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The microbial communities living in the human intestine can have profound impact on our well-being and health. However, we have limited understanding of the mechanisms that control this complex ecosystem. Here, based on a deep phylogenetic analysis of the intestinal microbiota in a thousand western adults, we identify groups of bacteria that exhibit robust bistable abundance distributions. These bacteria are either abundant or nearly absent in most individuals, and exhibit decreased temporal stability at the intermediate abundance range. The abundances of these bimodally distributed bacteria vary independently, and their abundance distributions are not affected by short-term dietary interventions. However, their contrasting alternative states are associated with host factors such as ageing and overweight. We propose that the bistable groups reflect tipping elements of the intestinal microbiota, whose critical transitions may have profound health implications and diagnostic potential. Intestinal microbes can have important effects on our health. Here, the authors analyse the gut microbiota composition in 1,000 western adults and find that certain bacteria are either abundant or nearly absent, and that these alternative states are associated with ageing and overweight.

Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. Current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution. We introduce the operational genomic unit (OGU) method, a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent of taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance, and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldom applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome data sets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project data set and more accurate prediction of human age by the gut microbiomes of Finnish individuals included in the FINRISK 2002 cohort. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate adoption of the OGU method in future metagenomics studies. IMPORTANCE Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. Current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution. To solve these challenges, we introduce operational genomic units (OGUs), which are the individual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition and (ii) permitting use of phylogeny-aware tools. Our analysis of real-world data sets shows that it is advantageous over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGUs as an effective practice in metagenomic studies.

Anaerobic digestion is the most successful bioenergy technology worldwide with, at its core, undefined microbial communities that have poorly understood dynamics. Here, we investigated the relationships of bacterial community structure (>400,000 16S rRNA gene sequences for 112 samples) with function (i.e., bioreactor performance) and environment (i.e., operating conditions) in a yearlong monthly time series of nine full-scale bioreactor facilities treating brewery wastewater (>20,000 measurements). Each of the nine facilities had a unique community structure with an unprecedented level of stability. Using machine learning, we identified a small subset of operational taxonomic units (OTUs; 145 out of 4,962), which predicted the location of the facility of origin for almost every sample (96.4% accuracy). Of these 145 OTUs, syntrophic bacteria were systematically overrepresented, demonstrating that syntrophs rebounded following disturbances. This indicates that resilience, rather than dynamic competition, played an important role in maintaining the necessary syntrophic populations. In addition, we explained the observed phylogenetic differences between all samples on the basis of a subset of environmental gradients (using constrained ordination) and found stronger relationships between community structure and its function rather than its environment. These relationships were strongest for two performance variables— methanogenic activity and substrate removal efficiency— both of which were also affected by microbial ecology because these variables were correlated with community evenness (at any given time) and variability in phylogenetic structure (over time), respectively. Thus, we quantified relationships between community structure and function, which opens the door to engineer communities with superior functions.

The elderly have reduced humoral immunity, as manifested by increased susceptibility to infections and impaired vaccine responses. To investigate the effects of aging on B-cell receptor (BCR) repertoire evolution during an immunological challenge, we used a phylogenetic distance metric to analyze Ig heavy-chain transcript sequences in both young and elderly individuals before and after influenza vaccination. We determined that BCR repertoires become increasingly specialized over a span of decades, but less plastic. In 50% of the elderly individuals, a large space in the repertoire was occupied by a small number of recall lineages that did not decline during vaccine response and contained hypermutated IgD⁺ B cells. Relative to their younger counterparts, older subjects demonstrated a contracted naive repertoire and diminished intralineage diversification, signifying a reduced substrate for mounting novel responses and decreased fine-tuning of BCR specificities by somatic hypermutation. Furthermore, a larger proportion of the repertoire exhibited premature stop codons in some elderly subjects, indicating that aging may negatively affect the ability of B cells to discriminate between functional and nonfunctional receptors. Finally, we observed a decreased incidence of radical mutations compared with conservative mutations in elderly subjects’ vaccine responses, which suggests that accumulating original antigenic sin may be limiting the accessible space for paratope evolution. Our findings shed light on the complex interplay of environmental and gerontological factors affecting immune senescence, and provide direct molecular characterization of the effects of senescence on the immune repertoire.

Microbes are difficult to culture. Consequently, the primary source of information about a fundamental evolutionary topic, life's diversity, is the environmental distribution of gene sequences. We report the most comprehensive analysis of the environmental distribution of bacteria to date, based on 21,752 16S rRNA sequences compiled from 111 studies of diverse physical environments. We clustered the samples based on similarities in the phylogenetic lineages that they contain and found that, surprisingly, the major environmental determinant of microbial community composition is salinity rather than extremes of temperature, pH, or other physical and chemical factors represented in our samples. We find that sediments are more phylogenetically diverse than any other environment type. Surprisingly, soil, which has high species-level diversity, has below-average phylogenetic diversity. This work provides a framework for understanding the impact of environmental factors on bacterial evolution and for the direction of future sequencing efforts to discover new lineages.

Background In microbiome data analysis, unsupervised clustering is often used to identify naturally occurring clusters, which can then be assessed for associations with characteristics of interest. In this work, we systematically compared beta diversity and clustering methods commonly used in microbiome analyses. We applied these to four published datasets where highly distinct microbiome profiles could be seen between sample groups, as well a clinical dataset with less clear separation between groups. Results Although no single method outperformed the others consistently, we did identify the key scenarios where certain methods can underperform. Specifically, the Bray Curtis (BC) metric resulted in poor clustering in a dataset where high-abundance OTUs were relatively rare. In contrast, the unweighted UniFrac (UU) metric clustered poorly on dataset with a high prevalence of low-abundance OTUs. To explore these hypotheses about BC and UU, we systematically modified the properties of the poorly performing datasets and found that this approach resulted in improved BC and UU performance. Based on these observations, we rationally combined BC and UU to generate a novel metric. We tested its performance while varying the relative contributions of each metric and also compared it with another combined metric, the generalized UniFrac distance. The proposed metric showed high performance across all datasets. Conclusions Our systematic evaluation of clustering performance in these five datasets demonstrates that there is no existing clustering method that universally performs best across all datasets. We propose a combined metric of BC and UU that capitalizes on the complementary strengths of the two metrics. syUurMUnqpLuEn-KfNXmw Video abstract

Tephritid fruit fly species display a diversity of host plant specialisation on a scale from monophagy to polyphagy. Furthermore, while some species prefer ripening fruit, a few are restricted to damaged or rotting fruit. Such a diversity of host plant use may be reflected in the microbial symbiont diversity of tephritids and their grade of dependency on their microbiomes. Here, we investigated the microbiome of six tephritid species from three genera, including species that are polyphagous pests (Bactrocera tryoni, Bactrocera neohumeralis, Bactrocera jarvisi, Ceratitis capitata) and a monophagous specialist (Bactrocera cacuminata). These were compared with the microbiome of a non-pestiferous but polyphagous tephritid species that is restricted to damaged or rotting fruit (Dirioxa pornia). The bacterial community associated with whole fruit flies was analysed by 16S ribosomal DNA (rDNA) amplicon pyrosequencing to detect potential drivers of taxonomic composition. Overall, the dominant bacterial families were Enterobacteriaceae and Acetobacteraceae (both Proteobacteria), and Streptococcaceae and Enterococcaceae (both Firmicutes). Comparisons across species and genera found different microbial composition in the three tephritid genera, but limited consistent differentiation between Bactrocera species. Within Bactrocera species, differentiation of microbial composition seemed to be influenced by the environment, possibly including their diets; beyond this, tephritid species identity or ecology also had an effect. The microbiome of D. pornia was most distinct from the other five species, which may be due to its ecologically different niche of rotting or damaged fruit, as opposed to ripening fruit favoured by the other species. Our study is the first amplicon pyrosequencing study to compare the microbiomes of tephritid species and thus delivers important information about the turnover of microbial diversity within and between fruit fly species and their potential application in pest management strategies.

Background Coalescent methods that use multi-locus sequence data are powerful tools for identifying putatively reproductively isolated lineages, though this approach has rarely been used for the study of microbial groups that are likely to harbor many unrecognized species. Among microbial symbionts, integrating genetic species delimitation methods with trait data that could indicate reproductive isolation, such as host specificity data, has rarely been used despite its potential to inform species limits. Here we test the ability of an integrative approach combining genetic and host specificity data to delimit species within the avian malaria parasite genus Leucocytozoon in central Alaska. Results We sequenced seven nuclear loci for 69 Leucocytozoon samples and used multiple species delimitation methods (GMYC and BPP models), tested for differences in host infection patterns among putative species based on 406 individual infections, and characterized parasite morphology. We found that cryptic morphology has masked a highly diverse Leucocytozoon assemblage, with most species delimitation methods recovering support for at least 21 separate species that occur sympatrically and have divergent host infection patterns. Reproductive isolation among putative species appears to have evolved despite low mtDNA divergence, and in one instance two Leucocytozoon cytb haplotypes that differed by a single base pair (~ 0.2% divergence) were supported as separate species. However, there was no consistent association between mtDNA divergence and species limits. Among cytb haplotypes that differed by one to three base pairs we observed idiosyncratic patterns of nuclear and ecological divergence, with cytb haplotype pairs found to be either conspecific, reproductively isolated with no divergence in host specificity, or reproductively isolated with divergent patterns of host specialization. Conclusion Integrating multi-locus genetic species delimitation methods and non-traditional ecological data types such as host specificity provide a novel view of the diversity of avian malaria parasites that has been missed previously using morphology and mtDNA barcodes. Species delimitation methods show that Leucocytozoon is highly species-rich in Alaska, and the genus is likely to harbor extraordinary species-level diversity worldwide. Integrating genetic and ecological data will be an important approach for understanding the diversity and evolutionary history of microbial symbionts moving forward.

Background Microbiome studies commonly use 16S rRNA gene amplicon sequencing to characterize microbial communities. Errors introduced at multiple steps in this process can affect the interpretation of the data. Here we evaluate the accuracy of operational taxonomic unit (OTU) generation, taxonomic classification, alpha- and beta-diversity measures for different settings in QIIME, MOTHUR and a pplacer-based classification pipeline, using a novel software package: DECARD. Results In-silico we generated 100 synthetic bacterial communities approximating human stool microbiomes to be used as a gold-standard for evaluating the colligative performance of microbiome analysis software. Our synthetic data closely matched the composition and complexity of actual healthy human stool microbiomes. Genus-level taxonomic classification was correctly done for only 50.4–74.8% of the source organisms. Miscall rates varied from 11.9 to 23.5%. Species-level classification was less successful, (6.9–18.9% correct); miscall rates were comparable to those of genus-level targets (12.5–26.2%). The degree of miscall varied by clade of organism, pipeline and specific settings used. OTU generation accuracy varied by strategy (closed, de novo or subsampling), reference database, algorithm and software implementation. Shannon diversity estimation accuracy correlated generally with OTU-generation accuracy. Beta-diversity estimates with Double Principle Coordinate Analysis (DPCoA) were more robust against errors introduced in processing than Weighted UniFrac. The settings suggested in the tutorials were among the worst performing in all outcomes tested. Conclusions Even when using the same classification pipeline, the specific OTU-generation strategy, reference database and downstream analysis methods selection can have a dramatic effect on the accuracy of taxonomic classification, and alpha- and beta-diversity estimation. Even minor changes in settings adversely affected the accuracy of the results, bringing them far from the best-observed result. Thus, specific details of how a pipeline is used (including OTU generation strategy, reference sets, clustering algorithm and specific software implementation) should be specified in the methods section of all microbiome studies. Researchers should evaluate their chosen pipeline and settings to confirm it can adequately answer the research question rather than assuming the tutorial or standard-operating-procedure settings will be adequate or optimal.

UniFrac is an important tool in microbiome research that is used for phylogenetically comparing microbiome profiles to one another. Here, we adapt UniFrac to operate on graphics processing units, enabling a 1,000× computational improvement. UniFrac is an important tool in microbiome research that is used for phylogenetically comparing microbiome profiles to one another (beta diversity). Striped UniFrac recently added the ability to split the problem into many independent subproblems, exhibiting nearly linear scaling but suffering from memory contention. Here, we adapt UniFrac to graphics processing units using OpenACC, enabling greater than 1,000× computational improvement, and apply it to 307,237 samples, the largest 16S rRNA V4 uniformly preprocessed microbiome data set analyzed to date. IMPORTANCE UniFrac is an important tool in microbiome research that is used for phylogenetically comparing microbiome profiles to one another. Here, we adapt UniFrac to operate on graphics processing units, enabling a 1,000× computational improvement. To highlight this advance, we perform what may be the largest microbiome analysis to date, applying UniFrac to 307,237 16S rRNA V4 microbiome samples preprocessed with Deblur. These scaling improvements turn UniFrac into a real-time tool for common data sets and unlock new research questions as more microbiome data are collected.