Fig 4 - available from: BMC Bioinformatics
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Background
Computing centrality is a foundational concept in social networking that involves finding the most “central” or important nodes. In some biological networks defining importance is difficult, which then creates challenges in finding an appropriate centrality algorithm.
Results
We instead generalize the results of any k centrality algorit...
Contexts in source publication
Context 1
... We note patterns among these disagreements. Many times all three backbones are covered exactly once between two adjacent or three triad nodes. We argue that because of the fundamental properties of iteration, centrality is likely a "toss-up" in these situations. Take for example the triad [ x, y, z] in Fig. 4a. In this case x, y and z were found as central by iterative betweenness, closeness and degree respectively. However, suppose centrality is actually a "toss-up" between them, which would mean for example in iterative betweenness when x was found as most central, y and z had only slightly lower centrality values. In the next iteration x ...
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... centrality values. In the next iteration x would be removed along with edge y − z, causing y and z to lose all contributions from paths involving this triad (which by definition are likely significant if x was central). The same thing would happen when y was found by iterative closeness, and z by iterative degree. Adjacencies like the one in Fig. 4b have the same issue for the same reason, with x (or y) losing contributions from its central neighbor upon its ...
Citations
... Centrality metrics can also be used to identify keystone nodes within the network (we do note, however, that this is not always the case; see Banerjee et al. (2018) for further discussion on the identification of keystone nodes). The application of more advanced centrality algorithms such as MATria (Cickovski et al., 2019) can facilitate the identification of keystone and other important nodes by using iterative approaches to identify unified sets of central nodes that maximize levels of agreement between the various centrality metrics. ...
Understanding the sets of inter- and intraspecies interactions in microbial communities is a fundamental goal of microbial ecology. However, the study and quantification of microbial interactions poses several challenges due to their complexity, dynamic nature, and the sheer number of unique interactions within a typical community. To overcome such challenges, microbial ecologists must rely on various approaches to distill the system of study to a functional and conceptualizable level, allowing for a practical understanding of microbial interactions in both simplified and complex systems. This review broadly addresses the role of several conceptual approaches available for the microbial ecologist’s arsenal, examines specific tools used to accomplish such approaches, and describes how the assumptions, expectations, and philosophies underlying these tools change across scales of complexity.
Gut microbiota, or the collection of diverse microorganisms in a specific ecological niche, are known to significantly impact human health. Decreased gut microbiota production of short-chain fatty acids (SCFAs) has been implicated in type 2 diabetes mellitus (T2DM) disease progression. Most microbiome studies focus on ethnic majorities. This study aims to understand how the microbiome differs between an ethnic majority (the Dutch) and minority (the South-Asian Surinamese (SAS)) group with a lower and higher prevalence of T2DM, respectively. Microbiome data from the Healthy Life in an Urban Setting (HELIUS) cohort were used. Two age- and gender-matched groups were compared: the Dutch (n = 41) and SAS (n = 43). Microbial community compositions were generated via DADA2. Metrics of microbial diversity and similarity between groups were computed. Biomarker analyses were performed to determine discriminating taxa. Bacterial co-occurrence networks were constructed to examine ecological patterns. A tight microbiota cluster was observed in the Dutch women, which overlapped with some of the SAS microbiota. The Dutch gut contained a more interconnected microbial ecology, whereas the SAS network was dispersed, i.e., contained fewer inter-taxonomic correlational relationships. Bacteroides caccae, Butyricicoccus, Alistipes putredinis, Coprococcus comes, Odoribacter splanchnicus, and Lachnospira were enriched in the Dutch gut. Haemophilus, Bifidobacterium, and Anaerostipes hadrus discriminated the SAS gut. All but Lachnospira and certain strains of Haemophilus are known to produce SCFAs. The Dutch gut microbiome was distinguished from the SAS by diverse, differentially abundant SCFA-producing taxa with significant cooperation. The dynamic ecology observed in the Dutch was not detected in the SAS. Among several potential gut microbial biomarkers, Haemophilus parainfluenzae likely best characterizes the ethnic minority group, which is more predisposed to T2DM. The higher prevalence of T2DM in the SAS may be associated with the gut dysbiosis observed.
Purpose: Decreased gut microbiota production of short-chain fatty acids (SCFAs) has been implicated in type 2 diabetes mellitus (T2DM) disease progression. Most microbiome studies focus on ethnic majorities. This study aims to understand microbiome differences between an ethnic majority (the Dutch) and minority (the South-Asian Surinamese (SAS)) group with a lower and higher prevalence of T2DM, respectively.
Methods: Microbiome data from the Healthy Life in an Urban Setting (HELIUS) cohort were used. The 16S rRNA V4 region was sequenced. Two age- and gender-matched groups were compared: the Dutch (n = 41) and SAS (n = 43). Microbial compositions were generated via DADA2. Alpha and beta-diversity and Principal Coordinate Analysis (PCoA) were computed. DESeq2 differential bacterial abundance and LEfSe biomarker analyses were performed to determine discriminating features. Co-occurrence networks were constructed to examine gut ecology.
Results: A tight cluster of bacterial abundances was observed in the Dutch women, which overlapped with some of the SAS microbiomes. The Dutch gut contained a more interconnected microbial ecology, whereas the SAS network was dispersed. Bacteroides caccae, Butyricicoccus, Alistipes putredinis, Coprococcus comes, Odoribacter splanchnicus, and Lachnospira characterized the Dutch gut. Haemophilus, Bifidobacterium, and Anaerostipes hadrus characterized the SAS gut. All but Lachnospira and certain strains of Haemophilus are known SCFA producers.
Conclusion: The Dutch gut microbiome was distinguished from the SAS by diverse, differentially abundant SCFA-producing taxa with significant cooperation. The dynamic ecology observed in the Dutch was lost in the SAS. The higher prevalence of T2DM in the SAS may be associated with the dysbiosis observed.
