Biology subjects

Pollard, K. S.

Publications and source records attributed to Pollard, K. S..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

Single Cell Epigenetics Reveal Cell-Cell Communication Networks in Normal and Abnormal Cardiac Morphogenesis

Communication between myriad cell types during organ formation underlies proper morphogenesis1. In cardiac development, reciprocal signaling between mesoderm progenitors and neural crest cells is essential, and its disruption leads to congenital heart malformations, the most common human birth defect. However, mechanistic interrogation of temporal gene networks and cis regulatory elements in this crosstalk is limited2,3. Here, we integrated single cell chromatin accessibility and transcriptomics to establish an unbiased and temporal epigenomic map of the embryonic mouse heart over multiple stages and developed machine learning models to predict enhancers for heart and neural crest. We leveraged these advances to determine the consequences of dysregulated signaling at single cell resolution caused by deletion of TBX1, a transcription factor that causes morphogenetic defects of the cardiac outflow tract in humans and functions non-cell autonomously in cardiac mesodermal progenitors to direct pharyngeal neural crest differentiation4-6. Loss of Tbx1 in mice led to broad closure of chromatin regions enriched in cardiac progenitor transcription factor motifs within a narrow subset of cardiac mesodermal progenitors and correlated with diminished expression of numerous members of the fibroblast growth factor, retinoic acid, Notch and Semaphorin pathways. In affected progenitors, ectopic accessibility and expression of posterior heart field factors in the anterior heart field suggested impaired axial patterning. In response, a subset of cardiac neural crest cells displayed epigenomic and transcriptional defects, indicating a failure of differentiation corresponding to dysregulation of the anterior-posterior gradient of pharyngeal Hox gene expression. This study demonstrates that single-cell genomics and machine learning can generate a mechanistic model for how disruptions in cell communication selectively affect spatiotemporally dynamic regulatory networks in cardiogenesis.

developmental biology↗

bioRxiv2022

Maast: genotyping thousands of microbial strains efficiently

Genotyping single nucleotide polymorphisms (SNPs) of intraspecific genomes is a prerequisite to performing population genetic analysis and microbial epidemiology. However, existing algorithms fail to scale for species with thousands of sequenced strains, nor do they account for the biased sampling of strains that has produced considerable redundancy in genome databases. Here we present Maast, a tool that reduces the computational burden of SNP genotyping by leveraging this genomic redundancy. Maast implements a novel algorithm to dynamically identify a minimum set of phylogenetically diverse conspecific genomes that contains the maximum number of SNPs above a user-specified allele frequency. Then it uses these genomes to construct a SNP panel for each species. A species SNP panel enables Maast to rapidly genotype thousands of strains using a hybrid of whole-genome alignment and k-mer exact matching. Maast works with both genome assemblies and unassembled sequencing reads. Compared to existing genotyping methods, Maast is more accurate and up to two orders of magnitude faster. We demonstrate Maasts utility on species with thousands of genomes by reconstructing the genetic structure of Helicobacter pylori across the globe and tracking SARS-CoV-2 diversification during the COVID-19 outbreak. Maast is a fast, reliable SNP genotyping tool that empowers population genetic meta-analysis of microbes at an unrivaled scale. Availabilitysource code of Maast is available at https://github.com/zjshi/Maast. Contactkpollard@gladstone.ucsf.edu

bioinformatics↗

bioRxiv2022

Pitfalls of genotyping microbial communities with rapidly growing genome collections

Detecting genetic variants in metagenomic data is a priority for understanding the evolution, ecology, and functional characteristics of microbial communities. Many recent tools that perform this metagenotyping rely on aligning reads of unknown origin to a reference database of sequences from many species before calling variants. Using simulations designed to represent a wide range of scenarios, we demonstrate that diverse and closely related species both reduce the power and accuracy of reference-based metagenotyping. We identify multi-mapping reads as a prevalent source of errors and illustrate a tradeoff between retaining correct alignments versus limiting incorrect alignments, many of which map reads to the wrong species. Then we quantitatively evaluate several actionable mitigation strategies and review emerging methods with promise to further improve metagenotyping. These findings document a critical challenge that has come to light through the rapid growth of genome collections that push the limits of current alignment algorithms. Our results have implications beyond metagenotyping to the many tools in microbial genomics that depend upon accurate read mapping. HIGHLIGHTSO_LIMost microbial species are genetically diverse. Their single nucleotide variants can be genotyped using metagenomic data aligned to databases constructed from genome collections ("metagenotyping"). C_LIO_LIMicrobial genome collections have grown and now contain many pairs of closely related species. C_LIO_LIClosely related species produce high-scoring but incorrect alignments while also reducing the uniqueness of correct alignments. Both cause metagenotype errors. C_LIO_LIThis dilemma can be mitigated by leveraging paired-end reads, customizing databases to species detected in the sample, and adjusting post-alignment filters. C_LI

bioinformatics↗

bioRxiv2022

MIDAS2: Metagenomic Intra-species Diversity Analysis System

SummaryThe Metagenomic Intra-Species Diversity Analysis System (MIDAS) is a scalable metagenomic pipeline that identifies single nucleotide variants (SNVs) and gene copy number variants (CNVs) in microbial populations. Here, we present MIDAS2, which addresses the computational challenges presented by increasingly large reference genome databases, while adding functionality for building custom databases and leveraging paired-end reads to improve SNV accuracy. This fast and scalable reengineering of the MIDAS pipeline enables thousands of metagenomic samples to be efficiently genotyped. Availability and ImplementationThe source code is available at https://github.com/czbiohub/MIDAS2. The documentation is available at https://midas2.readthedocs.io/en/latest/. Supplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

An oligogenic inheritance test detects risk genes and their interactions in congenital heart defects and developmental comorbidities

Exome sequencing of thousands of families has revealed many individual risk genes for congenital heart defects (CHD), yet most cases cannot be explained by a single causal mutation. Further, those who carry de novo and inherited mutations in known risk genes often demonstrate variable phenotypes even within the same family, indicating the presence of genetic modifiers. To explore oligogenic causes of CHD without assessing billions of variant combinations, we developed an efficient, simulation-based method to detect gene sets that carry damaging variants in probands at a higher rate than expected given parental genotypes. We implemented this approach in software called Gene Combinations in Oligogenic Disease (GCOD) and applied it to a cohort of 3382 trios with exome sequencing. This analysis detected 353 high-confidence risk genes in 202 pairs that appear together in multiple probands but rarely or never appear in combination in their unaffected parents. Stratifying analyses by specific CHD diagnosis and considering gene combinations of higher orders yielded an additional 244 gene sets. The oligogenic genes we discovered cluster in pathways specific to heart development and suggest new molecular disease mechanisms, such as arylsulfatase activity and de novo nucleotide biosynthesis. Finally, by combining CHD families with an autism spectrum disorder cohort, we were able to detect 925 oligogenic sets transmitted in renal disease, a known co-morbidity of both conditions. As genome sequencing is applied to more families and other disorders, GCOD will enable detection of increasingly large, novel gene combinations, shedding light on combinatorial causes of genetic diseases.

genetics↗

bioRxiv2022

Reconstructing the 3D genome organization of Neanderthals reveals that chromatin folding shaped phenotypic and sequence divergence

Changes in gene regulation were a major driver of the divergence of archaic hominins (AHs)--Neanderthals and Denisovans--and modern humans (MHs). The three-dimensional (3D) folding of the genome is critical for regulating gene expression; however, its role in recent human evolution has not been explored because the degradation of ancient samples does not permit experimental determination of AH 3D genome folding. To fill this gap, we apply novel deep learning methods for inferring 3D genome organization from DNA sequence to Neanderthal, Denisovan, and diverse MH genomes. Using the resulting 3D contact maps across the genome, we identify 167 distinct regions with diverged 3D genome organization between AHs and MHs. We show that these 3D-diverged loci are enriched for genes related to the function and morphology of the eye, supra-orbital ridges, hair, lungs, immune response, and cognition. Despite these specific diverged loci, the 3D genome of AHs and MHs is more similar than expected based on sequence divergence, suggesting that the pressure to maintain 3D genome organization constrained hominin sequence evolution. We also find that 3D genome organization constrained the landscape of AH ancestry in MHs today: regions more tolerant of 3D variation are enriched for introgression in modern Eurasians. Finally, we identify loci where modern Eurasians have inherited novel 3D genome folding patterns from AH ancestors and validate folding differences in a high-frequency locus using Hi-C, revealing a putative molecular mechanism for phenotypes associated with archaic introgression. In summary, our application of deep learning to predict archaic 3D genome organization illustrates the potential of inferring molecular phenotypes from ancient DNA to reveal previously unobservable biological differences.

evolutionary biology↗

bioRxiv2022

Scalable microbial strain inference in metagenomic data using StrainFacts

While genome databases are nearing a complete catalog of species commonly inhabiting the human gut, their representation of intraspecific diversity is lacking for all but the most abundant and frequently studied taxa. Statistical deconvolution of allele frequencies from shotgun metagenomic data into strain genotypes and relative abundances is a promising approach, but existing methods are limited by computational scalability. Here we introduce StrainFacts, a method for strain deconvolution that enables inference across tens of thousands of metagenomes. We harness a "fuzzy" genotype approximation that makes the underlying graphical model fully differentiable, unlike existing methods. This allows parameter estimates to be optimized with gradient-based methods, speeding up model fitting by two orders of magnitude. A GPU implementation provides additional scalability. Extensive simulations show that StrainFacts can perform strain inference on thousands of metagenomes and has comparable accuracy to more computationally intensive tools. We further validate our strain inferences using single-cell genomic sequencing from a human stool sample. Applying StrainFacts to a collection of more than 10,000 publicly available human stool metagenomes, we quantify patterns of strain diversity, biogeography, and linkage-disequilibrium that agree with and expand on what is known based on existing reference genomes. StrainFacts paves the way for large-scale biogeography and population genetic studies of microbiomes using metagenomic data.

bioinformatics↗

bioRxiv2021

Single cell genome sequencing of laboratory mouse microbiota improves taxonomic and functional resolution of this model microbial community

Laboratory mice are widely studied as models of mammalian biology, including the microbiota. However, much of the taxonomic and functional diversity of the mouse gut microbiome is missed in current metagenomic studies, because genome databases have not achieved a balanced representation of the diverse members of this ecosystem. Towards solving this problem, we used flow cytometry and low-coverage sequencing to capture the genomes of 764 single cells from the stool of three laboratory mice. From these, we generated 298 high-coverage microbial genome assemblies, which we annotated for open reading frames and phylogenetic placement. These genomes increase the gene catalog and phylogenetic breadth of the mouse microbiota, adding 135 novel species with the greatest increase in diversity to the Muribaculaceae and Bacteroidaceae families. This new diversity also improves the read mapping rate, taxonomic classifier performance, and gene detection rate of mouse stool metagenomes. The novel microbial functions revealed through our single-cell genomes highlight previously invisible pathways that may be important for life in the murine gastrointestinal tract.

microbiology↗

bioRxiv2021

Autism risk gene POGZ promotes chromatin accessibility and expression of clustered synaptic genes

De novo mutations in POGZ, which encodes the chromatin regulator Pogo Transposable Element with ZNF Domain protein, are strongly associated with autism spectrum disorder (ASD). Here we find that in the developing mouse and human brain POGZ binds predominantly euchromatic loci and these are enriched for human neurodevelopmental disorder genes and transposable elements. We profile chromatin accessibility and gene expression in Pogz-/- mice and find that POGZ promotes chromatin accessibility of candidate regulatory elements (REs) and the expression of clustered synaptic genes. We further demonstrate that POGZ forms a nuclear complex and co-occupies loci with HP1{gamma} and ADNP, another high-confidence ASD risk gene. In Pogz+/- mice, Adnp expression is reduced. We postulate that reduced POGZ dosage disrupts cortical function through alterations in the POGZ-ADNP balance which modifies neuronal gene expression.

neuroscience↗

bioRxiv2021

CellWalkR: An R Package for integrating single-cell and bulk data to resolve regulatory elements

While single-cell open chromatin (scATAC-seq) data allows for the identification of cell type-specific regulatory regions, it is much sparser than bulk data. CellWalkR is an R package that performs an integration of external labeling and bulk epigenetic data with scATAC-seq using a network-based random walk model to help overcome this sparsity. Outputs include cell type labels for individual cells and regulatory regions. Availability and implementationCellWalkR is freely available as an R package under a GNU GPL-2.0 License, and can be accessed from https://github.com/PFPrzytycki/CellWalkR with an accompanying vignette for analyzing example data.

bioinformatics↗

bioRxiv2021

A Human Accelerated Region is a Leydig cell GLI2 Enhancer that Affects Male-Typical Behavior

Human accelerated regions (HARs) are sequences that have evolved at an accelerated rate in the human lineage. Some HARs are developmental enhancers. We used a massively parallel reporter assay (MPRA) to identify HARs with enhancer activity in a mammalian testis cell line. A subset of HARs exhibited differential activity between the human and chimpanzee orthologs, representing candidates for underlying unique human male reproductive biology. We further characterized one of these candidate testis enhancers, 2xHAR.238. CRISPR/Cas9-mediated deletion in a testis cell line and mice revealed that 2xHAR.238 enhances expression of Gli2, encoding a Hedgehog pathway effector, in testis Leydig cells. 4C-seq revealed that 2xHAR.238 contacts the Gli2 promoter, consistent with enhancer function. In adult male mice, deletion of 2xHAR.238 disrupted mouse male-typical behavior and male interest in female odor. Combined, our work identifies a HAR that promotes the expression of Gli2 in Leydig cells and may have contributed to the evolution of human male reproductive biology.

evolutionary biology↗

bioRxiv2021

Genome-wide variability in recombination activity is associated with meiotic chromatin organization

BackgroundRecombination enables reciprocal exchange of genomic information between parental chromosomes and successful segregation of homologous chromosomes during meiosis. Errors in this process lead to negative health outcomes, while variability in recombination rate affects genome evolution. In mammals, most crossovers occur in hotspots defined by PRDM9 motifs, though PRDM9 binding sites are not all equally hot. We hypothesize that dynamic patterns of meiotic genome folding are linked to recombination activity. ResultsWe apply an integrative bioinformatics approach to analyze how three-dimensional (3D) chromosomal organization during meiosis relates to rates of double-strandbreak (DSB) and crossover formation at PRDM9 hotspots. We show that active, spatially accessible genomic regions during meiotic prophase are associated with DSB-favoured hotspots, which further adopt a transient locally active configuration in early prophase. Conversely, crossover formation is depleted among DSBs in spatially accessible regions during meiotic prophase, particularly within gene bodies. We also find evidence that active chromatin regions have smaller average loop sizes in mammalian meiosis. Collectively, these findings establish that differences in chromatin architecture along chromosomal axes are associated with variable recombination activity. ConclusionsWe propose an updated framework describing how 3D organization of brush-loop chromosomes during meiosis may modulate recombination.

genomics↗

bioRxiv2021

Integration of Protein Interactome Networks with Congenital Heart Disease Variants Reveals Candidate Disease Genes

Congenital heart disease (CHD) is present in 1% of live births, yet identification of causal mutations remains a challenge despite large-scale genomic sequencing efforts. We hypothesized that genetic determinants for CHDs may lie in protein interactomes of GATA4 and TBX5, two transcription factors that cause CHDs. Defining their interactomes in human cardiac progenitors via affinity purification-mass spectrometry and integrating results with genetic data from the Pediatric Cardiac Genomic Consortium revealed an enrichment of de novo variants among proteins that interact with GATA4 or TBX5. A consolidative score that prioritized interactome members based on variant, gene, and proband features identified likely CHD-causing genes, including the epigenetic reader GLYR1. GLYR1 and GATA4 widely co-occupied cardiac developmental genes, resulting in co-activation, and the GLYR1 missense variant associated with CHD disrupted interaction with GATA4. This integrative proteomic and genetic approach provides a framework for prioritizing and interrogating the contribution of genetic variants in disease.

genetics↗

bioRxiv2020

Cell Layers: Uncovering clustering structure and knowledge in unsupervised single-cell transcriptomic analysis

MotivationUnsupervised clustering of single-cell transcriptomics is a powerful method for identifying cell populations. Static visualization techniques for single-cell clustering only display results for a single resolution parameter. Analysts will often evaluate more than one resolution parameter, but then only report one. ResultsWe developed Cell Layers, an interactive Sankey tool for the quantitative investigation of gene expression, coexpression, biological processes, and cluster integrity across clustering resolutions. Cell Layers enhances the interpretability of single-cell clustering by linking molecular data and cluster evaluation metrics, to provide novel insight into cell populations. Availability and implementationUpon request

bioinformatics↗

bioRxiv2020

Accurate and sensitive detection of microbial eukaryotes from metagenomic shotgun sequencing data

BackgroundMicrobial eukaryotes are found alongside bacteria and archaea in natural microbial systems, including host-associated microbiomes. While microbial eukaryotes are critical to these communities, they are challenging to study with shotgun sequencing techniques and are therefore often excluded. ResultsHere we present EukDetect, a bioinformatics method to identify eukaryotes in shotgun metagenomic sequencing data. Our approach uses a database of 521,824 universal marker genes from 241 conserved gene families, which we curated from 3,713 fungal, protist, non-vertebrate metazoan, and non-streptophyte archaeplastid genomes and transcriptomes. EukDetect has a broad taxonomic coverage of microbial eukaryotes, performs well on low-abundance and closely related species, and is resilient against bacterial contamination in eukaryotic genomes. Using EukDetect, we describe the spatial distribution of eukaryotes along the human gastrointestinal tract, showing that fungi and protists are present in the lumen and mucosa throughout the large intestine. We discover that there is a succession of eukaryotes that colonize the human gut during the first years of life, mirroring patterns of developmental succession observed in gut bacteria. By comparing DNA and RNA sequencing of paired samples from human stool, we find that many eukaryotes continue active transcription after passage through the gut, though some do not, suggesting they are dormant or nonviable. We analyze metagenomic data from the Baltic Sea and find that eukaryotes differ across locations and salinity gradients. Finally, we observe eukaryotes in Arabidopsis leaf samples, many of which are not identifiable from public protein databases. ConclusionsEukDetect provides an automated and reliable way to characterize eukaryotes in shotgun sequencing datasets from diverse microbiomes. We demonstrate that it enables discoveries that would be missed or clouded by false positives with standard shotgun sequence analysis. EukDetect will greatly advance our understanding of how microbial eukaryotes contribute to microbiomes.

bioinformatics↗

bioRxiv2020

An atlas of lamina-associated chromatin across thirteen human cell types reveals cell-type-specific and multiple subtypes of peripheral heterochromatin

Association of chromatin with lamin proteins at the nuclear periphery has emerged as a potential mechanism to coordinate cell type-specific gene expression and maintain cellular identity via gene silencing. Unlike many histone modifications and chromatin-associated proteins, lamin-associated domains (LADs) have yet to be mapped genome-wide in a diverse panel of human cell types, which has limited our understanding of the role peripheral chromatin plays in development and disease. To address this gap, we mapped LAMIN B1 (LB1) across twelve human cell types encompassing pluripotent stem cells, intermediate progenitors, and differentiated cells from all three germ layers. Integrative analyses of this atlas of peripheral chromatin with publicly available genomic data, as well as gene expression and repressive histone maps generated for this study, revealed that in all twelve cellular contexts lamin-associated chromatin is organized into at least two subtypes defined by differences in LB1 occupancy, gene expression, chromatin accessibility, transposable elements, replication timing, and radial positioning. Most genes gain or lose LB1 occupancy consistent with their cell type along developmental trajectories; however, we also identified examples where the enhancer, but not the gene body and promoter, change LAD state. Imaging of fluorescently labeled DNA in single cells validated these transitions and showed intermediate radial positioning of LADs that are gene dense, relatively accessible, and dynamic across development. This atlas represents the largest resource to date for peripheral chromatin organization studies.

developmental biology↗

bioRxiv2020

A Transcriptional Switch Governing Fibroblast Plasticity Underlies Reversibility of Chronic Heart Disease

In diseased organs, stress-activated signaling cascades alter chromatin, triggering broad shifts in transcription and cell state that exacerbate pathology. Fibroblast activation is a common stress response that worsens lung, liver, kidney and heart disease, yet its mechanistic basis remains poorly understood1,2. Pharmacologic inhibition of the BET family of transcriptional coactivators alleviates cardiac dysfunction and associated fibrosis, providing a tool to mechanistically interrogate maladaptive fibroblast states and modulate their plasticity as a potential therapeutic approach3-8. Here, we leverage dynamic single cell transcriptomic and epigenomic interrogation of heart tissue with and without BET inhibition to reveal a reversible transcriptional switch underlying stress-induced fibroblast activation. Transcriptomes of resident cardiac fibroblasts demonstrated robust and rapid toggling between the quiescent fibroblast and activated myofibroblast state in a manner that directly correlated with BET inhibitor exposure and cardiac function. Correlation of single cell chromatin accessibility with cardiac function revealed a novel set of reversibly accessible DNA elements that correlated with disease severity. Among the most dynamic elements was an enhancer regulating the transcription factor MEOX1, which was specifically expressed in activated myofibroblasts, occupied putative regulatory elements of a broad fibrotic gene program, and was required for TGF{beta}-induced myofibroblast activation. CRISPR interference of the most dynamic cis-element within the enhancer, marked by nascent transcription, prevented TGF{beta}-induced activation of Meox1. These findings identify MEOX1 as a central regulator of stress-induced myofibroblast activation associated with cardiac dysfunction. The plasticity and specificity of the BET-dependent regulation of MEOX1 in endogenous tissue fibroblasts provides new trans- and cis- targets for treating fibrotic disease.

molecular biology↗

bioRxiv2020

Ultra-rapid metagenotyping of the human gut microbiome

Sequence variation is used to quantify population structure and identify genetic determinants of phenotypes that vary within species. In the human microbiome and other environments, single nucleotide polymorphisms (SNPs) are frequently detected by aligning metagenomic sequencing reads to catalogs of genes or genomes. But this requires high-performance computing and enough read coverage to distinguish SNPs from sequencing errors. We solved these problems by developing the GenoTyper for Prokaytotes (GT-Pro), a suite of novel methods to catalog SNPs from genomes and use exact k-mer matches to perform ultra-fast reference-based SNP calling from metagenomes. Compared to read alignment, GT-Pro is more accurate and two orders of magnitude faster. We discovered 104 million SNPs in 909 human gut species, characterized their global population structure, and tracked pathogenic strains. GT-Pro democratizes strain-level microbiome analysis by making it possible to genotype hundreds of metagenomes on a personal computer. Software availabilityGT-Pro is available at https://github.com/zjshi/gt-pro.

bioinformatics↗