Biology subjects

Meyer, C. A.

Publications and source records attributed to Meyer, C. A..

17 recordsLinked to original sources

bioRxiv2026

Mutational consequences of perturbing DNA repair and chromatin state in Arabidopsis

Mutation rate varies throughout an organisms genome and correlates with many factors, including primary sequence, DNA methylation, gene content, chromatin accessibility, replication timing, and more. Prior work has shown that DNA repair pathways contribute to this variation by repairing certain regions more efficiently than others, sometimes through interactions with gene-associated histone modifications. However, little is known about the relative importance of different DNA repair pathways in shaping intragenomic variability in mutation rate, nor the mutational consequences of perturbing chromatin state. Here, we quantify somatic mutation rate in several DNA repair and chromatin-related Arabidopsis mutants using nanorate sequencing. We find that NER and MMR prevent a smaller fraction of mutations in transposable elements (TEs) compared to other regions of the genome, indicating these pathways are less efficient in heterochromatin. MMR appears to be more efficient in accessible chromatin regions, as its loss nearly abolishes the reduced mutation rate there. TC-NER is the only pathway with greater efficiency in genes than in non-genic non-TE regions, suggesting only TC-NER specifically targets genes. We assay six mutants for histone modifications/variants, but only one (h2a.w.7) displays an altered mutation rate. Instead, mutation rate is elevated in the chromatin remodeler mutant ddm1 and two RNA-directed DNA methylation (RdDM) mutants. The RdDM mutants have a doubled overall mutation rate, but this increase is not localized to RdDM target regions, implicating a transcriptional change, genomic instability, or a secondary function in DNA repair.

genomics↗

bioRxiv2025

Hybrids reveal accessible chromatin trans genetic associations

Crosses between genetically distant Zea mays (Maize) inbreds cause hybrid vigour (heterosis): an over-dominant increase in vegetative growth and grain yield. Although the contributing loci are unknown, heterosis strength varies polygenically, with crosses between stiff stalk and non-stiff stalk lineages often producing the highly heterotic F1 seed used for agriculture. As F1 genomes conserve the parental cis-regulatory state, F1 trans-interactions between haplotypes underpin F1 trait over-dominance and heterotic gene expression. Gene expression relies upon transcription factors binding cis-regulatory elements in accessible chromatin regions (ACRs) and recruiting RNA POLYMERASE II. Here, assay for transposase accessible chromatin sequencing (ATAC-seq) examined cis-regulatory element chromatin accessibility inheritance in three different F1 parent-offspring pairs, including a representative stiff stalk-by-non-stiff stalk (B73xOh43) cross. Using concatenated reference alignment, we categorized ACR modes of inheritance (additive, dominant, under/over-dominant), revealing that ACRs are largely inherited additively, and, concordantly, that chromatin accessibility has strong narrow-sense heritability. However, mirroring other heterotic phenotypes, the B73xOh43 cross exhibited the most dominant and over-dominant inheritance. Comparing allele-specific parent-to-offspring chromatin accessibility, we identified F1 differentially trans-regulated ACRs. Using a matched chromatin accessible diversity panel, we found B73xOh43 SNPs with more ACR trans-associations than expected due to chance. These trans-regulatory variants revealed how cell-division and anabolism are elevated in heterotic F1 seedlings. This multiple discovery filtering (MDF) approach to trans-regulatory variant detection is genomic modality agnostic and stands to empower trans-relationship dissection in diverse genetic models.

genetics↗

bioRxiv2025

NAB2-STAT6 Fusion Proteins Drive Nuclear Condensate Formation and Transcriptional Reprogramming in Solitary Fibrous Tumors

Solitary fibrous tumor (SFT) is a rare and aggressive sarcoma driven by NAB2-STAT6 gene fusions, yet effective targeted therapies remain unavailable. Here, we report that the NAB2ex4-STAT6ex2 fusion variant forms nuclear condensates via liquid-liquid phase separation (LLPS) in engineered fibroblast models and primary SFT cells. These condensates co-localize with BRD4S and EGR1, key transcriptional regulators, and are functionally active, driving widespread transcriptional reprogramming. Treatment with Mithramycin A, a compound that disrupts EGR1-DNA interactions, dissolves NAB2-STAT6 condensates and reverses their aberrant gene expression and chromatin binding signatures. Our findings uncover a previously unrecognized role for NAB2-STAT6 in condensate-mediated oncogenic signaling and provide a mechanistic rationale for condensate-targeted therapy in SFT.

cancer biology↗

bioRxiv2025

Expanding the DNA Motif Lexicon of the Transcriptional Regulatory Code

Transcriptional regulatory sequences in metazoans contain intricate combinations of transcription factor (TF) motifs. Stereospecific arrangements of simple motifs constitute composite elements (CEs) that enhance DNA-protein interaction specificity and enable combinatorial regulatory logic. Despite their importance, CEs remain underexplored. We advance CE discovery and functional characterization by developing an integrated framework that combines computational prediction, experimental testing and deep learning. The extended TF motif catalog comprises both synergistic and counteracting CEs, which are supported by evidence of TF binding in vivo and in vitro. A deep learning model GRACE trained on customized massively parallel reporter assays learns the lexicon of CEs at single-nucleotide resolution. Comparative analysis with a neural network model trained on chromatin accessibility demonstrates striking convergence and distinctions within the expanded regulatory lexicon, enabling joint predictions of motif contributions and the impact of variants on chromatin structure and transcriptional activity in diverse cellular contexts.

genomics↗

bioRxiv2025

Nanorate sequencing reveals the Arabidopsis somatic mutation landscape

The rate and spectrum of somatic mutations can diverge from that of germline mutations. This is because somatic tissues experience different mutagenic processes than germline tissues. Here, we use nanorate sequencing (NanoSeq) to identify somatic mutations in Arabidopsis shoots with high sensitivity. We report a somatic mutation rate of 3.6x10-8 mutations/bp, [~]4-5x the germline mutation rate. Somatic mutations displayed elevated signatures consistent with oxidative damage, UV damage, and transcription-coupled nucleotide excision repair. Both somatic and germline mutations were enriched in transposable elements and depleted in genes, but this depletion was greater in germline mutations. Somatic mutation rate correlated with proximity to the centromere, DNA methylation, chromatin accessibility, and gene/TE content, properties which were also largely true of germline mutations. We note DNA methylation and chromatin accessibility have different predicted effects on mutation rate for genic and non-genic regions; DNA methylation associates with a greater increase in mutation rate when in non-genic regions, and accessible chromatin associates with a lower mutation rate in non-genic regions but a higher mutation rate in genic regions. Together, these results characterize key differences and similarities in the genomic distribution of somatic and germline mutations.

genomics↗

bioRxiv2025

Identification of BET Inhibitors (BETi) Against Solitary Fibrous Tumor (SFT) Through High-Throughput Screening (HTS)

Cancers, especially fusion oncoprotein (FO)-driven hematological cancers and sarcomas, often develop from a low number of key mutations. Solitary Fibrous Tumor (SFT) is a rare mesenchymal tumor driven by the NAB2-STAT6 oncofusion gene. Currently, the treatment options for SFT remain limited, with anti-angiogenic drugs providing only partial responses and an average survival of two years. To address this challenge, we constructed SFT cell models harboring specific NAB2-STAT6 fusion transcripts using the CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) technology. High-throughput drug screens demonstrated that the BET inhibitor Mivebresib can differentially reduce proliferation in SFT cell models. Subsequently, BET inhibitors Mivebresib and BMS-986158 efficiently reduced tumor growth in an SFT patient-derived xenograft (PDX) animal model. Furthermore, our data showed that NAB2-STAT6 fusions may lead to higher levels of DNA damage in SFTs. Consequently, combining BET inhibitors with PARP (Poly (ADP-ribose) polymerase) or ATR inhibitors significantly enhanced anti-proliferative effects in SFT cells. Taken together, our study established BET inhibitors Mivebresib and BMS-986158 as promising anti-SFT agents. SignificanceNew therapies are a clinical need for patients with Solitary Fibrous Tumor. We demonstrated that BET inhibitors are highly active in the preclinical setting for the treatment of this sarcoma entity.

cancer biology↗

bioRxiv2024

Transcription factor dynamics, oscillation, and functions in human enteroendocrine cell differentiation

Enteroendocrine cells (EECs), which secrete serotonin (enterochromaffin cells, EC) or a dominant peptide hormone, serve vital physiologic functions. As with any adult human lineage, the basis for terminal cell diversity remains obscure. We replicated human EEC differentiation in vitro, mapped transcriptional and chromatin dynamics that culminate in discrete cell types, and studied abundant EEC precursors expressing selected transcription factors (TFs) and gene programs. Before expressing the pre-terminal factor NEUROD1, non-replicating precursors oscillated between epigenetically similar but transcriptionally distinct ASCL1+ and HES6hi cell states. Loss of either factor substantially accelerated EEC differentiation and disrupted EEC individuality; ASCL1 or NEUROD1 deficiency had opposing consequences on EC and hormone-producing cell features. Expressed late in EEC differentiation, the latter TFs mainly bind cis-elements that are accessible in undifferentiated stem cells and tailor the subsequent expression of TF combinations that specify EEC types. Thus, TF oscillations retard EEC maturation to enable accurate EEC diversification.

molecular biology↗

bioRxiv2023

scATAnno: Automated Cell Type Annotation for single-cell ATAC-seq Data

Recent advances in single-cell epigenomic techniques have created a growing demand for scATAC-seq analysis. One key analysis task is to determine cell type identity based on the epigenetic data. We introduce scATAnno, a python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates the reference atlases from publicly available datasets enabling accurate cell type annotation by integrating query data with reference atlases, without the use of scRNA-seq data. To enhance annotation accuracy, we have incorporated KNN-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compare and benchmark scATAnno against 7 other published approaches for cell annotation and show superior performance in multiple data sets and metrics. We showcase the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cell (PBMC), Triple Negative Breast Cancer (TNBC), and basal cell carcinoma (BCC), and demonstrate that scATAnno accurately annotates cell types across conditions. Overall, scATAnno is a useful tool for scATAC-seq reference building and cell type annotation in scATAC-seq data and can aid in the interpretation of new scATAC-seq datasets in complex biological systems.

bioinformatics↗

bioRxiv2022

MetaTiME: Meta-components of the Tumor Immune Microenvironment

Recent advances in single-cell RNA sequencing have revealed heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can reveal common cell types and states in the tumor microenvironment (TME). We developed a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.

genomics↗

bioRxiv2021

MIRA: Joint regulatory modeling of multimodal expression and chromatin accessibility in single cells

Rigorously comparing gene expression and chromatin accessibility in the same single cells could illuminate the logic of how coupling or decoupling of these mechanisms regulates fate commitment. Here, we present MIRA: Probabilistic Multimodal Models for Integrated Regulatory Analysis, a comprehensive methodology that systematically contrasts transcription and accessibility to infer the regulatory circuitry driving cells along developmental trajectories. MIRA leverages topic modeling of cell states and regulatory potential modeling of individual gene loci. MIRA thereby represents cell states in an efficient and interpretable latent space, infers high fidelity lineage trees, determines key regulators of fate decisions at branch points, and exposes the variable influence of local accessibility on transcription at distinct loci. Applied to epidermal maintenance differentiation and embryonic brain development from two different multimodal platforms, MIRA revealed that early developmental genes were tightly regulated by local chromatin landscape whereas terminal fate genes were titrated without requiring extensive chromatin remodeling.

bioinformatics↗

bioRxiv2021

Accurate estimation of intrinsic biases for improved analysis of chromatin accessibility sequencing data using SELMA

Genome-wide profiling of chromatin accessibility by DNase-seq or ATAC-seq has been widely used to identify regulatory DNA elements and transcription factor binding sites. However, enzymatic DNA cleavage exhibits intrinsic sequence biases that confound chromatin accessibility profiling data analysis. Existing computational tools are limited in their ability to account for such intrinsic biases and not designed for analyzing single-cell data. Here, we present Simplex Encoded Linear Model for Accessible Chromatin (SELMA), a computational method for systematic estimation of intrinsic cleavage biases from genomic chromatin accessibility profiling data. We demonstrate that SELMA yields accurate and robust bias estimation from both bulk and single-cell DNase-seq and ATAC-seq data. SELMA can utilize internal mitochondrial DNA data to improve bias estimation. We show that transcription factor binding inference from DNase footprints can be improved by incorporating estimated biases using SELMA. Furthermore, we show strong effects of intrinsic biases in single-cell ATAC-seq data, and develop the first single-cell ATAC-seq intrinsic bias correction model to improve cell clustering. SELMA can enhance the performance of existing bioinformatics tools and improve the analysis of both bulk and single-cell chromatin accessibility sequencing data.

bioinformatics↗

bioRxiv2021

CHIPS: A Snakemake pipeline for quality control and reproducible processing of chromatin profiling data

MotivationThe chromatin profile measured by ATAC-seq, ChIP-seq, or DNase-seq experiments can identify genomic regions critical in regulating gene expression and provide insights on biological processes such as diseases and development. However, quality control and processing chromatin profiling data involve many steps, and different bioinformatics tools are used at each step. It can be challenging to manage the analysis. ResultsWe developed a Snakemake pipeline called CHIPS (CHromatin enrichment Processor) to streamline the processing of ChIP-seq, ATAC-seq, and DNase-seq data. The pipeline supports single- and paired-end data and is flexible to start with FASTQ or BAM files. It includes basic steps such as read trimming, mapping, and peak calling. In addition, it calculates quality control metrics such as contamination profiles, PCR bottleneck coefficient, the fraction of reads in peaks, percentage of peaks overlapping with the union of public DNaseI hypersensitivity sites, and conservation profile of the peaks. For downstream analysis, it carries out peak annotations, motif finding, and regulatory potential calculation for all genes. The pipeline ensures that the processing is robust and reproducible. AvailabilityCHIPS is available at https://github.com/liulab-dfci/CHIPS

bioinformatics↗

bioRxiv2021

BCOR and BCORL1 mutations disrupt PRC1.1 repressive function in leukemia by unlinking the RING-PCGF1 enzymatic core from target genes

BCOR and its paralog BCORL1 encode subunits of the Polycomb repressive complex 1.1 (PRC1.1) and are recurrently mutated in myeloid malignancies. We show that leukemia-associated BCOR/BCORL1 mutations unlink the PRC1.1 RING-PCGF enzymatic core from the KDM2B-containing chromatin targeting auxiliary subcomplex, either by causing complete protein loss or expression of a C-terminally truncated protein lacking the PCGF Ub-like fold discriminator (PUFD) domain. By uncoupling PRC1.1 repressive function from target genes, BCOR/BCORL1 mutations activate aberrant cell signaling programs that confer acquired resistance to treatment. This study provides a mechanistic basis for Polycomb repressive dysfunction as a key oncogenic driver in myeloid malignancies and identifies a potential strategy for targeted therapy in BCOR-mutated cancer.

cancer biology↗

bioRxiv2020

In Vivo CRISPR Screens Identify E3 Ligase Cop1 as a Modulator of Macrophage Infiltration and Cancer Immunotherapy Target

Despite remarkable clinical efficacies of immune checkpoint blockade (ICB) in cancer treatment, ICB benefits in triple-negative breast cancer (TNBC) remain limited. Through pooled in vivo CRISPR knockout (KO) screens in syngeneic TNBC mouse models, we found that inhibition of the E3 ubiquitin ligase Cop1 in cancer cells decreases the secretion of macrophage-associated chemokines, reduces tumor macrophage infiltration, and shows synergy in anti-tumor immunity with ICB. Transcriptomics, epigenomics, and proteomics analyses revealed Cop1 functions through proteasomal degradation of the C/ebp{delta} protein. Cop1 substrate Trib2 functions as a scaffold linking Cop1 and C/ebp{delta}, which leads to polyubiquitination of C/ebp{delta}. Cop1 inhibition stabilizes C/ebp{delta} to suppress the expression of macrophage chemoattractant genes. Our integrated approach implicates Cop1 as a target for improving cancer immunotherapy efficacy by regulating chemokine secretion and macrophage levels in the TNBC tumor microenvironment. HighlightsO_LILarge-scale in vivo CRISPR screens identify new immune targets regulating the tumor microenvironment C_LIO_LICop1 knockout in cancer cells enhances anti-tumor immunity C_LIO_LICop1 modulates chemokine secretion and macrophage infiltration into tumors C_LIO_LICop1 targets C/ebp{delta} degradation via Trib2 and influences ICB response C_LI

cancer biology↗

bioRxiv2019

Inferring transcriptional regulators through integrative modeling ofpublic chromatin accessibility and ChIP-seq data

We developed Lisa (http://lisa.cistrome.org) to predict the transcriptional regulators (TRs) of differentially expressed or co-expressed gene sets. Based on the input gene sets, Lisa first uses compendia of public histone mark ChIP-seq and chromatin accessibility profiles to construct a chromatin model related to the regulation of these genes. Then using TR ChIP-seq peaks or imputed TR binding sites, Lisa probes the chromatin models using in silico deletion to find the most relevant TRs. Applied to gene sets derived from targeted TF perturbation experiments, Lisa boosted the performance of imputed TR cistromes, and outperformed alternative methods in identifying the perturbed TRs.

bioinformatics↗

bioRxiv2019

Determinants of transcription factor regulatory range

To characterize the genomic distances over which transcription factors (TFs) influence gene expression, we examined thousands of TF and histone modification ChIP-seq datasets and thousands of gene expression profiles. A model integrating these data revealed two classes of TF: one with short-range regulatory influence, the other with long-range regulatory influence. The two TF classes also had distinct chromatin-binding preferences and auto-regulatory properties. The regulatory range of a single TF bound within different topologically associating domains (TADs) depended on intrinsic TAD properties such as local gene density and G/C content, but also on the TAD chromatin state in specific cell types. Our results provide evidence that most TFs belong to one of these two functional classes, and that the regulatory range of long-range TFs is chromatin-state dependent. Thus, consideration of TF type, distance-to-target, and chromatin context is likely important in identifying TF regulatory targets and interpreting GWAS and eQTL SNPs.

genomics↗

bioRxiv2017

Integrative analysis and refined design of CRISPR knockout screens

Genome-wide CRISPR-Cas9 screen has been widely used to interrogate gene functions. However, the analysis remains challenging and rules to design better libraries beg further refinement. Here we present MAGeCK-NEST, which integrates protein-protein interaction (PPI), improves the inference accuracy when fewer guide-RNAs (sgRNAs) are available, and assesses screen qualities using information on PPI. MAGeCK-NEST also adopts a maximum-likelihood approach to remove sgRNA outliers, which are characterized with higher G-nucleotide counts, especially in regions distal from the PAM motif. Using MAGeCK-NEST, we found that choosing non-targeting sgRNAs as negative controls lead to strong bias, which can be mitigated by sgRNAs targeting the \"safe harbor\" regions. Custom-designed screens confirmed our findings, and further revealed that 19nt sgRNAs consistently gave the best signal-to-noise separation. Collectively, our method enabled robust calling of CRISPR screen hits and motivated the design of an improved genome-wide CRISPR screen library.

bioinformatics↗