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A significant portion of world population still fails to brush teeth daily. As a result, the majority of the global adult population is afflicted with chronic gingivitis, and if it is left untreated, some of them will eventually suffer from periodontitis., Most adults experience episodes of gingivitis, which can progress to the irreversible, chronic state of periodontitis, yet roles of plaque in gingivitis onset and progression to periodontitis remain elusive. Here, we longitudinally profiled the plaque metagenome, the plaque metabolome, and salivary cytokines in 40 adults who transited from naturally occurring gingivitis (NG) to healthy gingivae (baseline) and then to experimental gingivitis (EG). During EG, rapid and consistent alterations in plaque microbiota, metabolites, and salivary cytokines emerged as early as 24 to 72 h after oral-hygiene pause, defining an asymptomatic suboptimal health (SoH) stage of the gingivae. SoH features a swift, full activation of 11 salivary cytokines but a steep synergetic decrease of plaque-derived betaine and Rothia spp., suggesting an anti-gum inflammation mechanism by health-promoting symbionts. Global, cross-cohort meta-analysis revealed, at SoH, a greatly elevated microbiome-based periodontitis index driven by its convergence of both taxonomical and functional profiles toward the periodontitis microbiome. Finally, post-SoH gingivitis development accelerates oral microbiota aging by over 1 year within 28 days, with Rothia spp. depletion and Porphyromonas gingivalis elevation as hallmarks. Thus, the microbiome-defined, transient gum SoH stage is a crucial link among gingivitis, periodontitis, and aging.

A search-based strategy is useful for large-scale mining of microbiome data sets, such as a bird’s-eye view of the microbiome data space and disease diagnosis via microbiome big data. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes against the existing microbiome data sets on the basis of their similarity in taxonomic structure or functional profile., Metagenomic data sets from diverse environments have been growing rapidly. To ensure accessibility and reusability, tools that quickly and informatively correlate new microbiomes with existing ones are in demand. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes in the global metagenome data space based on the taxonomic or functional similarity of a whole microbiome to those in the database. MSE 2 consists of (i) a well-organized and regularly updated microbiome database that currently contains over 250,000 metagenomic shotgun and 16S rRNA gene amplicon samples associated with unified metadata collected from 798 studies, (ii) an enhanced search engine that enables real-time and fast (

Background Due to their much lower costs in experiment and computation than metagenomic whole-genome sequencing (WGS), 16S rRNA gene amplicons have been widely used for predicting the functional profiles of microbiome, via software tools such as PICRUSt 2. However, due to the potential PCR bias and gene profile variation among phylogenetically related genomes, functional profiles predicted from 16S amplicons may deviate from WGS-derived ones, resulting in misleading results. Results Here we present Meta-Apo, which greatly reduces or even eliminates such deviation, thus deduces much more consistent diversity patterns between the two approaches. Tests of Meta-Apo on > 5000 16S-rRNA amplicon human microbiome samples from 4 body sites showed the deviation between the two strategies is significantly reduced by using only 15 WGS-amplicon training sample pairs. Moreover, Meta-Apo enables cross-platform functional comparison between WGS and amplicon samples, thus greatly improve 16S-based microbiome diagnosis, e.g. accuracy of gingivitis diagnosis via 16S-derived functional profiles was elevated from 65 to 95% by WGS-based classification. Therefore, with the low cost of 16S-amplicon sequencing, Meta-Apo can produce a reliable, high-resolution view of microbiome function equivalent to that offered by shotgun WGS. Conclusions This suggests that large-scale, function-oriented microbiome sequencing projects can probably benefit from the lower cost of 16S-amplicon strategy, without sacrificing the precision in functional reconstruction that otherwise requires WGS. An optimized C++ implementation of Meta-Apo is available on GitHub (https://github.com/qibebt-bioinfo/meta-apo) under a GNU GPL license. It takes the functional profiles of a few paired WGS:16S-amplicon samples as training, and outputs the calibrated functional profiles for the much larger number of 16S-amplicon samples.

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. By traversing a composition-similarity-based network of 177,022 microbiomes, we show that although the compositions are distinct by habitat, each microbiome is on-average only seven neighbors from any other microbiome on Earth, indicating the inherent homology of microbiomes at the global scale. This network is scale-free, suggesting a high degree of stability and robustness in microbiome transition. By tracking the minimum spanning tree in this network, a global roadmap of microbiome dispersal was derived that tracks the potential paths of formulating and propagating microbiome diversity. Such search-based global microbiome networks, reconstructed within hours on just one computing node, provide a readily expanded reference for tracing the origin and evolution of existing or new microbiomes. IMPORTANCE It remains unclear whether and how compositional changes at the "community to community" level among microbiomes are linked to the origin and evolution of global microbiome diversity. Here we propose a microbiome transition model and a network-based analysis framework to describe and simulate the variation and dispersal of the global microbial beta-diversity across multiple habitats. The traversal of a transition network with 177,022 samples shows the inherent homology of microbiome at the global scale. Then a global roadmap of microbiome dispersal derived from the network tracks the potential paths of formulating and propagating microbiome diversity. Such search-based microbiome network provides a readily expanded reference for tracing the origin and evolution of existing or new microbiomes at the global scale.

During the past decade, tremendous amount of microbiome sequencing data has been generated to study on the dynamic associations between microbial profiles and environments. How to precisely and efficiently decipher large-scale of microbiome data and furtherly take advantages from it has become one of the most essential bottlenecks for microbiome research at present. In this mini-review, we focus on the three key steps of analyzing cross-study microbiome datasets, including microbiome profiling, data integrating and data mining. By introducing the current bioinformatics approaches and discussing their limitations, we prospect the opportunities in development of computational methods for the three steps, and propose the promising solutions to multi-omics data analysis for comprehensive understanding and rapid investigation of microbiome from different angles, which could potentially promote the data-driven research by providing a broader view of the “microbiome data space”.

Here, we present a search-based strategy for disease detection and classification, which detects diseased samples via their outlier novelty versus a database of samples from healthy subjects and then compares them to databases of samples from patients. This approach enables the identification of microbiome states associated with disease even in the presence of different cohorts, multiple sequencing platforms, or significant contamination., Microbiome-based disease classification depends on well-validated disease-specific models or a priori organismal markers. These are lacking for many diseases. Here, we present an alternative, search-based strategy for disease detection and classification, which detects diseased samples via their outlier novelty versus a database of samples from healthy subjects and then compares these to databases of samples from patients. Our strategy’s precision, sensitivity, and speed outperform model-based approaches. In addition, it is more robust to platform heterogeneity and to contamination in 16S rRNA gene amplicon data sets. This search-based strategy shows promise as an important first step in microbiome big-data-based diagnosis. IMPORTANCE Here, we present a search-based strategy for disease detection and classification, which detects diseased samples via their outlier novelty versus a database of samples from healthy subjects and then compares them to databases of samples from patients. This approach enables the identification of microbiome states associated with disease even in the presence of different cohorts, multiple sequencing platforms, or significant contamination.

Motivation An accurate and reliable distance (or dissimilarity) among shotgun metagenomes is fundamental to deducing the beta-diversity of microbiomes. To compute the distance at the species level, current methods either ignore the evolutionary relationship among species or fail to account for unclassified organisms that cannot be mapped to definite tip nodes in the phylogenic tree, thus can produce erroneous beta-diversity pattern. Results To solve these problems, we propose the Dynamic Meta-Storms (DMS) algorithm to enable the comprehensive comparison of metagenomes on the species level with both taxonomy and phylogeny profiles. It compares the identified species of metagenomes with phylogeny, and then dynamically places the unclassified species to the virtual nodes of the phylogeny tree via their higher-level taxonomy information. Its high speed and low memory consumption enable pairwise comparison of 100 000 metagenomes (synthesized from 3688 bacteria) within 6.4 h on a single computing node. Availability and implementation An optimized implementation of DMS is available on GitHub (https://github.com/qibebt-bioinfo/dynamic-meta-storms) under a GNU GPL license. It takes the species-level profiles of metagenomes as input, and generates their pairwise distance matrix. The bacterial species-level phylogeny tree and taxonomy information of MetaPhlAn2 have been integrated into this implementation, while customized tree and taxonomy are also supported. Supplementary information Supplementary data are available at Bioinformatics online.

To quantitatively measure the beta diversities between microbiomes, Microbiome Search Engine (MSE) (1) calculates phylogeny similarity using operational taxonomy unit (OTU) profiles; for both query and database samples, all 16S rRNA gene sequences are mapped to the Greengenes database (version 13-8) (2) for reference-based OTU picking with a 97% cutoff. Thus, in MSE, the comparison between query and database samples is approximately at the species level (3), although the actual taxonomic resolution varies according to taxon, due to differences in the evolutionary rates of the 16S rRNAs. Moreover, in MSE, both the relative abundance (with 16S rRNA gene copy number normalization [4]) and the phylogenetic structures of OTUs are utilized for similarity calculation (as in UniFrac [5, 6]), yet the speed is optimized by nonrecursive computing to enable real-time responses (7). By comparing the query sample (i.e., dust from university dormitories) provided by Sun …