TrpNet: Understanding Tryptophan Metabolism across Gut Microbiome
Abstract
:1. Introduction
2. Results
2.1. Literature Search and Intestinal Tryptophan Metabolism
2.2. Curation of Genome-Scale Metabolic Models
2.3. Development of a Database for Tryptophan Metabolism and Functional Prediction
2.4. Case Studies
2.4.1. Myocardial Infarct (MI) Case Study
2.4.2. IBD Case Study
3. Discussion
4. Materials and Methods
4.1. Literature Review
4.2. GEMs Collection
4.3. TrpNet Implementation
4.4. Sample Collection for MI Case Study
4.5. Sample Collection for IBD Case Study
4.6. Logistic Regression Model for Predicting Metabolite Profiles
- Different taxonomy levels and their combinations were evaluated for their predictive values. Models were ranked by Akaike information criterion (AIC). The genus level combined with the host type was selected as the best predictor;
- The models were further optimized by Bayesian logistic regression coupled with a fast Pareto smoothed leave-one-out cross-validation for the penalized likelihood estimation [67]. These models capture the metabolite production potential (PM, G) for the underlying metabolite (M) of interest in every genus (G) for a given host type;
- The predicted probability (PM,G) was multiplied by the genus abundance table obtained from 16S rRNA sequencing data to compute the accumulated production potential for each metabolite of interest for each sample;
- The results of all samples were normalized by total sum scaling to be comparable with metabolomics data.
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Predictors | IA | Indole | IAAlD | IAM | IAA | ILA | IPA | Tryptamine |
---|---|---|---|---|---|---|---|---|
Bacteroides | 0.8786 | 310.4118 | 1.5256 | 0.5621 | 2.9424 | 69.0048 | 0.8515 | 0.1484 |
Bifidobacterium | 0.8712 | 0.0421 | 0.4879 | 0.6081 | 1.0597 | 103.1476 | 0.8393 | 8.5582 |
Clostridium | 413.0681 | 2.2526 | 1.6328 | 106.6308 | 0.8401 | 89.0063 | 638.3164 | 3.473 |
Desulfovibrio | 0.9738 | 1.5226 | 1.2451 | 0.8139 | 14.9215 | 0.8931 | 0.9676 | 0.3856 |
Enterococcus | 0.9225 | 2.1667 | 0.8861 | 0.7017 | 0.0392 | 0.6446 | 0.9017 | 1.0872 |
Escherichia | 0.936 | 241.1231 | 0.087 | 0.7997 | 3.9424 | 226.036 | 0.9161 | 9.0402 |
Eubacterium | 0.9783 | 3.7121 | 0.622 | 0.8615 | 16.7253 | 0.8792 | 0.9723 | 0.4817 |
Lactobacillus | 0.8536 | 0.0324 | 1.9794 | 0.5087 | 1.765 | 36.9424 | 0.8227 | 0.4338 |
Mouse.gut | 2.5942 | 2.2931 | 1.1424 | 0.937 | 1.8319 | 6.9211 | 2.9119 | 0.4471 |
Parabacteroides | 0.9668 | 0.1841 | 0.9344 | 0.8384 | 1.2605 | 13.8291 | 0.9569 | 0.4585 |
Prevotella | 0.9145 | 1.6855 | 0.7029 | 0.5947 | 0.473 | 0.7286 | 0.8969 | 0.1589 |
Ruminococcus | 0.9718 | 0.7449 | 0.4029 | 0.8035 | 7.2895 | 0.8861 | 0.9651 | 0.3699 |
Streptococcus | 0.8959 | 0.6288 | 0.7287 | 0.5533 | 0.527 | 0.6756 | 0.8749 | 19.0245 |
Predictors | IA | Indole | IAAlD | IAM | IAA | ILA | IPA | Tryptamine |
---|---|---|---|---|---|---|---|---|
Bacteroides | 0.8855 | 1595.5832 | 1.4595 | 0.5265 | 2.6489 | 79.5618 | 0.8575 | 0.1214 |
Bifidobacterium | 0.9025 | 0.0371 | 0.7658 | 0.5674 | 0.7158 | 175.3057 | 0.8781 | 5.5164 |
Clostridium | 414.0254 | 1.8606 | 1.2366 | 91.4966 | 1.6268 | 81.5643 | 606.5603 | 2.8663 |
Desulfovibrio | 0.9683 | 1.55 | 0.6004 | 0.79 | 47.2592 | 0.8512 | 0.9593 | 0.3552 |
Enterococcus | 0.9478 | 2.561 | 1.2673 | 0.7037 | 0.0322 | 0.7858 | 0.9333 | 0.9879 |
Escherichia | 0.9639 | 318.856 | 0.081. | 0.7677 | 4.0763 | 607.7244 | 0.9531 | 4.6206 |
Eubacterium | 0.981 | 2.0208 | 1.2466 | 0.8559 | 10.5365 | 0.9035 | 0.9753 | 0.463 |
Human.gut | 21.3204 | 1.4413 | 0.7778 | 342.7406 | 2.1685 | 20.4853 | 37.332 | 1876.3277 |
Lactobacillus | 0.8757 | 0.1256 | 2.4492 | 0.5055 | 1.5343 | 52.7602 | 0.8462 | 0.412 |
Parabacteroides | 0.976 | 0.1964 | 1.3015 | 0.8271 | 1.681 | 28.2969 | 0.9687 | 0.4121 |
Prevotella | 0.9479 | 3.5273 | 2.0026 | 0.7035 | 1.294 | 0.7879 | 0.9332 | 0.2527 |
Ruminococcus | 0.9663 | 0.6021 | 0.3477 | 0.7784 | 4.9819 | 0.8487 | 0.9562 | 0.3397 |
Streptococcus | 0.8871 | 0.451 | 0.7435 | 0.5304 | 0.3467 | 0.6357 | 0.8596 | 18.3925 |
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Lu, Y.; Chong, J.; Shen, S.; Chammas, J.-B.; Chalifour, L.; Xia, J. TrpNet: Understanding Tryptophan Metabolism across Gut Microbiome. Metabolites 2022, 12, 10. https://doi.org/10.3390/metabo12010010
Lu Y, Chong J, Shen S, Chammas J-B, Chalifour L, Xia J. TrpNet: Understanding Tryptophan Metabolism across Gut Microbiome. Metabolites. 2022; 12(1):10. https://doi.org/10.3390/metabo12010010
Chicago/Turabian StyleLu, Yao, Jasmine Chong, Shiqian Shen, Joey-Bahige Chammas, Lorraine Chalifour, and Jianguo Xia. 2022. "TrpNet: Understanding Tryptophan Metabolism across Gut Microbiome" Metabolites 12, no. 1: 10. https://doi.org/10.3390/metabo12010010