Dataset Information

Molecular detection of eukaryotes in a single human stool sample from Senegal.

ABSTRACT: BACKGROUND: Microbial eukaryotes represent an important component of the human gut microbiome, with different beneficial or harmful roles; some species are commensal or mutualistic, whereas others are opportunistic or parasitic. The diversity of eukaryotes inhabiting humans remains relatively unexplored because of either the low abundance of these organisms in human gut or because they have received limited attention from a whole-community perspective. METHODOLOGY/PRINCIPAL FINDING: In this study, a single fecal sample from a healthy African male was studied using both culture-dependent methods and extended molecular methods targeting the 18S rRNA and ITS sequences. Our results revealed that very few fungi, including Candida spp., Galactomyces spp., and Trichosporon asahii, could be isolated using culture-based methods. In contrast, a relatively a high number of eukaryotic species could be identified in this fecal sample when culture-independent methods based on various primer sets were used. A total of 27 species from one sample were found among the 977 analyzed clones. The clone libraries were dominated by fungi (716 clones/977, 73.3%), corresponding to 16 different species. In addition, 187 sequences out of 977 (19.2%) corresponded to 9 different species of plants; 59 sequences (6%) belonged to other micro-eukaryotes in the gut, including Entamoeba hartmanni and Blastocystis sp; and only 15 clones/977 (1.5%) were related to human 18S rRNA sequences. CONCLUSION: Our results revealed a complex eukaryotic community in the volunteer's gut, with fungi being the most abundant species in the stool sample. Larger investigations are needed to assess the generality of these results and to understand their roles in human health and disease.

SUBMITTER: Hamad I

PROVIDER: S-EPMC3396631 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Molecular detection of eukaryotes in a single human stool sample from Senegal.

Hamad Ibrahim I Sokhna Cheikh C Raoult Didier D Bittar Fadi F

PloS one 20120713 7

<h4>Background</h4>Microbial eukaryotes represent an important component of the human gut microbiome, with different beneficial or harmful roles; some species are commensal or mutualistic, whereas others are opportunistic or parasitic. The diversity of eukaryotes inhabiting humans remains relatively unexplored because of either the low abundance of these organisms in human gut or because they have received limited attention from a whole-community perspective.<h4>Methodology/principal finding</h4 ...[more]

PMID: 22808282

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Project description:BACKGROUND:Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore ( https://bioconductor.org/packages/singscore ). RESULTS:We use multiple cancer data sets to compare singscore against widely-used methods, including GSVA, z-score, PLAGE, and ssGSEA. Our approach does not depend upon background samples and scores are thus stable regardless of the composition and number of samples being scored. In contrast, scores obtained by GSVA, z-score, PLAGE and ssGSEA can be unstable when less data are available (NS?<?25). The singscore method performs as well as the best performing methods in terms of power, recall, false positive rate and computational time, and provides consistently high and balanced performance across all these criteria. To enhance the impact and utility of our method, we have also included a set of functions implementing visual analysis and diagnostics to support the exploration of molecular phenotypes in single samples and across populations of data. CONCLUSIONS:The singscore method described here functions independent of sample composition in gene expression data and thus it provides stable scores, which are particularly useful for small data sets or data integration. Singscore performs well across all performance criteria, and includes a suite of powerful visualization functions to assist in the interpretation of results. This method performs as well as or better than other scoring approaches in terms of its power to distinguish samples with distinct biology and its ability to call true differential gene sets between two conditions. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification.

Dataset Information

Molecular detection of eukaryotes in a single human stool sample from Senegal.

Publications

Molecular detection of eukaryotes in a single human stool sample from Senegal.

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