The pan-microbiome profiling system Taxa4Meta identifies clinical dysbiotic features and classifies diarrheal disease
Qinglong Wu, … , Todd J. Treangen, Tor C. Savidge
Qinglong Wu, … , Todd J. Treangen, Tor C. Savidge
Published November 14, 2023
Citation Information: J Clin Invest. 2024;134(2):e170859. https://doi.org/10.1172/JCI170859.
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Research Article Gastroenterology Infectious disease

The pan-microbiome profiling system Taxa4Meta identifies clinical dysbiotic features and classifies diarrheal disease

Abstract

Targeted metagenomic sequencing is an emerging strategy to survey disease-specific microbiome biomarkers for clinical diagnosis and prognosis. However, this approach often yields inconsistent or conflicting results owing to inadequate study power and sequencing bias. We introduce Taxa4Meta, a bioinformatics pipeline explicitly designed to compensate for technical and demographic bias. We designed and validated Taxa4Meta for accurate taxonomic profiling of 16S rRNA amplicon data acquired from different sequencing strategies. Taxa4Meta offers significant potential in identifying clinical dysbiotic features that can reliably predict human disease, validated comprehensively via reanalysis of individual patient 16S data sets. We leveraged the power of Taxa4Meta’s pan-microbiome profiling to generate 16S-based classifiers that exhibited excellent utility for stratification of diarrheal patients with Clostridioides difficile infection, irritable bowel syndrome, or inflammatory bowel diseases, which represent common misdiagnoses and pose significant challenges for clinical management. We believe that Taxa4Meta represents a new “best practices” approach to individual microbiome surveys that can be used to define gut dysbiosis at a population-scale level.

Authors

Qinglong Wu, Shyam Badu, Sik Yu So, Todd J. Treangen, Tor C. Savidge

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Figure 4

Supervised classification achieved by pan-microbiome profiling.

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Supervised classification achieved by pan-microbiome profiling. (A) β-Di...
(A) β-Diversity analysis of collapsed Taxa4Meta species profiles for V1–V3 and V3–V5 amplicon data generated from the same DNA extracts. The pairwise Wilcoxon test with Benjamini-Hochberg correction shows that the difference between the 2 groups is not significant. (B) Receiver operating characteristic (ROC) analysis of supervised classification using 16S region–specific versus pan-microbiome genera. The random forest trainer was used for supervised classification analysis, and the roc.test function from the pROC package was used for comparison of ROC curves. Statistical significance was determined using DeLong testing (**P < 0.01). (C) β-Diversity analysis of multiple CDI cohorts (training data sets 22–27) using collapsed Taxa4Meta species profiles. The pairwise Wilcoxon test with Benjamini-Hochberg correction shows that the difference between the disease and control groups is significant (***P < 0.001). (D) Improved cross-validation of CDI and control subjects using pan-microbiome profiles of 454 and Illumina data. Ten iterations of random, stratified subsampling of training sets were performed, and the random forest trainer was used for supervised classification analysis. The pairwise Wilcoxon test with Benjamini-Hochberg correction shows that the difference between the 2 groups is not significant. Data are presented as mean ± SD. Area under the curve (AUC) and classification accuracy (CA) were calculated, and the ANOSIM test was performed with 999 permutations.