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<p>Interactions among the gut microbiome, dysregulated immune responses, and genetic factors contribute to the pathogenesis of inflammatory bowel disease (IBD). Nlrx1^−/− mice have exacerbated disease severity, colonic lesions, and increased inflammatory markers. Global transcriptomic analyses demonstrate enhanced mucosal antimicrobial defense response, chemokine and cytokine expression, and epithelial cell metabolism in colitic Nlrx1^−/− mice compared to wild-type (WT) mice. Cell-specificity studies using cre-lox mice demonstrate that the loss of NLRX1 in intestinal epithelial cells (IEC) recapitulate the increased sensitivity to DSS colitis observed in whole body Nlrx1^−/− mice. Further, organoid cultures of Nlrx1^−/− and WT epithelial cells confirm the altered patterns of proliferation, amino acid metabolism, and tight junction expression. These differences in IEC behavior can impact the composition of the microbiome. Microbiome analyses demonstrate that colitogenic bacterial taxa such as Veillonella and Clostridiales are increased in abundance in Nlrx1^−/− mice and in WT mice co-housed with Nlrx1^−/− mice. The transfer of an Nlrx1^−/−-associated gut microbiome through co-housing worsens disease in WT mice confirming the contributions of the microbiome to the Nlrx1^−/− phenotype. To validate NLRX1 effects on IEC metabolism mediate gut–microbiome interactions, restoration of WT glutamine metabolic profiles through either exogenous glutamine supplementation or administration of 6-diazo-5-oxo-l-norleucine abrogates differences in inflammation, microbiome, and overall disease severity in Nlrx1^−/− mice. The influence NLRX1 deficiency on SIRT1-mediated effects is identified to be an upstream controller of the Nlrx1^−/− phenotype in intestinal epithelial cell function and metabolism. The altered IEC function and metabolisms leads to changes in barrier permeability and microbiome interactions, in turn, promoting greater translocation and inflammation and resulting in an increased disease severity. In conclusion, NLRX1 is an immunoregulatory molecule and a candidate modulator of the interplay between mucosal inflammation, metabolism, and the gut microbiome during IBD.</p

<i>In silico</i> simulation of altered commensal bacteria regrowth during <i>Clostridium difficile</i> infection.

<p>Four cases were tested with variations to the inhibition of the commensal bacteria growth: inhibited by both neutrophils and inflamed epithelial cells (N and E_i), by only neutrophils (N), by only inflamed epithelial cells (E_i), and by neither (none). Resulting changes in species populations for each case are shown: (a) <i>baiCD</i>-containing commensal species, (b) <i>C</i>. <i>difficile</i>, (c) activated neutrophils, and (d) iTreg cells in the lamina propria.</p

Commensal bacteria regrowth inhibited with <i>Clostridium difficile</i> infection.

<p>(a) The <i>baiCD</i> content is decreased by antibiotic treatment in both control and <i>C</i>. <i>difficile</i> challenged mice and further decreased post-infection in the <i>C</i>. <i>difficile</i> challenged mice compared to the controls. (b and c) The expression of anti-microbial peptides DefB1 and S100A8 are upregulated with infection. Data points and error bars represent mean ± standard error of the mean (SEM). Asterisks (*) mark significance (p≤0.05) in comparison between control and <i>C</i>. <i>difficile</i> infected mice (n = 10).</p

Simulated dynamics of mucosal immune response to <i>Clostridium difficile</i>.

<p>Modeling results following calibration and validation of the host response model in populations of (a) <i>C</i>. <i>difficile</i>, (b) protective commensal bacteria, (c) infection-exacerbating commensal bacteria, (d) lamina propria T helper 17 cells, (e) effector dendritic cells, (f) infiltrating neutrophils, (g) regulatory T cells, (h) tolerogenic dendritic cells and (i) activated macrophages. Lines represent simulation results, filled points represent experimental calibration data and unfilled points represent experimental validation data.</p

HIV HPV Diagram.

<p>A diagram for the co-infection model (5). The left side of the figure represents the HIV dynamics wherein the interaction between target CD4+ T cells (<i>T</i>), productively infected CD4+ T cells (<i>I</i>) and HIV (<i>V</i>) are shown. The figure also includes the effect of reverse transcriptase (<i>RT</i>) and protein inhibitor (<i>PI</i>) (shown by red line—inhibition). The right side of the figure represents the HPV dynamics wherein the interaction between infected basal cells (<i>Y</i>_<i>1</i>), suprabasal transit-amplifying cells (<i>Y</i>_<i>2</i>), HPV specific (<i>E</i>) cells and HPV (<i>W</i>) are shown. The systems biology markup language (SBML) compliant network of interactions between HIV (<i>V</i>) and HPV (<i>W</i>) is created using CellDesigner [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168133#pone.0168133.ref040" target="_blank">40</a>] (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168133#pone.0168133.s001" target="_blank">S1 Fig</a>).</p

Parameters.

<p>Parameters.</p

HIV/HPV dynamics under chronic HPV at the start of cART.

<p>(a) HPV <i>W</i>; (b) CD4+ T cells <i>T</i> as given by model (5) under cART. Here, <i>ε</i> = 0.5 per day, <i>ε</i>_<i>RT</i> = 0.95, <i>ε</i>_<i>PI</i> = 0.5, and all other parameters are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168133#pone.0168133.t001" target="_blank">Table 1</a>. Initial conditions are <i>T</i>_<i>0</i> = 3.3x10⁵ cells per ml; <i>I</i>_<i>0</i> = 2.4x10⁵ cells per ml; <i>V</i>_<i>0</i> = 4.8x10⁴ virions per ml; <i>Y</i>_<i>10</i> = 3.2x10³ cells; <i>Y</i>_<i>20</i> = 1.6 x10⁴ cells; <i>W</i>_<i>0</i> = 1.8x10⁷ virions; <i>E</i>_<i>0</i> = 0.01 cells, and <i>t</i> = 0 is the start of cART. Moreover, we found two instances when HPV infection stays chronic in the presence of cART, namely strong drug efficacy, <i>ε</i>_<i>RT</i> = 0.95 and <i>ε</i>_<i>PI</i> = 0.5, and AIDS level CD4+ T cells, ≤ 1.7×10⁵ cells per ml; and, inefficient drug therapy, <i>ε</i>_<i>RT</i> = 0.2 and <i>ε</i>_<i>PI</i> = 0 and intermediate CD4+ T cell levels ≤ 2.6×10⁵ cells per ml.</p

Modeling the Mechanisms by Which HIV-Associated Immunosuppression Influences HPV Persistence at the Oral Mucosa

<div><p>Human immunodeficiency virus (HIV)-infected patients are at an increased risk of co-infection with human papilloma virus (HPV), and subsequent malignancies such as oral cancer. To determine the role of HIV-associated immune suppression on HPV persistence and pathogenesis, and to investigate the mechanisms underlying the modulation of HPV infection and oral cancer by HIV, we developed a mathematical model of HIV/HPV co-infection. Our model captures known immunological and molecular features such as impaired HPV-specific effector T helper 1 (Th1) cell responses, and enhanced HPV infection due to HIV. We used the model to determine HPV prognosis in the presence of HIV infection, and identified conditions under which HIV infection alters HPV persistence in the oral mucosa system. The model predicts that conditions leading to HPV persistence during HIV/HPV co-infection are the permissive immune environment created by HIV and molecular interactions between the two viruses. The model also determines when HPV infection continues to persist in the short run in a co-infected patient undergoing antiretroviral therapy. Lastly, the model predicts that, under efficacious antiretroviral treatment, HPV infections will decrease in the long run due to the restoration of CD4+ T cell numbers and protective immune responses.</p></div

HIV/HPV co-infection.

<p>(a) HPV <i>W</i> and (b) CTL <i>E</i> as given by model (6) for <i>ε</i> = 0.5 per day, parameters are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168133#pone.0168133.t001" target="_blank">Table 1</a>, and = 10⁶ cells per ml (blue solid lines); = 5x10⁵ cells per ml (red dashed lines); = 3.3x10⁵ cells per ml (green dotted lines); and = 2x10⁵ cells per ml (purple dashed-dotted lines).</p

Varying oncogene expression rates.

<p>(a) Bifurcation diagram showing cleared <i>W</i> (area below the curve) versus chronic <i>W</i> (area above the curve) as the <i>tat</i> effect and CTL carrying capacity vary. Here, the criterion for HPV clearance is given by Eq (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168133#pone.0168133.e011" target="_blank">7</a>); (b) HPV <i>W;</i> and (c) CTL <i>E</i> as given by model (6) for parameters listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168133#pone.0168133.t001" target="_blank">Table 1</a> and <i>ε =</i> 0.1 (blue solid lines), <i>ε =</i> 0.5 (red dashed lines), and <i>ε =</i> 0.9 (green dotted lines).</p