Nucleosome density shapes kilobase-scale regulation by a mammalian chromatin remodeler.

Nearly all essential nuclear processes act on DNA packaged into arrays of nucleosomes. However, our understanding of how these processes (for example, DNA replication, RNA transcription, chromatin extrusion and nucleosome remodeling) occur on individual chromatin arrays remains unresolved. Here, to address this deficit, we present SAMOSA-ChAAT: a massively multiplex single-molecule footprinting approach to map the primary structure of individual, reconstituted chromatin templates subject to virtually any chromatin-associated reaction. We apply this method to distinguish between competing models for chromatin remodeling by the essential imitation switch (ISWI) ATPase SNF2h: nucleosome-density-dependent spacing versus fixed-linker-length nucleosome clamping. First, we perform in vivo single-molecule nucleosome footprinting in murine embryonic stem cells, to discover that ISWI-catalyzed nucleosome spacing correlates with the underlying nucleosome density of specific epigenomic domains. To establish causality, we apply SAMOSA-ChAAT to quantify the activities of ISWI ATPase SNF2h and its parent complex ACF on reconstituted nucleosomal arrays of varying nucleosome density, at single-molecule resolution. We demonstrate that ISWI remodelers operate as density-dependent, length-sensing nucleosome sliders, whose ability to program DNA accessibility is dictated by single-molecule nucleosome density. We propose that the long-observed, context-specific regulatory effects of ISWI complexes can be explained in part by the sensing of nucleosome density within epigenomic domains. More generally, our approach promises molecule-precise views of the essential processes that shape nuclear physiology.

Massively multiplex single-molecule oligonucleosome footprinting.

Our understanding of the beads-on-a-string arrangement of nucleosomes has been built largely on high-resolution sequence-agnostic imaging methods and sequence-resolved bulk biochemical techniques. To bridge the divide between these approaches, we present the single-molecule adenine methylated oligonucleosome sequencing assay (SAMOSA). SAMOSA is a high-throughput single-molecule sequencing method that combines adenine methyltransferase footprinting and single-molecule real-time DNA sequencing to natively and nondestructively measure nucleosome positions on individual chromatin fibres. SAMOSA data allows unbiased classification of single-molecular 'states' of nucleosome occupancy on individual chromatin fibres. We leverage this to estimate nucleosome regularity and spacing on single chromatin fibres genome-wide, at predicted transcription factor binding motifs, and across human epigenomic domains. Our analyses suggest that chromatin is comprised of both regular and irregular single-molecular oligonucleosome patterns that differ subtly in their relative abundance across epigenomic domains. This irregularity is particularly striking in constitutive heterochromatin, which has typically been viewed as a conformationally static entity. Our proof-of-concept study provides a powerful new methodology for studying nucleosome organization at a previously intractable resolution and offers up new avenues for modeling and visualizing higher order chromatin structure.

Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies.

Correlation-based analysis of paired microbiome-metabolome data sets is becoming a widespread research approach, aiming to comprehensively identify microbial drivers of metabolic variation. To date, however, the limitations of this approach and other microbiome-metabolome analysis methods have not been comprehensively evaluated. To address this challenge, we have introduced a mathematical framework to quantify the contribution of each taxon to metabolite variation based on uptake and secretion fluxes. We additionally used a multispecies metabolic model to simulate simplified gut communities, generating idealized microbiome-metabolome data sets. We then compared observed taxon-metabolite correlations in these data sets to calculated ground truth taxonomic contribution values. We found that in simulations of both a representative simple 10-species community and complex human gut microbiota, correlation-based analysis poorly identified key contributors, with an extremely low predictive value despite the idealized setting. We further demonstrate that the predictive value of correlation analysis is strongly influenced by both metabolite and taxon properties, as well as by exogenous environmental variation. We finally discuss the practical implications of our findings for interpreting microbiome-metabolome studies.IMPORTANCE Identifying the key microbial taxa responsible for metabolic differences between microbiomes is an important step toward understanding and manipulating microbiome metabolism. To achieve this goal, researchers commonly conduct microbiome-metabolome association studies, comprehensively measuring both the composition of species and the concentration of metabolites across a set of microbial community samples and then testing for correlations between microbes and metabolites. Here, we evaluated the utility of this general approach by first developing a rigorous mathematical definition of the contribution of each microbial taxon to metabolite variation and then examining these contributions in simulated data sets of microbial community metabolism. We found that standard correlation-based analysis of our simulated microbiome-metabolome data sets can identify true contributions with very low predictive value and that its performance depends strongly on specific properties of both metabolites and microbes, as well as on those of the surrounding environment. Combined, our findings can guide future interpretation and validation of microbiome-metabolome studies.

Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss.

BACKGROUND: Metabolic dependencies between microbial species have a significant impact on the assembly and activity of microbial communities. However, the evolutionary origins of such dependencies and the impact of metabolic and genomic architecture on their emergence are not clear. RESULTS: To address these questions, we developed a novel framework, coupling a reductive evolution model with a multi-species genome-scale metabolic model to simulate the evolution of two-species microbial communities. Simulating thousands of independent evolutionary trajectories, we surprisingly found that under certain environmental and evolutionary settings metabolic dependencies emerged frequently even though our model does not include explicit selection for cooperation. Evolved dependencies involved cross-feeding of a diverse set of metabolites, reflecting constraints imposed by metabolic network architecture. We additionally found metabolic 'missed opportunities', wherein species failed to capitalize on metabolites made available by their partners. Examining the genes deleted in each evolutionary trajectory and the deletion timing further revealed both genome-wide properties and specific metabolic mechanisms associated with species interaction. CONCLUSION: Our findings provide insight into the evolution of cooperative interaction among microbial species and a unique view into the way such relationships emerge.

BURRITO: An Interactive Multi-Omic Tool for Visualizing Taxa-Function Relationships in Microbiome Data.

The abundance of both taxonomic groups and gene categories in microbiome samples can now be easily assayed via various sequencing technologies, and visualized using a variety of software tools. However, the assemblage of taxa in the microbiome and its gene content are clearly linked, and tools for visualizing the relationship between these two facets of microbiome composition and for facilitating exploratory analysis of their co-variation are lacking. Here we introduce BURRITO, a web tool for interactive visualization of microbiome multi-omic data with paired taxonomic and functional information. BURRITO simultaneously visualizes the taxonomic and functional compositions of multiple samples and dynamically highlights relationships between taxa and functions to capture the underlying structure of these data. Users can browse for taxa and functions of interest and interactively explore the share of each function attributed to each taxon across samples. BURRITO supports multiple input formats for taxonomic and metagenomic data, allows adjustment of data granularity, and can export generated visualizations as static publication-ready formatted figures. In this paper, we describe the functionality of BURRITO, and provide illustrative examples of its utility for visualizing various trends in the relationship between the composition of taxa and functions in complex microbiomes.

Adaptation of commensal proliferating Escherichia coli to the intestinal tract of young children with cystic fibrosis.

The mature human gut microbiota is established during the first years of life, and altered intestinal microbiomes have been associated with several human health disorders. Escherichia coli usually represents less than 1% of the human intestinal microbiome, whereas in cystic fibrosis (CF), greater than 50% relative abundance is common and correlates with intestinal inflammation and fecal fat malabsorption. Despite the proliferation of E. coli and other Proteobacteria in conditions involving chronic gastrointestinal tract inflammation, little is known about adaptation of specific characteristics associated with microbiota clonal expansion. We show that E. coli isolated from fecal samples of young children with CF has adapted to growth on glycerol, a major component of fecal fat. E. coli isolates from different CF patients demonstrate an increased growth rate in the presence of glycerol compared with E. coli from healthy controls, and unrelated CF E. coli strains have independently acquired this growth trait. Furthermore, CF and control E. coli isolates have differential gene expression when grown in minimal media with glycerol as the sole carbon source. While CF isolates display a growth-promoting transcriptional profile, control isolates engage stress and stationary-phase programs, which likely results in slower growth rates. Our results indicate that there is selection of unique characteristics within the microbiome of individuals with CF, which could contribute to individual disease outcomes.

High-resolution characterization of the human microbiome.

The human microbiome plays an important and increasingly recognized role in human health. Studies of the microbiome typically use targeted sequencing of the 16S rRNA gene, whole metagenome shotgun sequencing, or other meta-omic technologies to characterize the microbiome's composition, activity, and dynamics. Processing, analyzing, and interpreting these data involve numerous computational tools that aim to filter, cluster, annotate, and quantify the obtained data and ultimately provide an accurate and interpretable profile of the microbiome's taxonomy, functional capacity, and behavior. These tools, however, are often limited in resolution and accuracy and may fail to capture many biologically and clinically relevant microbiome features, such as strain-level variation or nuanced functional response to perturbation. Over the past few years, extensive efforts have been invested toward addressing these challenges and developing novel computational methods for accurate and high-resolution characterization of microbiome data. These methods aim to quantify strain-level composition and variation, detect and characterize rare microbiome species, link specific genes to individual taxa, and more accurately characterize the functional capacity and dynamics of the microbiome. These methods and the ability to produce detailed and precise microbiome information are clearly essential for informing microbiome-based personalized therapies. In this review, we survey these methods, highlighting the challenges each method sets out to address and briefly describing methodological approaches.