Dataset Information

GePMI: A statistical model for personal intestinal microbiome identification.

ABSTRACT: Human gut microbiomes consist of a large number of microbial genomes, which vary by diet and health conditions and from individual to individual. In the present work, we asked whether such variation or similarity could be measured and, if so, whether the results could be used for personal microbiome identification (PMI). To address this question, we herein propose a method to estimate the significance of similarity among human gut metagenomic samples based on reference-free, long k-mer features. Using these features, we find that pairwise similarities between the metagenomes of any two individuals obey a beta distribution and that a p value derived accordingly well characterizes whether two samples are from the same individual or not. We develop a computational framework called GePMI (Generating inter-individual similarity distribution for Personal Microbiome Identification) and apply it to several human gut metagenomic datasets (>300 individuals and >600 samples in total). From the results of GePMI, most of the human gut microbiomes can be identified (auROC?=?0.9470, auPRC?=?0.8702). Even after antibiotic treatment or fecal microbiota transplantation, the individual k-mer signature still maintains a certain specificity.

SUBMITTER: Wang Z

PROVIDER: S-EPMC6123480 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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GePMI: A statistical model for personal intestinal microbiome identification.

Wang Zicheng Z Lou Huazhe H Wang Ying Y Shamir Ron R Jiang Rui R Chen Ting T

NPJ biofilms and microbiomes 20180904

Human gut microbiomes consist of a large number of microbial genomes, which vary by diet and health conditions and from individual to individual. In the present work, we asked whether such variation or similarity could be measured and, if so, whether the results could be used for personal microbiome identification (PMI). To address this question, we herein propose a method to estimate the significance of similarity among human gut metagenomic samples based on reference-free, long <i>k</i>-mer fe ...[more]

PMID: 30210803

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Project description:The highly personalized human skin microbiome may serve as a viable marker in personal identification. Amplicon sequencing resolution using 16S rRNA cannot identify bacterial communities sufficiently to discriminate between individuals. Thus, novel higher-resolution genetic markers are required for forensic purposes. The clustered regularly interspaced short palindromic repeats (CRISPRs) are prokaryotic genetic elements that can provide a history of infections encountered by the bacteria. The sequencing of CRISPR spacers may provide phylogenetic information with higher resolution than other markers. However, using spacer sequencing for discrimination of personal skin microbiome is difficult due to limited information on CRISPRs in human skin microbiomes. It remains unclear whether personal microbiome discrimination can be achieved using spacer diversity or which CRISPRs will be forensically relevant. We identified common CRISPRs in the human skin microbiome via metagenomic reconstruction and used amplicon sequencing for deep sequencing of spacers. We successfully reconstructed 24 putative CRISPR arrays using metagenomic data sets. A total of 1,223,462 reads from three CRISPR arrays revealed that spacers in the skin microbiome were highly personalized, and conserved repeats were commonly shared between individuals. These individual specificities observed using CRISPR typing were confirmed by comparing the CRISPR diversity to microbiome diversity assessed using 16S rRNA amplicon sequencing. CRISPR typing achieved 95.2% accuracy in personal classification, whereas 16S rRNA sequencing only achieved 52.6%. These results suggest that sequencing CRISPRs in the skin microbiome may be a more powerful approach for personal identification and ecological studies compared to conventional 16S rRNA sequencing.IMPORTANCE Microbial community diversity analysis can be utilized to characterize the personal microbiome that varies between individuals. CRISPR sequences, which reflect virome structure, in the human skin environment may be highly personalized similar to the structures of individual viromes. In this study, we identified 24 putative CRISPR arrays using a shotgun metagenome data set of the human skin microbiome. The findings of this study expand our understanding of the nature of CRISPRs by identifying novel CRISPR candidates. We developed a method to efficiently determine the diversity of three CRISPR arrays. Our analysis revealed that the CRISPR spacer diversity in the human skin microbiome is highly personalized compared with the microbiome diversity assessed by 16S rRNA sequencing, providing a new perspective on the study of the skin microbiome.

Project description:Objective The relationship between intestinal microbiome and colorectal cancer (CRC) progression is unclear. This study aims to identify the intestinal microbiome associated with CRC progression and construct predictive labels to support the accurate assessment and treatment of CRC. Method The 192 patients included in the study were divided into stage I-II and stage III-IV CRC patients according to the pathological stages, and preoperative stools were collected from both groups for 16S rDNA sequencing of the intestinal microbiota. Pearson correlation and Spearman correlation coefficient analysis were used to analyze the differential intestinal microbiome and the correlation with tumor microenvironment and to predict the functional pathway. XGBoost model (XGB) and Random Forest model (RF) were used to construct the microbiome-based signature. The total RNA extraction from 17 CRC tumor simples was used for transcriptome sequencing. Result The Simpson index of intestinal microbiome in stage III-IV CRC were significantly lower than those in stage I-II CRC. Proteus, Parabacteroides, Alistipes and Ruminococcus etc. are significantly enriched genus in feces of CRC patients with stage III-IV. ko00514: Other types of O − glycan biosynthesis pathway is relevant with CRC progression. Alistipes indistinctus was positively correlated with mast cells, immune activators IL-6 and IL6R, and GOBP_PROTEIN_FOLDING_IN_ENDOPLASMIC_RETICULUM dominantly. The Random Forest (RF) model and eXtreme Gradient Boosting (XGBoost) model constructed with 42 CRC progression-associated differential bacteria were effective in distinguishing CRC patients between stage I-II and stage III-IV. Conclusions The abundance and diversity of intestinal microbiome may increase gradually with the occurrence and progression of CRC. Elevated fetal abundance of Proteus, Parabacteroides, Alistipes and Ruminococcus may contribute to CRC progression. Enhanced synthesis of O − glycans may result in CRC progression. Alistipes indistinctus may play a facilitated role in mast cell maturation by boosting IL-6 production. Alistipes indistinctus may work in the correct folding of endoplasmic reticulum proteins in CRC, reducing ER stress and prompting the survival and deterioration of CRC, which may owe to the enhanced PERK expression and activation of downstream UPR by Alistipes indistinctus. The CRC progression-associated differential intestinal microbiome identified in our study can be served as potential microbial markers for CRC staging prediction. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-023-04119-1.

Dataset Information

GePMI: A statistical model for personal intestinal microbiome identification.

Publications

GePMI: A statistical model for personal intestinal microbiome identification.

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