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

Bhar, Anirban; Gierse, Laurin Christopher; Meene, Alexander; Wang, Haitao; Karte, Claudia; Schwaiger, Theresa; Schröder, Charlotte; Mettenleiter, Thomas C.; Urich, Tim; Riedel, Katharina; Kaderali, Lars

doi:10.3389/fmicb.2022.979320

Cited by 5 publications

(3 citation statements)

References 68 publications

(87 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…R. intestinalis inhibits the development of Crohn's disease (CD) by increasing the differentiation of anti-inflammatory Tregs, which may provide the basis of new therapeutic strategies for CD (Shen et al, 2022). Such a decrease of butyrate-producing taxa, such as Blautia and Eubacterium (rectale), in influenza A virus infection was proven in the previous 16s studies (Fuentes et al, 2021;Bhar et al, 2022). Strikingly, in the pathway analysis, results displayed a strong association between the depleted species and reduced pathways, implying that the protective role of the gut microbiome in the population could be caused by their biosynthesis functions.…”

Section: Discussionmentioning

confidence: 95%

Lower gut abundance of Eubacterium rectale is linked to COVID-19 mortality

Liu,

Chan,

Chan

et al. 2023

Front. Cell. Infect. Microbiol.

View full text Add to dashboard Cite

IntroductionEmerging preclinical and clinical studies suggest that altered gut microbiome composition and functions are associated with coronavirus 2019 (COVID- 19) severity and its long-term complications. We hypothesize that COVID-19 outcome is associated with gut microbiome status in population-based settings.MethodsGut metagenomic data of the adult population consisting of 2871 subjects from 16 countries were obtained from ExperimentHub through R, while the dynamic death data of COVID-19 patients between January 22, 2020 and December 8, 2020 in each country was acquired from Johns Hopkins Coronavirus Resource Center. An adjusted stable mortality rate (SMR) was used to represent these countries’ mortality and correlated with the mean relative abundance (mRA) of healthy adult gut microbiome species.ResultsAfter excluding bacterial species with low prevalence (prevalence <0.2 in the included countries), the β-diversity was significantly higher in the countries with high SMR when compared with those with median or low SMR (p <0.001). We then identified the mRA of two butyrate producers, Eubacterium rectale and Roseburia intestinalis, that were negatively correlated with SMR during the study period. And the reduction of these species was associated with severer COVID-19 manifestation.ConclusionPopulation-based microbiome signatures with the stable mortality rate of COVID-19 in different countries suggest that altered gut microbiome composition and functions are associated with mortality of COVID-19.

show abstract

Section: Discussionmentioning

confidence: 95%

Lower gut abundance of Eubacterium rectale is linked to COVID-19 mortality

Liu,

Chan,

Chan

et al. 2023

Front. Cell. Infect. Microbiol.

View full text Add to dashboard Cite

show abstract

“…Cluster algorithms are now widely used not only in social (Hoffman et al, 2018 ) or technological (Faloutsos et al, 1999 ) but also in biological and health‐related (Bhar et al, 2022 ; Fell & Wagner, 2000 ) network analysis. The fecal microbiota can also be considered a network in which the microbial composition of a sample represents the nodes and the distance between the samples and the respective edges of the network.…”

Section: Introductionmentioning

confidence: 99%

Time series cluster analysis reveals individual assignment of microbiota in captive tiger (Panthera tigris) and wildebeest (Connochaetes taurinus)

Zoelzer

Schneider

Dierkes

2023

Ecology and Evolution

View full text Add to dashboard Cite

Fecal microbiota variability and individuality are well studied in humans and also in farm animals (related to diet‐ or disease‐specific influences), but very little is known for exotic zoo‐housed animals. This includes a wide range of species that differ greatly in microbiota composition and variation. For example, herbivorous species show a very similar and constant fecal microbiota over time, whereas carnivorous species appear to be highly variable in fecal microbial diversity and composition. Our objective was to determine whether species‐specific and individual‐specific clustering patterns were observed in the fecal microbiota of wildebeest ( Connochaetes taurinus ) and tigers ( Panthera tigris ). We collected 95 fecal samples of 11 animal individuals that were each sampled over eight consecutive days and analyzed those with Illumina MiSeq sequencing of the V3–V4 region of the 16SrRNA gene. In order to identify species or individual clusters, we applied two different agglomerative hierarchical clustering algorithms – a community detection algorithm and Ward's linkage. Our results showed that both, species‐specific and individual‐specific clustering is possible, but more reliable results were achieved when applying dynamic time warping which finds the optimal alignment between different time series. Furthermore, the bacterial families that distinguish individuals from each other in both species included daily occurring core bacteria (e.g., Acidaminococcaceae in wildebeests or Clostridiaceae in tigers) as well as individual dependent and more fluctuating bacterial families. Our results suggest that while it is necessary to consider multiple consecutive samples per individual, it is then possible to characterize individual abundance patterns in fecal microbiota in both herbivorous and carnivorous species. This would allow establishing individual microbiota profiles of animals housed in zoos, which is a basic prerequisite to quickly detect deviations and use microbiome analysis as a non‐invasive and cost‐effective tool in animal welfare.

show abstract

“…Cluster algorithms are now widely used not only in social (Hoffman et al, 2018) or technological (Faloutsos et al, 1999) but also in biological and health-related (Bhar et al, 2022;Fell & Wagner, 2000) network analysis. The fecal microbiota can also be considered a network in which the microbial composition of a sample represents the nodes and the distance between the samples and the respective edges of the network.…”

Section: Introductionmentioning

confidence: 99%

Characterization of the herbivore and carnivore faecal microbiota with special emphasis on diet and host-specificity

Zölzer

View full text Add to dashboard Cite

Research on the human and animal microbiome has become increasingly important in recent years. It is now widely accepted the gut microbiome is of crucial importance to health, as it is involved in a large number of physiological processes. The term ‘microbiome’ refers to the all living microorganisms including their genes and metabolites in a defined environment, while the specific composition of microorganisms consisting of bacteria, archaea and protozoa is referred to as the ‘microbiota’ (Lane-Petter, 1962; Lederberg and McCray, 2001). In recent years, research has focused on various of these communities in the soil (Fierer, 2017), water (Sunagawa et al., 2015), air (Leung et al., 2014) and especially in the human gut. However, this topic is also becoming increasingly relevant for the conservation of endangered species. In the face of global mass extinctions and the listing of over 42,000 animal species as ‘critically endangered’, conservation breeding programmes are more important than ever (Díaz et al., 2019; IUCN, 2022). The responsibility for these tasks lies with zoological institutions, which are dedicated to animal conservation and the continuous monitoring of animal welfare. Microbiome research offers a non-invasive method to support species conservation. By analysing faecal samples, microbial markers can be identified that provide important information about the health status and reproductive cycle of animals (Weingrill et al., 2004; Antwis et al., 2019). Zoological facilities also provide an ideal research environment for comparing individuals from different habitats. In addition, all necessary metadata such as age, sex, kinship or medical treatment are documented and can be used for the analysis. This is the starting point for this thesis. In order to identify such microbial markers, it is necessary to understand the microbiome of a variety of animal species. The first aim is therefore to characterise the faecal microbiota of 31 mammalian species, focusing on herbivores and carnivores. It could be shown that they differ significantly in terms of both microbial diversity and microbiota composition. Herbivorous species express a very diverse microbial composition, consisting mainly of cellulose-degrading taxa of the families Fibrobacteraceae or Spirochaetaceae. In contrast, the microbiota of carnivorous species is less diverse and is dominated by protein-degrading Fusobacteriaceae and Clostridiaceae. In addition, this thesis proves that the microbiota of herbivorous species is highly consistent, whereas the microbiota of carnivorous species is highly variable. The results of this study provide important insights for the sampling scheme of future projects. Especially when analysing carnivorous species, single samples are not sufficient to capture the full variability of the microbiome. These results lead to the question of whether this variability can be explained by daily fluctuations in the individual microbiome and whether this can be used to distinguish between species or individuals. Using individual longitudinal data and a combined approach of clustering algorithms and dynamic time warping, it is shown that such a distinction is possible at the species and individual level. This was confirmed for both a carnivorous (Panthera tigris) and a herbivorous (Connochaetes taurinus) species. These results confirm the influence of the host individual on the faecal microbiota, in addition to the often described influence of diet (Ley et al., 2008a; Kartzinel et al., 2019). Based on the knowledge gained from these studies, a methodology has been developed that will enable the conservation of species in the field to be supported by microbiome research in the future. The focus here lays on the identification of host-specific metadata based on the faecal microbiota. The developed regression model is able to distinguish between carnivorous, herbivorous and omnivorous hosts with up to 99% accuracy. In addition, a more accurate phylogenetic classification of the family (Canidae, Felidae, Ursidae, Herpestidae) can be made for carnivorous hosts. For herbivorous hosts, the model can predict the respective digestive system with up to 100% accuracy, distinguishing between ruminants, hindgut fermenters and a simple digestive system. The acquisition of host-specific metadata from an unknown faecal sample is an important step towards establishing microbiome research in species conservation. Field studies in particular will benefit from such new methods. Usually, costly microsatellite analysis and high-quality host DNA are required to obtain host-specific information from faecal samples. The newly developed method offers a less costly and labour-intensive alternative to conventional techniques and opens up a more accessible field for microbiome research in the field.

show abstract

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

Cited by 5 publications

References 68 publications

Lower gut abundance of Eubacterium rectale is linked to COVID-19 mortality

Lower gut abundance of Eubacterium rectale is linked to COVID-19 mortality

Time series cluster analysis reveals individual assignment of microbiota in captive tiger (Panthera tigris) and wildebeest (Connochaetes taurinus)

Characterization of the herbivore and carnivore faecal microbiota with special emphasis on diet and host-specificity

Contact Info

Product

Resources

About