Integrated microbiome and metabolome analysis reveals a novel interplay between commensal bacteria and metabolites in colorectal cancer

Study information

Reviewed Marked as Reviewed by Peace Sandy on 2024-1-4

study design

case-control

Citation

PMID PubMed identifier for scientific articles.

31281534

DOI Digital object identifier for electronic documents.

10.7150/thno.35186

URI

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6592169/

Authors

Yang Y, Misra BB, Liang L, Bi D, Weng W, Wu W, Cai S, Qin H, Goel A, Li X, Ma Y

Journal

Theranostics

Year

2019

Keywords:

biomarkers, colorectal cancer, gut, metabolomics, microbiome

Rationale: Colorectal cancer (CRC) is a malignant tumor with the third highest morbidity rate among all cancers. Driven by the host's genetic makeup and environmental exposures, the gut microbiome and its metabolites have been implicated as the causes and regulators of CRC pathogenesis. We assessed human fecal samples as noninvasive and unbiased surrogates to catalog the gut microbiota and metabolome in patients with CRC. Methods: Fecal samples collected from CRC patients (CRC group, n = 50) and healthy volunteers (H group, n = 50) were subjected to microbiome (16S rRNA gene sequencing) and metabolome (gas chromatography-mass spectrometry, GC-MS) analyses. The datasets were analyzed individually and integrated for combined analysis using various bioinformatics approaches. Results: Fecal metabolomic analysis led to the identification of 164 metabolites spread across 40 metabolic pathways in both groups. In addition, there were 42 and 17 metabolites specific to the H and CRC groups, respectively. Sequencing of microbial diversity revealed 1084 operational taxonomic units (OTUs) across the two groups, and there was less species diversity in the CRC group than in the H group. Seventy-six discriminatory OTUs were identified for the microbiota of H volunteers and CRC patients. Integrated analysis correlated CRC-associated microbes with metabolites, such as polyamines (cadaverine and putrescine). Conclusions: Our results provide substantial evidence of a novel interplay between the gut microbiome and metabolome (i.e., polyamines), which is drastically perturbed in CRC. Microbe-associated metabolites can be used as diagnostic biomarkers in therapeutic explorations.

Experiment 1

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Reviewed Marked as Reviewed by Peace Sandy on 2024-1-4

Curated date: 2022/01/28

Curator: Itslanapark

Revision editor(s): WikiWorks, Itslanapark, Atrayees, Peace Sandy

Subjects

Location of subjects: China

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 carcinoma

Group 0 name Corresponds to the control (unexposed) group for case-control studies: healthy controls

Group 1 name Corresponds to the case (exposed) group for case-control studies: Colorectal Cancer 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: 50

Group 1 sample size Number of subjects in the case (exposed) group: 50

Antibiotics exclusion Number of days without antibiotics usage (if applicable) and other antibiotics-related criteria used to exclude participants (if any): 2 months

Lab analysis

Sequencing type: 16S

16S variable region One or more hypervariable region(s) of the bacterial 16S gene: V3-V4

Sequencing platform Manufacturer and experimental platform used for quantifying microbial abundance: Illumina

Statistical Analysis

Data transformation Data transformation applied to microbial abundance measurements prior to differential abundance testing (if any).: relative abundances

Statistical test: LEfSe

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

LDA Score above Threshold for the linear discriminant analysis (LDA) score for studies using the popular LEfSe tool: 4

Alpha Diversity

Shannon Estimator of species richness and species evenness: more weight on species richness: increased

Chao1 Abundance-based estimator of species richness: increased

Simpson Estimator of species richness and species evenness: more weight on species evenness: unchanged

Signature 1

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Curated date: 2022/02/20

Curator: Itslanapark

Revision editor(s): WikiWorks, Itslanapark, Aiyshaaaa, Peace Sandy, Davvve

Source: Figure 2(b)

Description: The differences in abundance between the H group and the CRC groups.

Abundance in Group 1: increased abundance in Colorectal Cancer patients

NCBI	Quality Control	Links
Akkermansia
Enterobacteriaceae
Escherichia/Shigella sp.
Fusobacteriaceae
Fusobacteriales
Fusobacteriia
Fusobacteriota
Fusobacterium
Lentisphaera
Parvimonas
Porphyromonas
Pseudomonadales
Pseudomonadota
Gammaproteobacteria

Revision editor(s): WikiWorks, Itslanapark, Aiyshaaaa, Peace Sandy, Davvve

Signature 2

Edit History Delete

Mark reviewed

Your review change was submitted

Needs review

Curated date: 2022/02/20

Curator: Itslanapark

Revision editor(s): WikiWorks, Itslanapark, Aiyshaaaa, Peace Sandy, Davvve

Source: Figure 2(b)

Description: The differences in abundance between the H group and the CRC groups.

Abundance in Group 1: decreased abundance in Colorectal Cancer patients

NCBI	Quality Control	Links
Acidaminococcaceae
Actinomycetota
Bacillota
Bifidobacteriaceae
Bifidobacteriales
Bifidobacterium
Blautia
Clostridia
Eubacteriales
Eubacterium coprostanoligenes
Eubacterium oxidoreducens
Faecalibacterium
Lachnospira
Lachnospiraceae
Megamonas
Negativicutes
Oscillospiraceae
Parasutterella
Phascolarctobacterium
Pseudobutyrivibrio
Romboutsia
Selenomonadales
Solibacillus
Subdoligranulum

Revision editor(s): WikiWorks, Itslanapark, Aiyshaaaa, Peace Sandy, Davvve