HCNCp mRNA Transcripts Are Upregulated During Encystation

<div><p>SAGE revealed increased steady state abundance of HCNCp transcripts beginning at 12 hr encystation through 42 hr compared to trophozoite (“troph”) and 4 hr encystation.</p> <p>SAGE data is presented as percentage of all tags sampled at a given time point.</p></div

Traffic of HCNCp And Cyst Proteins During Growth And Differentiation

<div><p>Differential interference contrast (DIC) merged with DAPI images are shown to the left of each panel, HCNCp in green, anti-cyst proteins in red, and nucleic acid in blue (DAPI).</p> <p>In trophozoites, HCNCp localized to nuclei and nuclear envelope/ER.</p> <p>During encystation HCNCp co-localized with cyst proteins in encystation secretory vesicles (ESV) and to the cyst wall of water-resistant cysts.</p> <p>In mature cysts, most of the HCNCp was within the cell body.</p> <p>Scale bar is 5 µM.</p></div

demographics of paediatric (training) cohort.

<p>demographics of paediatric (training) cohort.</p

Best features to discriminate by activity levels.

<p>Activity levels are considered simultaneously, employing the Kruskal-Wallis test. Grey bar indicate the q-value and thus the strength of the association between the features and the disease state. Color bars indicate the average percentage of reads for each disease activity level.</p

Stratification of patients by activity levels.

<p>Overall microbial diversity as measured by the Shannon Diversity Index. Activity was assessed on the basis of patient symptoms using PCDAI and PUCAI clinical indices.</p

Taxa significantly associated with IBD.

<p>Center panel is a compositional heatmap of the selected taxa for each of the samples in the pediatric case-control study. Left panel indicates the significance of the association of each taxa with disease state, as measured by the q-value. Right panel shows a measure of effect size (Cohen’s delta), highlighting in red those taxa which are significantly more prevalent in IBD samples. Bottom panels show relevant metadata for each sample, including disease activity as measured by PUCAI <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039242#pone.0039242-Tomas1" target="_blank">[32]</a> and PCDAI indices <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039242#pone.0039242-Knights1" target="_blank">[33]</a>.</p

Accuracy of disease classification.

<p>(A) SLiME applied to Frank et al. biopsy data set. The black line indicates performance obtained when features were generated by taxonomical binning of the original sequence data (AUC  = 0.73); dashed line shows performance when features were selected based on their importance in the pediatric case-control data set and then applied to the Frank et al. study (AUC  = 0.71). (B) ROC curve for SLiME classification of active IBD patients vs controls in the pediatric case-control data set. Two different threshold selections are highlighted: circle, for which SLiME has 80.3% sensitivity and 69.7% specificity; triangle, for which SLiME has 45.8% sensitivity and 92.4% specificity.</p

Temporal Dynamics of In-Field Bioreactor Populations Reflect the Groundwater System and Respond Predictably to Perturbation

Temporal variability complicates testing the influences of environmental variability on microbial community structure and thus function. An in-field bioreactor system was developed to assess oxic versus anoxic manipulations on <i>in situ</i> groundwater communities. Each sample was sequenced (16S SSU rRNA genes, average 10,000 reads), and biogeochemical parameters are monitored by quantifying 53 metals, 12 organic acids, 14 anions, and 3 sugars. Changes in dissolved oxygen (DO), pH, and other variables were similar across bioreactors. Sequencing revealed a complex community that fluctuated in-step with the groundwater community and responded to DO. This also directly influenced the pH, and so the biotic impacts of DO and pH shifts are correlated. A null model demonstrated that bioreactor communities were driven in part not only by experimental conditions but also by stochastic variability and did not accurately capture alterations in diversity during perturbations. We identified two groups of abundant OTUs important to this system; one was abundant in high DO and pH and contained heterotrophs and oxidizers of iron, nitrite, and ammonium, whereas the other was abundant in low DO with the capability to reduce nitrate. In-field bioreactors are a powerful tool for capturing natural microbial community responses to alterations in geochemical factors beyond the bulk phase