Kinetics of <i>Listeria</i>-specific CD8 T cells in WT and E-FABP-deficient mice.

<p>WT and E-FABP-/- mice were infected with 5x10⁶ Lm-OVA. OVA-specific CD8 T cells were detected by MHC class I tetramer (K^b-OVA_257-264) staining (A-B) or peptide (OVA_257-264) stimulated intracellular cytokine staining for IFN-γ (C-D). (A and C) Representative contour plots showing the frequency of OVA-specific CD8 T cells, amongst all CD8+ cells, at the indicated days following infection with Lm-OVA. (B and D) Total number of OVA-specific CD8 T cells in the spleen. Data (mean±s.d.) are from 3-mice/time point/group except day 63 E-FABP-/- (n = 2). Data are representative of two independent experiments and were analyzed by the Mann-Whitney test.</p

Phenotypic and functional characterization of <i>Listeria</i>-specific CD4 T cells.

<p>(A, C, E) Representative histograms and contour plots from day 7 or day 63 showing the frequency of LLO-specific CD4 T cells that are positive for the indicated cell surface marker or cytokine. (A) Open histogram is isotype control; gray histogram is CD43 (clone 1B11). (C) Open histogram is isotype control; gray histogram is CD27. (B, D, F) Total percentage of LLO-specific CD4 T cells positive for the indicated cell surface marker or cytokine on the indicated days. Data (mean±s.d.) are from 3-mice/time point/group except day 63 WT (n = 2). Data are representative of two independent experiments and were analyzed by the Mann-Whitney test.</p

Kinetics of <i>Listeria</i>-specific CD4 T cells in WT and E-FABP-deficient mice.

WT and E-FABP-/- mice were infected with 5x106 Lm-OVA. (A) Representative contour plots showing the frequency of LLO-specific CD4 T cells as detected by peptide (LLO190-201) stimulated intracellular cytokine staining for IFN-γ at the indicated days following infection with Lm-OVA. (B) Total number of LLO-specific CD4 T cells in the spleen. Data (mean±s.d.) are from 3-mice/time point/group except day 63 WT (n = 2). Data are representative of two independent experiments and were analyzed by the Mann-Whitney test.</p

Phenotypic and functional characterization of <i>Listeria</i>-specific CD8 T cells.

<p>(A, C, E, G) Representative histograms and contour plots from day 7 or day 63 showing the frequency of OVA-specific CD8 T cells that are positive for the indicated cell surface marker or cytokine. Open histogram is isotype control; gray histogram is CD43 (clone 1B11). (B, D, F, H) Total percentage of OVA-specific CD8 T cells positive for the indicated cell surface marker or cytokine on the indicated days. Data (mean±s.d.) are from 3-mice/time point/group except day 63 WT (n = 2). Data are representative of two independent experiments and were analyzed by the Mann-Whitney test.</p

Gut microbiota intervention strategy and possible immunological mechanism of action against severity to malaria.

Agents that can modulate gut microbiota composition or deplete gut bacteria that can influence gut or systemic immunity are shown. Dotted arrow indicates the potential interactions which need further validation. Color of arrows connect gut microbiota modifying agents and their impact on respective immune cells. Role of different immune populations to inhibit or exacerbate various stage and types of malaria are connected. Although, the exact mechanism on how gut microbiota impacts severe malaria is unknown, this figure provides a plausible connection between gut microbiota and malaria severity. Figure was created with BioRender.com.</p

Gut bacteria associated with human malaria.

To date 5 peer-reviewed studies have been published on the impact of the gut microbiota in Plasmodium infection in humans. A cohort of children in Kalifabougou, Mali was used for 2 prospective studies (Yilmaz and colleagues (2014) [18] and Yooseph and colleagues (2015) [28]). Additionally, 3 case-control studies investigated association of gut microbiota at the time of Plasmodium infection (Huwe and colleagues (2019) [32] and Easton and colleagues (2020) [33]) and SMA (Mandal and colleagues (2021) [6]). Prospective and case-control studies have their own advantages and disadvantages. Bacteria associated with the clinical outcome is shown. Pf: P. falciparum, neg: negative, pos: positive. Figure was created with BioRender.com.</p

Gut bacteria associated with nonhuman model of malaria.

Top 2 panels show baseline gut bacteria differentially abundant in mice either susceptible or resistant to P. yoelii 17XNL hyperparasitemia and P. chabaudi AS pregnancy outcomes. Fecal pellets are collected at baseline prior to Plasmodium infection to determine the gut microbiota composition. Bottom 2 panels show the shift in gut microbiota composition during Plasmodium infection in mice and monkeys. In nonlethal infection models, fecal pellet microbiota composition at peak parasitemia is compared to before Plasmodium infection. Bacteria that are significantly increased (up arrow) or decreased (down arrow) are shown. P. yoelii 17XL causes lethal infection due to hyperparasitemia while P. berghei ANKA causes mortality due to experimental cerebral malaria. Gut fecal samples are collected before mortality and compared to baseline gut microbiota. Changes in bacteria population at phylum, family, and genus level are shown. Figure was created with BioRender.com.</p

SCFA quantification in the feces of mice from different vendors.

Fecal pellets were collected from 10 mice from each vendor, except for Jax (n = 9). Data were analyzed by ordinary one-way ANOVA test followed by Tukey’s multiple comparison test. p<0.05 was considered statistically significant.</p

Correlation analysis between propionic acid levels in the feces of mice and corresponding parasite density.

Pearson’s coefficient was calculated, followed by estimation of two-tailed p values.</p

Ion monitoring mode for individual short-chain fatty acids.

Ion monitoring mode for individual short-chain fatty acids.</p