Corrigendum: Chromatin structure and context-dependent sequence features control prime editing efficiency.

[This corrects the article DOI: 10.3389/fgene.2023.1222112.].

Novel SOX10 indel mutations drive schwannomas through impaired transactivation of myelination gene programs.

BACKGROUND: Schwannomas are common peripheral nerve sheath tumors that can cause severe morbidity given their stereotypic intracranial and paraspinal locations. Similar to many solid tumors, schwannomas and other nerve sheath tumors are primarily thought to arise due to aberrant hyperactivation of the RAS growth factor signaling pathway. Here, we sought to further define the molecular pathogenesis of schwannomas. METHODS: We performed comprehensive genomic profiling on a cohort of 96 human schwannomas, as well as DNA methylation profiling on a subset. Functional studies including RNA sequencing, chromatin immunoprecipitation-DNA sequencing, electrophoretic mobility shift assay, and luciferase reporter assays were performed in a fetal glial cell model following transduction with wildtype and tumor-derived mutant isoforms of SOX10. RESULTS: We identified that nearly one-third of sporadic schwannomas lack alterations in known nerve sheath tumor genes and instead harbor novel recurrent in-frame insertion/deletion mutations in SOX10, which encodes a transcription factor responsible for controlling Schwann cell differentiation and myelination. SOX10 indel mutations were highly enriched in schwannomas arising from nonvestibular cranial nerves (eg facial, trigeminal, vagus) and were absent from vestibular nerve schwannomas driven by NF2 mutation. Functional studies revealed these SOX10 indel mutations have retained DNA binding capacity but impaired transactivation of glial differentiation and myelination gene programs. CONCLUSIONS: We thus speculate that SOX10 indel mutations drive a unique subtype of schwannomas by impeding proper differentiation of immature Schwann cells.

Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation.

Understanding the impact of regulatory variants on complex phenotypes is a significant challenge because the genes and pathways that are targeted by such variants and the cell type context in which regulatory variants operate are typically unknown. Cell-type-specific long-range regulatory interactions that occur between a distal regulatory sequence and a gene offer a powerful framework for examining the impact of regulatory variants on complex phenotypes. However, high-resolution maps of such long-range interactions are available only for a handful of cell types. Furthermore, identifying specific gene subnetworks or pathways that are targeted by a set of variants is a significant challenge. We have developed L-HiC-Reg, a Random Forests regression method to predict high-resolution contact counts in new cell types, and a network-based framework to identify candidate cell-type-specific gene networks targeted by a set of variants from a genome-wide association study (GWAS). We applied our approach to predict interactions in 55 Roadmap Epigenomics Mapping Consortium cell types, which we used to interpret regulatory single nucleotide polymorphisms (SNPs) in the NHGRI-EBI GWAS catalogue. Using our approach, we performed an in-depth characterization of fifteen different phenotypes including schizophrenia, coronary artery disease (CAD) and Crohn's disease. We found differentially wired subnetworks consisting of known as well as novel gene targets of regulatory SNPs. Taken together, our compendium of interactions and the associated network-based analysis pipeline leverages long-range regulatory interactions to examine the context-specific impact of regulatory variation in complex phenotypes.

Chromatin structure and context-dependent sequence features control prime editing efficiency.

Prime editing (PE) is a highly versatile CRISPR-Cas9 genome editing technique. The current constructs, however, have variable efficiency and may require laborious experimental optimization. This study presents statistical models for learning the salient epigenomic and sequence features of target sites modulating the editing efficiency and provides guidelines for designing optimal PEs. We found that both regional constitutive heterochromatin and local nucleosome occlusion of target sites impede editing, while position-specific G/C nucleotides in the primer-binding site (PBS) and reverse transcription (RT) template regions of PE guide RNA (pegRNA) yield high editing efficiency, especially for short PBS designs. The presence of G/C nucleotides was most critical immediately 5' to the protospacer adjacent motif (PAM) site for all designs. The effects of different last templated nucleotides were quantified and observed to depend on the length of both PBS and RT templates. Our models found AGG to be the preferred PAM and detected a guanine nucleotide four bases downstream of the PAM to facilitate editing, suggesting a hitherto-unrecognized interaction with Cas9. A neural network interpretation method based on nonextensive statistical mechanics further revealed multi-nucleotide preferences, indicating dependency among several bases across pegRNA. Our work clarifies previous conflicting observations and uncovers context-dependent features important for optimizing PE designs.

Chromatin structure and context-dependent sequence features control prime editing efficiency.

Prime editor (PE) is a highly versatile CRISPR-Cas9 genome editing technique. The current constructs, however, have variable efficiency and may require laborious experimental optimization. This study presents statistical models for learning the salient epigenomic and sequence features of target sites modulating the editing efficiency and provides guidelines for designing optimal PEs. We found that both regional constitutive heterochromatin and local nucleosome occlusion of target sites impede editing, while position-specific G/C nucleotides in the primer binding site (PBS) and reverse transcription (RT) template regions of PE guide-RNA (pegRNA) yield high editing efficiency, especially for short PBS designs. The presence of G/C nucleotides was most critical immediately 5' to the protospacer adjacent motif (PAM) site for all designs. The effects of different last templated nucleotides were quantified and seen to depend on both PBS and RT template lengths. Our models found AGG to be the preferred PAM and detected a guanine nucleotide four bases downstream of PAM to facilitate editing, suggesting a hitherto-unrecognized interaction with Cas9. A neural network interpretation method based on nonextensive statistical mechanics further revealed multi-nucleotide preferences, indicating dependency among several bases across pegRNA. Our work clarifies previous conflicting observations and uncovers context-dependent features important for optimizing PE designs.

Spectral clustering of single-cell multi-omics data on multilayer graphs.

MOTIVATION: Single-cell sequencing technologies that simultaneously generate multimodal cellular profiles present opportunities for improved understanding of cell heterogeneity in tissues. How the multimodal information can be integrated to obtain a common cell type identification, however, poses a computational challenge. Multilayer graphs provide a natural representation of multi-omic single-cell sequencing datasets, and finding cell clusters may be understood as a multilayer graph partition problem. RESULTS: We introduce two spectral algorithms on multilayer graphs, spectral clustering on multilayer graphs and the weighted locally linear (WLL) method, to cluster cells in multi-omic single-cell sequencing datasets. We connect these algorithms through a unifying mathematical framework that represents each layer using a Hamiltonian operator and a mixture of its eigenstates to integrate the multiple graph layers, demonstrating in the process that the WLL method is a rigorous multilayer spectral graph theoretic reformulation of the popular Seurat weighted nearest neighbor (WNN) algorithm. Implementing our algorithms and applying them to a CITE-seq dataset of cord blood mononuclear cells yields results similar to the Seurat WNN analysis. Our work thus extends spectral methods to multimodal single-cell data analysis. AVAILABILITY AND IMPLEMENTATION: The code used in this study can be found at https://github.com/jssong-lab/sc-spectrum. All public data used in the article are accurately cited and described in Materials and Methods and in Supplementary Information. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SOX10 Regulates Melanoma Immunogenicity through an IRF4-IRF1 Axis.

Loss-of-function mutations of JAK1/2 impair cancer cell responsiveness to IFNγ and immunogenicity. Therefore, an understanding of compensatory pathways to activate IFNγ signaling in cancer cells is clinically important for the success of immunotherapy. Here we demonstrate that the transcription factor SOX10 hinders immunogenicity of melanoma cells through the IRF4-IRF1 axis. Genetic and pharmacologic approaches revealed that SOX10 repressed IRF1 transcription via direct induction of a negative regulator, IRF4. The SOX10-IRF4-IRF1 axis regulated PD-L1 expression independently of JAK-STAT pathway activity, and suppression of SOX10 increased the efficacy of combination therapy with an anti-PD-1 antibody and histone deacetylase inhibitor against a clinically relevant melanoma model. Thus, the SOX10-IRF4-IRF1 axis serves as a potential target that can bypass JAK-STAT signaling to immunologically warm up melanoma with a "cold" tumor immune microenvironment. SIGNIFICANCE: This study identifies a novel SOX10/IRF4 pathway that regulates noncanonical induction of IRF1 independent of the JAK-STAT pathway and can be targeted to improve the efficacy of anti-PD-1 therapy in melanoma.

Chd1 protects genome integrity at promoters to sustain hypertranscription in embryonic stem cells.

Stem and progenitor cells undergo a global elevation of nascent transcription, or hypertranscription, during key developmental transitions involving rapid cell proliferation. The chromatin remodeler Chd1 mediates hypertranscription in pluripotent cells but its mechanism of action remains poorly understood. Here we report a novel role for Chd1 in protecting genome integrity at promoter regions by preventing DNA double-stranded break (DSB) accumulation in ES cells. Chd1 interacts with several DNA repair factors including Atm, Parp1, Kap1 and Topoisomerase 2ß and its absence leads to an accumulation of DSBs at Chd1-bound Pol II-transcribed genes and rDNA. Genes prone to DNA breaks in Chd1 KO ES cells are longer genes with GC-rich promoters, a more labile nucleosomal structure and roles in chromatin regulation, transcription and signaling. These results reveal a vulnerability of hypertranscribing stem cells to accumulation of endogenous DNA breaks, with important implications for developmental and cancer biology.

Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning.

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.

Epigenomic tensor predicts disease subtypes and reveals constrained tumor evolution.

Understanding the epigenomic evolution and specificity of disease subtypes from complex patient data remains a major biomedical problem. We here present DeCET (decomposition and classification of epigenomic tensors), an integrative computational approach for simultaneously analyzing hierarchical heterogeneous data, to identify robust epigenomic differences among tissue types, differentiation states, and disease subtypes. Applying DeCET to our own data from 21 uterine benign tumor (leiomyoma) patients identifies distinct epigenomic features discriminating normal myometrium and leiomyoma subtypes. Leiomyomas possess preponderant alterations in distal enhancers and long-range histone modifications confined to chromatin contact domains that constrain the evolution of pathological epigenomes. Moreover, we demonstrate the power and advantage of DeCET on multiple publicly available epigenomic datasets representing different cancers and cellular states. Epigenomic features extracted by DeCET can thus help improve our understanding of disease states, cellular development, and differentiation, thereby facilitating future therapeutic, diagnostic, and prognostic strategies.

Measuring DNA mechanics on the genome scale.

Mechanical deformations of DNA such as bending are ubiquitous and have been implicated in diverse cellular functions1. However, the lack of high-throughput tools to measure the mechanical properties of DNA has limited our understanding of how DNA mechanics influence chromatin transactions across the genome. Here we develop 'loop-seq'-a high-throughput assay to measure the propensity for DNA looping-and determine the intrinsic cyclizabilities of 270,806 50-base-pair DNA fragments that span Saccharomyces cerevisiae chromosome V, other genomic regions, and random sequences. We found sequence-encoded regions of unusually low bendability within nucleosome-depleted regions upstream of transcription start sites (TSSs). Low bendability of linker DNA inhibits nucleosome sliding into the linker by the chromatin remodeller INO80, which explains how INO80 can define nucleosome-depleted regions in the absence of other factors2. Chromosome-wide, nucleosomes were characterized by high DNA bendability near dyads and low bendability near linkers. This contrast increases for deeper gene-body nucleosomes but disappears after random substitution of synonymous codons, which suggests that the evolution of codon choice has been influenced by DNA mechanics around gene-body nucleosomes. Furthermore, we show that local DNA mechanics affect transcription through TSS-proximal nucleosomes. Overall, this genome-scale map of DNA mechanics indicates a 'mechanical code' with broad functional implications.

Functional analysis of low-grade glioma genetic variants predicts key target genes and transcription factors.

BACKGROUND: Large-scale genome-wide association studies (GWAS) have implicated thousands of germline genetic variants in modulating individuals' risk to various diseases, including cancer. At least 25 risk loci have been identified for low-grade gliomas (LGGs), but their molecular functions remain largely unknown. METHODS: We hypothesized that GWAS loci contain causal single nucleotide polymorphisms (SNPs) that reside in accessible open chromatin regions and modulate the expression of target genes by perturbing the binding affinity of transcription factors (TFs). We performed an integrative analysis of genomic and epigenomic data from The Cancer Genome Atlas and other public repositories to identify candidate causal SNPs within linkage disequilibrium blocks of LGG GWAS loci. We assessed their potential regulatory role via in silico TF binding sequence perturbations, convolutional neural network trained on TF binding data, and simulated annealing-based interpretation methods. RESULTS: We built an interactive website (http://education.knoweng.org/alg3/) summarizing the functional footprinting of 280 variants in 25 LGG GWAS regions, providing rich information for further computational and experimental scrutiny. We identified as case studies PHLDB1 and SLC25A26 as candidate target genes of rs12803321 and rs11706832, respectively, and predicted the GWAS variant rs648044 to be the causal SNP modulating ZBTB16, a known tumor suppressor in multiple cancers. We showed that rs648044 likely perturbed the binding affinity of the TF MAFF, as supported by RNA interference and in vitro MAFF binding experiments. CONCLUSIONS: The identified candidate (causal SNP, target gene, TF) triplets and the accompanying resource will help accelerate our understanding of the molecular mechanisms underlying genetic risk factors for gliomas.

Single-Cell Profiling Reveals Divergent, Globally Patterned Immune Responses in Murine Skin Inflammation.

Inflammatory response heterogeneity has impeded high-resolution dissection of diverse immune cell populations during activation. We characterize mouse cutaneous immune cells by single-cell RNA sequencing, after inducing inflammation using imiquimod and oxazolone dermatitis models. We identify 13 CD45+ subpopulations, which broadly represent most functionally characterized immune cell types. Oxazolone pervasively upregulates Jak2/Stat3 expression across T cells and antigen-presenting cells (APCs). Oxazolone also induces Il4/Il13 expression in newly infiltrating basophils, and Il4ra and Ccl24, most prominently in APCs. In contrast, imiquimod broadly upregulates Il17/Il22 and Ccl4/Ccl5. A comparative analysis of single-cell inflammatory transcriptional responses reveals that APC response to oxazolone is tightly restricted by cell identity, whereas imiquimod enforces shared programs on multiple APC populations in parallel. These global molecular patterns not only contrast immune responses on a systems level but also suggest that the mechanisms of new sources of inflammation can eventually be deduced by comparison to known signatures.

ABC-GWAS: Functional Annotation of Estrogen Receptor-Positive Breast Cancer Genetic Variants.

Over the past decade, hundreds of genome-wide association studies (GWAS) have implicated genetic variants in various diseases, including cancer. However, only a few of these variants have been functionally characterized to date, mainly because the majority of the variants reside in non-coding regions of the human genome with unknown function. A comprehensive functional annotation of the candidate variants is thus necessary to fill the gap between the correlative findings of GWAS and the development of therapeutic strategies. By integrating large-scale multi-omics datasets such as the Cancer Genome Atlas (TCGA) and the Encyclopedia of DNA Elements (ENCODE), we performed multivariate linear regression analysis of expression quantitative trait loci, sequence permutation test of transcription factor binding perturbation, and modeling of three-dimensional chromatin interactions to analyze the potential molecular functions of 2,813 single nucleotide variants in 93 genomic loci associated with estrogen receptor-positive breast cancer. To facilitate rapid progress in functional genomics of breast cancer, we have created "Analysis of Breast Cancer GWAS" (ABC-GWAS), an interactive database of functional annotation of estrogen receptor-positive breast cancer GWAS variants. Our resource includes expression quantitative trait loci, long-range chromatin interaction predictions, and transcription factor binding motif analyses to prioritize putative target genes, causal variants, and transcription factors. An embedded genome browser also facilitates convenient visualization of the GWAS loci in genomic and epigenomic context. ABC-GWAS provides an interactive visual summary of comprehensive functional characterization of estrogen receptor-positive breast cancer variants. The web resource will be useful to both computational and experimental biologists who wish to generate and test their hypotheses regarding the genetic susceptibility, etiology, and carcinogenesis of breast cancer. ABC-GWAS can also be used as a user-friendly educational resource for teaching functional genomics. ABC-GWAS is available at http://education.knoweng.org/abc-gwas/.

Riemannian geometry and statistical modeling correct for batch effects and control false discoveries in single-cell surface protein count data.

Recent advances in next generation sequencing-based single-cell technologies have allowed high-throughput quantitative detection of cell-surface proteins along with the transcriptome in individual cells, extending our understanding of the heterogeneity of cell populations in diverse tissues that are in different diseased states or under different experimental conditions. Count data of surface proteins from the cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) technology pose new computational challenges, and there is currently a dearth of rigorous mathematical tools for analyzing the data. This work utilizes concepts and ideas from Riemannian geometry to remove batch effects between samples and develops a statistical framework for distinguishing positive signals from background noise. The strengths of these approaches are demonstrated on two independent CITE-seq data sets in mouse and human.

Adult diffuse glioma GWAS by molecular subtype identifies variants in D2HGDH and FAM20C.

BACKGROUND: Twenty-five germline variants are associated with adult diffuse glioma, and some of these variants have been shown to be associated with particular subtypes of glioma. We hypothesized that additional germline variants could be identified if a genome-wide association study (GWAS) were performed by molecular subtype. METHODS: A total of 1320 glioma cases and 1889 controls were used in the discovery set and 799 glioma cases and 808 controls in the validation set. Glioma cases were classified into molecular subtypes based on combinations of isocitrate dehydrogenase (IDH) mutation, telomerase reverse transcriptase (TERT) promoter mutation, and 1p/19q codeletion. Logistic regression was applied to the discovery and validation sets to test for associations of variants with each of the subtypes. A meta-analysis was subsequently performed using a genome-wide P-value threshold of 5 × 10-8. RESULTS: Nine variants in or near D-2-hydroxyglutarate dehydrogenase (D2HGDH) on chromosome 2 were genome-wide significant in IDH-mutated glioma (most significant was rs5839764, meta P = 2.82 × 10-10). Further stratifying by 1p/19q codeletion status, one variant in D2HGDH was genome-wide significant in IDH-mutated non-codeleted glioma (rs1106639, meta P = 4.96 × 10-8). Further stratifying by TERT mutation, one variant near FAM20C (family with sequence similarity 20, member C) on chromosome 7 was genome-wide significant in gliomas that have IDH mutation, TERT mutation, and 1p/19q codeletion (rs111976262, meta P = 9.56 × 10-9). Thirty-six variants in or near GMEB2 on chromosome 20 near regulator of telomere elongation helicase 1 (RTEL1) were genome-wide significant in IDH wild-type glioma (most significant was rs4809313, meta P = 2.60 × 10-10). CONCLUSIONS: Performing a GWAS by molecular subtype identified 2 new regions and a candidate independent region near RTEL1, which were associated with specific glioma molecular subtypes.

Epigenetic engineering of yeast reveals dynamic molecular adaptation to methylation stress and genetic modulators of specific DNMT3 family members.

Cytosine methylation is a ubiquitous modification in mammalian DNA generated and maintained by several DNA methyltransferases (DNMTs) with partially overlapping functions and genomic targets. To systematically dissect the factors specifying each DNMT's activity, we engineered combinatorial knock-in of human DNMT genes in Komagataella phaffii, a yeast species lacking endogenous DNA methylation. Time-course expression measurements captured dynamic network-level adaptation of cells to DNMT3B1-induced DNA methylation stress and showed that coordinately modulating the availability of S-adenosyl methionine (SAM), the essential metabolite for DNMT-catalyzed methylation, is an evolutionarily conserved epigenetic stress response, also implicated in several human diseases. Convolutional neural networks trained on genome-wide CpG-methylation data learned distinct sequence preferences of DNMT3 family members. A simulated annealing interpretation method resolved these preferences into individual flanking nucleotides and periodic poly(A) tracts that rotationally position highly methylated cytosines relative to phased nucleosomes. Furthermore, the nucleosome repeat length defined the spatial unit of methylation spreading. Gene methylation patterns were similar to those in mammals, and hypo- and hypermethylation were predictive of increased and decreased transcription relative to control, respectively, in the absence of mammalian readers of DNA methylation. Introducing controlled epigenetic perturbations in yeast thus enabled characterization of fundamental genomic features directing specific DNMT3 proteins.

Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform.

We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive "Knowledge Network." KnowEnG adheres to "FAIR" principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system's potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.

Targeted exon skipping with AAV-mediated split adenine base editors.

Techniques for exclusion of exons from mature transcripts have been applied as gene therapies for treating many different diseases. Since exon skipping has been traditionally accomplished using technologies that have a transient effect, it is particularly important to develop new techniques that enable permanent exon skipping. We have recently shown that this can be accomplished using cytidine base editors for permanently disabling the splice acceptor of target exons. We now demonstrate the application of CRISPR-Cas9 adenine deaminase base editors to disrupt the conserved adenine within splice acceptor sites for programmable exon skipping. We also demonstrate that by altering the amino acid sequence of the linker between the adenosine deaminase domain and the Cas9-nickase or by coupling the adenine base editor with a uracil glycosylase inhibitor, the DNA editing efficiency and exon-skipping rates improve significantly. Finally, we developed a split base editor architecture compatible with adeno-associated viral packaging. Collectively, these results represent significant progress toward permanent in vivo exon skipping through base editing and, ultimately, a new modality of gene therapy for the treatment of genetic diseases.