Vocal learning-associated convergent evolution in mammalian proteins and regulatory elements

Vocal production learning ("vocal learning") is a convergently evolved trait in vertebrates. To identify brain genomic elements associated with mammalian vocal learning, we integrated genomic, anatomical, and neurophysiological data from the Egyptian fruit bat (Rousettus aegyptiacus) with analyses of the genomes of 215 placental mammals. First, we identified a set of proteins evolving more slowly in vocal learners. Then, we discovered a vocal motor cortical region in the Egyptian fruit bat, an emergent vocal learner, and leveraged that knowledge to identify active cis-regulatory elements in the motor cortex of vocal learners. Machine learning methods applied to motor cortex open chromatin revealed 50 enhancers robustly associated with vocal learning whose activity tended to be lower in vocal learners. Our research implicates convergent losses of motor cortex regulatory elements in mammalian vocal learning evolution.

Machine learning reveals the diversity of human 3D chromatin contact patterns

Understanding variation in chromatin contact patterns across human populations is critical for interpreting non-coding variants and their ultimate effects on gene expression and phenotypes. However, experimental determination of chromatin contacts at a population-scale is prohibitively expensive. To overcome this challenge, we develop and validate a machine learning method to quantify the diversity 3D chromatin contacts at 2 kilobase resolution from genome sequence alone. We then apply this approach to thousands of diverse modern humans and the inferred human-archaic hominin ancestral genome. While patterns of 3D contact divergence genome-wide are qualitatively similar to patterns of sequence divergence, we find that 3D divergence in local 1-megabase genomic windows does not follow sequence divergence. In particular, we identify 392 windows with significantly greater 3D divergence than expected from sequence. Moreover, 26% of genomic windows have rare 3D contact variation observed in a small number of individuals. Using in silico mutagenesis we find that most sequence changes to do not result in changes to 3D chromatin contacts. However in windows with substantial 3D divergence, just one or a few variants can lead to divergent 3D chromatin contacts without the individuals carrying those variants having high sequence divergence. In summary, inferring 3D chromatin contact maps across human populations reveals diverse contact patterns. We anticipate that these genetically diverse maps of 3D chromatin contact will provide a reference for future work on the function and evolution of 3D chromatin contact variation across human populations.

ChromaFactor: deconvolution of single-molecule chromatin organization with non-negative matrix factorization

The investigation of chromatin organization in single cells holds great promise for identifying causal relationships between genome structure and function. However, analysis of single-molecule data is hampered by extreme yet inherent heterogeneity, making it challenging to determine the contributions of individual chromatin fibers to bulk trends. To address this challenge, we propose ChromaFactor, a novel computational approach based on non-negative matrix factorization that deconvolves single-molecule chromatin organization datasets into their most salient primary components. ChromaFactor provides the ability to identify trends accounting for the maximum variance in the dataset while simultaneously describing the contribution of individual molecules to each component. Applying our approach to two single-molecule imaging datasets across different genomic scales, we find that these primary components demonstrate significant correlation with key functional phenotypes, including active transcription, enhancer-promoter distance, and genomic compartment. ChromaFactor offers a robust tool for understanding the complex interplay between chromatin structure and function on individual DNA molecules, pinpointing which subpopulations drive functional changes and fostering new insights into cellular heterogeneity and its implications for bulk genomic phenomena.

Hybrid assemblies of microbiome Blastocystis protists reveal evolutionary diversification reflecting host ecology

The most prevalent microbial eukaryote in the human gut is Blastocystis , an obligate commensal protist also common in many other vertebrates. Blastocystis is descended from free-living stramenopile ancestors; how it has adapted to thrive within humans and a wide range of hosts is unclear. Here, we cultivated six Blastocystis strains spanning the diversity of the genus and generated highly contiguous, annotated genomes with long-read DNA-seq, Hi-C, and RNA-seq. Comparative genomics between these strains and two closely related stramenopiles with different lifestyles, the lizard gut symbiont Proteromonas lacertae and the free-living marine flagellate Cafeteria burkhardae , reveal the evolutionary history of the Blastocystis genus. We find substantial gene content variability between Blastocystis strains. Blastocystis isolated from an herbivorous tortoise has many plant carbohydrate metabolizing enzymes, some horizontally acquired from bacteria, likely reflecting fermentation within the host gut. In contrast, human- isolated Blastocystis have gained many heat shock proteins, and we find numerous subtype- specific expansions of host-interfacing genes, including cell adhesion and cell surface glycan genes. In addition, we observe that human-isolated Blastocystis have substantial changes in gene structure, including shortened introns and intergenic regions, as well as genes lacking canonical termination codons. Finally, our data indicate that the common ancestor of Blastocystis lost nearly all ancestral genes for heterokont flagella morphology, including cilia proteins, microtubule motor proteins, and ion channel proteins. Together, these findings underscore the huge functional variability within the Blastocystis genus and provide candidate genes for the adaptations these lineages have undergone to thrive in the gut microbiomes of diverse vertebrates.

SuPreMo: a computational tool for streamlining in silico perturbation using sequence-based predictive models

Computationally editing genome sequences is a common bioinformatics task, but current approaches have limitations, such as incompatibility with structural variants, challenges in identifying responsible sequence perturbations, and the need for vcf file inputs and phased data. To address these bottlenecks, we present Sequence Mutator for Predictive Models (SuPreMo), a scalable and comprehensive tool for performing in silico mutagenesis. We then demonstrate how pairs of reference and perturbed sequences can be used with machine learning models to prioritize pathogenic variants or discover new functional sequences.

FTLD targets brain regions expressing recently evolved genes

In frontotemporal lobar degeneration (FTLD), pathological protein aggregation is associated with a decline in human-specialized social-emotional and language functions. Most disease protein aggregates contain either TDP-43 (FTLD-TDP) or tau (FTLD-tau). Here, we explored whether FTLD targets brain regions that express genes containing human accelerated regions (HARs), conserved sequences that have undergone positive selection during recent human evolution. To this end, we used structural neuroimaging from patients with FTLD and normative human regional transcriptomic data to identify genes expressed in FTLD-targeted brain regions. We then integrated primate comparative genomic data to test our hypothesis that FTLD targets brain regions expressing recently evolved genes. In addition, we asked whether genes expressed in FTLD-targeted brain regions are enriched for genes that undergo cryptic splicing when TDP-43 function is impaired. We found that FTLD-TDP and FTLD-tau subtypes target brain regions that express overlapping and distinct genes, including many linked to neuromodulatory functions. Genes whose normative brain regional expression pattern correlated with FTLD cortical atrophy were strongly associated with HARs. Atrophy-correlated genes in FTLD-TDP showed greater overlap with TDP-43 cryptic splicing genes compared with atrophy-correlated genes in FTLD-tau. Cryptic splicing genes were enriched for HAR genes, and vice versa, but this effect was due to the confounding influence of gene length. Analyses performed at the individual-patient level revealed that the expression of HAR genes and cryptically spliced genes within putative regions of disease onset differed across FTLD-TDP subtypes. Overall, our findings suggest that FTLD targets brain regions that have undergone recent evolutionary specialization and provide intriguing potential leads regarding the transcriptomic basis for selective vulnerability in distinct FTLD molecular-anatomical subtypes.

Sequence-based machine learning reveals 3D genome differences between bonobos and chimpanzees

Phenotypic divergence between closely related species, including bonobos and chimpanzees (genus Pan), is largely driven by variation in gene regulation. The 3D structure of the genome mediates gene expression; however, genome folding differences in Pan are not well understood. Here, we apply machine learning to predict genome-wide 3D genome contact maps from DNA sequence for 56 bonobos and chimpanzees, encompassing all five extant lineages. We use a pairwise approach to estimate 3D divergence between individuals from the resulting contact maps in 4,420 1 Mb genomic windows. While most pairs were similar, ∼17% were predicted to be substantially divergent in genome folding. The most dissimilar maps were largely driven by single individuals with rare variants that produce unique 3D genome folding in a region. We also identified 89 genomic windows where bonobo and chimpanzee contact maps substantially diverged, including several windows harboring genes associated with traits implicated in Pan phenotypic divergence. We used in silico mutagenesis to identify 51 3D-modifying variants in these bonobo-chimpanzee divergent windows, finding that 34 or 66.67% induce genome folding changes via CTCF binding motif disruption. Our results reveal 3D genome variation at the population-level and identify genomic regions where changes in 3D folding may contribute to phenotypic differences in our closest living relatives.

Exploring the Roles of RNAs in Chromatin Architecture Using Deep Learning

Recent studies have highlighted the impact of both transcription and transcripts on 3D genome organization, particularly its dynamics. Here, we propose a deep learning framework, called AkitaR, that leverages both genome sequences and genome-wide RNA-DNA interactions to investigate the roles of chromatin-associated RNAs (caRNAs) on genome folding in HFFc6 cells. In order to disentangle the cis- and trans-regulatory roles of caRNAs, we compared models with nascent transcripts, trans-located caRNAs, open chromatin data, or DNA sequence alone. Both nascent transcripts and trans-located caRNAs improved the models' predictions, especially at cell-type-specific genomic regions. Analyses of feature importance scores revealed the contribution of caRNAs at TAD boundaries, chromatin loops and nuclear sub-structures such as nuclear speckles and nucleoli to the models' predictions. Furthermore, we identified non-coding RNAs (ncRNAs) known to regulate chromatin structures, such as MALAT1 and NEAT1, as well as several novel RNAs, RNY5, RPPH1, POLG-DT and THBS1-IT, that might modulate chromatin architecture through trans-interactions in HFFc6. Our modeling also suggests that transcripts from Alus and other repetitive elements may facilitate chromatin interactions through trans R-loop formation. Our findings provide new insights and generate testable hypotheses about the roles of caRNAs in shaping chromatin organization.

In silico discovery of repetitive elements as key sequence determinants of 3D genome folding

Natural and experimental genetic variants can modify DNA loops and insulating boundaries to tune transcription, but it is unknown how sequence perturbations affect chromatin organization genome wide. We developed a deep-learning strategy to quantify the effect of any insertion, deletion, or substitution on chromatin contacts and systematically scored millions of synthetic variants. While most genetic manipulations have little impact, regions with CTCF motifs and active transcription are highly sensitive, as expected. Our unbiased screen and subsequent targeted experiments also point to noncoding RNA genes and several families of repetitive elements as CTCF-motif-free DNA sequences with particularly large effects on nearby chromatin interactions, sometimes exceeding the effects of CTCF sites and explaining interactions that lack CTCF. We anticipate that our disruption tracks may be of broad interest and utility as a measure of 3D genome sensitivity, and our computational strategies may serve as a template for biological inquiry with deep learning.

Maast: genotyping thousands of microbial strains efficiently

Existing single nucleotide polymorphism (SNP) genotyping algorithms do not scale for species with thousands of sequenced strains, nor do they account for conspecific redundancy. Here we present a bioinformatics tool, Maast, which empowers population genetic meta-analysis of microbes at an unrivaled scale. Maast implements a novel algorithm to heuristically identify a minimal set of diverse conspecific genomes, then constructs a reliable SNP panel for each species, and enables rapid and accurate genotyping using a hybrid of whole-genome alignment and k-mer exact matching. We demonstrate Maast's utility by genotyping thousands of Helicobacter pylori strains and tracking SARS-CoV-2 diversification.

Spatial and temporal organization of the genome: Current state and future aims of the 4D nucleome project

The four-dimensional nucleome (4DN) consortium studies the architecture of the genome and the nucleus in space and time. We summarize progress by the consortium and highlight the development of technologies for (1) mapping genome folding and identifying roles of nuclear components and bodies, proteins, and RNA, (2) characterizing nuclear organization with time or single-cell resolution, and (3) imaging of nuclear organization. With these tools, the consortium has provided over 2,000 public datasets. Integrative computational models based on these data are starting to reveal connections between genome structure and function. We then present a forward-looking perspective and outline current aims to (1) delineate dynamics of nuclear architecture at different timescales, from minutes to weeks as cells differentiate, in populations and in single cells, (2) characterize cis-determinants and trans-modulators of genome organization, (3) test functional consequences of changes in cis- and trans-regulators, and (4) develop predictive models of genome structure and function.

Culturing of a complex gut microbial community in mucin-hydrogel carriers reveals strain- and gene-associated spatial organization

Microbial community function depends on both taxonomic composition and spatial organization. While composition of the human gut microbiome has been deeply characterized, less is known about the organization of microbes between regions such as lumen and mucosa and the microbial genes regulating this organization. Using a defined 117 strain community for which we generate high-quality genome assemblies, we model mucosa/lumen organization with in vitro cultures incorporating mucin hydrogel carriers as surfaces for bacterial attachment. Metagenomic tracking of carrier cultures reveals increased diversity and strain-specific spatial organization, with distinct strains enriched on carriers versus liquid supernatant, mirroring mucosa/lumen enrichment in vivo. A comprehensive search for microbial genes associated with this spatial organization identifies candidates with known adhesion-related functions, as well as novel links. These findings demonstrate that carrier cultures of defined communities effectively recapitulate fundamental aspects of gut spatial organization, enabling identification of key microbial strains and genes.

Genetic Determinants of the Interventricular Septum Are Linked to Ventricular Septal Defects and Hypertrophic Cardiomyopathy

BACKGROUND

A large proportion of genetic risk remains unexplained for structural heart disease involving the interventricular septum (IVS) including hypertrophic cardiomyopathy and ventricular septal defects. This study sought to develop a reproducible proxy of IVS structure from standard medical imaging, discover novel genetic determinants of IVS structure, and relate these loci to diseases of the IVS, hypertrophic cardiomyopathy, and ventricular septal defect.

METHODS

We estimated the cross-sectional area of the IVS from the 4-chamber view of cardiac magnetic resonance imaging in 32 219 individuals from the UK Biobank which was used as the basis of genome wide association studies and Mendelian randomization.

RESULTS

Measures of IVS cross-sectional area at diastole were a strong proxy for the 3-dimensional volume of the IVS (Pearson r=0.814, P=0.004), and correlated with anthropometric measures, blood pressure, and diagnostic codes related to cardiovascular physiology. Seven loci with clear genomic consequence and relevance to cardiovascular biology were uncovered by genome wide association studies, most notably a single nucleotide polymorphism in an intron of CDKN1A (rs2376620; β, 7.7 mm2 [95% CI, 5.8-11.0]; P=6.0×10-10), and a common inversion incorporating KANSL1 predicted to disrupt local chromatin structure (β, 8.4 mm2 [95% CI, 6.3-10.9]; P=4.2×10-14). Mendelian randomization suggested that inheritance of larger IVS cross-sectional area at diastole was strongly associated with hypertrophic cardiomyopathy risk (pIVW=4.6×10-10) while inheritance of smaller IVS cross-sectional area at diastole was associated with risk for ventricular septal defect (pIVW=0.007).

CONCLUSIONS

Automated estimates of cross-sectional area of the IVS supports discovery of novel loci related to cardiac development and Mendelian disease. Inheritance of genetic liability for either small or large IVS, appears to confer risk for ventricular septal defect or hypertrophic cardiomyopathy, respectively. These data suggest that a proportion of risk for structural and congenital heart disease can be localized to the common genetic determinants of size and shape of cardiovascular anatomy.

Comparing chromatin contact maps at scale: methods and insights

Comparing chromatin contact maps is an essential step in quantifying how three-dimensional (3D) genome organization shapes development, evolution, and disease. However, no gold standard exists for comparing contact maps, and even simple methods often disagree. In this study, we propose novel comparison methods and evaluate them alongside existing approaches using genome-wide Hi-C data and 22,500 in silico predicted contact maps. We also quantify the robustness of methods to common sources of biological and technical variation, such as boundary size and noise. We find that simple difference-based methods such as mean squared error are suitable for initial screening, but biologically informed methods are necessary to identify why maps diverge and propose specific functional hypotheses. We provide a reference guide, codebase, and benchmark for rapidly comparing chromatin contact maps at scale to enable biological insights into the 3D organization of the genome.

Evolutionary constraint and innovation across hundreds of placental mammals

Zoonomia is the largest comparative genomics resource for mammals produced to date. By aligning genomes for 240 species, we identify bases that, when mutated, are likely to affect fitness and alter disease risk. At least 332 million bases (~10.7%) in the human genome are unusually conserved across species (evolutionarily constrained) relative to neutrally evolving repeats, and 4552 ultraconserved elements are nearly perfectly conserved. Of 101 million significantly constrained single bases, 80% are outside protein-coding exons and half have no functional annotations in the Encyclopedia of DNA Elements (ENCODE) resource. Changes in genes and regulatory elements are associated with exceptional mammalian traits, such as hibernation, that could inform therapeutic development. Earth's vast and imperiled biodiversity offers distinctive power for identifying genetic variants that affect genome function and organismal phenotypes.

Relating enhancer genetic variation across mammals to complex phenotypes using machine learning

Protein-coding differences between species often fail to explain phenotypic diversity, suggesting the involvement of genomic elements that regulate gene expression such as enhancers. Identifying associations between enhancers and phenotypes is challenging because enhancer activity can be tissue-dependent and functionally conserved despite low sequence conservation. We developed the Tissue-Aware Conservation Inference Toolkit (TACIT) to associate candidate enhancers with species' phenotypes using predictions from machine learning models trained on specific tissues. Applying TACIT to associate motor cortex and parvalbumin-positive interneuron enhancers with neurological phenotypes revealed dozens of enhancer-phenotype associations, including brain size-associated enhancers that interact with genes implicated in microcephaly or macrocephaly. TACIT provides a foundation for identifying enhancers associated with the evolution of any convergently evolved phenotype in any large group of species with aligned genomes.

Evolutionary constraint and innovation across hundreds of placental mammals

Zoonomia is the largest comparative genomics resource for mammals produced to date. By aligning genomes for 240 species, we identify bases that, when mutated, are likely to affect fitness and alter disease risk. At least 332 million bases (~10.7%) in the human genome are unusually conserved across species (evolutionarily constrained) relative to neutrally evolving repeats, and 4552 ultraconserved elements are nearly perfectly conserved. Of 101 million significantly constrained single bases, 80% are outside protein-coding exons and half have no functional annotations in the Encyclopedia of DNA Elements (ENCODE) resource. Changes in genes and regulatory elements are associated with exceptional mammalian traits, such as hibernation, that could inform therapeutic development. Earth's vast and imperiled biodiversity offers distinctive power for identifying genetic variants that affect genome function and organismal phenotypes.

Integrating gene annotation with orthology inference at scale

Annotating coding genes and inferring orthologs are two classical challenges in genomics and evolutionary biology that have traditionally been approached separately, limiting scalability. We present TOGA (Tool to infer Orthologs from Genome Alignments), a method that integrates structural gene annotation and orthology inference. TOGA implements a different paradigm to infer orthologous loci, improves ortholog detection and annotation of conserved genes compared with state-of-the-art methods, and handles even highly fragmented assemblies. TOGA scales to hundreds of genomes, which we demonstrate by applying it to 488 placental mammal and 501 bird assemblies, creating the largest comparative gene resources so far. Additionally, TOGA detects gene losses, enables selection screens, and automatically provides a superior measure of mammalian genome quality. TOGA is a powerful and scalable method to annotate and compare genes in the genomic era.

Leveraging base-pair mammalian constraint to understand genetic variation and human disease

Thousands of genomic regions have been associated with heritable human diseases, but attempts to elucidate biological mechanisms are impeded by an inability to discern which genomic positions are functionally important. Evolutionary constraint is a powerful predictor of function, agnostic to cell type or disease mechanism. Single-base phyloP scores from 240 mammals identified 3.3% of the human genome as significantly constrained and likely functional. We compared phyloP scores to genome annotation, association studies, copy-number variation, clinical genetics findings, and cancer data. Constrained positions are enriched for variants that explain common disease heritability more than other functional annotations. Our results improve variant annotation but also highlight that the regulatory landscape of the human genome still needs to be further explored and linked to disease.

Three-dimensional genome rewiring in loci with human accelerated regions

Human accelerated regions (HARs) are conserved genomic loci that evolved at an accelerated rate in the human lineage and may underlie human-specific traits. We generated HARs and chimpanzee accelerated regions with an automated pipeline and an alignment of 241 mammalian genomes. Combining deep learning with chromatin capture experiments in human and chimpanzee neural progenitor cells, we discovered a significant enrichment of HARs in topologically associating domains containing human-specific genomic variants that change three-dimensional (3D) genome organization. Differential gene expression between humans and chimpanzees at these loci suggests rewiring of regulatory interactions between HARs and neurodevelopmental genes. Thus, comparative genomics together with models of 3D genome folding revealed enhancer hijacking as an explanation for the rapid evolution of HARs.

The contribution of historical processes to contemporary extinction risk in placental mammals

Species persistence can be influenced by the amount, type, and distribution of diversity across the genome, suggesting a potential relationship between historical demography and resilience. In this study, we surveyed genetic variation across single genomes of 240 mammals that compose the Zoonomia alignment to evaluate how historical effective population size (N_e) affects heterozygosity and deleterious genetic load and how these factors may contribute to extinction risk. We find that species with smaller historical N_e carry a proportionally larger burden of deleterious alleles owing to long-term accumulation and fixation of genetic load and have a higher risk of extinction. This suggests that historical demography can inform contemporary resilience. Models that included genomic data were predictive of species' conservation status, suggesting that, in the absence of adequate census or ecological data, genomic information may provide an initial risk assessment.

Mammalian evolution of human cis-regulatory elements and transcription factor binding sites

Understanding the regulatory landscape of the human genome is a long-standing objective of modern biology. Using the reference-free alignment across 241 mammalian genomes produced by the Zoonomia Consortium, we charted evolutionary trajectories for 0.92 million human candidate cis-regulatory elements (cCREs) and 15.6 million human transcription factor binding sites (TFBSs). We identified 439,461 cCREs and 2,024,062 TFBSs under evolutionary constraint. Genes near constrained elements perform fundamental cellular processes, whereas genes near primate-specific elements are involved in environmental interaction, including odor perception and immune response. About 20% of TFBSs are transposable element-derived and exhibit intricate patterns of gains and losses during primate evolution whereas sequence variants associated with complex traits are enriched in constrained TFBSs. Our annotations illuminate the regulatory functions of the human genome.

A genomic timescale for placental mammal evolution

The precise pattern and timing of speciation events that gave rise to all living placental mammals remain controversial. We provide a comprehensive phylogenetic analysis of genetic variation across an alignment of 241 placental mammal genome assemblies, addressing prior concerns regarding limited genomic sampling across species. We compared neutral genome-wide phylogenomic signals using concatenation and coalescent-based approaches, interrogated phylogenetic variation across chromosomes, and analyzed extensive catalogs of structural variants. Interordinal relationships exhibit relatively low rates of phylogenomic conflict across diverse datasets and analytical methods. Conversely, X-chromosome versus autosome conflicts characterize multiple independent clades that radiated during the Cenozoic. Genomic time trees reveal an accumulation of cladogenic events before and immediately after the Cretaceous-Paleogene (K-Pg) boundary, implying important roles for Cretaceous continental vicariance and the K-Pg extinction in the placental radiation.

The functional and evolutionary impacts of human-specific deletions in conserved elements

Conserved genomic sequences disrupted in humans may underlie uniquely human phenotypic traits. We identified and characterized 10,032 human-specific conserved deletions (hCONDELs). These short (average 2.56 base pairs) deletions are enriched for human brain functions across genetic, epigenomic, and transcriptomic datasets. Using massively parallel reporter assays in six cell types, we discovered 800 hCONDELs conferring significant differences in regulatory activity, half of which enhance rather than disrupt regulatory function. We highlight several hCONDELs with putative human-specific effects on brain development, including HDAC5, CPEB4, and PPP2CA. Reverting an hCONDEL to the ancestral sequence alters the expression of LOXL2 and developmental genes involved in myelination and synaptic function. Our data provide a rich resource to investigate the evolutionary mechanisms driving new traits in humans and other species.

Oligogenic Architecture of Rare Noncoding Variants Distinguishes 4 Congenital Heart Disease Phenotypes

BACKGROUND

Congenital heart disease (CHD) is highly heritable, but the power to identify inherited risk has been limited to analyses of common variants in small cohorts.

METHODS

We performed reimputation of 4 CHD cohorts (n=55 342) to the TOPMed reference panel (freeze 5), permitting meta-analysis of 14 784 017 variants including 6 035 962 rare variants of high imputation quality as validated by whole genome sequencing.

RESULTS

Meta-analysis identified 16 novel loci, including 12 rare variants, which displayed moderate or large effect sizes (median odds ratio, 3.02) for 4 separate CHD categories. Analyses of chromatin structure link 13 of the genome-wide significant loci to key genes in cardiac development; rs373447426 (minor allele frequency, 0.003 [odds ratio, 3.37 for Conotruncal heart disease]; P=1.49×10^-8) is predicted to disrupt chromatin structure for 2 nearby genes BDH1 and DLG1 involved in Conotruncal development. A lead variant rs189203952 (minor allele frequency, 0.01 [odds ratio, 2.4 for left ventricular outflow tract obstruction]; P=1.46×10^-8) is predicted to disrupt the binding sites of 4 transcription factors known to participate in cardiac development in the promoter of SPAG9. A tissue-specific model of chromatin conformation suggests that common variant rs78256848 (minor allele frequency, 0.11 [odds ratio, 1.4 for Conotruncal heart disease]; P=2.6×10^-8) physically interacts with NCAM1 (P_FDR=1.86×10^-27), a neural adhesion molecule acting in cardiac development. Importantly, while each individual malformation displayed substantial heritability (observed h2 ranging from 0.26 for complex malformations to 0.37 for left ventricular outflow tract obstructive disease) the risk for different CHD malformations appeared to be separate, without genetic correlation measured by linkage disequilibrium score regression or regional colocalization.

CONCLUSIONS

We describe a set of rare noncoding variants conferring significant risk for individual heart malformations which are linked to genes governing cardiac development. These results illustrate that the oligogenic basis of CHD and significant heritability may be linked to rare variants outside protein-coding regions conferring substantial risk for individual categories of cardiac malformation.

Comparing chromatin contact maps at scale: methods and insights

Comparing chromatin contact maps is an essential step in quantifying how three-dimensional (3D) genome organization shapes development, evolution, and disease. However, no gold standard exists for comparing contact maps, and even simple methods often disagree. In this study, we propose novel comparison methods and evaluate them alongside existing approaches using genome-wide Hi-C data and 22,500 in silico predicted contact maps. We also quantify the robustness of methods to common sources of biological and technical variation, such as boundary size and noise. We find that simple difference-based methods such as mean squared error are suitable for initial screening, but biologically informed methods are necessary to identify why maps diverge and propose specific functional hypotheses. We provide a reference guide, codebase, and benchmark for rapidly comparing chromatin contact maps at scale to enable biological insights into the 3D organization of the genome.

Identifying species-specific k-mers for fast and accurate metagenotyping with Maast and GT-Pro

Genotyping single-nucleotide polymorphisms (SNPs) in microbiomes enables strain-level quantification. In this protocol, we describe a computational pipeline that performs fast and accurate SNP genotyping using metagenomic data. We first demonstrate how to use Maast to catalog SNPs from microbial genomes. Then we use GT-Pro to extract unique SNP-covering k-mers, optimize a data structure for storing these k-mers, and finally perform metagenotyping. For proof of concept, the protocol leverages public whole-genome sequences to metagenotype a synthetic community. For complete details on the use and execution of this protocol, please refer to Shi et al. (2022a)1 and Shi et al. (2022b).2.

Machine learning dissection of human accelerated regions in primate neurodevelopment

Using machine learning (ML), we interrogated the function of all human-chimpanzee variants in 2,645 human accelerated regions (HARs), finding 43% of HARs have variants with large opposing effects on chromatin state and 14% on neurodevelopmental enhancer activity. This pattern, consistent with compensatory evolution, was confirmed using massively parallel reporter assays in chimpanzee and human neural progenitor cells. The species-specific enhancer activity of HARs was accurately predicted from the presence and absence of transcription factor footprints in each species. Despite these striking cis effects, activity of a given HAR sequence was nearly identical in human and chimpanzee cells. This suggests that HARs did not evolve to compensate for changes in the trans environment but instead altered their ability to bind factors present in both species. Thus, ML prioritized variants with functional effects on human neurodevelopment and revealed an unexpected reason why HARs may have evolved so rapidly.

Leveraging Base Pair Mammalian Constraint to Understand Genetic Variation and Human Disease

Although thousands of genomic regions have been associated with heritable human diseases, attempts to elucidate biological mechanisms are impeded by a general inability to discern which genomic positions are functionally important. Evolutionary constraint is a powerful predictor of function that is agnostic to cell type or disease mechanism. Here, single base phyloP scores from the whole genome alignment of 240 placental mammals identified 3.5% of the human genome as significantly constrained, and likely functional. We compared these scores to large-scale genome annotation, genome-wide association studies (GWAS), copy number variation, clinical genetics findings, and cancer data sets. Evolutionarily constrained positions are enriched for variants explaining common disease heritability (more than any other functional annotation). Our results improve variant annotation but also highlight that the regulatory landscape of the human genome still needs to be further explored and linked to disease.

Cross-ancestry, cell-type-informed atlas of gene, isoform, and splicing regulation in the developing human brain

Genomic regulatory elements active in the developing human brain are notably enriched in genetic risk for neuropsychiatric disorders, including autism spectrum disorder (ASD), schizophrenia, and bipolar disorder. However, prioritizing the specific risk genes and candidate molecular mechanisms underlying these genetic enrichments has been hindered by the lack of a single unified large-scale gene regulatory atlas of human brain development. Here, we uniformly process and systematically characterize gene, isoform, and splicing quantitative trait loci (xQTLs) in 672 fetal brain samples from unique subjects across multiple ancestral populations. We identify 15,752 genes harboring a significant xQTL and map 3,739 eQTLs to a specific cellular context. We observe a striking drop in gene expression and splicing heritability as the human brain develops. Isoform-level regulation, particularly in the second trimester, mediates the greatest proportion of heritability across multiple psychiatric GWAS, compared with eQTLs. Via colocalization and TWAS, we prioritize biological mechanisms for ~60% of GWAS loci across five neuropsychiatric disorders, nearly two-fold that observed in the adult brain. Finally, we build a comprehensive set of developmentally regulated gene and isoform co-expression networks capturing unique genetic enrichments across disorders. Together, this work provides a comprehensive view of genetic regulation across human brain development as well as the stage-and cell type-informed mechanistic underpinnings of neuropsychiatric disorders.

Massively parallel characterization of psychiatric disorder-associated and cell-type-specific regulatory elements in the developing human cortex

Nucleotide changes in gene regulatory elements are important determinants of neuronal development and disease. Using massively parallel reporter assays in primary human cells from mid-gestation cortex and cerebral organoids, we interrogated the cis-regulatory activity of 102,767 sequences, including differentially accessible cell-type specific regions in the developing cortex and single-nucleotide variants associated with psychiatric disorders. In primary cells, we identified 46,802 active enhancer sequences and 164 disorder-associated variants that significantly alter enhancer activity. Activity was comparable in organoids and primary cells, suggesting that organoids provide an adequate model for the developing cortex. Using deep learning, we decoded the sequence basis and upstream regulators of enhancer activity. This work establishes a comprehensive catalog of functional gene regulatory elements and variants in human neuronal development.

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 tools that perform this metagenotyping rely on aligning reads of unknown origin to a database of sequences from many species before calling variants. In this synthesis, we investigate how databases of increasingly diverse and closely related species have pushed the limits of current alignment algorithms, thereby degrading the performance of metagenotyping tools. We identify multi-mapping reads as a prevalent source of errors and illustrate a trade-off between retaining correct alignments versus limiting incorrect alignments, many of which map reads to the wrong species. Then we evaluate several actionable mitigation strategies and review emerging methods showing promise to further improve metagenotyping in response to the rapid growth in genome collections. Our results have implications beyond metagenotyping to the many tools in microbial genomics that depend upon accurate read mapping.

An atlas of lamina-associated chromatin across twelve human cell types reveals an intermediate chromatin subtype

BACKGROUND

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, lamina-associated domains (LADs) are mapped genome-wide in relatively few genetically normal human cell types, which limits our understanding of the role peripheral chromatin plays in development and disease.

RESULTS

To address this gap, we map LAMIN B1 occupancy across twelve human cell types encompassing pluripotent stem cells, intermediate progenitors, and differentiated cells from all three germ layers. Integrative analyses of this atlas with gene expression and repressive histone modification maps reveal that lamina-associated chromatin in all twelve cell types is organized into at least two subtypes defined by differences in LAMIN B1 occupancy, gene expression, chromatin accessibility, transposable elements, replication timing, and radial positioning. Imaging of fluorescently labeled DNA in single cells validates these subtypes and shows radial positioning of LADs with higher LAMIN B1 occupancy and heterochromatic histone modifications primarily embedded within the lamina. In contrast, the second subtype of lamina-associated chromatin is relatively gene dense, accessible, dynamic across development, and positioned adjacent to the lamina. Most genes gain or lose LAMIN B1 occupancy consistent with cell types along developmental trajectories; however, we also identify examples where the enhancer, but not the gene body and promoter, changes LAD state.

CONCLUSIONS

Altogether, this atlas represents the largest resource to date for peripheral chromatin organization studies and reveals an intermediate chromatin subtype.

Chromatin Remodeling Drives Immune-Fibroblast Crosstalk in Heart Failure Pathogenesis

Chronic inflammation and tissue fibrosis are common stress responses that worsen organ function, yet the molecular mechanisms governing their crosstalk are poorly understood. In diseased organs, stress-induced changes in gene expression fuel maladaptive cell state transitions and pathological interaction between diverse cellular compartments. Although chronic fibroblast activation worsens dysfunction of lung, liver, kidney, and heart, and exacerbates many cancers, the stress-sensing mechanisms initiating the transcriptional activation of fibroblasts are not well understood. Here, we show that conditional deletion of the transcription co-activator Brd4 in Cx3cr1-positive myeloid cells ameliorates heart failure and is associated with a dramatic reduction in fibroblast activation. Analysis of single-cell chromatin accessibility and BRD4 occupancy in vivo in Cx3cr1-positive cells identified a large enhancer proximal to Interleukin-1 beta (Il1b), and a series of CRISPR deletions revealed the precise stress-dependent regulatory element that controlled expression of Il1b in disease. Secreted IL1B functioned non-cell autonomously to activate a p65/RELA-dependent enhancer near the transcription factor MEOX1, resulting in a profibrotic response in human cardiac fibroblasts. In vivo, antibody-mediated IL1B neutralization prevented stress-induced expression of MEOX1, inhibited fibroblast activation, and improved cardiac function in heart failure. The elucidation of BRD4-dependent crosstalk between a specific immune cell subset and fibroblasts through IL1B provides new therapeutic strategies for heart disease and other disorders of chronic inflammation and maladaptive tissue remodeling.

MIDAS2: Metagenomic Intra-species Diversity Analysis System

SUMMARY

The Metagenomic Intra-Species Diversity Analysis System (MIDAS) is a scalable metagenomic pipeline that identifies single nucleotide variants (SNVs) and gene copy number variants 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 IMPLEMENTATION

The source code is available at https://github.com/czbiohub/MIDAS2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genotyping Microbial Communities with MIDAS2: From Metagenomic Reads to Allele Tables

The Metagenomic Intra-Species Diversity Analysis System 2 (MIDAS2) is a scalable pipeline that identifies single nucleotide variants and gene copy number variants in metagenomes using comprehensive reference databases built from public microbial genome collections (metagenotyping). MIDAS2 is the first metagenotyping tool with functionality to control metagenomic read mapping filters and to customize the reference database to the microbial community, features that improve the precision and recall of detected variants. In this article we present four basic protocols for the most common use cases of MIDAS2, along with supporting protocols for installation and use. In addition, we provide in-depth guidance on adjusting command line parameters, editing the reference database, optimizing hardware utilization, and understanding the metagenotyping results. All the steps of metagenotyping, from raw sequencing reads to population genetic analysis, are demonstrated with example data in two downloadable sequencing libraries of single-end metagenomic reads representing a mixture of multiple bacterial species. This set of protocols empowers users to accurately genotype hundreds of species in thousands of samples, providing rich genetic data for studying the evolution and strain-level ecology of microbial communities. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Species prescreening Basic Protocol 2: Download MIDAS reference database Basic Protocol 3: Population single nucleotide variant calling Basic Protocol 4: Pan-genome copy number variant calling Support Protocol 1: Installing MIDAS2 Support Protocol 2: Command line inputs Support Protocol 3: Metagenotyping with a custom collection of genomes Support Protocol 4: Metagenotyping with advanced parameters.

Enhancer Function and Evolutionary Roles of Human Accelerated Regions

Human accelerated regions (HARs) are the fastest-evolving sequences in the human genome. When HARs were discovered in 2006, their function was mysterious due to scant annotation of the noncoding genome. Diverse technologies, from transgenic animals to machine learning, have consistently shown that HARs function as gene regulatory enhancers with significant enrichment in neurodevelopment. It is now possible to quantitatively measure the enhancer activity of thousands of HARs in parallel and model how each nucleotide contributes to gene expression. These strategies have revealed that many human HAR sequences function differently than their chimpanzee orthologs, though individual nucleotide changes in the same HAR may have opposite effects, consistent with compensatory substitutions. To fully evaluate the role of HARs in human evolution, it will be necessary to experimentally and computationally dissect them across more cell types and developmental stages.

Host and gut bacteria share metabolic pathways for anti-cancer drug metabolism

Pharmaceuticals have extensive reciprocal interactions with the microbiome, but whether bacterial drug sensitivity and metabolism is driven by pathways conserved in host cells remains unclear. Here we show that anti-cancer fluoropyrimidine drugs inhibit the growth of gut bacterial strains from 6 phyla. In both Escherichia coli and mammalian cells, fluoropyrimidines disrupt pyrimidine metabolism. Proteobacteria and Firmicutes metabolized 5-fluorouracil to its inactive metabolite dihydrofluorouracil, mimicking the major host mechanism for drug clearance. The preTA operon was necessary and sufficient for 5-fluorouracil inactivation by E. coli, exhibited high catalytic efficiency for the reductive reaction, decreased the bioavailability and efficacy of oral fluoropyrimidine treatment in mice and was prevalent in the gut microbiomes of colorectal cancer patients. The conservation of both the targets and enzymes for metabolism of therapeutics across domains highlights the need to distinguish the relative contributions of human and microbial cells to drug efficacy and side-effect profiles.

Transcription Factor GATA4 Regulates Cell Type-Specific Splicing Through Direct Interaction With RNA in Human Induced Pluripotent Stem Cell-Derived Cardiac Progenitors

BACKGROUND

GATA4 (GATA-binding protein 4), a zinc finger-containing, DNA-binding transcription factor, is essential for normal cardiac development and homeostasis in mice and humans, and mutations in this gene have been reported in human heart defects. Defects in alternative splicing are associated with many heart diseases, yet relatively little is known about how cell type- or cell state-specific alternative splicing is achieved in the heart. Here, we show that GATA4 regulates cell type-specific splicing through direct interaction with RNA and the spliceosome in human induced pluripotent stem cell-derived cardiac progenitors.

METHODS

We leveraged a combination of unbiased approaches including affinity purification of GATA4 and mass spectrometry, enhanced cross-linking with immunoprecipitation, electrophoretic mobility shift assays, in vitro splicing assays, and unbiased transcriptomic analysis to uncover GATA4's novel function as a splicing regulator in human induced pluripotent stem cell-derived cardiac progenitors.

RESULTS

We found that GATA4 interacts with many members of the spliceosome complex in human induced pluripotent stem cell-derived cardiac progenitors. Enhanced cross-linking with immunoprecipitation demonstrated that GATA4 also directly binds to a large number of mRNAs through defined RNA motifs in a sequence-specific manner. In vitro splicing assays indicated that GATA4 regulates alternative splicing through direct RNA binding, resulting in functionally distinct protein products. Correspondingly, knockdown of GATA4 in human induced pluripotent stem cell-derived cardiac progenitors resulted in differential alternative splicing of genes involved in cytoskeleton organization and calcium ion import, with functional consequences associated with the protein isoforms.

CONCLUSIONS

This study shows that in addition to its well described transcriptional function, GATA4 interacts with members of the spliceosome complex and regulates cell type-specific alternative splicing via sequence-specific interactions with RNA. Several genes that have splicing regulated by GATA4 have functional consequences and many are associated with dilated cardiomyopathy, suggesting a novel role for GATA4 in achieving the necessary cardiac proteome in normal and stress-responsive conditions.

Brain-wide perception of the emotional valence of light is regulated by distinct hypothalamic neurons

Salient sensory stimuli are perceived by the brain, which guides both the timing and outcome of behaviors in a context-dependent manner. Light is such a stimulus, which is used in treating mood disorders often associated with a dysregulated hypothalamic-pituitary-adrenal stress axis. Relationships between the emotional valence of light and the hypothalamus, and how they interact to exert brain-wide impacts remain unclear. Employing larval zebrafish with analogous hypothalamic systems to mammals, we show in free-swimming animals that hypothalamic corticotropin releasing factor (CRFHy) neurons promote dark avoidance, and such role is not shared by other hypothalamic peptidergic neurons. Single-neuron projection analyses uncover processes extended by individual CRFHy neurons to multiple targets including sensorimotor and decision-making areas. In vivo calcium imaging uncovers a complex and heterogeneous response of individual CRFHy neurons to the light or dark stimulus, with a reduced overall sum of CRF neuronal activity in the presence of light. Brain-wide calcium imaging under alternating light/dark stimuli further identifies distinct and distributed photic response neuronal types. CRFHy neuronal ablation increases an overall representation of light in the brain and broadly enhances the functional connectivity associated with an exploratory brain state. These findings delineate brain-wide photic perception, uncover a previously unknown role of CRFHy neurons in regulating the perception and emotional valence of light, and suggest that light therapy may alleviate mood disorders through reducing an overall sum of CRF neuronal activity.

Cell Layers: uncovering clustering structure in unsupervised single-cell transcriptomic analysis

Motivation

Unsupervised 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.

Results

We developed Cell Layers, an interactive Sankey tool for the quantitative investigation of gene expression, co-expression, 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, providing novel insight into cell populations.

Availability and implementation

https://github.com/apblair/CellLayers.

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.

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.

Strain-resolved analysis in a randomized trial of antibiotic pretreatment and maintenance dose delivery mode with fecal microbiota transplant for ulcerative colitis

Fecal microbiota transplant is a promising therapy for ulcerative colitis. Parameters maximizing effectiveness and tolerability are not yet clear, and it is not known how import the transmission of donor microbes to patients is. Here (clinicaltrails.gov: NCT03006809) we have tested the effects of antibiotic pretreatment and compared two modes of maintenance dose delivery, capsules versus enema, in a randomized, pilot, open-label, 2 × 2 factorial design with 22 patients analyzed with mild to moderate UC. Clinically, the treatment was well-tolerated with favorable safety profile. Of patients who received antibiotic pretreatment, 6 of 11 experienced remission after 6 weeks of treatment, versus 2 of 11 non-pretreated patients (log odds ratio: 1.69, 95% confidence interval: −0.25 to 3.62). No significant differences were found between maintenance dosing via capsules versus enema. In exploratory analyses, microbiome turnover at both the species and strain levels was extensive and significantly more pronounced in the pretreated patients. Associations were also revealed between taxonomic turnover and changes in the composition of primary and secondary bile acids. Together these findings suggest that antibiotic pretreatment contributes to microbiome engraftment and possibly clinical effectiveness, and validate longitudinal strain tracking as a powerful way to monitor the dynamics and impact of microbiota transfer.

Fast and accurate metagenotyping of the human gut microbiome with GT-Pro

Single nucleotide polymorphisms (SNPs) in metagenomics are used to quantify population structure, track strains and identify genetic determinants of microbial phenotypes. However, existing alignment-based approaches for metagenomic SNP detection require high-performance computing and enough read coverage to distinguish SNPs from sequencing errors. To address these issues, we developed the GenoTyper for Prokaryotes (GT-Pro), a suite of methods to catalog SNPs from genomes and use unique k-mers to rapidly genotype these SNPs from metagenomes. Compared to methods that use read alignment, GT-Pro is more accurate and two orders of magnitude faster. Using high-quality genomes, we constructed a catalog of 104 million SNPs in 909 human gut species and used unique k-mers targeting this catalog to characterize the global population structure of gut microbes from 7,459 samples. GT-Pro enables fast and memory-efficient metagenotyping of millions of SNPs on a personal computer.

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

Summary

CellWalkR is an R package that integrates single-cell open chromatin data with cell type labels and bulk epigenetic data to identify cell type-specific regulatory regions. A Graphics Processing Unit (GPU) implementation and downsampling strategies enable thousands of cells to be processed in seconds. CellWalkR’s user-friendly interface provides interactive analysis and visualization of cell labels and regulatory region mappings.

Availability and implementation

CellWalkR 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.

Supplementary information

Supplementary data are available at Bioinformatics online.

Transcription factor protein interactomes reveal genetic determinants in heart disease

Congenital heart disease (CHD) is present in 1% of live births, yet identification of causal mutations remains challenging. We hypothesized that genetic determinants for CHDs may lie in the protein interactomes of transcription factors whose mutations cause CHDs. Defining the interactomes of two transcription factors haplo-insufficient in CHD, GATA4 and TBX5, within human cardiac progenitors, and integrating the results with nearly 9,000 exomes from proband-parent trios revealed an enrichment of de novo missense variants associated with CHD within the interactomes. Scoring variants of interactome members based on residue, gene, and proband features identified likely CHD-causing genes, including the epigenetic reader GLYR1. GLYR1 and GATA4 widely co-occupied and co-activated cardiac developmental genes, and the identified GLYR1 missense variant disrupted interaction with GATA4, impairing in vitro and in vivo function in mice. This integrative proteomic and genetic approach provides a framework for prioritizing and interrogating genetic variants in heart disease.

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

Deleterious genetic variants in POGZ, which encodes the chromatin regulator Pogo Transposable Element with ZNF Domain protein, are strongly associated with autism spectrum disorder (ASD). Although it is a high-confidence ASD risk gene, the neurodevelopmental functions of POGZ remain unclear. Here we reveal the genomic binding of POGZ in the developing forebrain at euchromatic loci and gene regulatory elements (REs). We profile chromatin accessibility and gene expression in Pogz-/- mice and show that POGZ promotes the active chromatin state and transcription of clustered synaptic genes. We further demonstrate that POGZ forms a nuclear complex and co-occupies loci with ADNP, another high-confidence ASD risk gene, and provide evidence that POGZ regulates other neurodevelopmental disorder risk genes as well. Our results reveal a neurodevelopmental function of an ASD risk gene and identify molecular targets that may elucidate its function in ASD.

Single-cell epigenomics reveals mechanisms of human cortical development

During mammalian development, differences in chromatin state coincide with cellular differentiation and reflect changes in the gene regulatory landscape1. In the developing brain, cell fate specification and topographic identity are important for defining cell identity2 and confer selective vulnerabilities to neurodevelopmental disorders3. Here, to identify cell-type-specific chromatin accessibility patterns in the developing human brain, we used a single-cell assay for transposase accessibility by sequencing (scATAC-seq) in primary tissue samples from the human forebrain. We applied unbiased analyses to identify genomic loci that undergo extensive cell-type- and brain-region-specific changes in accessibility during neurogenesis, and an integrative analysis to predict cell-type-specific candidate regulatory elements. We found that cerebral organoids recapitulate most putative cell-type-specific enhancer accessibility patterns but lack many cell-type-specific open chromatin regions that are found in vivo. Systematic comparison of chromatin accessibility across brain regions revealed unexpected diversity among neural progenitor cells in the cerebral cortex and implicated retinoic acid signalling in the specification of neuronal lineage identity in the prefrontal cortex. Together, our results reveal the important contribution of chromatin state to the emerging patterns of cell type diversity and cell fate specification and provide a blueprint for evaluating the fidelity and robustness of cerebral organoids as a model for cortical development.

Accelerated RNA detection using tandem CRISPR nucleases

Direct, amplification-free detection of RNA has the potential to transform molecular diagnostics by enabling simple on-site analysis of human or environmental samples. CRISPR-Cas nucleases offer programmable RNA-guided RNA recognition that triggers cleavage and release of a fluorescent reporter molecule, but long reaction times hamper their detection sensitivity and speed. Here, we show that unrelated CRISPR nucleases can be deployed in tandem to provide both direct RNA sensing and rapid signal generation, thus enabling robust detection of ~30 molecules per µl of RNA in 20 min. Combining RNA-guided Cas13 and Csm6 with a chemically stabilized activator creates a one-step assay that can detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA extracted from respiratory swab samples with quantitative reverse transcriptase PCR (qRT-PCR)-derived cycle threshold (Ct) values up to 33, using a compact detector. This Fast Integrated Nuclease Detection In Tandem (FIND-IT) approach enables sensitive, direct RNA detection in a format that is amenable to point-of-care infection diagnosis as well as to a wide range of other diagnostic or research applications.