Probiotic Bacteria Produce Conjugated Linoleic Acid Locally in the Gut That Targets Macrophage PPAR γ to Suppress Colitis

Inflammatory bowel disease (IBD) therapies are modestly successful and associated with significant side effects. Thus, the investigation of novel approaches to prevent colitis is important. Probiotic bacteria can produce immunoregulatory metabolites in vitro such as conjugated linoleic acid (CLA), a polyunsaturated fatty acid with potent anti-inflammatory effects. This study aimed to investigate the cellular and molecular mechanisms underlying the anti-inflammatory efficacy of probiotic bacteria using a mouse model of colitis. The immune modulatory mechanisms of VSL#3 probiotic bacteria and CLA were investigated in a mouse model of DSS colitis. Colonic specimens were collected for histopathology, gene expression and flow cytometry analyses. Immune cell subsets in the mesenteric lymph nodes (MLN), spleen, blood and colonic lamina propria cells were phenotypically and functionally characterized. Fecal samples and colonic contents were collected to determine the effect of VSL#3 and CLA on gut microbial diversity and CLA production. CLA and VSL#3 treatment ameliorated colitis and decreased colonic bacterial diversity, a finding that correlated with decreased gut pathology. Colonic CLA concentrations were increased in response to probiotic bacterial treatment, but without systemic distribution in blood. VSL#3 and CLA decreased macrophage accumulation in the MLN of mice with DSS colitis. The loss of PPAR γ in myeloid cells abrogated the protective effect of probiotic bacteria and CLA in mice with DSS colitis. Probiotic bacteria modulate gut microbial diversity and favor local production of CLA in the colon that targets myeloid cell PPAR γ to suppress colitis

A Global Metabolic Shift Is Linked to Salmonella Multicellular Development

Bacteria can elaborate complex patterns of development that are dictated by temporally ordered patterns of gene expression, typically under the control of a master regulatory pathway. For some processes, such as biofilm development, regulators that initiate the process have been identified but subsequent phenotypic changes such as stress tolerance do not seem to be under the control of these same regulators. A hallmark feature of biofilms is growth within a self-produced extracellular matrix. In this study we used metabolomics to compare Salmonella cells in rdar colony biofilms to isogenic csgD deletion mutants that do not produce an extracellular matrix. The two populations show distinct metabolite profiles. Even though CsgD controls only extracellular matrix production, metabolite signatures associated with cellular adaptations associated with stress tolerances were present in the wild type but not the mutant cells. To further explore these differences we examine the temporal gene expression of genes implicated in biofilm development and stress adaptations. In wild type cells, genes involved in a metabolic shift to gluconeogenesis and various stress-resistance pathways exhibited an ordered expression profile timed with multicellular development even though they are not CsgD regulated. In csgD mutant cells, the ordered expression was lost. We conclude that the induction of these pathways results from production of, and growth within, a self produced matrix rather than elaboration of a defined genetic program. These results predict that common physiological properties of biofilms are induced independently of regulatory pathways that initiate biofilm formation

Metabolomic Profiling in Cattle Experimentally Infected with <i>Mycobacterium avium</i> subsp. <i>paratuberculosis</i>

<div><p>The sensitivity of current diagnostics for Johne's disease, a slow, progressing enteritis in ruminants caused by <i>Mycobacterium avium</i> subsp. <i>paratuberculosis (</i>MAP<i>)</i>, is too low to reliably detect all infected animals in the subclinical stage. The objective was to identify individual metabolites or metabolite profiles that could be used as biomarkers of early MAP infection in ruminants. In a monthly follow-up for 17 months, calves infected at 2 weeks of age were compared with aged-matched controls. Sera from all animals were analyzed by ¹H nuclear magnetic resonance spectrometry. Spectra were acquired, processed, and quantified for analysis. The concentration of many metabolites changed over time in all calves, but some metabolites only changed over time in either infected or non-infected groups and the change in others was impacted by the infection. Hierarchical multivariate statistical analysis achieved best separation between groups between 300 and 400 days after infection. Therefore, a cross-sectional comparison between 1-year-old calves experimentally infected at various ages with either a high- or a low-dose and age-matched non-infected controls was performed. Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS DA) yielded distinct separation of non-infected from infected cattle, regardless of dose and time (3, 6, 9 or 12 months) after infection. Receiver Operating Curves demonstrated that constructed models were high quality. Increased isobutyrate in the infected cattle was the most important agreement between the longitudinal and cross-sectional analysis. In general, high- and low-dose cattle responded similarly to infection. Differences in acetone, citrate, glycerol and iso-butyrate concentrations indicated energy shortages and increased fat metabolism in infected cattle, whereas changes in urea and several amino acids (AA), including the branched chain AA, indicated increased protein turnover. In conclusion, metabolomics was a sensitive method for detecting MAP infection much sooner than with current diagnostic methods, with individual metabolites significantly distinguishing infected from non-infected cattle.</p></div

Solution Structure of the Dimeric SAM Domain of MAPKK Ste11 and its interactions with the Adaptor Protein Ste50 from the Budding Yeast: Implications for Ste11 Activation and Signal Transmission Through the Ste50-Ste11 Complex

NRC publication: Ye

2D supervised OPLS-DA scores plot demonstrating the clustering pattern obtained for animals with known discrete infection status in dairy calves exactly 1-year-old dairy calves (R²Y = 0.65, Q²Y = 0.52).

<p>The metabolites were trimmed to the 16 most discriminating ones based on an analysis including all 53 metabolites.</p

2D unsupervised PCA scores plot of infected and non-infected cattle at 12 months old.

<p>The clustering pattern between animals in first and second run can be seen.</p

OPLS-DA coefficient plot of the 16 most discriminating metabolites; bars with negative values indicating metabolites that are significantly lower in MAP-infected than in non-infected and bars with positive value indicating metabolites which are significantly higher in MAP-infected than in non-infected animals exactly 1 year of age (R²Y = 0.65, Q²Y = 0.52).

<p>Only metabolites with a confidence interval that did not cross the zero line had significant changes (p<0.05).</p

OPLS-DA coefficient plot of all metabolites; bars with negative values indicating metabolites that are significantly lower in A) non-infected versus high dose MAP-infected (R²Y = 0.72, Q²Y = 0.42), B) in non-infected versus low dose infected animal (R²Y = 0.86, Q²Y = 0.33), and C) non-infected against all MAP-infected animals (R²Y = 0.76, Q²Y = 0.44), and bars with positive value indicating metabolites which are significantly higher in those same groups of infected animals exactly 1 year of age.

<p>Only metabolites with a confidence interval that did not cross the zero line had significant changes (p<0.05). The negative coefficients indicate metabolites that are significantly lower in negative versus MAP-infected animals and vice versa.</p

Boxplots of selected metabolites (acetone, asparagine, aspartate, betaine, glycerol, isobutyrate, isoleucine, leucine, mannose, threonine, tyrosine) for MAP-infected animals (n = 35) and non-infected age matched controls (n = 16).

<p>Significant differences with the control group by posthoc testing (Scheffe) after ANOVA are indicated by * (P<0.05).</p

ROC curve for the trimmed O-PLS DA model including the 16 most significant metabolites.

<p>The AUC was 0.984.</p