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@@ -291,7 +292,7 @@ <h3>Unveiling protist-bacteria interactions through computational metabolic mode |
291 | 292 | </div> |
292 | 293 | </div> |
293 | 294 | <p>Tritrichomonas musculus (Tmu) is a common protist in the mouse gut. As many gut protists, Tmu is a pathobiont, capable of commensalism and pathogenesis. Previous studies suggest that gut bacteria could mediate protist’s switch to pathogens, at least in part, through metabolic interactions. To further explore the role of such interactions in Tmu pathogenesis we performed computational simulations of the mice gut. |
294 | | - We reconstructed genome scale metabolic models (GEMs) of Tmu and 266 mouse gut bacteria. We applied these GEMs to simulate the impact of 45 diferent diets on Tmu growth, and its metabolic interplay with gut bacteria, using the BacArena modeling platform. For the diets we considered regular mouse chow with 1 of 45 diferent saccharides as carbon source. |
| 295 | + We reconstructed genome scale metabolic models (GEMs) of Tmu and 266 mouse gut bacteria. We applied these GEMs to simulate the impact of 45 different diets on Tmu growth, and its metabolic interplay with gut bacteria, using the BacArena modeling platform. For the diets we considered regular mouse chow with 1 of 45 different saccharides as carbon source. |
295 | 296 | Among the tested carbon sources, we found that glucose, maltose and maltose oligomers increased Tmu numbers, while xylan reduced them. The amount of secreted succinate, a key modulator of host immunity, depended on the diet as well, with glucan resulting in the highest succinate levels, and pectin in the lowest. Finally, we also identified cross-feeding interactions that depended on diet. Under maltose, Tmu feeds alanine to bacteria, and bacteria feed Tmu with phenylalanine and riboflavin. In contrast, when grown with xylan, bacteria feed Tmu with riboflavin but stop receiving alanine from it. |
296 | 297 | Our simulations revealed an important role of diet on Tmu growth, succinate secretion and Tmu-bacteria interactions. These diets will be validated in vivo to assess if we can promote commensalism in Tmu, and eventually other gut protists.</p> |
297 | 298 | </div> |
@@ -417,7 +418,7 @@ <h3><span class="poster-badge">#6</span>Exploring the microbiome of broiler chic |
417 | 418 | Benjamin P. Willing<sup>2</sup>, Shane Carey<sup>2</sup>, John Parkinson<sup>3,4</sup> |
418 | 419 | </p> |
419 | 420 | <div class="affiliations"> |
420 | | - <p><sup>1</sup>Molecular Medicine, SickKids Reseaerch Institute, |
| 421 | + <p><sup>1</sup>Molecular Medicine, SickKids Research Institute, |
421 | 422 | <sup>2</sup>Department of Agricultural, Food and Nutritional Science, University of Alberta, |
422 | 423 | <sup>3</sup>Department of Molecular Genetics, University of Toronto, |
423 | 424 | <sup>4</sup>SickKids Research Institute |
@@ -896,7 +897,7 @@ <h3><span class="poster-badge">#35</span>Tool assisted curation of gene predicti |
896 | 897 | <p><sup></sup>Dalhousie University, Halifax, Nova Scotia</p> |
897 | 898 | </div> |
898 | 899 | </div> |
899 | | - <p>Prediction of protein coding genes in eukaryotic genomes has improved drastically in the last decades but remains a challenge in non-model organisms. Once a new genome has been sequenced, it is standard practice to apply gene prediction pipelines that integrate ab initio predictors with RNA-seq and protein data, followed by extensive but careful curation of the predicted genes. The curation process can be very laborious and time consuming. After applying the BRAKER2 gene prediction pipeline on the newly sequenced genome of the metamonad Ergobibamus cyprinoides, we noticed that many genes were predicted to have introns that were not supported by the RNA-seq data. False introns can lead to errors in gene models, such as truncated genes or artificial mergers of neighboring genes. To remedy this problem, we developed a python script, fix_genes_with_false_introns.py, that automatically identified such genes and replaced them with new gene models that only include supported introns. The new models were generally in line with how we would have manually curated the model. Although the script only fixes one type of gene prediction error, we found it drastically reduced the amount of time and work necessary to complete the curation process. The script is publically available on our GitHub page github.com/Dalhousie-ICG/icg-shared-scripts</p> |
| 900 | + <p>Prediction of protein coding genes in eukaryotic genomes has improved drastically in the last decades but remains a challenge in non-model organisms. Once a new genome has been sequenced, it is standard practice to apply gene prediction pipelines that integrate ab initio predictors with RNA-seq and protein data, followed by extensive but careful curation of the predicted genes. The curation process can be very laborious and time consuming. After applying the BRAKER2 gene prediction pipeline on the newly sequenced genome of the metamonad Ergobibamus cyprinoides, we noticed that many genes were predicted to have introns that were not supported by the RNA-seq data. False introns can lead to errors in gene models, such as truncated genes or artificial mergers of neighboring genes. To remedy this problem, we developed a python script, fix_genes_with_false_introns.py, that automatically identified such genes and replaced them with new gene models that only include supported introns. The new models were generally in line with how we would have manually curated the model. Although the script only fixes one type of gene prediction error, we found it drastically reduced the amount of time and work necessary to complete the curation process. The script is publicly available on our GitHub page github.com/Dalhousie-ICG/icg-shared-scripts</p> |
900 | 901 | </div> |
901 | 902 |
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902 | 903 | <div class="abstract" id="Min-Cho"> |
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