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Biology subjects

Agar, N. Y. R.

Publications and source records attributed to Agar, N. Y. R..

10 recordsLinked to original sources
bioRxiv2026

H3K27M-driven hypertranscription leads to a new targetable dependency in diffuse midline gliomas

Diffuse midline gliomas (DMGs) are driven by the H3K27M oncohistone--a challenging therapeutic target. However, conventional therapeutic modalities are never curative. Against this backdrop, we address an important unresolved question--are there H3K27M-induced oncogenic vulnerabilities that can be exploited for therapeutic benefit. We show that H3K27M induces hypertranscription, thus identifying hypertranscription as a new molecular feature of H3K27M-driven DMGs. We demonstrate this finding in genetic mouse models, human DMG cells, and primary tumor specimens. We further demonstrate that H3K27M-induced hypertranscription perturbs replication, heightens basal replication stress, and enhances sensitivity to ATR inhibition. In exploring therapeutic implications of these findings, we document brain penetrance, target engagement, and therapeutic efficacy of a clinical-stage ATR inhibitor (alnodesertib) in vitro and in intracranial DMG xenografts. We further demonstrate synergistic activity of alnodesertib with radiotherapy--the current standard of care for DMGs. These findings provide the mechanistic underpinning and preclinical rationale for including alnodesertib as monotherapy and in combination with radiation in clinical trials for children with H3K27M DMGs. The broad implications of our studies highlight ATR inhibition as a therapy for aggressive human cancers displaying hypertranscription.

cancer biology↗
bioRxiv2025

Nitrogen metabolism profiling reveals cell state-specific pyrimidine synthesis pathway choice

Conventional stable isotope tracing assays track one or several metabolites. However, cells use an array of nutrients to sustain nitrogen metabolic pathways. This incongruency hampers a system level understanding of cellular nitrogen metabolism. Therefore, we created a platform to simultaneously trace 30 nitrogen isotope-labeled metabolites. This platform revealed that while primitive cells engage both de novo and salvage pyrimidine synthesis pathways, differentiated cells nearly exclusively salvage uridine despite expressing de novo pathway enzymes. This link between cell state and pyrimidine synthesis routes persisted in physiological contexts, including primary murine and human tissues and tumor xenografts. Mechanistically, we found that Ser1900 phosphorylation of CAD, the first enzyme of the de novo pathway, was enriched in primitive cells and that mimicking this modification in differentiated cells abrogated their preference for pyrimidine salvage. Collectively, we establish a method for nitrogen metabolism profiling and define a mechanism of cell state-specific pyrimidine synthesis pathway choice.

cell biology↗
bioRxiv2025

A pathogenic subpopulation of human glioma associated macrophages linked to glioma progression

Malignant gliomas follow two distinct natural histories: de novo high grade tumors such as glioblastoma, or lower grade tumors with a propensity to transform into high grade disease. Despite differences in tumor genotype, both entities converge on a common histologically aggressive phenotype, and the basis for this progression is unknown. Glioma associated macrophages (GAM) have been implicated in this process, however GAMs are ontologically and transcriptionally diverse, rendering isolation of pathogenic subpopulations challenging. Since macrophage contextual gene programs are orchestrated by transcription factors acting on cis-acting promoters and enhancers in gene regulatory networks (GRN), we hypothesized that functional populations of GAMs can be resolved through GRN inference. Here we show via parallel single cell RNA and ATAC sequencing that a subpopulation of human GAMs can be defined by a GRN centered around the Activator Protein-1 transcription factor FOSL2 preferentially enriched in high grade tumors. Using this GRN we nominate ANXA1 and HMOX1 as surrogate cell surface markers for activation, thus permitting prospective isolation and functional validation in human GAMs. These cells, termed malignancy associated GAMs (mGAMs) are pro-invasive, pro-angiogenic, pro-proliferative, possess intact antigen presentation but skew T-cells towards a CD4+FOXP3+ phenotype under hypoxia. Ontologically, mGAMs share somatic mitochondrial mutations with peripheral blood monocytes, and their presence correlates with high grade disease irrespective of underlying tumor mutation status. Furthermore, spatio-temporally mGAMs occupy distinct metabolic niches; mGAMs directly induce proliferation and mesenchymal transition of low grade glioma cells and accelerate tumor growth in vivo upon co-culture. Finally mGAMs are preferentially enriched in patients with newly transformed regions in human gliomas, supporting the view that mGAMs play a pivotal role in glioma progression and may represent a plausible therapeutic target in human high-grade glioma.

cancer biology↗
bioRxiv2024

Pooled nanoparticle screening using a chemical barcoding approach

We report the development of a small molecule-based barcoding platform for pooled screening of nanoparticle delivery. Using aryl halide-based tags (halocodes), we achieve high-sensitivity detection via gas chromatography coupled with mass spectrometry or electron capture. This enables barcoding and tracking of nanoparticles with minimal halocode concentrations and without altering their physicochemical properties. To demonstrate the utility of our platform for pooled screening, we synthesized a halocoded library of polylactide-co-glycolide (PLGA) nanoparticles and quantified uptake in ovarian cancer cells in a pooled manner. Our findings correlate with conventional fluorescence-based assays. Additionally, we demonstrate the potential of halocodes for spatial mapping of nanoparticles using mass spectrometry imaging (MSI). Halocoding presents an accessible and modular nanoparticle screening platform capable of quantifying delivery of pooled nanocarrier libraries in a range of biological settings.

bioengineering↗
bioRxiv2021

MIAAIM: Multi-omics image integration and tissue state mapping using topological data analysis and cobordism learning

High-parameter tissue imaging enables detailed molecular analysis of single cells in their spatial environment. However, the comprehensive characterization and mapping of tissue states through multimodal imaging across different physiological and pathological conditions requires data integration across multiple imaging systems. Here, we introduce MIAAIM (Multi-omics Image Alignment and Analysis by Information Manifolds) a modular, reproducible computational framework for aligning data across bioimaging technologies, modeling continuities in tissue states, and translating multimodal measures across tissue types. We demonstrate MIAAIMs workflows across diverse imaging platforms, including histological stains, imaging mass cytometry, and mass spectrometry imaging, to link cellular phenotypic states with molecular microenvironments in clinical biopsies from multiple tissue types with high cellular complexity. MIAAIM provides a robust foundation for the development of computational methods to integrate multimodal, high-parameter tissue imaging data and enable downstream computational and statistical interrogation of tissue states.

systems biology↗
bioRxiv2021

Characterization of metabolic compartmentalization in the liver using spatially resolved metabolomics

Cells adapt their metabolism to physiological stimuli, and metabolic heterogeneity exists between cell types, within tissues, and subcellular compartments. The liver plays an essential role in maintaining whole-body metabolic homeostasis and is structurally defined by metabolic zones. These zones are well-understood on the transcriptomic level, but have not been comprehensively characterized on the metabolomic level. Mass spectrometry imaging (MSI) can be used to map hundreds of metabolites directly from a tissue section, offering an important advance to investigate metabolic heterogeneity in tissues compared to extraction-based metabolomics methods that analyze tissue metabolite profiles in bulk. We established a workflow for the preparation of tissue specimens for matrix-assisted laser desorption/ionization (MALDI) MSI and achieved broad coverage of central carbon, nucleotide, and lipid metabolism pathways. We used this approach to visualize the effect of nutrient stress and excess on liver metabolism. Our data revealed a highly organized metabolic compartmentalization in livers, which becomes disrupted under nutrient stress conditions. Fasting caused changes in glucose metabolism and increased the levels of fatty acids in the circulation. In contrast, a prolonged high-fat diet (HFD) caused lipid accumulation within liver tissues with clear zonal patterns. Fatty livers had higher levels of purine and pentose phosphate related metabolites, which generates reducing equivalents to counteract oxidative stress. This MALDI MSI approach allowed the visualization of liver metabolic compartmentalization at high resolution and can be applied more broadly to yield new insights into metabolic heterogeneity in vivo.

biochemistry↗
bioRxiv2021

De Novo Pyrimidine Synthesis is a Targetable Vulnerability in IDH Mutant Glioma

Mutations affecting isocitrate dehydrogenase (IDH) enzymes are prevalent in glioma, leukemia, and other cancers. Although mutant IDH inhibitors are effective against leukemia, they appear less active in aggressive glioma, underscoring the need for alternative treatment strategies. Through a chemical synthetic lethality screen, we discovered that IDH1 mutant glioma cells are hypersensitive to drugs targeting enzymes in the de novo pyrimidine nucleotide synthesis pathway, including dihydroorotate dehydrogenase (DHODH). We developed a genetically engineered mouse model of mutant IDH1-driven astrocytoma and used it and multiple patient-derived models to show that the brain-penetrant DHODH inhibitor BAY 2402234 displays monotherapy efficacy against IDH mutant gliomas. Mechanistically, this vulnerability selectively applies to de novo pyrimidine, but not purine, synthesis because glioma cells engage disparate programs to produce these nucleotide species and because IDH oncogenes increase DNA damage upon nucleotide pool imbalance. Our work outlines a tumor-selective, biomarker-guided therapeutic strategy that is poised for clinical translation.

cancer biology↗
bioRxiv2021

massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation

MotivationMass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high dimensionality, and spectral non-linearity. Preprocessing, including peak picking, has been used to reduce raw data complexity, however peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation. ResultsWe propose a deep learning model, massNet, that provides the desired qualities of scalability, non-linearity, and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The models performance was assessed using cross-validation, and the results demonstrate higher accuracy and a 174-fold gain in speed compared to the established classical machine learning method, support vector machine. Availability and ImplementationThe code is publicly available on GitHub.

bioinformatics↗
bioRxiv2020

msiPL: Non-linear Manifold and Peak Learning of Mass Spectrometry Imaging Data Using Artificial Neural Networks

Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving clinical diagnosis, biomarker discovery, metabolomics research and pharmaceutical applications. The large data size and high dimensional nature of MSI pose computational and memory complexities that hinder accurate identification of biologically-relevant molecular patterns. We propose msiPL, a robust and generic probabilistic generative model based on a fully-connected variational autoencoder for unsupervised analysis and peak learning of MSI data. The method can efficiently learn and visualize the underlying non-linear spectral manifold, reveal biologically-relevant clusters of tumor heterogeneity and identify underlying informative m/z peaks. The method provides a probabilistic parametric mapping to allow a trained model to rapidly analyze a new unseen MSI dataset in a few seconds. The computational model features a memory-efficient implementation using a minibatch processing strategy to enable the analyses of big MSI data (encompassing more than 1 million high-dimensional datapoints) with significantly less memory. We demonstrate the robustness and generic applicability of the application on MSI data of large size from different biological systems and acquired using different mass spectrometers at different centers, namely: 2D Matrix-Assisted Laser Desorption Ionization (MALDI) Fourier Transform Ion Cyclotron Resonance (FT ICR) MSI data of human prostate cancer, 3D MALDI Time-of-Flight (TOF) MSI data of human oral squamous cell carcinoma, 3D Desorption Electrospray Ionization (DESI) Orbitrap MSI data of human colorectal adenocarcinoma, 3D MALDI TOF MSI data of mouse kidney, and 3D MALDI FT ICR MSI data of a patient-derived xenograft (PDX) mouse brain model of glioblastoma. SignificanceMass spectrometry imaging (MSI) provides detailed molecular characterization of a tissue specimen while preserving spatial distributions. However, the complex nature of MSI data slows down the processing time and poses computational and memory challenges that hinder the analysis of multiple specimens required to extract biologically relevant patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Here, we present a generative probabilistic deep-learning model that can analyze and non-linearly visualize MSI data independent of the nature of the specimen and of the MSI platform. We demonstrate robustness of the method with application to different tissue types, and envision it as a new generation of rapid and robust analysis for mass spectrometry data.

bioinformatics↗