Table_1_Parasite-Microbiota Interactions With the Vertebrate Gut: Synthesis Through an Ecological Lens.pdf

<p>The vertebrate gut teems with a large, diverse, and dynamic bacterial community that has pervasive effects on gut physiology, metabolism, and immunity. Under natural conditions, these microbes share their habitat with a similarly dynamic community of eukaryotes (helminths, protozoa, and fungi), many of which are well-known parasites. Both parasites and the prokaryotic microbiota can dramatically alter the physical and immune landscape of the gut, creating ample opportunities for them to interact. Such interactions may critically alter infection outcomes and affect overall host health and disease. For instance, parasite infection can change how a host interacts with its bacterial flora, either driving or protecting against dysbiosis and inflammatory disease. Conversely, the microbiota can alter a parasite's colonization success, replication, and virulence, shifting it along the parasitism-mutualism spectrum. The mechanisms and consequences of these interactions are just starting to be elucidated in an emergent transdisciplinary area at the boundary of microbiology and parasitology. However, heterogeneity in experimental designs, host and parasite species, and a largely phenomenological and taxonomic approach to synthesizing the literature have meant that common themes across studies remain elusive. Here, we use an ecological perspective to review the literature on interactions between the prokaryotic microbiota and eukaryotic parasites in the vertebrate gut. Using knowledge about parasite biology and ecology, we discuss mechanisms by which they may interact with gut microbes, the consequences of such interactions for host health, and how understanding parasite-microbiota interactions may lead to novel approaches in disease control.</p

Disruption of <i>TgPHIL1</i> Alters Specific Parameters of <i>Toxoplasma gondii</i> Motility Measured in a Quantitative, Three-Dimensional Live Motility Assay

<div><p><i>T. gondii</i> uses substrate-dependent gliding motility to invade cells of its hosts, egress from these cells at the end of its lytic cycle and disseminate through the host organism during infection. The ability of the parasite to move is therefore critical for its virulence. <i>T. gondii</i> engages in three distinct types of gliding motility on coated two-dimensional surfaces: twirling, circular gliding and helical gliding. We show here that motility in a three-dimensional Matrigel-based environment is strikingly different, in that all parasites move in irregular corkscrew-like trajectories. Methods developed for quantitative analysis of motility parameters along the smoothed trajectories demonstrate a complex but periodic pattern of motility with mean and maximum velocities of 0.58±0.07 µm/s and 2.01±0.17 µm/s, respectively. To test how a change in the parasite's crescent shape might affect trajectory parameters, we compared the motility of Δ<i>phil1</i> parasites, which are shorter and wider than wild type, to the corresponding parental and complemented lines. Although comparable percentages of parasites were moving for all three lines, the Δ<i>phil1</i> mutant exhibited significantly decreased trajectory lengths and mean and maximum velocities compared to the parental parasite line. These effects were either partially or fully restored upon complementation of the Δ<i>phil1</i> mutant. These results show that alterations in morphology may have a significant impact on <i>T. gondii</i> motility in an extracellular matrix-like environment, provide a possible explanation for the decreased fitness of Δ<i>phil1</i> parasites <i>in vivo</i>, and demonstrate the utility of the quantitative three-dimensional assay for studying parasite motility.</p></div

<i>T. gondii</i> moves in a corkscrew-like manner in three dimensions.

<p>(<b>A</b>) RH strain <i>T. gondii</i> tachyzoites expressing a tandem tomato fluorescence cassette (RH-OVA-tdTomato) were injected into a mouse earflap and imaged by two-photon laser scanning microscopy. A maximum intensity projection (MIP) shows parasites that move in a corkscrew-like fashion (<i>e.g.</i>, dashed white box). Scale bar = 29 µm. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085763#pone.0085763.s009" target="_blank">Video S1</a> for the corresponding movie. (<b>A'</b>) Higher magnification view of dashed white box in (A); scale bar = 12 µm. (<b>B</b>) Assembly and dimensions of the imaging (“Pitta”) chamber. Coverslips were assembled using double-sided tape, and perfused with a 1∶3∶3 mixture of parasites treated with Hoechst 33342, 3D motility media and chilled Matrigel, respectively. (<b>C</b>) Pitta chambers were incubated at 27°C for 7 min, followed by 2 min equilibration in the preheated 35°C microscope enclosure. Fluorescent parasite nuclei were imaged for 60 s in a 402 µm×401 µm×40 µm volume (approximately 67 z-stacks, with 41 z-slices captured every 0.88 s) by time-lapse fluorescence videomicroscopy. Datasets were visualized during acquisition by generating MIPs, tracked using the Imaris software, and then analyzed using the Bugs software as indicated in the workflow. (<b>D</b>) A MIP showing that parasites also move in corkscrew-like trajectories in Matrigel. Scale bar = 50 µm. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085763#pone.0085763.s011" target="_blank">Video S3</a> for the corresponding movie. (<b>D'</b>) Higher magnification view of dashed white box in (D); scale bar = 10 µm. The colour scheme for all MIPs was inverted for better visualization of parasite trajectories.</p

3D motility of the Δ<i>phil1</i> parasites.

<p>(<b>A</b>) MIPs for wild-type (RH), <i>TgPHIL1</i> knockout (Δ<i>phil1</i>) and complemented (Comp) parasites. Scale bar = 50 µm. The colour scheme for all MIPs was inverted for better visualization of parasite trajectories. The percentage of total parasites moving (<b>B</b>) was comparable for the three parasite lines, but the cumulative frequency distribution (<b>C</b>) and histogram (<b>D</b>) of the smoothed trajectory lengths for RH (black), Δ<i>phil1</i> (red) and Comp parasites (grey) reveal that the Δ<i>phil1</i> parasites do not move as far as the RH or Comp parasites within the same timeframe (Kolmogorov-Smirnov test, D = 0.199, p<0.0001 and D = 0.114, p<0.0001, respectively). The Δ<i>phil1</i> parasites also exhibited significantly decreased mean velocity compared to the RH parasites (<b>E</b>) and significantly reduced maximum velocity compared to both RH and Comp parasites (<b>F</b>) (paired t-test, significance indicated by asterisks). Closed data points are the results from five independent experiments comparing RH and Δ<i>phil1</i> parasites; open data points are the results from four independent experiments comparing Δ<i>phil1</i> and Comp parasites. Each of the independent experiments (assigned a different colour in the scatter plot) was performed in either triplicate or quadruplicate. The total number of parasites analyzed was 6,467 for RH, 9,305 for Δ<i>phil1</i> and 3,743 for Comp. * p<0.05, ** p<0.001, ns = not significant.</p

Summary of 3D motility parameters.

<p>Summary of 3D motility parameters.</p

Visualization and analysis of two representative 3D trajectories of parasites in Matrigel.

<p>(<b>A</b>) and (<b>B</b>) Positional coordinates for two representative wild-type (RH) parasite trajectories are visualized as connected, discrete trackpoints (white), and overlaid with the trajectory after smoothing (green). Scale bar = 7 µm. (<b>C</b>) and (<b>D</b>) Plots of velocity (red), curvature (green) and torsion (blue) values along the length of the parasite trajectories shown in panels A and B, respectively. The curvature and torsion values would be constant through time for a regular helix; the variation in these measurements along the parasite trajectories shows that they move in irregular-shaped yet periodically fluctuating corkscrews.</p

T_H1 and T_H2 lymphocyte populations do not vary by region.

<p>A) Representative gating strategy showing IFNγ and IL-4 staining of CD4⁺ cells isolated from four regions of the intestine in two subjects (CAP 20 and CAP23). B) IFNγ and C) IL-4 production by CD4⁺ isolated from each region for all subjects. Differences between regions are not statistically significant (two tailed Mann-Whitney). TI: Terminal Ileum, ICV: Ileocecal Valve, AO: Appendiceal Orifice, SC: Sigmoid Colon.</p

Cytokine staining panel.

<p>Cytokine staining panel.</p

Transcriptional profiling analysis identifies regional variations of gene expression in the intestinal tract.

<p>A) Unsupervised hierarchical clustering analysis was used to organize gene probes and samples. Each row represents an individual gene probe and each column represents an individual sample. Black indicates the median level of expression; yellow, greater then median expression; blue, less than median expression. Horizontal bars at the top of the figure indicate the dispersal of samples according to biopsy location (blue: terminal ileum; red: ileocecal valve; green: appendiceal orifice; purple: sigmoid colon). Data was filtered for probes with expressions levels that vary by a standard deviation of at least 1.0 to yield n = 2,351 unique gene probes. B) Unsupervised principal component analysis showed segregation of terminal ileum samples from colon samples along PC1 and segregation of distal (sigmoid) from proximal colon (ileocecal valve and appendiceal orifice) along PC2. C) Multiclass statistical analysis of microarrays (SAM) identified 2,079 unique gene probes that vary significantly among the sites biopsied (FDR 0%). D) Gene ontology analysis of these gene probes was performed in order to classify genes according to biological processes. Of the 2,079 unique significant gene probes, 304 were classified as relating to the immune system.</p

CD4+ viability among biopsy locations.

<p>Mean and standard deviations are shown above.</p><p>Only comparison of CD4⁺ viability between the appendiceal orifice and sigmoid colon was found to be statistically significant.</p