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 1

Influence of 16S amplicon sequence length, orientation, and variable region on taxonomic and clustering accuracy.

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Influence of 16S amplicon sequence length, orientation, and variable reg...
Simulated 16S sequences of variable length were generated from known input taxa (ground truth) in the NCBI 16S RefSeq database. Taxonomic annotation was determined for accuracy from simulated reads using the BLCA tool. Confidence scores from the data output were used for statistical calculations. (A) Schematic representation showing how increasing amplicon length improves taxonomic accuracy. (B) Spearman correlations of VSEARCH-based de novo clustering with 99% similarity for 16S V1–V3 amplicons of varying length derived from the same parent 16S sequence. The optimal sequence length range for clustering is highlighted (orange boxes). Results for other 16S variable regions are presented in Supplemental Figure 1, and Spearman correlation results for other clustering/denoising tools are provided in Supplemental Table 2. (C and D) Both the confidence score and accuracy of taxonomic assignment for simulated amplicons are significantly affected by sequence length and orientation. Supplemental Figure 3 provides additional results for other 16S variable regions. “Org.” denotes the original amplicon length without trimming. Statistical analysis indicates a significant difference (P < 0.05, Wilcoxon test) between correct and incorrect genus/species annotations at each amplicon length.