Bacterial–bacterial interactions.

<p>The composition of nasopharyngeal microbiota is constantly subject to interactions between species. Bacterial species can interact with other bacterial species by competition and synergism. Synergism can be characterized by, for instance, the production of components that favors another species, as shown for the production of outer membrane vesicles. These may contain factors that are able to inactivate complement factor C3, thereby allowing another species to escape the immune system. Production of substances by one species, for example hydrogen peroxide (H₂O₂), may eliminate its competitor. The immune system may also be involved in competition, as one bacterium has fewer escape mechanisms to evade the immune system than another and therefore may use co-inhabitants to survive, whereas the reverse phenomenon (i.e., one species may trigger the immune system to combat the other species) may also occur. In addition, since PhC (phosphorylcholine) is shown to be immunogenic and some species may be able to switch off PhC expression whereas others cannot, there might be a selective advantage. Another form of competition involves competition for the same host receptor, as demonstrated for PhC and its receptor platelet activating factor receptor (PAFr). Moreover, one species may use neuraminidase to cut off the sialic acids (SA) that other bacteria may require for attachment to host receptors, therefore inhibiting adherence of the other bacterial species. H₂O₂, hydrogen peroxide; PAFr, platelet activating factor receptor; PhC, phosphorylcholine; NA, neuraminidase; SA, sialic acid (SA); rSA, receptor for sialic acids; Ab, antibodies.</p

Viral–bacterial interactions.

<p>(A) Viral–bacterial interaction on the respiratory epithelial surface. Viral presence is thought to predispose the respiratory niche to bacterial colonization by different mechanisms. First, viruses may render the epithelium more susceptible to bacterial colonization by altering the mucosal surfaces. Ciliae may be damaged, leading to decreased mucociliar function of the respiratory epithelium. Additionally, due to viral-induced damage and loss of integrity of the epithelium layer, bacterial colonization may be enhanced and translocation may be increased. Virus-infected cells may decrease the expression of antimicrobial peptides, as shown for β-defensins, thereby affecting the natural defense of the host epithelium. Viral neuraminidase (NA) activity is able to cleave sialic acids residues, thereby giving access to bacterial receptors that were covered by these residues. Finally, viruses may induce bacterial colonization and replication both directly and indirectly, the latter by inducing upregulation of various receptors required for bacterial adherence, including PAFr, CAECAM-1, P5F, ICAM-1, and G-protein. PAFr, platelet activating factor receptor; ICAM-1, intracellular adhesion molecule 1; P5 fimbriae, outer membrane protein P5-homologous fimbriae; CAECAM-1, carcinoembryonic adhesion molecule-1; PhC, phosphorylcholine; SA, sialic acids; rSA, receptor for sialic acids; NA, neuraminidase; mRNA, messenger RNA, AMPs, antimicrobial peptides. (B) Viral–bacterial interaction in relation to the host immune system. Viruses may also induce changes in immune function favorable to bacterial invasion: fewer NK cells may be recruited into the tissue and their functionality may be suboptimal as a consequence of viral infection. Virus-induced IFN-α and IFN-β may impair recruitment and functionality of neutrophils, and subsequently induce apoptosis of neutrophils recruited to combat the viral invader. Furthermore, IFN-γ seems to negatively affect the activity of macrophages. Viral-infected monocytes appear less effective in ingesting and killing bacteria, predisposing them to bacterial overgrowth and invasion. Viral infection seems to impair TLR pathways, induce production of the anti-inflammatory cytokine IL-10, and decrease the concentration of the pro-inflammatory cytokine TNF-α, generally affecting adequate immune responses to bacterial infections. Black arrows indicate increased (↑) or decreased (↓) activity or functionality of a cytokine. IFN, interferon; TNF, tumor necrosis factor; TLR, toll like receptor; IL, interleukin; NK cell, natural killer cell.</p

Viral–bacterial interaction based on data available from human, animal, and in vitro studies.

<p>Virus (column one) and respective bacterium (column two) for which interactions were observed (column three), and source of evidence: from human studies (column four), animal studies (column five), or in vitro studies (column six) showing type of epithelium tested.</p><p>NA, data not available from literature.</p

Viral detection in respiratory samples in asymptomatic children.

a<p>Related to geographical area.</p>b<p>Number of samples tested.</p>c<p>Stratified for season.</p>d<p>Picornavirus general.</p><p>M, months of age; Y, years of age; HRV, human rhinoviruses; EV, entero viruses; AdV, adeno viruses; HBoV, human bocavirus; RSV, respiratory syncytial virus; hMPV, human metapneumovirus; CoV, corona viruses; IV, influenza viruses; PIV, para-influenza viruses; NS, not specified.</p

Antimicrobial susceptibility of <i>Streptococcus pneumoniae</i> strains carried by children infected with HIV.

<p>Antimicrobial susceptibility of <i>Streptococcus pneumoniae</i> strains carried by children infected with HIV.</p

Represents the results from univariate regression analysis. Significant correlations between 15 variables and the top 100 OTUs are depicted.

<p>The x-axis depicts the respective OTUs and the y–axis the 15 variables tested. The heatmap shows significant correlations (p-value less than 0.05) from univariate analysis. Blue squares show positive changes in relative abundance, whereas red squares show negative correlations. The intensity of colour correlates with the magnitude of the (log) fold change value (see colour key).</p

Represents results from multivariate regression analysis following bivariate regression analysis.

<p>The x-axis depicts the by bivariate regression selected 68 OTUs and the y–axis the selected variables. The heatmap shows significant correlations (p-value less than 0.05) from multivariate analysis. Blue squares show positive changes in relative abundance, whereas red squares show negative correlations. The intensity of colour correlates with the magnitude of the (log) fold change value (see colour key).</p

Represents the results from bivariate regression analysis.

<p>Significant correlations between 14 variables and the top 100 OTUs are depicted after correcting for the correlation between season and OUT composition. The x-axis depicts the respective OTUs and the y–axis the 15 variables tested. The heatmap shows significant correlations (p-value less than 0.05) from univariate analysis. Blue squares show positive changes in relative abundance, whereas red squares show negative correlations. The intensity of colour correlates with the magnitude of the (log) fold change value (see colour key).</p

Represent the results from multivariate logistic regression analysis following CCA.

<p>Significant correlations are depicted between the by CCA selected 12 variables and 31 OTU’s. The x-axis depicts the respective OTUs and the y–axis the 12 variables tested. The heatmap shows significant correlations (p-value less than 0.05) from multivariate analysis. The colour represents the effect size and direction of the correlation. Blue squares show positive changes in relative abundance, whereas red squares show negative correlations. The intensity of color correlates with the magnitude of the (log) fold change value (see colour key).</p

Correlations between variables and microbiome composition at OTU level identified by the different types of analysis.

<p>In <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050267#pone-0050267-g001" target="_blank">figure 1</a>, results from Canonical Correlation Analysis (CCA) are depicted, where each variable is plotted with the weight of this variable from the first order and second order variates as coordinates. Pairs of variables with relatively large weights in the same direction represent positive correlations and variables whose weights have opposite directions exhibit inverse correlations.After applying the canonical correlation analysis with a cut off p-value <0.15, 12 external variables (in blue color) and 31 OTUs (in old rose color) remained as potential determinants of microbiota composition.</p