Conjugated linoleic acid-related molecules correlated to parasite burden.

<p>Conjugated linoleic acid-related molecules correlated to parasite burden.</p

Experimental Chagas disease-induced perturbations of the fecal microbiome and metabolome

<div><p><i>Trypanosoma cruzi</i> parasites are the causative agents of Chagas disease. These parasites infect cardiac and gastrointestinal tissues, leading to local inflammation and tissue damage. Digestive Chagas disease is associated with perturbations in food absorption, intestinal traffic and defecation. However, the impact of <i>T</i>. <i>cruzi</i> infection on the gut microbiota and metabolome have yet to be characterized. In this study, we applied mass spectrometry-based metabolomics and 16S rRNA sequencing to profile infection-associated alterations in fecal bacterial composition and fecal metabolome through the acute-stage and into the chronic stage of infection, in a murine model of Chagas disease. We observed joint microbial and chemical perturbations associated with <i>T</i>. <i>cruzi</i> infection. These included alterations in conjugated linoleic acid (CLA) derivatives and in specific members of families <i>Ruminococcaceae</i> and <i>Lachnospiraceae</i>, as well as alterations in secondary bile acids and members of order Clostridiales. These results highlight the importance of multi-‘omics’ and poly-microbial studies in understanding parasitic diseases in general, and Chagas disease in particular.</p></div

Cholic acid/deoxycholic acid-related molecules correlated with parasite burden.

<p>Cholic acid/deoxycholic acid-related molecules correlated with parasite burden.</p

Infection with <i>T</i>. <i>cruzi</i> perturbs the fecal microbiome and metabolome.

<p>(<b>A</b>) Intestinal parasite burden progression during the acute and chronic stage of the disease. Parasite signal peaked at day 35 post-infection and decreased into the chronic stage of the disease. (<b>B, C</b>) Principal coordinates analysis (PCoA) showing clustering of high parasite burden samples (dotted oval) compared to uninfected and low parasite burden samples for fecal microbiome (<b>B</b>, weighted UniFrac distance metric) and fecal metabolome (<b>C</b>, Bray-Curtis-Faith distance metric). Each sphere represents a single sample from one mouse at a given timepoint. Spheres are colored by intestinal parasite burden, with darkest spheres coming from samples collected at the peak of infection, when parasite burden is highest. Samples collected prior to infection are shown by large spheres, with all other samples represented by small spheres. (<b>D,E</b>) Random forest classification error between infected and uninfected samples using microbiome (<b>D</b>) and metabolome (<b>E</b>) datasets. Classifier is unable to distinguish between infected and uninfected samples initially. Classification accuracy improves over the acute stage of infection, with near-perfect classification 21 days post-infection. Error then increases during the chronic disease stage, although classification accuracy remains better than pre-infection.</p

Joint microbial and chemical alterations during experimental Chagas disease.

<p>(<b>A</b>) Procrustes analysis of microbiome and metabolome data, showing similar overall trends for microbiome and metabolome: lack of segregation between infected and uninfected samples at day 0, and clear separation at day 21. Less separation was observed 90 days post-infection. Connected spheres came from the same sample. Black lines indicate metabolome data and red lines microbiome data. (<b>B</b>) Network of correlated CLA derivatives (outer perimeter) and bacterial OTUs (central circles and V shapes), as identified by WGCNA analysis (correlation coefficients > 0, p-value <0.05). Known metabolizing bacterial genera are V-shaped, all other OTUs are represented by circles. OTU nodes are colored based on corresponding bacterial order, to highlight members of the Bacteroidales, Bifidobacteriales, Clostridiales, Lactobacillales orders. (<b>C</b>) Network of correlated cholic acid derivatives and bacterial OTUs, as identified by WGCNA analysis (correlation coefficients > 0, p-value <0.05). Known metabolizing bacterial genera are V-shaped, all other OTUs are represented by circles. OTU nodes are colored based on corresponding bacterial order.</p

An Image-Based Algorithm for Precise and Accurate High Throughput Assessment of Drug Activity against the Human Parasite <i>Trypanosoma cruzi</i>

<div><p>We present a customized high content (image-based) and high throughput screening algorithm for the quantification of <i>Trypanosoma cruzi</i> infection in host cells. Based solely on DNA staining and single-channel images, the algorithm precisely segments and identifies the nuclei and cytoplasm of mammalian host cells as well as the intracellular parasites infecting the cells. The algorithm outputs statistical parameters including the total number of cells, number of infected cells and the total number of parasites per image, the average number of parasites per infected cell, and the infection ratio (defined as the number of infected cells divided by the total number of cells). Accurate and precise estimation of these parameters allow for both quantification of compound activity against parasites, as well as the compound cytotoxicity, thus eliminating the need for an additional toxicity-assay, hereby reducing screening costs significantly. We validate the performance of the algorithm using two known drugs against <i>T.cruzi</i>: Benznidazole and Nifurtimox. Also, we have checked the performance of the cell detection with manual inspection of the images. Finally, from the titration of the two compounds, we confirm that the algorithm provides the expected half maximal effective concentration (EC50) of the anti-<i>T. cruzi</i> activity.</p></div

Sequential steps of individual nucleus segmentation process.

<p>(<b>A</b>) Enhanced image. (<b>B</b>) <b>∼</b> (<b>H</b>) <b>Nuclei region segmentation.</b> (<b>B</b>) Laplacian of Gaussian. (<b>C</b>) Gray-scale dilation. (<b>D</b>) Gaussian smoothing. (<b>E</b>) Subtract (D) from (A). (<b>F</b>) Gray-scale opening. (<b>G</b>) Gaussian smoothing. (<b>H</b>) Segmented nuclei regions obtained by the Otsu's thresholding method. (<b>I</b>) <b>∼</b> (<b>J</b>) <b>Two criteria for identifying clustered nuclei.</b> (<b>I</b>) Identification based on boundary curvature. (<b>J</b>) Identification based on convexity. Red arrows and dots in (I) represent local minimum curvature points and corresponding found corner points. In both (I) and (J), red contours refer isolated nucleus and green contours refer clustered nuclei. (<b>K</b>) <b>∼</b> (<b>L</b>) <b>Clustered nuclei splitting using GVF segmentation method.</b> (<b>K</b>) Gradient vector fields (red arrows) and the sink points (green dots) of clustered nuclei. (<b>L</b>) Splitting results.</p

Image enhancement process.

<p>(<b>A</b>) Original image of maximum intensity 6461. (<b>B</b>) Cumulative histogram of intensity. The maximum intensity is rescaled to the intensity of higher 1% cumulative intensity level. (<b>C</b>) Intensity equalized image. The maximum intensity has decreased to 1805. (<b>D</b>) Estimated shading map of (C). (<b>E</b>) Biased illumination was corrected from (C). The numbers in the boxes of (C) and (E) are average intensities of nuclei in the yellow dashed circles.</p

Nuclei region segmentation results for examples of difficult cases.

<p>(<b>A</b>) Parasites are too close to a nucleus. (<b>B</b>) Nucleus intensity is too high. (<b>C</b>) Parasites intensities are too high. (<b>Second row</b>) Parasite-removal images by the proposed method. (<b>Third row</b>) Nuclei masks by Otsu's thresholding method applied to the second row images. (<b>Fourth row</b>) Boundaries of segmented nuclei regions (green contours) overlapped to the original images.</p

<i>T. cruzi</i> fluorescence images.

<p>(<b>A</b>) Negative control image (infected host cells). (<b>B</b>) Positive control image (uninfected host cells). (<b>C</b>) 3D surface plot of infected host cells in the yellow boxed region of (A). (<b>D</b>) 3D surface plot of uninfected host cells in the yellow boxed region of (B). Note that intensity ranges of (A) and (B) are rescaled for enhanced visibility.</p