Commensal microbiota-dependent tryptophan catabolism within the gastrointestinal tract is known to exert profound effects upon host physiology, including the maintenance of epithelial barrier and immune function. A number of abundant microbiota-derived tryptophan metabolites exhibit activation potential for the aryl hydrocarbon receptor (AHR). Gene expression facilitated by AHR activation through the presence of dietary or microbiota-generated metabolites can influence gastrointestinal homeostasis and confer protection from intestinal challenges. Utilizing untargeted mass spectrometry-based metabolomics profiling, combined with AHR activity screening assays, we identify four previously unrecognized tryptophan metabolites, present in mouse cecal contents and human stool, with the capacity to activate AHR. Using GC/MS and LC/MS platforms, quantification of these novel AHR activators, along with previously established AHR-activating tryptophan metabolites, was achieved, providing a relative order of abundance. Using physiologically relevant concentrations and quantitative gene expression analyses, the relative efficacy of these tryptophan metabolites with regard to mouse or human AHR activation potential is examined. These data reveal indole, 2-oxindole, indole-3-acetic acid and kynurenic acid as the dominant AHR activators in mouse cecal contents and human stool from participants on a controlled diet. Here we provide the first documentation of the relative abundance and AHR activation potential of a panel of microbiota-derived tryptophan metabolites. Furthermore, these data reveal the human AHR to be more sensitive, at physiologically relevant concentrations, to tryptophan metabolite activation than mouse AHR. Additionally, correlation analyses indicate a relationship linking major tryptophan metabolite abundance with AHR activity, suggesting these cecal/fecal metabolites represent biomarkers of intestinal AHR activity.

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The high-throughput gas chromatography-mass spectrometry (GC-MS) technology offers a powerful means of analyzing a large number of chemical and biological samples. One of the important analyses of GC-MS data is compound identification. In this work, novel spectral similarity measures based on the discrete wavelet and Fourier transforms were proposed. The proposed methods are composite similarities that are composed of weighted intensities and wavelet/Fourier coefficients using cosine correlation. The performance of the proposed approaches along with the existing similarity measures was evaluated using the NIST Chemistry WebBook mass database maintained by the National Institute of Standards and Technology (NIST) as a library of reference spectra and repetitive mass spectral data as query spectra. The analysis results showed that the identification accuracies of the wavelet/Fourier transform-based methods were improved by 2.02% and 1.95%, respectively, comparing the weighted dot product (cosine correlation) and by 3.01% and 3.08%, respectively, comparing to the composite similarity measure. The improved identification accuracy demonstrates that the proposed approaches outperformed the existing similarity measures in the literature.

Compound identification is a key component of data analysis in the applications of gas chromatography–mass spectrometry (GC-MS). Currently, the most widely used compound identification is mass spectrum matching, in which dot product and its composite version are employed as spectral similarity measures. Several forms of transformations for fragment ion intensities have also been proposed to increase the accuracy of compound identification. In this study, we introduced partial and semi-partial correlations as mass spectral similarity measures and applied them to identify compounds along with different transformations of peak intensity. The mixture versions of the proposed method were also developed to further improve the accuracy of compound identification. To demonstrate the performance of the proposed spectral similarity measures, the National Institute of Standard Technology (NIST) mass spectral library and replicate spectral library were used as the reference library and the query spectra, respectively. Identification results showed that the mixture partial and semi-partial correlations always outperform both the dot-product and its composite measure. The mixture similarity with semi-partial correlation has the highest accuracy of 84.6% in compound identification with a transformation of (0.53, 1.3) for fragment ion intensity and m/z value, respectively.

Compound identification in gas chromatography–mass spectrometry (GC-MS) is usually achieved by matching query spectra to spectra present in a reference library. Although several spectral similarity measures have been developed and compared using a small reference library, it still remains unknown how the relationship between the spectral similarity measure and the size of reference library affects on the identification accuracy as well as the optimal weight factor. We used three reference libraries to investigate the dependency of the optimal weight factor, spectral similarity measure and the size of reference library. Our study demonstrated that the optimal weight factor depends on not only spectral similarity measure but also the size of reference library. The mixture semi-partial correlation measure outperforms all existing spectral similarity measures in all tested reference libraries, in spite of the computational expense. Furthermore, the accuracy of compound identification using a larger reference library in future is estimated by varying the size of reference library. Simulation study indicates that the mixture semi-partial correlation measure will have the best performance with the increase of reference library in future.