Dataset Information

Integrating next-generation sequencing and traditional tongue diagnosis to determine tongue coating microbiome.

ABSTRACT: Tongue diagnosis is a unique method in traditional Chinese medicine (TCM). This is the first investigation on the association between traditional tongue diagnosis and the tongue coating microbiome using next-generation sequencing. The study included 19 gastritis patients with a typical white-greasy or yellow-dense tongue coating corresponding to TCM Cold or Hot Syndrome respectively, as well as eight healthy volunteers. An Illumina paired-end, double-barcode 16S rRNA sequencing protocol was designed to profile the tongue-coating microbiome, from which approximately 3.7 million V6 tags for each sample were obtained. We identified 123 and 258 species-level OTUs that were enriched in patients with Cold/Hot Syndromes, respectively, representing "Cold Microbiota" and "Hot Microbiota". We further constructed the tongue microbiota-imbalanced networks associated with Cold/Hot Syndromes. The results reveal an important connection between the tongue-coating microbiome and traditional tongue diagnosis, and illustrate the potential of the tongue-coating microbiome as a novel holistic biomarker for characterizing patient subtypes.

SUBMITTER: Jiang B

PROVIDER: S-EPMC3515809 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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Integrating next-generation sequencing and traditional tongue diagnosis to determine tongue coating microbiome.

Jiang Bai B Liang Xujun X Chen Yang Y Ma Tao T Liu Liyang L Li Junfeng J Jiang Rui R Chen Ting T Zhang Xuegong X Li Shao S

Scientific reports 20121206

Tongue diagnosis is a unique method in traditional Chinese medicine (TCM). This is the first investigation on the association between traditional tongue diagnosis and the tongue coating microbiome using next-generation sequencing. The study included 19 gastritis patients with a typical white-greasy or yellow-dense tongue coating corresponding to TCM Cold or Hot Syndrome respectively, as well as eight healthy volunteers. An Illumina paired-end, double-barcode 16S rRNA sequencing protocol was desi ...[more]

PMID: 23226834

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Project description:BackgroundMetagenomic next-generation sequencing (mNGS) is emerging as a promising technique for pathogens detection. However, reports on the application of mNGS in mixed pulmonary infection remain scarce.MethodsFrom July 2018 to March 2019, 55 cases were enrolled in this retrospective analysis. Cases were classified into mixed pulmonary infection (36 [65.5%]) and non-mixed pulmonary infection (19 [34.5%]) according to primary diagnoses. The performances of mNGS and conventional test on mixed pulmonary infection diagnosis and pathogen identification were compared.ResultsThe sensitivity of mNGS in mixed pulmonary infection diagnosis was much higher than that of conventional test (97.2% vs 13.9%; P < 0.01), but the specificity was the opposite (63.2% vs 94.7%; P = 0.07). The positive predictive value of mNGS was 83.3% (95% CI, 68.0-92.5%), and the negative predictive value was 92.3% (95% CI, 62.1-99.6%). A total of 5 (9.1%) cases were identified as mixed pulmonary infection by both conventional tests and mNGS, however, the pathogens identification results were consistent between these two methods in only 1 (1.8%) case. In summary, the pathogens detected by mNGS in 3 (5.5%) cases were consistent with those by conventional test, and only 1 (1.8%) case was mixed pulmonary infection. According to our data, mNGS had a broader spectrum for pathogen detection than conventional tests. In particular, application of mNGS improved the diagnosis of pulmonary fungal infections. Within the 55 cases, mNGS detected and identified fungi in 31 (56.4%) cases, of which only 10 (18.2%) cases were positive for the same fungi by conventional test. The most common pathogen detected by mNGS was Human cytomegalovirus in our study, which was identified in 19 (34.5%) cases of mixed pulmonary infection. Human cytomegalovirus and Pneumocystis jirovecii, which were detected in 7 (12.7%) cases, were the most common co-pathogens in the group of mixed pulmonary infection.ConclusionsmNGS is a promising technique to detect co-pathogens in mixed pulmonary infection, with potential benefits in speed and sensitivity.Trial registration(retrospectively registered): ChiCTR1900023727. Registrated 9 JUNE 2019.

Dataset Information

Integrating next-generation sequencing and traditional tongue diagnosis to determine tongue coating microbiome.

Publications

Integrating next-generation sequencing and traditional tongue diagnosis to determine tongue coating microbiome.

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