Metagenomic Analysis of Common Intestinal Diseases Reveals Relationships among Microbial Signatures and Powers Multidisease Diagnostic Models
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Study information
-
Quality control
- Retracted paper
- Contamination issues suspected
- Batch effect issues suspected
- Uncontrolled confounding suspected
- Results are suspect (various reasons)
- Tags applied
study design
Citation
PMID PubMed identifier for scientific articles.
DOI Digital object identifier for electronic documents.
Authors
Jiang P, Wu S, Luo Q, Zhao XM, Chen WH
Journal
mSystems
Year
2021
Keywords:
gut dysbiosis, human microbiome, intestinal disease, machine learning-based disease classification, noninvasive disease diagnosis
Common intestinal diseases such as Crohn's disease (CD), ulcerative colitis (UC), and colorectal cancer (CRC) share clinical symptoms and altered gut microbes, necessitating cross-disease comparisons and the use of multidisease models. Here, we performed meta-analyses on 13 fecal metagenome data sets of the three diseases. We identified 87 species and 65 pathway markers that were consistently changed in multiple data sets of the same diseases. According to their overall trends, we grouped the disease-enriched marker species into disease-specific and disease-common clusters and revealed their distinct phylogenetic relationships; species in the CD-specific cluster were phylogenetically related, while those in the CRC-specific cluster were more distant. Strikingly, UC-specific species were phylogenetically closer to CRC, likely because UC patients have higher risk of CRC. Consistent with their phylogenetic relationships, marker species had similar within-cluster and different between-cluster metabolic preferences. A portion of marker species and pathways correlated with an indicator of leaky gut, suggesting a link between gut dysbiosis and human-derived contents. Marker species showed more coordinated changes and tighter inner-connections in cases than the controls, suggesting that the diseased gut may represent a stressed environment and pose stronger selection on gut microbes. With the marker species and pathways, we constructed four high-performance (including multidisease) models with an area under the receiver operating characteristic curve (AUROC) of 0.87 and true-positive rates up to 90%, and explained their putative clinical applications. We identified consistent microbial alterations in common intestinal diseases, revealed metabolic capacities and the relationships among marker bacteria in distinct states, and supported the feasibility of metagenome-derived multidisease diagnosis.IMPORTANCE Gut microbes have been identified as potential markers in distinguishing patients from controls in colorectal cancer, ulcerative colitis, and Crohn's disease individually, whereas there lacks a systematic analysis to investigate the exclusive microbial shifts of these enteropathies with similar clinical symptoms. Our meta-analysis and cross-disease comparisons identified consistent microbial alterations in each enteropathy, revealed microbial ecosystems among marker bacteria in distinct states, and demonstrated the necessity and feasibility of metagenome-based multidisease classifications. To the best of our knowledge, this is the first study to construct multiclass models for these common intestinal diseases.
Experiment 1
Reviewed Marked as Reviewed by Peace Sandy on 2024-2-7
Curated date: 2022/01/21
Curator: Itslanapark
Revision editor(s): Itslanapark, WikiWorks, Peace Sandy
Subjects
- Host species Species from which microbiome was sampled. Contact us to have more species added.
- Homo sapiens
- Body site Anatomical site where microbial samples were extracted from according to the Uber Anatomy Ontology
- Feces Cow dung,Cow pat,Droppings,Dung,Excrement,Excreta,Faeces,Fecal material,Fecal matter,Fewmet,Frass,Guano,Matières fécales@fr,Merde@fr,Ordure,Partie de la merde@fr,Piece of shit,Porción de mierda@es,Portion of dung,Portion of excrement,Portion of faeces,Portion of fecal material,Portion of fecal matter,Portion of feces,Portion of guano,Portion of scat,Portionem cacas,Scat,Spoor,Spraint,Stool,Teil der fäkalien@de,Feces,feces
- Condition The experimental condition / phenotype studied according to the Experimental Factor Ontology
- Colorectal cancer cancer of colorectum,cancer of large bowel,cancer of large intestine,cancer of the large bowel,colon cancer,colorectal cancer,colorectum cancer,CRC,large intestine cancer,malignant colorectal neoplasm,malignant colorectal tumor,malignant colorectum neoplasm,malignant large bowel neoplasm,malignant large bowel tumor,malignant large intestine neoplasm,malignant large intestine tumor,malignant neoplasm of colorectum,malignant neoplasm of large bowel,malignant neoplasm of large intestine,malignant neoplasm of the large bowel,malignant neoplasm of the large intestine,malignant tumor of large bowel,malignant tumor of large intestine,malignant tumor of the large bowel,malignant tumor of the large intestine,Colorectal cancer
- Group 0 name Corresponds to the control (unexposed) group for case-control studies
- Non disease controls
- Group 1 name Corresponds to the case (exposed) group for case-control studies
- CRC patients
- Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group
- Patients diagnosed with colorectal cancer
- Group 0 sample size Number of subjects in the control (unexposed) group
- 632
- Group 1 sample size Number of subjects in the case (exposed) group
- 354
Lab analysis
- Sequencing type
- 16S
- 16S variable region One or more hypervariable region(s) of the bacterial 16S gene
- Not specified
Statistical Analysis
- Data transformation Data transformation applied to microbial abundance measurements prior to differential abundance testing (if any).
- relative abundances
- Statistical test
- Mann-Whitney (Wilcoxon)
- Significance threshold p-value or FDR threshold used for differential abundance testing (if any)
- 0.05
- MHT correction Have statistical tests be corrected for multiple hypothesis testing (MHT)?
- Yes
- Confounders controlled for Confounding factors that have been accounted for by stratification or model adjustment
- age, body mass index, sex
Alpha Diversity
- Shannon Estimator of species richness and species evenness: more weight on species richness
- unchanged
Signature 1
Reviewed Marked as Reviewed by Peace Sandy on 2024-2-7
Curated date: 2022/01/24
Curator: Itslanapark
Revision editor(s): Itslanapark, Peace Sandy, WikiWorks
Source: Figure 1
Description: Disease-specific and shared microbial markers showed distinct prevalence profiles in patients and controls. (A) Microbial markers and their trends (i.e., case- or control-enriched) in common intestinal diseases. Species significantly enriched in cases (or controls) of corresponding disease in meta-analysis are shown (fdr < 0.05 in meta-analysis, Benjamini-Hochberg FDR correction), with their phylum shown on top. Red indicates case-enriched species and blue indicates control-enriched ones.
Abundance in Group 1: decreased abundance in CRC patients
Revision editor(s): Itslanapark, Peace Sandy, WikiWorks
Signature 2
Reviewed Marked as Reviewed by Peace Sandy on 2024-2-7
Curated date: 2022/01/25
Curator: Itslanapark
Revision editor(s): WikiWorks, Itslanapark, Aiyshaaaa, Peace Sandy
Source: Figure 1
Description: Disease-specific and shared microbial markers showed distinct prevalence profiles in patients and controls. (A) Microbial markers and their trends (i.e., case- or control-enriched) in common intestinal diseases. Species significantly enriched in cases (or controls) of corresponding disease in meta-analysis are shown (fdr < 0.05 in meta-analysis, Benjamini-Hochberg FDR correction), with their phylum shown on top. Red indicates case-enriched species and blue indicates control-enriched ones.
Abundance in Group 1: increased abundance in CRC patients
Revision editor(s): WikiWorks, Itslanapark, Aiyshaaaa, Peace Sandy
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