Biology subjects

Melnick, A.

Publications and source records attributed to Melnick, A..

16 recordsLinked to original sources

bioRxiv2026

Intravital single-cell behavior profiling reveals disrupted germinal center B cell motility and interactions by EZH2 gain-of-function mutation

Germinal center (GC) B-cells give rise to the majority of non-Hodgkin lymphomas, underscoring the need to pinpoint critical processes that initiate and drive lymphomagenesis. Lymphoma driver mutations can alter GC B cell functions and B cell fate decisions. Here, we studied how EZH2 oncogenic mutation in GC B cells alters cellular motility and interactions with T follicular helper (Tfh) cells and follicular dendritic cells (FDCs) to determine B cell fate. By combining intravital imaging, single-cell behavior analyses, and RNA sequencing, we uncover how lymphoma-associated EZH2 mutations reprogram the behaviors of GC B cells in vivo. We found that EZH2 mutations increased single-cell motility speeds and morphological plasticity of GC B cells, redirecting migration toward the FDC-rich light zone subregions rather than to the dark zone. Although mutant EZH2 GC B cells exhibited normal engagement quality with FDCs, they showed shorter interaction times and reduced surface engagement with Tfh cells. Notably, EZH2 mutant B cells required prior contact with FDC before engaging with Tfh cells, thus impairing DZ recycling. This motility phenotype scaled with local mutant clone abundance, suggesting a behavioral strategy underlying how mutant cells outcompete WT cells. Lastly, we developed scMOTIPh, a computational framework that integrates single-cell behavioral features with transcriptomic profiles. Applying scMOTIPh to mutant GC B cells within the FDC-rich zone revealed enhanced ATP production, metabolic and antigen-presentation programs, and suppression of cell-death pathways, which is consistent with a tendency for malignant transformation and survival fitness. These findings provide an in vivo, single-cell view of how an epigenetic lesion rewires the local microenvironment by modulating single-cell behaviors within native GCs, revealing a dynamic mechanism for early lymphomagenesis.

immunology↗

bioRxiv2026

Spatial Proteomics Reveals Dual-Compartment Immune Evasion Architecture and Core Lymphomagenic Stromal Niches in Follicular Lymphoma

Follicular lymphomas (FL) depend on microenvironmental signals for survival, and the composition of the tumor microenvironment may influence clinical outcome. How it is spatially organized, and how that organization drives immune evasion, has not been established. We applied two complementary 39-marker imaging mass cytometry panels to 156 follicular lymphoma tumor cores, generating a spatial atlas of 4.2 million cells resolved into nine tissue compartments. FL follicles operated as immune-privileged zones: CD8 T cells were largely excluded, and those that did infiltrate were exhausted in a gradient that peaked at the GC core and dissolved at the follicle boundary. A Treg-enriched barrier at that boundary separated the intrafollicular program from an effector-rich interfollicular compartment. Within follicles, CD14-high follicular dendritic cells defined an activated stromal niche that supported tumor B cell proliferation and predicted shorter progression-free and overall survival. Across patients, per-cell CD14 intensity was the strongest predictor of progression, remaining prognostic independently of FLIPI, grade, and stage. VISTA, not PD-L1, dominated checkpoint expression across all myeloid subtypes in both follicular and interfollicular zones, and was further amplified on M2 macrophages in patients carrying EP300 mutations. S100A9+ MDSC-like cells were the strongest myeloid predictor of histologic transformation. Agent-based modeling of this architecture predicted synergistic tumor clearance when anti-VISTA was combined with a CD20-CD3 bispecific antibody. These findings establish that FL uses tissue architecture as an immune evasion mechanism and identify VISTA and CD14-high follicular dendritic cells as candidate therapeutic targets.

Cancer Biology↗

bioRxiv2025

Transcriptome sequencing of Hodgkin lymphoma Hodgkin and Reed-Stenberg cells reveals escape from NK cell recognition and an unfolded protein response

The mutational profile of classic Hodgkin lymphoma (cHL) overlaps with that of related B cell lymphomas, including primary mediastinal B cell lymphoma (PMBL), and yet these are different histologically and clinically. To discover the molecular features that distinguish cHL, we deployed flow cytometric cell sorting and low-input RNA sequencing to generate full transcriptome data from viable, isolated Hodgkin and Red-Sternberg (HRS) cells from eighteen primary tumors, alongside matched intra-tumoral non-neoplastic B cells and four cell lines. Comparison of HRS cells to normal cellular subsets revealed evidence of abortive plasma cell differentiation, with an unfolded protein response signature, shared with plasma cell neoplasms, but not other B-cell lymphoma types. Comparison of cHL to PMBL revealed similarities but also key differences in B cell differentiation programs accompanied by upregulation of genes involved in microtubule cytoskeleton organization in cHL, which may be related to the unique multinucleated nature of HRS cells. In HRS cells, we also observed a downregulation of SLAM family receptors, which are crucial for NK cell activation, providing a potential mechanism for immune evasion from NK-mediated killing. STATEMENT OF SIGNIFICANCEThis study defines a unique transcriptional program in classic Hodgkin Lymphoma (cHL) marked by oncogenic signaling, chromatin integrity, DNA repair, and immune escape including loss of NK-activating receptors. HRS cells resemble plasma cells and have an unfolded protein response signature, which distinguishes them from diffuse large and primary mediastinal B cell lymphomas.

cancer biology↗

bioRxiv2025

Deep dynamical models of single-cell multiomic velocities predict loss-of-function and rescue perturbations in B cells

We present DynaVelo, a generative neural ordinary differential equation model that learns the joint dynamics of gene expression and transcription factor (TF) motif activities in evolving cell systems using single-cell multiome with joint gene expression and chromatin accesibility readout. DynaVelo leverages partial RNA velocity information together with single-cell TF motif accessibility data to improve the modeling of cell state dynamics and identification of TF drivers. We show that DynaVelo recovers the complex and bifurcating in vivo dynamics of wildtype murine germinal center (GC) B cells and reveals how these cell dynamics change under loss-of-function mutations in epigenetic regulators Arid1a and Ctcf. DynaVelo resolves how TF motif activities evolve along latent time trajectories using analysis of training cells or through generated trajectories from the model. In silico perturbation analysis further enables DynaVelo to infer dynamic and cell-state-specific gene regulatory networks (GRNs), recovering many known TF-to-gene edges in the wildtype GC GRN and predicting those that are disrupted in mutants. Finally, in silico gene and TF perturbations allow both the prediction of cell dynamics under loss-of-function genetic mutations and the identification of TF perturbations to rescue loss-of-function dynamic and immunological phenotypes. This analysis predicted that Ctcf knockout would rescue Arid1a loss-of-function phenotype in the GC reaction and nominated Bcl6 and Stat3 as additional TFs whose knockout would rescue Arid1a loss. We validated these predictions in vivo using double heterozygous mutant mice, confirming rescue of the Arid1a dark zone phenotype in all cases and quantitatively assessing model predictions using multiome in Arid1aHet;CtcfHet double heterozygous mice. DynaVelo therefore provides a powerful new deep learning framework for modeling and perturbing dynamic cell systems by harnessing single-cell multiome data sets.

systems biology↗

bioRxiv2024

PAIRWISE: Deep Learning-based Prediction of Effective Personalized Drug Combinations in Cancer

Combination therapies offer promise for improving cancer treatment efficacy and preventing recurrence. However, identifying optimal drug combinations tailored to specific cancer subtypes and individual patients is extremely challenging due to the vast number of possible combinations and tumor heterogeneity. To address this gap, we take a machine learning approach combining deep learning with transfer learning to incorporate prior scientific knowledge and predict drug synergy based on tumor-specific transcriptome profiles. This approach, called PAIRWISE, explicitly modeled synergistic effects of drug combinations in cancer cell lines or individual tumor samples based on drug chemical structures, drug targets, and transcriptomes of inferred samples. PAIRWISE outperformed competing models with an area under the receiver operating characteristic curve (AUROC) of 0.85 on held-out cancer cell lines. When applied to an independent dataset of combinations with Bruton Tyrosine Kinase inhibitors (BTKi) in Diffuse Large B Cell Lymphoma (DLBCL) cell lines, PAIRWISE accurately predicted synergistic drug combinations with an AUROC of 0.72. To further confirm the robustness of PAIRWISE predictions, we performed an in silico, patient profile-directed screen for other compounds that would synergize with BTKi in DLBCL patients, and confirmed the synergy of the predictions using a panel of eight non-Hodgkin lymphoma cell lines. These findings demonstrate the ability of PAIRWISE to nominate effective personalized drug combinations, accelerating the development of precision oncology.

bioinformatics↗

bioRxiv2024

MALT1 protease inhibition restrains glioblastoma progression by reversing tumor-associated macrophage-dependent immunosuppression

MALT1 protease is an intracellular signaling molecule that promotes tumor progression via cancer cell-intrinsic and cancer cell-extrinsic mechanisms. MALT1 has been mostly studied in lymphocytes, and little is known about its role in tumor-associated macrophages. Here, we show that MALT1 plays a key role in glioblastoma (GBM)-associated macrophages. Mechanistically, GBM tumor cells induce a MALT1-NF-{kappa}B signaling axis within macrophages, leading to macrophage migration and polarization toward an immunosuppressive phenotype. Inactivation of MALT1 protease promotes transcriptional reprogramming that reduces migration and restores a macrophage "M1-like" phenotype. Preclinical in vivo analysis shows that MALT1 inhibitor treatment results in increased immuno-reactivity of GBM-associated macrophages and reduced GBM tumor growth. Further, the addition of MALT1 inhibitor to temozolomide reduces immunosuppression in the tumor microenvironment, which may enhance the efficacy of this standard-of-care chemotherapeutic. Together, our findings suggest that MALT1 protease inhibition represents a promising macrophage-targeted immunotherapeutic strategy for the treatment of GBM. Graphical abstract. O_FIG O_LINKSMALLFIG WIDTH=196 HEIGHT=200 SRC="FIGDIR/small/614808v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@18c969eorg.highwire.dtl.DTLVardef@f48f0dorg.highwire.dtl.DTLVardef@1dbbbb1org.highwire.dtl.DTLVardef@ff8237_HPS_FORMAT_FIGEXP M_FIG The effects of tumor cell-induced CARD9-BCL10-MALT1 (CBM) activation (left) and MALT1 protease inhibition (right) on GBM associated macrophages in the tumor microenvironment. Cartoon of cellular components of a GBM tumor with an immunosuppressive TME characterized by "M2-like macrophages" (left) and conversion to a more immune-reactive tumor microenvironment characterized by "M1-like macrophages and increased effector T-cells (right) as a result of MALT1 protease inhibition. C_FIG

cancer biology↗

bioRxiv2023

ChromaFold predicts the 3D contact map from single-cell chromatin accessibility

The identification of cell-type-specific 3D chromatin interactions between regulatory elements can help to decipher gene regulation and to interpret the function of disease-associated non-coding variants. However, current chromosome conformation capture (3C) technologies are unable to resolve interactions at this resolution when only small numbers of cells are available as input. We therefore present ChromaFold, a deep learning model that predicts 3D contact maps and regulatory interactions from single-cell ATAC sequencing (scATAC-seq) data alone. ChromaFold uses pseudobulk chromatin accessibility, co-accessibility profiles across metacells, and predicted CTCF motif tracks as input features and employs a lightweight architecture to enable training on standard GPUs. Once trained on paired scATAC-seq and Hi-C data in human cell lines and tissues, ChromaFold can accurately predict both the 3D contact map and peak-level interactions across diverse human and mouse test cell types. In benchmarking against a recent deep learning method that uses bulk ATAC-seq, DNA sequence, and CTCF ChIP-seq to make cell-type-specific predictions, ChromaFold yields superior prediction performance when including CTCF ChIP-seq data as an input and comparable performance without. Finally, fine-tuning ChromaFold on paired scATAC-seq and Hi-C in a complex tissue enables deconvolution of chromatin interactions across cell subpopulations. ChromaFold thus achieves state-of-the-art prediction of 3D contact maps and regulatory interactions using scATAC-seq alone as input data, enabling accurate inference of cell-type-specific interactions in settings where 3C-based assays are infeasible.

bioinformatics↗

bioRxiv2023

Cdc73 protects Notch-induced T-cell leukemia cells from DNA damage and mitochondrial stress

Activated Notch signaling is highly prevalent in T-cell acute lymphoblastic leukemia (T-ALL) but pan-Notch inhibitors were toxic in clinical trials. To find alternative ways to target Notch signals, we investigated Cell division cycle 73 (Cdc73), which is a Notch cofactor and component of transcriptional machinery, a potential target in T-ALL. While we confirmed previous work that CDC73 interacts with NOTCH1, we also found that the interaction in T-ALL was context-dependent and facilitated by the lymphoid transcription factor ETS1. Using mouse models, we showed that Cdc73 is important for Notch-induced T-cell development and T-ALL maintenance. Mechanistically, Cdc73, Ets1, and Notch intersect chromatin at promoters and enhancers to activate oncogenes and genes that are important for DNA repair and oxidative phosphorylation. Consistently, Cdc73 deletion in T-ALL cells induced DNA damage and impaired mitochondrial function. Our data suggests that Cdc73 might promote a gene expression program that was eventually intersected by Notch to mitigate the genotoxic and metabolic stresses of elevated Notch signaling. We also provide mechanistic support for testing inhibitors of DNA repair, oxidative phosphorylation, and transcriptional machinery. Inhibiting pathways like Cdc73 that intersect with Notch at chromatin might constitute a strategy to weaken Notch signals without directly targeting the Notch complex.

cancer biology↗

bioRxiv2022

Profiling emergent behaviors from single-cell motility

Cell motility plays an essential role in many biological processes as cells move and interact within their local microenvironments. Current methods for quantifying cell motility typically involve tracking individual cells over time, but the results are often presented as averaged values across cell populations. While informative, these ensemble approaches have limitations in assessing cellular heterogeneity and identifying generalizable patterns of single-cell behaviors, at baseline and in response to perturbations. In this study, we introduce CaMI, a computational framework designed to leverage the single-cell nature of motility data. CaMI identifies and classifies distinct spatio-temporal behaviors of individual cells, enabling robust classification of single-cell motility patterns in a large dataset (n=74,253 cells). This framework allows quantification of spatial and temporal heterogeneities, determination of single-cell motility behaviors across various biological conditions, and provides a visualization scheme for direct interpretation of dynamic cell behaviors. Importantly, CaMI reveals insights that conventional cell motility analyses may overlook, showcasing its utility in uncovering robust biological insights. Together, we present a multivariate framework to classify emergent patterns of single-cell motility, emphasizing the critical role of cellular heterogeneity in shaping cell behaviors across populations. TeaserA computational framework to identify and classify single-cell motility patterns and phenotypic heterogeneity across biological conditions.

bioengineering↗

bioRxiv2020

The SEQC2 Epigenomics Quality Control (EpiQC) Study: Comprehensive Characterization of Epigenetic Methods, Reproducibility, and Quantification

Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and a global resource for epigenetics research from the FDAs Epigenomics Quality Control (EpiQC) Group. The study design leverages seven human cell lines that are designated as reference materials and publicly available from the National Institute of Standards and Technology (NIST) and Genome in a Bottle (GIAB) consortium. These samples were subject to a variety of genome-wide methylation interrogation approaches across six independent laboratories, with a primary focus was on 5-methylcytosine modifications. Each sample was processed in two or more technical replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), one enzymatic deamination method (EMseq), targeted methylation sequencing (Illumina Methyl Capture EPIC), and single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, BWAMeth, and GemBS), we found overall high concordance between assays (R=0.87-R0.93), differences in efficency of read mapping and CpG capture and coverage, and platform performance. The data provided herein can guide continued used of these reference materials in epigenomics assays, as well as provide best practices for epigenomics research and experimental design in future studies.

genomics↗

bioRxiv2020

Cyclin D3 drives inertial cell cycling in dark zone germinal center B cells

During affinity maturation, germinal center (GC) B cells alternate between proliferation and so-matic hypermutation in the dark zone (DZ) and affinity-dependent selection in the light zone (LZ). This anatomical segregation imposes that the vigorous proliferation that allows clonal expansion of positively-selected GC B cells takes place ostensibly in the absence of the signals that triggered selection in the LZ, as if by "inertia." We find that such inertial cycles specifically require the cell cycle regulator cyclin D3. Cyclin D3 dose-dependently controls the extent to which B cells proliferate in the DZ and is essential for effective clonal expansion of GC B cells in response to strong T follicular helper (Tfh) cell help. Introduction into the Ccnd3 gene of a Burkitt lymphoma-associated gain-of-function mutation (T283A) leads to larger GCs with increased DZ proliferation and, in older mice, to clonal B cell lymphoproliferation, suggesting that the DZ inertial cell cycle program can be coopted by B cells undergoing malignant transformation.

immunology↗

bioRxiv2020

Cell-free DNA (cfDNA) and exosome profiling from a year-long humanspaceflight reveals circulating biomarkers

The health impact of prolonged space flight on the human body is not well understood. Liquid biopsies based on cell-free DNA (cfDNA) or exosome analysis provide a noninvasive approach to monitor the dynamics of genomic, epigenomic and proteomic biomarkers, and the occurrence of DNA damage, physiological stress, and immune responses. To study the molecular consequences of spaceflight we profiled cfDNA isolated from plasma of an astronaut (TW) during a year-long mission on the International Space Station (ISS), sampling before, during, and after spaceflight, and compared the results to cfDNA profiling of the subjects identical twin (HR) who remained on Earth, as well as healthy donors. We characterized cfDNA concentration and fragment size, and the positioning of nucleosomes on cfDNA, observing a significant increase in the proportion of cell-free mitochondrial DNA inflight, suggesting that cf-mtDNA is a potential biomarker for space flight-associated stress, and that this result was robust to ambient transit from the International Space Station (ISS). Analysis of exosomes isolated from post-flight plasma revealed a 30-fold increase in circulating exosomes and distinct exosomal protein cargo, including brain-derived peptides, in TW compared to HR and all known controls. This study provides the first longitudinal analysis of astronaut cfDNA during spaceflight, as well as the first exosome profiles, and highlights cf-mtDNA levels as a potential biomarker for physiological stress or immune system responses related to microgravity, radiation exposure, and other unique environmental conditions on the ISS.

cell biology↗

bioRxiv2020

KDM5 inhibition offers a novel therapeutic strategy for the treatment of KMT2D mutant lymphomas

Loss-of-function mutations in KMT2D are a striking feature of the germinal centre (GC) lymphomas, resulting in decreased H3K4 methylation and altered gene expression. We hypothesised that inhibition of the KDM5 family, which demethylates H3K4me3/me2, would re-establish H3K4 methylation and restore the expression of genes repressed upon loss of KMT2D. KDM5-inhibition increased H3K4me3 levels and caused an anti-proliferative response in vitro, which was markedly greater in both endogenous and CRISPR-edited KMT2D mutant DLBCL cell lines, whilst tumour growth was inhibited in KMT2D mutant xenografts in vivo. KDM5-inhibition reactivated both KMT2D-dependent and -independent genes, resulting in diminished B-cell receptor signalling and altered expression of BCL2 family members, including BCL2 itself, allowing it to synergise with agents targeting these pathways. KDM5-inhibition may offer an effective therapeutic strategy for ameliorating KMT2D loss-of-function mutations in GC-lymphomas.Statement of significance We detail a novel way of reverting the effects of loss-of-function mutations in the histone methyltransferase KMT2D by inhibiting the KDM5 demethylase family, increasing levels of H3K4me3 and restoring expression of KMT2D regulated genes.Competing Interest StatementThe authors have declared no competing interest.View Full Text

cancer biology↗

bioRxiv2020

Host, Viral, and Environmental Transcriptome Profiles of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused thousands of deaths worldwide, including >18,000 in New York City (NYC) alone. The sudden emergence of this pandemic has highlighted a pressing clinical need for rapid, scalable diagnostics that can detect infection, interrogate strain evolution, and identify novel patient biomarkers. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs, plus a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, bacterial, and viral profiling. We applied both technologies across 857 SARS-CoV-2 clinical specimens and 86 NYC subway samples, providing a broad molecular portrait of the COVID-19 NYC outbreak. Our results define new features of SARS-CoV-2 evolution, nominate a novel, NYC-enriched viral subclade, reveal specific host responses in interferon, ACE, hematological, and olfaction pathways, and examine risks associated with use of ACE inhibitors and angiotensin receptor blockers. Together, these findings have immediate applications to SARS-CoV-2 diagnostics, public health, and new therapeutic targets.

molecular biology↗

bioRxiv2019

Chromatin activation as a unifying principle underlying pathogenic mechanisms in multiple myeloma

Multiple myeloma (MM) is a plasma cell neoplasm associated with a broad variety of genetic lesions. In spite of this genetic heterogeneity, MMs share a characteristic malignant phenotype whose underlying molecular basis remains poorly characterized. In the present study, we examined plasma cells from MM using a multi-epigenomics approach and demonstrated that when compared to normal B cells, malignant plasma cells showed an extensive activation of regulatory elements, in part affecting co-regulated adjacent genes. Among target genes upregulated by this process, we found members of the NOTCH, NFkB, mTOR1 signaling and p53 signaling pathways. Other activated genes included sets involved in osteoblast differentiation and response to oxidative stress, all of which have been shown to be associated with the MM phenotype and clinical behavior. We functionally characterized MM specific active distant enhancers controlling the expression of thioredoxin (TXN), a major regulator of cellular redox status, and in addition identified PRDM5 as a novel essential gene for MM. Collectively our data indicates that aberrant chromatin activation is a unifying feature underlying the malignant plasma cell phenotype.

cancer biology↗

bioRxiv2019

Selective inhibition of HDAC3 targets synthetic vulnerabilities and activates immune surveillance in lymphoma

CREBBP mutations are highly recurrent in B-cell lymphomas and either inactivate its histone acetyltransferase (HAT) domain or truncate the protein. Herein, we show that these two classes of mutations yield different degrees of disruption of the epigenome, with HAT mutations being more severe and associated with inferior clinical outcome. Genes perturbed by CREBBP mutation are direct targets of the BCL6/HDAC3 onco-repressor complex. Accordingly, we show that HDAC3 selective inhibitors fully reverse CREBBP mutant aberrant epigenetic programming resulting in: a) growth inhibition of lymphoma cells through induction of BCL6 target genes such as CDKN1A and b) restoration of immune surveillance due to induction of BCL6 repressed IFN pathway and antigen presentation genes. By reactivating these genes, exposure to HDAC3-i restored the ability of tumor infiltrating lymphocytes to kill DLBCL cells in an MHC II and MHC I dependent manner. Hence HDAC3-i represent a novel mechanism-based immune-epigenetic therapy for CREBBP mutant lymphomas. STATEMENT OF SIGNIFICANCEWe have leveraged the molecular characterization of different types of CREBBP mutations to define a rational approach for targeting these mutations through selective inhibition of HDAC3. This represents an attractive therapeutic avenue for targeting synthetic vulnerabilities in CREBBP mutant cells in tandem with promoting anti-tumor immunity.

cancer biology↗