The impact of gut microbiome enterotypes on ulcerative colitis: identifying key bacterial species and revealing species co-occurrence networks using machine learning
The impact of gut microbiome enterotypes on ulcerative colitis: identifying key bacterial species and revealing species co-occurrence networks using machine learning is a research paper published in Gut Microbes (2023). On theSindex it has a DataRank of 1.3. It has been cited 50 times, with 42 citing works in its 1-hop citation network.
Abstract
Ulcerative colitis (UC) is a chronic inflammatory intestinal disease affecting the colon and rectum, with its pathogenesis attributed to genetic background, environmental factors, and gut microbes. This study aimed to investigate the role of enterotypes in UC by conducting a hierarchical analysis, determining differential bacteria using machine learning, and performing Species Co-occurrence Network (SCN) analysis. Fecal bacterial data were collected from UC patients, and a 16S rRNA metagenomic analysis was performed using the QIIME2 bioinformatics pipeline. Enterotype clustering was conducted at the family level, and deep neural network (DNN) classification models were trained for UC and healthy controls (HC) in each enterotype. Results from eleven 16S rRNA gut microbiome datasets revealed three enterotypes: Bacteroidaceae (ET-B), Lachnospiraceae (ET-L), and Clostridiaceae (ET-C). Ruminococcus (R. gnavus) abundance was significantly higher in UC subjects with ET-B and ET-C than in those with ET-L. R. gnavus also showed a positive correlation with Clostridia in UC SCN for ET-B and ET-C subjects, with a higher correlation in ET-C subjects. Conversely, Odoribacter (O.) splanchnicus and Bacteroides (B.) uniformis exhibited a positive correlation with tryptophan metabolism and AMP-activated protein kinase (AMPK) signaling pathways, while R. gnavus showed a negative correlation. In vitro co-culture experiments with Clostridium (C.) difficile demonstrated that fecal microbiota from ET-B subjects had a higher abundance of C. difficile than ET-L subjects. In conclusion, the ET-B enterotype predisposes individuals to UC, with R. gnavus as a potential risk factor and O. splanchnicus and B. uniformis as protective bacteria, and those with UC may have ultimately become ET-C.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
DataRank Breakdown
Base Score Contribution
0.590
From this paper's citation signal
Citation Network Contribution
0.734
From 29 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- DADA2: High-resolution sample inference from Illumina amplicon dataNature Methods201635,281 citationsDataRank 1.6
- Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial CommunitiesApplied and Environmental Microbiology200921,605 citationsDataRank 1.5
- Metagenomic biomarker discovery and explanationGenome Biology201116,383 citationsDataRank 1.5
- Enterotypes of the human gut microbiomeNature20117,570 citationsDataRank 1.3
- Meta-analysis of gut microbiome studies identifies disease-specific and shared responsesNature Communications20171,023 citationsDataRank 11.8Top 18%
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 45% comes from its base citations and 55% from the citation network (29 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
Click a node to highlight its connections. Use scroll to zoom. Drag to pan.