Correlation Network Analysis Applied to Complex Biofilm Communities

Abstract

The complexity of the human microbiome makes it difficult to reveal organizational principles of the community and even more challenging to generate testable hypotheses. It has been suggested that in the gut microbiome species such as Bacteroides thetaiotaomicron are keystone in maintaining the stability and functional adaptability of the microbial community. In this study, we investigate the interspecies associations in a complex microbial biofilm applying systems biology principles. Using correlation network analysis we identified bacterial modules that represent important microbial associations within the oral community. We used dental plaque as a model community because of its high diversity and the well known species-species interactions that are common in the oral biofilm. We analyzed samples from healthy individuals as well as from patients with periodontitis, a polymicrobial disease. Using results obtained by checkerboard hybridization on cultivable bacteria we identified modules that correlated well with microbial complexes previously described. Furthermore, we extended our analysis using the Human Oral Microbe Identification Microarray (HOMIM), which includes a large number of bacterial species, among them uncultivated organisms present in the mouth. Two distinct microbial communities appeared in healthy individuals while there was one major type in disease. Bacterial modules in all communities did not overlap, indicating that bacteria were able to effectively re-associate with new partners depending on the environmental conditions. We then identified hubs that could act as keystone species in the bacterial modules. Based on those results we then cultured a not-yet-cultivated microorganism, Tannerella sp. OT286 (clone BU063). After two rounds of enrichment by a selected helper (Prevotella oris OT311) we obtained colonies of Tannerella sp. OT286 growing on blood agar plates. This system-level approach would open the possibility of manipulating microbial communities in a targeted fashion as well as associating certain bacterial modules to clinical traits (e.g.: obesity, Crohn's disease, periodontal disease, etc).

High-Resolution Taxonomic Profiling of the Subgingival Microbiome for Biomarker Discovery and Periodontitis Diagnosis

“…Within the periodontitis community, four bacterial modules were obtained and were shown to be similar to those of a previous study of 13,261 subgingival plaque samples from 185 individuals (47). The brown module was reproduced in both studies and consisted of the red complex pathogens and several additional biomarkers of periodontitis (46). Although these analyses were confined to cultivated species, they provide an excellent reference to test whether similar modules would be detectable in our community profiles.…”

“…In a polymicrobial community, the interactions between the individual species are key to understanding the shift from a healthy community toward dysbiosis (45). An interaction network constructed from checkerboard and microarray analysis of subgingival plaque samples (3,079 periodontitis samples and 89 samples from healthy individuals) identified one highly connected community in individuals with disease and two distinct communities in healthy individuals (46). Within the periodontitis community, four bacterial modules were obtained and were shown to be similar to those of a previous study of 13,261 subgingival plaque samples from 185 individuals (47).…”

Network topology reveals high connectance levels and few key microbial genera within soils

“…The study of networked systems has received great attention in the last years, especially in the mathematical and social sciences, mainly as result of the increasing availability to obtain and analyse large datasets. These methods have been applied to the study of various biological contexts including healthy microbiota in human microbiome (Duran-Pinedo et al, 2011;, cancer (Choi et al, 2005), food webs (Estrada, 2007), marine microbial community (Steele et al, 2011), and recently this technique have been used to better understand soil microbial processes by examining complex interactions among microbes (Prasad et al, 2011;Roesch et al, 2012). The use of network analysis in microbial ecology has the potential for exploring inter-taxa correlations allowing an integrated understanding of soil microbial community structure and the ecological rules.…”

Untargeted metabolic profiling reveals geography as the strongest predictor of metabolic phenotypes of a cosmopolitan weed

“…Co‐occurrence networks have been implemented within community ecology using high‐throughput sequencing data, providing insight into ecological interactions between species (Barberán, Bates, Casamayor, & Fierer, 2012; Duran‐Pinedo, Paster, Teles, & Frias‐Lopez, 2011). Here we use this approach on metabolic feature data, using features that were shown to differ significantly with geographic region (30 features in total).…”