Human Stool Sample
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA693434 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR13503534_1.fastq.gz | Fastqsanger.gz | |||
| SRR13503534_2.fastq.gz | Fastqsanger.gz | |||
| SRR13503535_1.fastq.gz | Fastqsanger.gz | |||
| SRR13503535_2.fastq.gz | Fastqsanger.gz | |||
| SRR13503536_1.fastq.gz | Fastqsanger.gz |
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Project description:Human 16S stool microbiome Metagenome
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Project description:Background and Aims: RNA biomarkers derived from sloughed enterocytes would provide an ideal, non-invasive method for early detection of colorectal cancer (CRC) and precancerous adenomas. To realize this goal, a highly reliable method to isolate preserved human RNA from stool samples is needed. Here we develop a protocol to identify RNA biomarkers associated with CRC to assess the use of these biomarkers for noninvasive screening of disease. Methods: Stool samples were collected from 454 patients prior to a colonoscopy. A nucleic acid extraction protocol was developed to isolate human RNA from 330 stool samples and transcript abundances were estimated by microarray analysis. This 330-patient cohort was split into a training set of 265 individuals to develop a machine learning model and a testing set of 65 individuals to determine the model’s ability to detect colorectal neoplasms. Results: Analysis of the transcriptome from 265 individuals identified 200 transcript clusters as differentially expressed (p<0.03). These transcripts were used to build a Support Vector Machine (SVM) based model to classify 65 individuals within the testing set. This SVM algorithm attained a 95% sensitivity for precancerous adenomas and a 65% sensitivity for CRC (stage I-IV). The machine learning algorithm attained a specificity of 59% for healthy individuals and an overall accuracy of 72.3%. Conclusions: We developed an RNA-based neoplasm detection model that is sensitive for CRC and precancerous adenomas. The model allows for non-invasive assessment of tumors and could potentially be used to provide clinical guidance for individuals within the screening population for colorectal cancer.
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Project description:Understanding gene expression by bacteria during the actual course of human infection may provide important insights into microbial pathogenesis. In this study, we evaluated the transcriptional profile of Vibrio cholerae, the causative agent of cholera, in clinical specimens from cholera patients. We collected samples of human stool and vomitus that were positive by dark-field microscopy for abundant vibrios and used a microarray to compare gene expression in organisms recovered directly from the early and late stages of human infection. Our results reveal that V. cholerae gene expression within the human host environment differs from patterns defined in in vitro models of pathogenesis. tcpA, the major subunit of the essential V. cholerae colonization factor, was significantly more highly expressed in early compared with late infection; however, the genes encoding cholera toxin were not highly expressed in either phase of human infection. Furthermore, expression of the virulence regulators, toxRS and tcpPH, was uncoupled. Interestingly, the pattern of gene expression indicates that the human upper intestine may be a uniquely suitable environment for the transfer of genetic elements that are important in the evolution of pathogenic strains of V. cholerae. These findings provide a more detailed assessment of the transcriptome of V. cholerae in the human host than previous studies of organisms in stool alone and have implications for cholera control and the design of improved vaccines. The V. cholerae microarray consists of 3,890 full-length PCR products representing the annotated open reading frames from the initial release of the V. cholerae N16961 genome. Each labeling and hybridization was performed in duplicate. Genomic DNA was used as a universal internal control for the quality of the microarray and to allow for the comparison of results across multiple experiments. Data were normalized using locally-weighted regression (Lowess) to obtain the relative abundance of each transcript as an intensity ratio with respect to that of genomic DNA. High correlation coefficients were observed between technical replicates (Pearsonâs correlation coefficient (r) > 0.80) and between results of separate clinical specimens of vomitus (r > 0.77) and of stool (r > 0.80). Hence, the results from the two clinical vomitus specimens and the five clinical stool specimens were pooled. Fold changes for the relative expression of a given gene between the two clinical specimens were calculated by dividing the normalized median intensity ratios with respect to genomic DNA.
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