Metabolic pathway enrichment across layers.

<p>Based on the enzyme content of the different species found in different layers of the biofilm (with layers labeled from 1 to 4, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077617#pone-0077617-g001" target="_blank">Fig. 1</a>), one can estimate whether any given layer is enriched for specific metabolic functions. Enzyme and pathway enrichments for each layer are computed based on a standard GSEA algorithm. Black boxes in the pathways-by-layers matrix denote enrichment of a particular KEGG pathway in a given layer. The pathway abbreviations are as follows: APM-Arginine and proline metabolism, BAOLN-Biosynthesis of alkaloids derived from ornithine lysine and nicotinic acid, CFP-Carbon fixation pathways in prokaryotes, GDM-Glyoxylate and dicarboxylate metabolism, GST-Glycine, serine and threonine metabolism, NM-Nitrogen metabolism, PCM-Porphyrin and chlorophyll metabolism, PGI-Pentose and glucuronate interconversions, PPM-Propanoate metabolism, PYM-Pyruvate metabolism, TBB-Terpenoid backbone biosynthesis, and TCA-Tricarboxylic acid cycle.</p

Expected synergy between metabolic networks as a function of metabolic distance.

<p>The synergy is computed as the count of elementary flux modes (pathways) that are feasible for a metabolic network that is the union of two networks with a given Jaccard’s distance from each other, normalized to the count of elementary flux modes of the constituent networks. The count of elementary flux modes can be thought of as an estimate of the number of distinct metabolic tasks that the network can perform, i.e. its versatility. Hence, the graph shows how the versatility of two conjoined networks relative to the constituent networks is maximal for an intermediate Jaccard’s distance between such networks. 100 random paired networks were generated for each of several possible Jaccard’s distances. Bar heights reflect the average normalized increase in the number of elementary flux modes, whereas error bars represent the standard error of the mean.</p

Metabolic Proximity in the Order of Colonization of a Microbial Community

<div><p>Microbial biofilms are often composed of multiple bacterial species that accumulate by adhering to a surface and to each other. Biofilms can be resistant to antibiotics and physical stresses, posing unresolved challenges in the fight against infectious diseases. It has been suggested that early colonizers of certain biofilms could cause local environmental changes, favoring the aggregation of subsequent organisms. Here we ask whether the enzyme content of different microbes in a well-characterized dental biofilm can be used to predict their order of colonization. We define a metabolic distance between different species, based on the overlap in their enzyme content. We next use this metric to quantify the average metabolic distance between neighboring organisms in the biofilm. We find that this distance is significantly smaller than the one observed for a random choice of prokaryotes, probably reflecting the environmental constraints on metabolic function of the community. More surprisingly, this metabolic metric is able to discriminate between observed and randomized orders of colonization of the biofilm, with the observed orders displaying smaller metabolic distance than randomized ones. By complementing these results with the analysis of individual vs. joint metabolic networks, we find that the tendency towards minimal metabolic distance may be counter-balanced by a propensity to pair organisms with maximal joint potential for synergistic interactions. The trade-off between these two tendencies may create a “sweet spot” of optimal inter-organism distance, with possible broad implications for our understanding of microbial community organization.</p></div

Convergent Synthesis of Novel Muramyl Dipeptide Analogues: Inhibition of Porphyromonas gingivalis-Induced Pro-inflammatory Effects by High Doses of Muramyl Dipeptide

Porphyromonas gingivalis (<i>P.g.</i>)-induced TNF-α can be affected by muramyl dipeptide (MDP) in a biphasic concentration-dependent manner. We found that in <i>P.g.</i>-exposed macrophages, treatment with 10 μg/mL of MDP (MDP-low) up-regulated TNF-α by 29%, while 100 μg/mL or higher (MDP-high) significantly decreased it (16% to 38%). MDP-high was found to affect the ubiquitin-editing enzyme A20 and activator protein 1 (AP1). An AP1 binding site was found in the promoter region of A20. A20 promoter activity was up-regulated after transfection of AP1 cDNA in cells. Four analogues of MDP (<b>3</b>–<b>6</b>) were prepared through a convergent strategy involving the synthesis of two unique carbohydrate fragments, <b>7a</b> and <b>7b</b>, using the peptide coupling reagents, EDCI and HOAt. Analogue <b>4</b> improved MDP function and <i>P.g.</i>-induced activities. We propose a new signaling pathway for TNF-α induction activated after exposing macrophages to both <i>P.g.</i> and MDP-high or analogue <b>4</b>

Distributions of alternative metrics for correct and randomized orders of colonization.

<p>(A) Similar to what shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077617#pone-0077617-g001" target="_blank">Fig. 1B</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077617#pone-0077617-g002" target="_blank">Fig. 2</a>, we computed inter-species distance between organisms along paths that respect (red) or do not respect (blue) the layered order of colonization of the Kolenbrander map. Here, however, as opposed to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077617#pone-0077617-g002" target="_blank">Fig. 2</a>, we compute the Jaccard distance between two species based on their profiles of non-enzyme genes (as identifiable through KEGG KO numbers). (B) The correct and incorrect orders of colonization are compared based on an information metric, rather than on the Jaccard distance. In walking along a colonization order path from one organism to the next, we compute (in Nats) the amount of information added due to the presence of previously absent enzymes. The added information for each pair of adjacent organisms is summed to form the added information score, along paths that respect (red) or do not respect (blue) the layered order of colonization. The purple distribution is obtained by computing the added information scores for orders of colonization that reflect the layered structure, but walk through it in reverse order (i.e. from the outer layer downwards towards the salivary pellicle).</p

A simplified model of dental biofilm.

<p>(A) Rectangular nodes represent components of the salivary pellicle while circular nodes represent organisms in the biofilm. Lines represent known interactions (often mediated by adhesin molecules) between different components of the biofilm. The organism abbreviations are as follows: Layer 1: SM-<i>Streptococcus mitis</i>, SS-<i>Streptococcus sanguinis</i> and SG-<i>Streptococcus gordonii</i>. Layer 2: CO-<i>Capnocytophaga ochracea</i>,VS-<i>Veionella</i> (represented by <i>Veillonella parvula</i>), and PA-<i>Propionibacterium acnes</i>. Layer 3: FN-<i>Fusobacterium nucleatum</i>. Layer 4: AA-<i>Aggregatibacter actinomycetemcomitans</i>, TD-<i>Treponema denticola</i>, ES-<i>Eubacterium</i> (represented by <i>Eubacterium eligens</i>), and PG-<i>Porphyromonas gingivalis</i>. The salivary receptors have the following abbreviations: SMU-Sialylated mucins, BCF-Bacterial cell fragment, PRP-Proline rich protein, SA-Salivary agglutinin, S- Statherin and AAM-Alpha amylase. (B) A schematic representation of one of the many possible step-wise orders of colonization that conforms to the layered organization inferred from the literature, i.e. is such that the path that walks through the different species is monotonically departing from the salivary pellicle upwards. In our calculations of inter-species metabolic distances, we average the distances between any two species connected by a segment. This calculation is performed for all paths that reflect the order of colonization, giving rise to the distributions shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077617#pone-0077617-g002" target="_blank">Fig. 2</a>. (C) A schematic representation of one of the many possible randomized orders of colonization that do not follow the order of the literature-derived layers; for the 11 organisms present in the biofilm there are 11! possible permutations.</p

M1 polarization is inhibited in BMMΦ from obese mice after <i>P.</i><i>gingivalis</i> infection.

<p>(A) Uninfected BMMΦ from lean (LN) and obese (OB) mice were left unlabeled or were labeled with PE-conjugated anti-mouse CD11b and analyzed by FACS to determine macrophages differentiation. (B) Uninfected BMMΦ from LN and OB mice were left unlabeled or were labeled with PE-conjugated anti-mouse F4/80 and analyzed by FACS to determine macrophages maturation. (C) BMMΦ from LN and OB mice were infected with <i>P. gingivalis</i> (P.g) for 1, 4 and 24 h. Cells were collected and labeled with PE-conjugated anti-mouse IL-12 and FITC-conjugated anti-mouse IL-10. The percentages of M1 and M2 macrophages were measured by FACS analysis. Data represent one of three independent experiments with consistent results. (D) Percentage of BMMΦ from LN and OB mice that present an M1 polarization (high levels of IL-12 and low levels of IL-10) and an M2 polarization (high levels of IL-10 and low levels of IL-12).</p

Partial Restoration of Macrophage Alteration from Diet-Induced Obesity in Response to <i>Porphyromonas gingivalis</i> Infection

<div><p>Obesity is a chronic inflammatory disease that weakens macrophage innate immune response to infections. Since M1 polarization is crucial during acute infectious diseases, we hypothesized that diet-induced obesity inhibits M1 polarization of macrophages in the response to bacterial infections. Bone marrow macrophages (BMMΦ) from lean and obese mice were exposed to live <i>Porphyromonas gingivalis</i> (<i>P. gingivalis</i>) for three incubation times (1 h, 4 h and 24 h). Flow cytometry analysis revealed that the M1 polarization was inhibited after <i>P. gingivalis</i> exposure in BMMΦ from obese mice when compared with BMMΦ from lean counterparts. Using a computational approach in conjunction with microarray data, we identified switching genes that may differentially control the behavior of response pathways in macrophages from lean and obese mice. The two most prominent switching genes were thrombospondin 1 and arginase 1. Protein expression levels of both genes were higher in obese BMMΦ than in lean BMMΦ after exposure to <i>P. gingivalis</i>. Inhibition of either thrombospondin 1 or arginase 1 by specific inhibitors recovered the M1 polarization of BMMΦ from obese mice after <i>P. gingivalis</i> exposure. These data indicate that thrombospondin 1 and arginase 1 are important bacterial response genes, whose regulation is altered in macrophages from obese mice.</p></div

Thbs1 and Arg1 expression after <i>P.</i><i>gingivalis</i> infection.

<p>BMMΦ from lean (LN) and obese (OB) mice were stimulated with <i>P. gingivalis</i> (P.g) for 1, 4 and 24 h. (A) The protein levels of Thbs1 and Arg1 were detected by Western blot using whole cell lysates. (B) The intensities of the bands were converted into quantitative data. The expression of Thbs and Arg1 were normalized to β-Actin.</p

Schematic of the procedure for identifying switching genes.

<p>Microarray data for the BMMΦ from lean and obese mice in response to infection was used to create two Self-Organizing Maps (SOMs). Differentially expressed clusters from the lean and obese SOMs with at least one gene in common were identified and plotted as shown. The left and right graphs show the behavior of genes in the lean and obese BMMΦ, respectively. The green dashed circle refers to a lean cluster, as taken from the lean SOM, where all the gene trajectories, shown in green, have similar behavior over the three time points. These genes lose their clustered behavior over time when expressed in obese BMMΦ. Similarly, the blue dashed circle refers to an obese cluster, as taken from the obese SOM. All the trajectories shown in blue are clustered when genes are expressed in obese BMMΦ, and dispersed when genes are expressed in lean BMMΦ. The red line represents the time behavior of the switching gene, which moves from the lean cluster to the obese cluster due to the change in condition. This gene appears to activate/deactivate the genes in the lean and obese clusters.</p