A Scale-Free, Fully Connected Global Transition Network Underlies Known Microbiome Diversity

Jing, Gongchao; Zhang, Yufeng; Liu, Lu; Wang, Zengbin; Sun, Zheng; Knight, Rob; Su, Xiaoquan; Xu, Jian

doi:10.1128/msystems.00394-21

Cited by 5 publications

(4 citation statements)

References 39 publications

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“…Co-occurrence network analysis revealed that different topological characteristics between UN and HC groups might be linked to different inter-species interactions or system efficiency [ 38 ]. Although the gut microbiota networks in both groups had scale-free properties, the hub nodes, which play a critical role in preserving the overall functionality of the network, were different [ 39 – 41 ]. Notably, the gut microbiota network of undernourished children exhibited higher clustering coefficient and modularity, along with lower graph density, lower average degree, and longer average path length.…”

Section: Discussionmentioning

confidence: 99%

Uncovering the complexity of childhood undernutrition through strain-level analysis of the gut microbiome

Chang,

Zhang,

Wang

et al. 2024

BMC Microbiol

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Background Undernutrition (UN) is a critical public health issue that threatens the lives of children under five in developing countries. While evidence indicates the crucial role of the gut microbiome (GM) in UN pathogenesis, the strain-level inspection and bacterial co-occurrence network investigation in the GM of UN children are lacking. Results This study examines the strain compositions of the GM in 61 undernutrition patients (UN group) and 36 healthy children (HC group) and explores the topological features of GM co-occurrence networks using a complex network strategy. The strain-level annotation reveals that the differentially enriched species between the UN and HC groups are due to discriminated strain compositions. For example, Prevotella copri is mainly composed of P. copri ASM1680343v1 and P. copri ASM345920v1 in the HC group, but it is composed of P. copri ASM346549v1 and P. copri ASM347465v1 in the UN group. In addition, the UN-risk model constructed at the strain level demonstrates higher accuracy (AUC = 0.810) than that at the species level (AUC = 0.743). With complex network analysis, we further discovered that the UN group had a more complex GM co-occurrence network, with more hub bacteria and a higher clustering coefficient but lower information transfer efficiencies. Moreover, the results at the strain level suggested the inaccurate and even false conclusions obtained from species level analysis. Conclusions Overall, this study highlights the importance of examining the GM at the strain level and investigating bacterial co-occurrence networks to advance our knowledge of UN pathogenesis.

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Section: Discussionmentioning

confidence: 99%

Uncovering the complexity of childhood undernutrition through strain-level analysis of the gut microbiome

Chang,

Zhang,

Wang

et al. 2024

BMC Microbiol

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“…Massive data sets suggested that microbiome structures are intensively distinct across habitat types ( 9 , 18 ). Different microbes have their preference for the 16S rRNA gene variable region in amplification sensitivity and nucleotide sequence recognizability.…”

Section: Resultsmentioning

confidence: 99%

“…Massive data sets suggested that microbiome structures are intensively distinct across habitat types ( 18 ), causing microbes that are unevenly distributed ( 9 ). For instance, Firmicutes and Bacteroidetes dominate the human gut microbiota, while Proteobacteria and Cyanobacteria are prevalent in natural environments ( 19 , 20 ).…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Assessment of 16S rRNA Gene Amplicon Sequencing for Microbiome Profiling across Multiple Habitats

et al. 2023

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“…In order to evaluate the performance of the proposed community detection algorithm MCCD-WN, we used the LFR benchmark (Lancichinetti et al, 2008 ; Lancichinetti and Fortunato, 2009 ), which is a generalization of Girvan-Newman benchmark (Girvan and Newman, 2002 ) and assumes a power-law degree distribution of the nodes, similar to biological networks (Jing et al, 2021 ). The LFR benchmark uses a parameter called mixing parameter (μ w ) which is delineated as the ratio of the external degree of a node to the total degree of the node.…”

Section: Methodsmentioning

confidence: 99%

Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus infection

et al. 2022

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Influenza A Virus (IAV) infection followed by bacterial pneumonia often leads to hospitalization and death in individuals from high risk groups. Following infection, IAV triggers the process of viral RNA replication which in turn disrupts healthy gut microbial community, while the gut microbiota plays an instrumental role in protecting the host by evolving colonization resistance. Although the underlying mechanisms of IAV infection have been unraveled, the underlying complex mechanisms evolved by gut microbiota in order to induce host immune response following IAV infection remain evasive. In this work, we developed a novel Maximal-Clique based Community Detection algorithm for Weighted undirected Networks (MCCD-WN) and compared its performance with other existing algorithms using three sets of benchmark networks. Moreover, we applied our algorithm to gut microbiome data derived from fecal samples of both healthy and IAV-infected pigs over a sequence of time-points. The results we obtained from the real-life IAV dataset unveil the role of the microbial families Ruminococcaceae, Lachnospiraceae, Spirochaetaceae and Prevotellaceae in the gut microbiome of the IAV-infected cohort. Furthermore, the additional integration of metaproteomic data enabled not only the identification of microbial biomarkers, but also the elucidation of their functional roles in protecting the host following IAV infection. Our network analysis reveals a fast recovery of the infected cohort after the second IAV infection and provides insights into crucial roles of Desulfovibrionaceae and Lactobacillaceae families in combating Influenza A Virus infection. Source code of the community detection algorithm can be downloaded from https://github.com/AniBhar84/MCCD-WN.

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A Scale-Free, Fully Connected Global Transition Network Underlies Known Microbiome Diversity

Abstract: Microbiomes are inherently linked by their structural similarity, yet the global features of such similarity are not clear. Here, we propose as a solution a search-based microbiome transition network.

Cited by 5 publications

References 39 publications

Uncovering the complexity of childhood undernutrition through strain-level analysis of the gut microbiome

Uncovering the complexity of childhood undernutrition through strain-level analysis of the gut microbiome

Comprehensive Assessment of 16S rRNA Gene Amplicon Sequencing for Microbiome Profiling across Multiple Habitats

Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus infection

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