Biology subjects

McNally, C. P.

Publications and source records attributed to McNally, C. P..

8 recordsLinked to original sources

bioRxiv2025

Pervasive and programmed nucleosome distortion patterns on single mammalian chromatin fibers

We present a genome-scale method to map the single-molecule co-occupancy of structurally distinct nucleosomes, subnucleosomes, and other protein-DNA interactions via long-read high-resolution adenine methyltransferase footprinting. Iteratively Defined Lengths of Inaccessibility (IDLI) classifies nucleosomes on the basis of shared patterns of intranucleosomal accessibility, into: i.) minimally-accessible chromatosomes; ii.) octasomes with stereotyped DNA accessibility from superhelical locations (SHLs) {+/-}1 through {+/-}7; iii.) highly-accessible unwrapped nucleosomes; and iv.) subnucleosomal species, such as hexasomes, tetrasomes, and other short DNA protections. Applying IDLI to mouse embryonic stem cell (mESC) chromatin, we discover widespread nucleosomal distortion on individual mammalian chromatin fibers, with >85% of nucleosomes surveyed displaying degrees of intranucleosomally accessible DNA. We observe epigenomic-domain-specific patterns of distorted nucleosome co-occupancy and positioning, including at enhancers, promoters, and mouse satellite repeat sequences. Nucleosome distortion is programmed by the presence of bound transcription factors (TFs) at cognate motifs; occupied TF binding sites are differentially decorated by distorted nucleosomes compared to unbound sites, and degradation experiments establish direct roles for TFs in structuring binding-site proximal nucleosomes. Finally, we apply IDLI in the context of primary mouse hepatocytes, observing evidence for pervasive nucleosomal distortion in vivo. Further genetic experiments reveal a role for the hepatocyte master regulator FOXA2 in directly impacting nucleosome distortion at hepatocyte-specific regulatory elements in vivo. Our work suggests extreme--but regulated--plasticity in nucleosomal DNA accessibility at the single-molecule level. Further, our study offers an essential new framework to model transcription factor binding, nucleosome remodeling, and cell-type specific gene regulation across biological contexts.

genomics↗

bioRxiv2024

Perturbation-aware predictive modeling of RNA splicing using bidirectional transformers

Predicting molecular function directly from DNA sequence remains a grand challenge in computational and molecular biology. Here, we engineer and train bidirectional transformer models to predict the chemical grammar of alternative human mRNA splicing leveraging the largest perturbative full-length RNA dataset to date. By combining high-throughput single-molecule long-read "chemical transcriptomics" in human cells with transformer models, we train AllSplice - a nucleotide foundation model that achieves state-of-the-art prediction of canonical and noncanonical splice junctions across the human transcriptome. We demonstrate improved performance achieved through incorporation of diverse noncanonical splice sites in its training set that were identified through long-read RNA data. Leveraging chemical perturbations and multiple cell types in the data, we fine-tune AllSplice to train ChemSplice - the first predictive model of sequence-dependent and cell-type specific alternative splicing following programmed cellular perturbation. We anticipate the broad application of AllSplice, ChemSplice, and other models fine-tuned on this foundation to myriad areas of RNA therapeutics development.

genomics↗

bioRxiv2023

The single-molecule accessibility landscape of newly replicated mammalian chromatin

The higher-order structure of newly replicated (i.e. nascent) chromatin fibers remains poorly-resolved, limiting our understanding of how epigenomes are maintained across cell divisions. To address this, we present Replication-Aware Single-molecule Accessibility Mapping (RASAM), a long-read sequencing method that nondestructively measures genome-wide replication-status and protein-DNA interactions simultaneously on intact chromatin templates. We report that individual human and mouse nascent chromatin fibers are hyperaccessible compared to steady-state chromatin. This hyperaccessibility occurs at two, coupled length-scales: first, individual nucleosome core particles on nascent DNA exist as a mixture of partially-unwrapped nucleosomes and other subnucleosomal species; second, newly-replicated chromatin fibers are significantly enriched for irregularly-spaced nucleosomes on individual DNA molecules. Focusing on specific cis-regulatory elements (e.g. transcription factor binding sites; active transcription start sites [TSSs]), we discover unique modes by which nascent chromatin hyperaccessibility is resolved at the single-molecule level: at CCCTC-binding factor (CTCF) binding sites, CTCF and nascent nucleosomes compete for motifs on nascent chromatin fibers, resulting in quantitatively-reduced CTCF occupancy and motif accessibility post-replication; at active TSSs, high levels of steady-state chromatin accessibility are preserved, implying that nucleosome free regions (NFRs) are rapidly re-established behind the fork. Our study introduces a new paradigm for studying higher-order chromatin fiber organization behind the replication fork. More broadly, we uncover a unique organization of newly replicated chromatin that must be reset by active processes, providing a substrate for epigenetic reprogramming.

genomics↗

bioRxiv2021

Single-fiber nucleosome density shapes the regulatory output of a mammalian chromatin remodeling enzyme

ATP-dependent chromatin remodelers regulate the DNA accessibility required of virtually all nuclear processes. Biochemical studies have provided insight into remodeler action at the nucleosome level, but how these findings translate to activity on chromatin fibers in vitro and in vivo remains poorly understood. Here, we present a massively multiplex single-molecule platform allowing high-resolution mapping of nucleosomes on fibers assembled on mammalian genomic sequences. We apply this method to distinguish between competing models for chromatin remodeling by the essential ISWI ATPase SNF2h: linker-length-dependent dynamic positioning versus fixed-linker-length static clamping. Our single-fiber data demonstrate that SNF2h operates as a density-dependent, length-sensing chromatin remodeler whose ability to decrease or increase DNA accessibility depends on single-fiber nucleosome density. In vivo, this activity manifests as different regulatory modes across epigenomic domains: at canonically-defined heterochromatin, SNF2h generates evenly-spaced nucleosome arrays of multiple nucleosome repeat lengths; at SNF2h-dependent accessible sites, SNF2h slides nucleosomes to increase accessibility of motifs for the essential transcription factor CTCF. Overall, our generalizable approach provides molecularly-precise views of the processes that shape nuclear physiology. Concurrently, our data illustrate how a mammalian chromatin remodeling enzyme can effectively sense nucleosome density to induce diametrically-opposed regulatory effects within the nucleus.

genomics↗

bioRxiv2020

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 both active and silent human epigenomic domains. Our analyses suggest that chromatin is comprised of a diverse array 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. 1-sentence summaryHigh-throughput single-molecule real-time footprinting of chromatin arrays reveals heterogeneous patterns of oligonucleosome occupancy.

genomics↗

bioRxiv2018

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

Correlation-based analysis of paired microbiome-metabolome datasets is becoming a widespread research approach, aiming to comprehensively identify microbial drivers of metabolic variation. To date, however, the limitations of this approach have not been comprehensively evaluated. To address this challenge, we introduce a mathematical framework to quantify the contribution of each taxon to metabolite variation based on uptake and secretion fluxes. We additionally use a multi-species metabolic model to simulate simplified gut communities, generating idealized microbiome-metabolome datasets. We then compare observed taxon-metabolite correlations in these datasets to calculated ground-truth taxonomic contribution values. We find that in simulations of both a model 10-species community and of complex human gut microbiota, correlation-based analysis poorly identifies key contributors, with 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 exogenous environmental variation. We finally discuss the practical implications of our findings for interpreting microbiome-metabolome studies.\n\nImportanceIdentifying the key microbial taxa responsible for metabolic differences between microbiomes is an important step towards 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 datasets of microbial community metabolism. We found that standard correlation-based analysis of our simulated microbiome-metabolome datasets identifies 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 the surrounding environment. Combined, our findings can guide future interpretation and validation of microbiome-metabolome studies.

microbiology↗

bioRxiv2017

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.

bioinformatics↗

bioRxiv2017

Metabolic Model-Based Analysis of the Emergence of Bacterial Cross-Feeding through Extensive Gene Loss

Metabolic dependencies between microbial species are common and have a significant impact on the assembly and resilience of microbial communities. However, the origins of such metabolic dependencies, the evolutionary forces that drive metabolic cross-feeding, and the impact of metabolic and genomic architecture on their emergence are not clear. To address these questions, we developed a novel simulation-based framework coupling a model of reductive evolution with a multi-species genome-scale model of microbial metabolism. We used this framework to model the evolution of a two-species microbial community, simulating thousands of independent evolutionary trajectories and investigating the link between genome reductive evolution and the emergence of metabolic interactions. Surprisingly, even though our model does not impose explicit selection for cooperation, metabolic dependencies emerged in nearly half of all evolutionary runs. Evolved dependencies involved cross-feeding of a diverse set of metabolites at varying frequencies, reflecting various constraints imposed by the metabolic network architecture. We additionally found metabolic missed opportunities, wherein species failed to capitalize on metabolites made available by their partners. When cross-feeding did evolve, it generally emerged immediately after a metabolite became available, but a complete dependence on such cross-fed metabolites often evolved relatively slowly. Examining the genes deleted and retained in each evolutionary trajectory and the timing of gene deletion events along these trajectories, we were further able to identify both genome-wide properties and specific gene retentions that were associated with metabolic phenotypes. Our findings provide insight into the evolution of cooperative metabolic interaction among microbial species, offering a unique view into the way such relationships could emerge in natural settings.

microbiology↗