Modelling spatial patterns in host‐associated microbial communities

Ruuskanen, Matti O.; Sommeria‐Klein, Guilhem; Havulinna, Aki S.; Niiranen, Teemu J.; Lahti, Leo

doi:10.1111/1462-2920.15462

Cited by 17 publications

(9 citation statements)

References 138 publications

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“…It is possible that these differences contributed to the inconsistencies in the microbial associations. However, the geographic distance between the regions and the differences in ethnic and lifestyle features of the subpopulations are more likely to have caused the partly inconsistent taxon associations ( 38 ).…”

Section: Discussionmentioning

confidence: 99%

Gut Microbiome Composition Is Predictive of Incident Type 2 Diabetes in a Population Cohort of 5,572 Finnish Adults

et al. 2022

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OBJECTIVE To examine the previously unknown long-term association between gut microbiome composition and incident type 2 diabetes in a representative population cohort. RESEARCH DESIGN AND METHODS We collected fecal samples from 5,572 Finns (mean age 48.7 years; 54.1% women) in 2002 who were followed up for incident type 2 diabetes until 31 December 2017. The samples were sequenced using shotgun metagenomics. We examined associations between gut microbiome composition and incident diabetes using multivariable-adjusted Cox regression models. We first used the eastern Finland subpopulation to obtain initial findings and validated these in the western Finland subpopulation. RESULTS Altogether, 432 cases of incident diabetes occurred over the median follow-up of 15.8 years. We detected four species and two clusters consistently associated with incident diabetes in the validation models. These four species were Clostridium citroniae (hazard ratio [HR] 1.21; 95% CI 1.04–1.42), C. bolteae (HR 1.20; 95% CI 1.04–1.39), Tyzzerella nexilis (HR 1.17; 95% CI 1.01–1.36), and Ruminococcus gnavus (HR 1.17; 95% CI 1.01–1.36). The positively associated clusters, cluster 1 (HR 1.18; 95% CI 1.02–1.38) and cluster 5 (HR 1.18; 95% CI 1.02–1.36), mostly consisted of these same species. CONCLUSIONS We observed robust species-level taxonomic features predictive of incident type 2 diabetes over long-term follow-up. These findings build on and extend previous mainly cross-sectional evidence and further support links between dietary habits, metabolic diseases, and type 2 diabetes that are modulated by the gut microbiome. The gut microbiome can potentially be used to improve disease prediction and uncover novel therapeutic targets for diabetes.

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Section: Discussionmentioning

confidence: 99%

Gut Microbiome Composition Is Predictive of Incident Type 2 Diabetes in a Population Cohort of 5,572 Finnish Adults

et al. 2022

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“…The epiphytic bacteria on macroalgae, and surface bacterial communities in general, tend to be heavily influenced by environmental conditions [31,32] and often composed of functionally redundant taxa [33]. Yet, within this variability a suite of taxa frequently found on macroalgal surfaces is emerging across studies, including Saprospiraceae, Granulosicoccus, and Flavobacteria [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Alternative approaches to identify core bacteria in Fucus distichus microbiome and assess their distribution and host-specificity

Park

Davis

Lajoie

et al. 2022

Environmental Microbiome

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Background Identifying meaningful ecological associations between host and components of the microbiome is challenging. This is especially true for hosts such as marine macroalgae where the taxonomic composition of the microbiome is highly diverse and variable in space and time. Identifying core taxa is one way forward but there are many methods and thresholds in use. This study leverages a large dataset of microbial communities associated with the widespread brown macroalga, Fucus distichus, across sites and years on one island in British Columbia, Canada. We compare three different methodological approaches to identify core taxa at the amplicon sequence variant (ASV) level from this dataset: (1) frequency analysis of taxa on F. distichus performed over the whole dataset, (2) indicator species analysis (IndVal) over the whole dataset that identifies frequent taxa that are enriched on F. distichus in comparison to the local environment, and (3) a two-step IndVal method that identifies taxa that are consistently enriched on F. distichus across sites and time points. We then investigated a F. distichus time-series dataset to see if those core taxa are seasonally consistent on another remote island in British Columbia, Canada. We then evaluate host-specificity of the identified F. distichus core ASVs using comparative data from 32 other macroalgal species sampled at one of the sites. Results We show that a handful of core ASVs are consistently identified by both frequency analysis and IndVal approaches with alternative definitions, although no ASVs were always present on F. distichus and IndVal identified a diverse array of F. distichus indicator taxa across sites on Calvert Island in multiple years. Frequency analysis captured a broader suit of taxa, while IndVal was better at identifying host-specific microbes. Finally, two-step IndVal identified hundreds of indicator ASVs for particular sites/timepoints but only 12 that were indicators in a majority (> 6 out of 11) of sites/timepoints. Ten of these ASVs were also indicators on Quadra Island, 250 km away. Many F. distichus-core ASVs are generally found on multiple macroalgal species, while a few ASVs are highly specific to F. distichus. Conclusions Different methodological approaches with variable set thresholds influence core identification, but a handful of core taxa are apparently identifiable as they are widespread and temporally associated with F. distichus and enriched in comparison to the environment. Moreover, we show that many of these core ASVs of F. distichus are found on multiple macroalgal hosts, indicating that most occupy a macroalgal generalist niche rather than forming highly specialized associations with F. distichus. Further studies should test whether macroalgal generalists or specialists are more likely to engage in biologically important exchanges with host.

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“…The ten most abundant genera, which were well documented and made up over 50% of skin microbiota within each sample, were Cutibacterium , Staphylococcus, Acinetobacter, Streptococcus, Enhydrobacter, and Corynebacterium. Thus, suggesting , geographical and ethnic differences in the human population are the factors that could explain some of the differences observed in the skin microbiota ( Leung, Wilkins & Lee, 2015 ; Ruuskanen et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Skin microbiota diversity among genetically unrelated individuals of Indian origin

Potbhare

RaviKumar

Munukka

et al. 2022

PeerJ

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Background Human skin harbors complex transient and resident microbial communities that show intra- & inter-individual variation due to various environmental and host-associated factors such as skin site, diet, age, gender, genetics, or the type and use of cosmetics. This variation remains largely uncharacterized in the Indian population; hence, the present study aims to characterize the variation in skin microbiota among individuals of Indian origin and quantify associations with age, diet, and geography. Methods Axillary sweat samples from genetically unrelated individuals (N = 58) residing in the three geographical locations of Maharashtra, India, were collected using a sterile cotton swab. Bacterial DNA was extracted using a standard protocol and checked for quality. Variable regions (V3–V4) of the 16S rRNA gene were sequenced using the Illumina platform. We used standard methods from microbiota bioinformatics, including alpha and beta diversity, community typing, and differential abundance, to quantify the association of skin microbiota with age, diet, and geographical location. Results Our study indicated the prevalence of phyla- Firmicutes, Proteobacteria, and Actinobacteria, consistent with previous reports on skin microbiota composition of the world population level. The alpha diversity (Shannon index) was significantly associated with the age group (Kruskal–Wallis test, p = 0.02), but not with geography (p = 0.62) or diet (p = 0.74). The overall skin microbiota community composition was significantly associated with geographical location based on Community State Types (CST) analysis and PERMANOVA (R2 = 0.07, p = 0.01). Differential abundance analysis at the genus level indicated a distinctively high abundance of Staphylococcus and Corynebacterium among individuals of the Pune district. Pseudomonas and Anaerococcus were abundant in individuals from Ahmednagar whereas, Paenibacillus, Geobacillus, Virgibacillus, Jeotgalicoccus, Pullulanibacillus, Delsulfosporomusa, Citinovibrio, and Calditerricola were abundant in individuals from Nashik district. Conclusion Our work provides one of the first characterizations of skin microbiota variation in different sub-populations in India. The analysis quantifies the level of individuality, as contrasted to the other factors of age, geography, and diet, thus helping to evaluate the applicability of skin microbiota profiles as a potential biomarker to stratify individuals.

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Modelling spatial patterns in host‐associated microbial communities

Cited by 17 publications

References 138 publications

Gut Microbiome Composition Is Predictive of Incident Type 2 Diabetes in a Population Cohort of 5,572 Finnish Adults

Gut Microbiome Composition Is Predictive of Incident Type 2 Diabetes in a Population Cohort of 5,572 Finnish Adults

Alternative approaches to identify core bacteria in Fucus distichus microbiome and assess their distribution and host-specificity

Skin microbiota diversity among genetically unrelated individuals of Indian origin

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