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. 2025 Jun 26;25(1):371.
doi: 10.1186/s12866-025-04106-0.

[1]The human gut microbiota in IBD, characterizing hubs, the core microbiota and terminal nodes: a network-based approach

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[1]The human gut microbiota in IBD, characterizing hubs, the core microbiota and terminal nodes: a network-based approach

Theresa Geese et al. BMC Microbiol. .

Abstract

Background: Dysbiosis, an imbalance in the bacterial composition of the human gut microbiota, is linked to inflammatory bowel disease (IBD). Advances in biological techniques have generated vast microbiota datasets, presenting both opportunities and challenges for clinical research in that field. Network theory offers powerful tools to analyze these complex datasets.

Methods: Utilizing genetically unrelated individuals from the Kiel IBD-KC cohort, we compared network properties of the gut microbiota between patients with inflammatory bowel disease (IBD, n = 522) and healthy controls (n = 365), and between Crohn's disease (CD, n = 230) and Ulcerative Colitis (UC, n = 280). Correlation-based microbial networks were constructed, with genera as nodes and significant pairwise correlations as edges. We used centrality measures to identify key microbial constituents, called hubs, and suggest a network-based definition for a core microbiota. Using Graphlet theoretical approaches, we analyzed network topology and individual node roles.

Results: Global network properties differed between cases and controls, with controls showing a potentially more robust network structure characterized by e.g., a greater number of components and a lower edge density. Local network properties varied across all groups. For cases and both UC and CD, Faecalibacterium and Veillonella, and for unaffected controls Bacteroides, Blautia, Clostridium XIVa, and Clostridium XVIII emerged as unique hubs in the respective networks. Graphlet analysis revealed significant differences in terminal node orbits among all groups. Four genera which act as hubs in one state, were found to be terminal nodes in the opposite disease state: Bacteroides, Clostridium XIVa, Faecalibacterium, and Subdoligranulum. Comparing our network-based core microbiota definition with a conventional one showed an overlap in approximately half of the core taxa, while core taxa identified through our new definition maintained high abundance.

Conclusion: The network-based approach complements previous investigations of alteration of the human gut microbiota in IBD by offering a different perspective that extends beyond a focus solely on highly abundant taxa. Future studies should further investigate functional roles of hubs and terminal nodes as potential targets for interventions and preventions. Additionally, the advantages of the newly proposed network-based core microbiota definition, should be investigated more systematically.

Keywords: Centrality measures; Core microbiota; Graphlets; Gut microbiota; Hub and terminal nodes; Inflammatory bowel disease; Network analysis.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Written informed consent was obtained from each included participant. Study protocols conform to the ethical guidelines of the 1975 Declaration of Helsinki and have been priorly approved by the local ethics committee of the Medical Faculty at Kiel University, Germany (AZ 117/13). Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Graphlets up to four nodes. Graphlets with k = 2 to k = 4 nodes. Node colors correspond to orbit types within each graphlet, labeling refers to orbits. Adapted from [26]
Fig. 2
Fig. 2
Alpha diversity expressed through the Shannon index (a) and Chao1 index (b), colors depict the different groups. Both indices show higher diversity in the control group, and CD patients having the lowest diversity. Significance is tested using the Mann–Whitney test
Fig. 3
Fig. 3
Node color depicts the cluster, node size is scaled according to the sum of normalized counts, hubs are highlighted by a black circle, and for clarity only edges corresponding to an absolute association > = 0.15 are plotted and labels of nodes are shortened. The color and thickness of the edges indicate the direction (red for negative, green for positive) and strength of the Pearson correlation coefficient. Same layout is used for both groups and gray nodes depict genera that are not connected and/or only present in the other group
Fig. 4
Fig. 4
Closeness against betweenness centrality measures for all genera of networks constructed for cases (a) and controls (b), CD (c) and UC (d), color scale represents degree value. For better visibility the x-axis is log-scaled. Additionally, each subplot displays the Spearman correlation coefficients for the associations between closeness and betweenness (clos_betw), degree and betweenness (deg_betw), and degree and closeness (deg_clos)
Fig. 5
Fig. 5
Barplots depict min/max normalized centrality values for betweenness (yellow), closeness (orange), and degree (brown) for hubs from controls, cases, CD, and UC patients. Boxes present genera present in controls but not cases and vice versa, and genera present in CD but not UC and vice versa
Fig. 6
Fig. 6
Number and percentage of genera in the core of cases (left, blue) and controls (right, pink), following definition 1 (prevalence and abundance) compared to definition 2 (hubs), depicting the intersection and distinct sets
Fig. 7
Fig. 7
Comparing the cumulative abundance, average prevalence, and sum of all three centrality values, summed (averaged for centrality) over all core members identified by definition 1 (each left) and definition 2 (each right) for cases (blue) and controls (pink)
Fig. 8
Fig. 8
Number and percentage of genera in the core of CD (left, turquoise) and UC (right, light green) patients, following definition 1 (prevalence and abundance) compared to definition 2 (hubs), depicting the intersection and distinct sets
Fig. 9
Fig. 9
Comparing the cumulative abundance, average prevalence, and sum of all three centrality values, summed (averaged for centrality) over all core members identified by definition 1 (each left) and definition 2 (each right) for CD (turquoise) and UC (light green) patients
Fig. 10
Fig. 10
Presented are two graphlet correlation matrices computed for two different networks (based on cases (left GCM1) and controls (right GCM2)). The graphlet correlation matrix quantifies the pairwise similarity (Spearman correlation coefficient) of subgraph patterns (network's node orbits) in each network. In both cases, the color of each square represents the correlation coefficient between the corresponding pairs of graphlets. The diagonal of the matrices corresponds to the self-correlations of each graphlet. The matrix at the bottom shows the absolute difference between the graphlet correlation matrices for both networks. Positive values (blue) in the difference matrix indicate an increase in similarity, while negative values (red) indicate a decrease. Orbits are sorted according to their topological role

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