives the expansion of Tbet^hi atypical memory B cells

<div><p>Many chronic infections, including malaria and HIV, are associated with a large expansion of CD21⁻CD27⁻ ‘atypical’ memory B cells (MBCs) that exhibit reduced B cell receptor (BCR) signaling and effector functions. Little is known about the conditions or transcriptional regulators driving atypical MBC differentiation. Here we show that atypical MBCs in malaria-exposed individuals highly express the transcription factor T-bet, and that T-bet expression correlates inversely with BCR signaling and skews toward IgG3 class switching. Moreover, a longitudinal analysis of a subset of children suggested a correlation between the incidence of febrile malaria and the expansion of T-bet^hi B cells. The Th1-cytokine containing supernatants of malaria-stimulated PBMCs plus BCR cross linking induced T-bet expression in naïve B cells that was abrogated by neutralizing IFN-γ or blocking the IFN-γ receptor on B cells. Accordingly, recombinant IFN-γ plus BCR cross-linking drove T-bet expression in peripheral and tonsillar B cells. Consistent with this, Th1-polarized Tfh (Tfh-1) cells more efficiently induced T-bet expression in naïve B cells. These data provide new insight into the mechanisms underlying atypical MBC differentiation.</p></div

MPXV infection induced changes in chemokine receptor expression on NK cells in LNs.

<p>Frequency of chemokine receptor expression on NK cell subsets from the axillary LNs of day 8-9 MPXV-infected NHPs. Statistical analyses were permutation re-sampling tests with Bonferroni-adjusted p-values computed using R (**p<0.01). </p

MPXV infection induced changes in the expression of chemokine receptors on blood NK cells.

<p>PBMCs from different time points following MPXV inoculation were stained for NK markers and chemokine receptors as described in the material and methods. The chemokine receptor expression was revealed by gating on individual NK cell subsets. A) Expression kinetics of CXCR3, CCR5, CCR6, and CCR7 on NK cell subsets following MPXV inoculation. B) Representative analysis of CCR5 expression on NK cells at days 0 and day 7 post-inoculation. Statistical analyses were permutation re-sampling tests with Holm-adjusted p-values computed using R (*p<0.05).</p

T-bet^hi B cells of malaria-exposed children express markers associated with atypical MBCs.

<p>Ex vivo expression of cell surface proteins on total CD19⁺ B cells stratified by level of T-bet expression in Malian children (n = 10). p values determined by paired Student’s <i>t</i> test with Bonferroni corrections for multiple comparisons where appropriate. ****<i>P</i><0.0001, ***<i>P</i><0.001, **<i>P</i><0.01, *<i>P</i><0.05, ns = not significant.</p

NK cell frequency and absolute number in LNs at days 8–9 after MPXV inoculation.

<p>A) Frequency of total NK cells in the LN lymphocyte gate and frequency of NK subsets in the NK cell gate. B) Total NK cell number and numbers of NK subsets in the LN. Total NK cell number was calculated based on the frequency of NKG2A+ NK cells in the lymphocyte gate and the total live cell number of individual LNs. The total number of NK cell subsets was calculated based on the frequency of each subset within the NK cell gate and the total NK cell number in the LN. C) Dot-plot showing the marked variation of NK cell subset distribution in two representative subjects induced by MPXV infection. Statistical analyses of A) and B) were computed using two-way ANOVA in Graph-pad Prism software with Bonferroni post-tests and <i>p</i> values with significant change are shown (* p<0.05, **p<0.01, *** p<0.001, **** p<0.0001). Results were confirmed using bootstrap re-sampling test computed with the R project for statistical computing.</p