15. ゼミノームの放射線治療成績(第5回佐藤外科例会,第488回千葉医学会例会)

Performance of the MDSINE inference algorithms on simulated data with different sequencing depths. Simulations assumed an underlying dynamical systems model with ten species observed over 30 days with 27 time points sampled and an invading species at day 10. Performance of the four MDSINE inference algorithms, maximum likelihood ridge regression (MLRR), maximum likelihood constrained ridge regression (MLCRR), Bayesian adaptive lasso (BAL), and Bayesian variable selection (BVS), were compared. Algorithm performance was assessed using four different metrics: (a) root mean-square error (RMSE) for microbial growth rates; (b) RMSE for microbial interaction parameters; (c) RMSE for prediction of microbe trajectories on held-out subjects given only initial microbe concentrations for the held-out subject; and (d) area under the receiver operator curve (AUC ROC) for the underlying microbial interaction network. Lower RMSE values indicate superior performance, whereas higher AUC ROC values indicate superior performance. (PDF 182 kb

Additional file 6: Figure S4. of MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Forecasts of microbial concentration trajectories for the gnotobiotic mice probiotic stability experiments. The forecasts were obtained using a hold-one-subject-out procedure. Briefly, MDSINE was run on all data from all but one of the mice (the held-out subject) and model parameters were inferred. Using the inferred model parameters (including for the perturbation) and the measured concentrations of the microbiota at an initial time point for the held-out mouse, the trajectories of the microbiota for the held-out mouse were then forecast for all the remaining time points; the procedure was repeated for each mouse in turn. Solid lines denote predicted trajectories and symbols denote actual data. (PDF 7552 kb

Additional file 1: Tables S1–4. of MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Supplemental tables. (DOCX 21 kb

Additional file 7: Figure S5. of MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Predicted stability and steady state concentrations (log10 ng strain DNA/μg total fecal DNA) for all combinations of the 13 Clostridia strains in mice fed either high-fiber (standard) or low-fiber diets in the probiotic stability experiment. Columns and rows were ordered using hierarchical clustering using Euclidean distance with Ward linkage. No significant differences were found in the predicted stable biodiversity profiles between the high-fiber and low-fiber dietary regimes (number of strains across all predicted stable states was not significantly different; Wilcoxon rank sum test p value = 0.096). (PDF 4845 kb

Additional file 5: Figure S3. of MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Experimental design for probiotic stability studies in gnotobiotic mice. Seven adult germ-free mice were gavaged with 13 Clostridia strains from the VE202 probiotic cocktail [24]. Five mice were maintained on a standard high-fiber diet for 5 weeks, after which mice were switched to a low-fiber diet for 2 weeks and then switched back to the high-fiber diet for another 2 weeks; an additional two mice were inoculated with the same strains but were not subjected to the low-fiber dietary perturbation. Fecal pellets were collected at days 1–21 (daily), 23, 25, 27, 29, 31, 33, 35–60 (daily), 62, 63, and 65 for the five mice receiving the low-fiber dietary perturbation and at days 1–21 (daily), 23, 25, 27, and 29 for the two mice not receiving the perturbation. (PDF 42 kb

Clarithromycin expands CD11b⁺Gr-1⁺ cells via the STAT3/Bv8 axis to ameliorate lethal endotoxic shock and post-influenza bacterial pneumonia

<div><p>Macrolides are used to treat various inflammatory diseases owing to their immunomodulatory properties; however, little is known about their precise mechanism of action. In this study, we investigated the functional significance of the expansion of myeloid-derived suppressor cell (MDSC)-like CD11b⁺Gr-1⁺ cells in response to the macrolide antibiotic clarithromycin (CAM) in mouse models of shock and post-influenza pneumococcal pneumonia as well as in humans. Intraperitoneal administration of CAM markedly expanded splenic and lung CD11b⁺Gr-1⁺ cell populations in naïve mice. Notably, CAM pretreatment enhanced survival in a mouse model of lipopolysaccharide (LPS)-induced shock. In addition, adoptive transfer of CAM-treated CD11b⁺Gr-1⁺ cells protected mice against LPS-induced lethality via increased IL-10 expression. CAM also improved survival in post-influenza, CAM-resistant pneumococcal pneumonia, with improved lung pathology as well as decreased interferon (IFN)-γ and increased IL-10 levels. Adoptive transfer of CAM-treated CD11b⁺Gr-1⁺ cells protected mice from post-influenza pneumococcal pneumonia. Further analysis revealed that the CAM-induced CD11b⁺Gr-1⁺ cell expansion was dependent on STAT3-mediated Bv8 production and may be facilitated by the presence of gut commensal microbiota. Lastly, an analysis of peripheral blood obtained from healthy volunteers following oral CAM administration showed a trend toward the expansion of human MDSC-like cells (Lineage⁻HLA-DR⁻CD11b⁺CD33⁺) with increased arginase 1 mRNA expression. Thus, CAM promoted the expansion of a unique population of immunosuppressive CD11b⁺Gr-1⁺ cells essential for the immunomodulatory properties of macrolides.</p></div

CAM-treated CD11b⁺Gr-1⁺ cells exhibit an immunosuppressive phenotype.

<p>(A) Top 25 upregulated and downregulated genes determined by a microarray analysis in splenic CD11b⁺Gr-1⁺ cells sorted from vehicle- and CAM-treated mice. *<i>Stxbp6</i> (chromosome 12:45956210–46175345). **<i>Stxbp6</i> (chromosome 12:45953470–45956090). Results are presented as fold changes relative to the expression levels of each gene in vehicle-treated CD11b⁺Gr-1⁺ cells. (B) Arginase activity in the spleen of vehicle- and CAM-treated mice (n = 4 per group). N.D., not detected. (C) Immunofluorescence staining of Gr-1 and arginase-1 in the lungs of mice treated with CAM daily for three consecutive days (n = 4 per group). Scale bar, 200 μm. (D) The concentration of nitric oxide (NO) in spleen extracts of vehicle- and CAM-treated mice (n = 4 per group) ***<i>p</i> < 0.001 by the Mann–Whitney U-test. (E) Expression of the surface marker CD244 on splenic CD11b⁺Ly-6G⁺ cells determined by flow cytometry (n = 4 per group). (F–H) Cytokine profile of the culture supernatant from bone marrow-derived macrophages (BMDMs) with or without equal numbers of vehicle-treated or CAM-treated CD11b⁺Gr-1⁺ cells (5 × 10⁵ cells) in the spleen: TNF-α (F), IFN-γ (G), and IL-10 (H). Representative data for three independent experiments are shown. Data are expressed as the mean ± SEM. ***<i>p</i> < 0.001 by a one-way ANOVA with Tukey’s multiple comparison tests.</p

CAM improves survival in post-influenza pneumococcal pneumonia via an essential contribution of CD11b⁺Gr-1⁺ cells.

<p>(A) Survival rate of post-influenza pneumococcal pneumonia mice treated with vehicle, ampicillin (ABPC) (100 mg/kg), or clarithromycin (CAM) (100 mg/kg) (n = 37 per group). *<i>p</i> < 0.05, ***<i>p</i> < 0.001 by the log-rank test. (B) Cell counts in bronchoalveolar lavage fluid (BALF) obtained from mice with post-influenza pneumococcal pneumonia treated with vehicle, ABPC (100 mg/kg), or CAM (100 mg/kg) (n = 7–8 per group). Data are presented as the mean ± SEM. *<i>p</i> < 0.05. **<i>p</i> < 0.01, ***<i>p</i> < 0.001 by a two-way ANOVA with Tukey’s multiple comparison tests. (C and D) Bacterial load in the lungs (C) and blood (D) of post-influenza pneumococcal pneumonia mice treated with vehicle, ABPC (100 mg/kg), or CAM (100 mg/kg) at 18 and 36 h after pneumococcal infection (n = 15–16 per group). Data are presented as the mean ± SEM. *<i>p</i> < 0.05 by a two-way ANOVA with Tukey’s multiple comparison tests. (E) Lung H&E staining at 48 h after pneumococcal infection. Representative data for 5 mice per group are shown. Scale bar, 100 μm. (F–I) Levels of IFN-γ (F) and IL-10 (G) in BALF were measured by ELISA. The levels of IFN-γ (H) and IL-10 (I) in serum were measured by ELISA (n = 7–8 per group). Data are presented as the mean ± SEM. *<i>p</i> < 0.05. **<i>p</i> < 0.01. ***<i>p</i> < 0.001 by a two-way ANOVA with Tukey’s multiple comparison tests. (J) Survival rate of mice following adoptive transfer of CD11b⁺Gr-1⁺ cells treated with vehicle, ABPC (100 mg/kg), or CAM (100 mg/kg) in post-influenza pneumococcal pneumonia mice (n = 34 per group). Combined data for two independent experiments are shown. *<i>p</i> < 0.05. **<i>p</i> < 0.01 by the log-rank test. (K) Survival rate of vehicle-, ABPC-, and CAM-treated mice intranasally inoculated with recombinant IFN-γ (16 μg/kg) or PBS at 30 min and 24 h after pneumococcal infection (n = 20 per group). (L) Survival rate of vehicle- or CAM-treated WT and <i>Ifng</i>^<i>-/-</i> mice with post-influenza pneumococcal pneumonia (n = 7–16 per group).</p

CAM ameliorates LPS-endotoxin shock via the essential contribution of CD11b⁺Gr-1⁺ cells.

<p>(A) Survival rate for LPS (50 mg/kg)-endotoxin shock in mice pretreated with vehicle or CAM (100 mg/day) daily for three consecutive days (n = 36 per group). *<i>p</i> = 0.0009 by the log-rank test. (B–D) Cytokine profiles in serum 12 h after LPS challenge in vehicle- or CAM-treated mice: TNF-α (B), IFN-γ (C), and IL-10 (D). (n = 5–6 per group). Data are represented as the mean ± SEM. **<i>p</i> < 0.01. ***<i>p</i> < 0.001 by the Mann–Whitney U-tests. (E and F) Representative two-parameter dot plots of CD11b⁺Gr-1⁺ cells in the spleen (E) and lungs (F) of mice intraperitoneally treated with vehicle or CAM (100 mg/day) daily for three consecutive days, followed by intraperitoneal injection with PBS or LPS (50 mg/kg) (n = 4 per group). (G) Quantification of CD11b⁺Gr-1⁺ cells in the spleen and lungs sorted from intraperitoneally vehicle- and CAM-treated (once a day for 3 days), followed by intraperitoneally LPS-treated mice (n = 4 per group). *<i>p</i> < 0.05, **<i>p</i> < 0.01 by Mann–Whitney U-tests. (H) Survival rate for LPS-endotoxin shock in vehicle- and CAM-injected mice pretreated with either anti-Gr-1 antibody (250 μg/mouse) or control IgG (n = 20–21 per group) 24 h before LPS challenge. *<i>p</i> = 0.0128 by the log-rank test. (I) Survival rate for LPS-endotoxin shock in vehicle- and CAM-injected mice pretreated with either anti-Gr-1 antibody (250 μg/mouse) or control IgG (n = 25–26 per group) 1 h before initiation of CAM treatment (i.e., 73 h before LPS challenge). Combined data for two independent experiments are shown. ***<i>p</i> < 0.001 by the log-rank test. (J) Adoptive transfer of CAM-treated CD11b⁺Gr-1⁺ cells improved the survival rate in LPS endotoxin shock (n = 24 per group). *<i>p</i> = 0.0023 by the log-rank test. (K-M) TNF-α (K), IFN-γ (L), and IL-10 (M) levels in serum at 12 h after intraperitoneal LPS injection (n = 5–6 per group). Data are presented as the mean ± SEM. *<i>p</i> < 0.05. **<i>p</i> < 0.01. ***<i>p</i> < 0.001 by the Mann–Whitney U-tests.</p

Dysbiosis and compositional alterations with aging in the gut microbiota of patients with heart failure

<div><p>Emerging evidence has suggested a potential impact of gut microbiota on the pathophysiology of heart failure (HF). However, it is still unknown whether HF is associated with dysbiosis in gut microbiota. We investigated the composition of gut microbiota in patients with HF to elucidate whether gut microbial dysbiosis is associated with HF. We performed 16S ribosomal RNA gene sequencing of fecal samples obtained from 12 HF patients and 12 age-matched healthy control (HC) subjects, and analyzed the differences in gut microbiota. We further compared the composition of gut microbiota of 12 HF patients younger than 60 years of age with that of 10 HF patients 60 years of age or older. The composition of gut microbial communities of HF patients was distinct from that of HC subjects in both unweighted and weighted UniFrac analyses. <i>Eubacterium rectale</i> and <i>Dorea longicatena</i> were less abundant in the gut microbiota of HF patients than in that of HC subjects. Compared to younger HF patients, older HF patients had diminished proportions of Bacteroidetes and larger quantities of Proteobacteria. The genus <i>Faecalibacterium</i> was depleted, while <i>Lactobacillus</i> was enriched in the gut microbiota of older HF patients. These results suggest that patients with HF harbor significantly altered gut microbiota, which varies further according to age. New concept of heart-gut axis has a great potential for breakthroughs in the development of novel diagnostic and therapeutic approach for HF.</p></div