Biology subjects

Friedman, S.

Publications and source records attributed to Friedman, S..

13 recordsLinked to original sources

bioRxiv2026

Biomarker-Targeted O6-Guanine Alkylation Potentiates ATR Inhibitor Response in De Novo and Relapsed/Refractory AML

Acute myeloid leukemia (AML) remains limited by high relapse rates and a scarcity of biomarker-directed therapies, underscoring the need to identify actionable vulnerabilities and mechanisms of therapeutic resistance. O6-methylguanine-DNA methyltransferase (MGMT), a DNA repair enzyme that directly reverses mutagenic O6-alkylguanine lesions, is epigenetically silenced in multiple cancers. Although MGMT silencing is predictive of temozolomide (TMZ) response in glioblastoma with significant survival advantage, prior clinical trials of TMZ in AML have shown only modest responses. Therefore, the prevalence and therapeutic relevance of MGMT silencing, as well as genetic factors that modify its therapeutic response in AML, remain to be comprehensively investigated. Here, we first profiled MGMT status across a diverse cohort of 23 de novo and 16 relapsed/refractory (R/R) primary AMLs using an integrated analysis of MGMT mRNA expression, promoter methylation, and protein expression. We found that approximately 25-30% of AMLs harbor MGMT silencing, in contrast to consistent MGMT expression in healthy CD34+ hematopoietic stem and progenitor cells. We further identified frequent loss of mismatch repair (MMR) proteins in these AML cohorts, a known resistance mechanism against TMZ in glioblastoma. Using CRISPR knockout screening and genetically engineered AML models, we found that MMR loss also drives both upfront and acquired TMZ resistance in MGMT-silenced AML. These findings identify MMR deficiency as an important and previously underappreciated genetic contributor to the modest responses observed in prior trials of TMZ in AML. To therapeutically exploit MGMT silencing while bypassing genetic determinants of resistance, including MMR deficiency, we evaluated clinically and preclinically characterized MGMT-dependent DNA-alkylating agents and identified a fluoroethylating analog of TMZ, N3-(2-fluoroethyl) imidazotetrazine (KL50), with pronounced and selective activity in MGMT-silenced AML. KL50 retained antileukemic activity irrespective of MMR status, significantly prolonging survival in humanized MISTRG6 mice harboring primary AML patient-derived xenografts. Mechanistically, KL50 induced DNA damage through the time-dependent formation of interstrand DNA crosslinks, bypassing MMR dependence and triggering a replication stress response dominated by ATR signaling. Pharmacologic ATR inhibition synergized with KL50, producing marked antileukemic activity and significantly extending survival across AML models without compromising hematologic safety. Together, these findings establish MGMT silencing as a prevalent and therapeutically actionable biomarker in AML, define MMR status as a key determinant of TMZ response in AML but not KL50 sensitivity, and provide a translational rationale for combining low-dose O6-fluoroethylating imidazotetrazines with ATR inhibitors to target both de novo and R/R AML across diverse genetic backgrounds.

cancer biology↗

bioRxiv2026

Machine learning cross-platform proteomic imputation enables protein quality scoring and replication of epidemiological associations

High-throughput affinity-based proteomics has advanced biomedical research, yet fundamental, persistent discordance between mainstream platforms (SomaScan and Olink) routinely undermines the replication of findings. This platform-driven non-replication complicates downstream biological validation and biomarker prioritization. Here, we develop a machine learning-based framework for cross-platform protein value imputation to resolve this translational bottleneck. Using paired proteomic data measured by both SomaScan and Olink from 5,325 participants of the Multi-Ethnic Study of Atherosclerosis, we developed models to impute cross-platform measurements and applied them to two independent and demographically distinct cohorts (Cardiovascular Health Study [N=3,171] and UK Biobank [UKB; N=41,405]) for external validation. Our bi-directional model 1) established an imputation performance-based protein fidelity index, validated against gold-standard measurements from Atherosclerosis Risk in Communities study (N=101) and Nurses Health Study (N=54), 2) enabled imputation of platform-exclusive protein measurements, and 3) facilitated calibration of overlapping proteins. We demonstrate the utility of this framework through three applications: 1) fidelity-informed analyses enhanced the replication of biomarker discovery, 2) recovery of SomaScan signals that were previously inaccessible in UKBs original Olink measurements, and 3) improved replication performance for overlapping proteins. Our study offers a translational roadmap that allows researchers to achieve reliable epidemiological replication, target specific assays for future optimization, and prioritize biological signal over platform noise.

bioinformatics↗

bioRxiv2025

NAPRT expression and epigenetic regulation in pediatric rhabdomyosarcoma as a potential biomarker for NAMPT inhibition

PurposeNew treatments are needed to improve survival in children with rhabdomyosarcoma (RMS). NAD biosynthesis, regulated by the enzymes NAPRT and NAMPT, represents a metabolic vulnerability due to high NAD turnover in cancers. Although NAMPT inhibitors (NAMPTi) show preclinical promise, clinical translation has been limited by toxicity and the lack of predictive biomarkers. Here, we evaluated NAPRT expression in RMS and its potential as an actionable biomarker to guide NAMPTi therapy. Experimental DesignNAPRT promoter methylation, transcript levels, and protein expression were assessed in RMS cells, PDXs, and primary tumors (n=109) from the Childrens Oncology Group. In vitro sensitivity to NAMPTi was tested in molecularly diverse and isogenic RMS cell lines, examining the role of NAPRT expression in mediating cytotoxicity and the ability of nicotinic acid (NA) to rescue viability. In vivo efficacy was assessed using NAPRT-isogenic orthotopic xenograft models. ResultsNAPRT promoter hypermethylation was found in a subset of RMS models and patient samples. Immunohistochemistry showed loss of NAPRT protein in 30-40% of tumors, defined as <1% tumor cell staining. Methylation modestly correlated with protein expression. NAPRT-silenced cells were highly sensitive to NAMPTi, driven by NAD depletion and not reversible with NA. In vivo, NAMPTi induced significant tumor regression, which was not abrogated with NA administration in NAPRT-silenced models. ConclusionsNAPRT loss occurs in a subset of RMS, offering a potential strategy to expand the therapeutic window of NAMPTi. Further research is needed to understand NAPRT regulation and optimize biomarker assay strategies for use in future clinical trials.

cancer biology↗

bioRxiv2025

Multi-scale machine learning model predicts muscle and functional disease progression in FSHD

Facioscapulohumeral muscular dystrophy (FSHD) is a genetic neuromuscular disorder characterized by progressive muscle degeneration with substantial variability in severity and progression patterns. FSHD is a highly heterogeneous disease; however, current clinical metrics used tracking disease progression lack sensitivity for personalized assessment, which greatly limits the design and execution of clinical trials. This study introduces a multi-scale machine learning framework leveraging whole-body magnetic resonance imaging (MRI) and clinical data to predict regional, muscle, joint, and functional progression in FSHD. The goal this work is to create a digital twin of individual FSHD patients that can be leveraged in clinical trials. Using a combined dataset of over 100 patients from seven studies, MRI-derived metrics--including fat fraction, lean muscle volume, and fat spatial heterogeneity at baseline--were integrated with clinical and functional measures. A three-stage random forest model was developed to predict annualized changes in muscle composition and a functional outcome (timed up-and-go (TUG)). All model stages revealed strong predictive performance in separate holdout datasets. After training, the models predicted fat fraction change with a root mean square error (RMSE) of 2.16% and lean volume change with a RMSE of 8.1ml in a holdout testing dataset. Feature analysis revealed that metrics fat heterogeneity within muscle predicts muscle-level progression. The stage 3 model that combined functional muscle groups and predicted change in TUG with a RMSE of 0.6 seconds, in the holdout testing dataset. This study demonstrates the machine learning models incorporating individual muscle and performance data can effectively predict MRI disease progression and functional performance of complex tasks, addressing the heterogeneity and nonlinearity inherent in FSHD. Further studies incorporating larger longitudinal cohorts as well as comprehensive clinical and functional measures will allow for expanding and refining this model. As many neuromuscular diseases are characterized by varability and heterogeneity similar to FSHD, such approaches have broad applicability.

bioengineering↗

bioRxiv2024

Transpiration Insights: Estimating Transpiration Through Advanced Modeling

O_LIWe conducted research to predict daily transpiration in crops by utilizing a combination of machine learning (ML) models combined with extensive transpiration data from gravimetric load cells and ambient sensors. Our aim was to improve the accuracy of transpiration estimates. C_LIO_LIData were collected from hundreds of plant specimens growing in two semi-controlled greenhouses over seven years, automatically measuring key physiological traits (serves as our ground truth data) and meteorological variables with high temporal resolution and accuracy. We trained Decision tree, Random Forest, XGBoost, and Neural Network models on this dataset to predict daily transpiration. C_LIO_LIThe Random Forest and XGBoost models demonstrated high accuracy in predicting the whole plant transpiration, with R{superscript 2} values of 0.89 on the test set (cross-validation) and R2 = 0.82 on holdout experiments. Ambient temperature was identified as the most influential environmental factors affecting transpiration. C_LIO_LIOur results emphasize the potential of ML for precise water management in agriculture, and simplify some of the complex and dynamic environmental forces that shape transpiration. C_LI

plant biology↗

bioRxiv2024

Therapeutic Activity of Resolvin D1 (RvD1) in Murine MASH

Background and AimsRecent studies have highlighted the beneficial effect of resolvin D1 (RvD1), a DHA-derived specialized pro-resolving mediator, on metabolic dysfunction-associated steatohepatitis (MASH), but the underlying mechanisms are not well understood. Our study aims to determine the mechanism by which RvD1 protects against MASH progression. MethodsRvD1 was administered to mice with experimental MASH, followed by bulk and single-cell RNA sequencing analysis. Primary cells including bone marrow-derived macrophages (BMDMs), Kupffer cells, T cells, and primary hepatocytes were isolated to elucidate the effect of RvD1 on inflammation, cell death, and fibrosis regression genes. ResultsHepatic tissue levels of RvD1 were decreased in murine and human MASH, likely due to an expansion of pro-inflammatory M1-like macrophages with diminished ability to produce RvD1. Administering RvD1 reduced inflammation, cell death, and liver fibrosis. Mechanistically, RvD1 reduced inflammation by suppressing the Stat1-Cxcl10 signaling pathway in macrophages and prevented hepatocyte death by alleviating ER stress-mediated apoptosis. Moreover, RvD1 induced Mmp2 and decreased Acta2 expression in hepatic stellate cells (HSCs), and promoted Mmp9 and Mmp12 expression in macrophages, leading to fibrosis regression in MASH. ConclusionsRvD1 reduces Stat1-mediated inflammation, mitigates ER stress-induced apoptosis, and promotes MMP-mediated fibrosis regression in MASH. This study highlights the therapeutic potential of RvD1 to treat MASH. Impact and implicationsMetabolic dysfunction-associated steatohepatitis (MASH) is an increasing healthcare burden worldwide. Current treatments for MASH and its sequelae are very limited. Recent studies highlighted the therapeutic benefit of specialized pro-resolving mediators (SPMs), including resolvin D1 (RvD1), in liver diseases. However, the mechanisms underlying these beneficial effects are not well understood. Based on unbiased transcriptomic analyses using bulk and single-cell RNA sequencing in RvD1-treated MASH livers, we show that RvD1 suppresses Stat1-mediated inflammatory responses and ER stress-induced apoptosis, and induces gene expression related to fibrosis regression. Our study provides new mechanistic insight into the role of RvD1 in MASH and highlights its therapeutic potential to treat MASH. HighlightsO_LILiver RvD1 levels are decreased in MASH patients and MASH mice C_LIO_LIRvD1 administration suppresses Stat1-mediated inflammatory response C_LIO_LIRvD1 administration alleviates ER stress-induced apoptosis C_LIO_LIRvD1 administration induces fibrosis regression gene expression C_LI

pharmacology and toxicology↗

bioRxiv2024

Whole-Plant Physiological Identification and Quantification of Disease Progression

Visual estimates of plant symptoms are traditionally used to quantify disease severity. Yet, the methodologies used to assess these phenotypes are often subjective and do not allow tracking of disease progression from very early stages. Here, we hypothesized that quantitative analysis of whole-plant physiological vital functions can be used to objectively determine plant health, providing a more sensitive way to detect disease. We studied the tomato wilt that is caused by Fusarium oxysporum f. sp. lycopersici. Physiological performance of infected and non-infected tomato plants was compared using a whole-plant pot-based lysimeter functional-phenotyping system in a semi-environmentally controlled greenhouse. Water-balance traits of the plants were measured continuously and simultaneously in a quantitative manner. Infected plants exhibited early reductions in transpiration and biomass gain, which preceded visual disease symptoms. These changes in transpiration proved to be effective quantitative indicators for assessing both plant susceptibility to infection and virulence of the fungus. Physiological changes linked to fungal outgrowth and toxin release contributed to reduced hydraulic conductance during initial infection stages. The functional-phenotyping method objectively captures early-stage disease progression, advancing plant disease research and management. This approach emphasizes the potential of quantitative whole-plant physiological analysis over traditional visual estimates for understanding and detecting plant diseases.

plant biology↗

bioRxiv2023

Spatial and single-cell transcriptomics illuminate bat immunity and barrier tissue evolution

The Egyptian fruit bat displays tolerance to lethal viruses and unique dietary adaptations, but the molecular basis for this is poorly understood. To this end, we generated detailed maps of bat gut, lung and blood cells using spatial and single-cell transcriptomics. We compared bat with mouse and human cells to reveal divergence in genetic programs associated with environmental interactions and immune responses. Complement system genes are transcriptionally divergent, uniquely expressed in bat lung and gut epithelium, and undergo rapid coding-sequence evolution. Specifically in the tip of the gut villus, bat enterocytes express evolutionarily young genes while lacking expression of genes related to specific nutrient absorption. Profiling immune stimulation of PBMCs revealed a monocyte subset with conserved cross-species interferon expression, suggesting strong constraints to avoid an excessive immune response. Our study thus uncovers conserved and divergent immune pathways in bat tissues, providing a unique resource to study bat immunity and evolution.

genomics↗

bioRxiv2023

PARG inhibition induces nuclear aggregation of PARylated PARP1

PARG inhibitors are currently under clinical development for the treatment of DNA repair-deficient cancers, however, their precise mechanism of action is still unclear. Here we report that PARG inhibition causes increased nuclear PARylated PARP1 that limits PARP1 chromatin binding in response to DNA damage. This PARylated PARP1 accumulates as aggregates at sites distinct from the site of DNA damage, leading to the mis-localization of PARP1. Additionally, these aggregates are formed through PAR chains as abrogating PARP1 catalytic activity prevents their formation. Finally, these PARP1 nuclear aggregates persist long-term and are associated with cleaved cytoplasmic PARP1, a cell death hallmark, which ultimately leads to a non-apoptotic form of cell death. Overall, our data uncovers a novel mechanism of PARG inhibitor cytotoxicity, which will inform ongoing clinical studies.

cancer biology↗

bioRxiv2023

Cholesterol-associated Locus EHBP1 Protects against NASH Fibrosis

SUMMARYHepatic free cholesterol contributes to fibrosis in nonalcoholic steatohepatitis (NASH), but how hepatic cholesterol metabolism becomes dysregulated in NASH is not completely understood. We show here that human fibrotic NASH livers have decreased EHBP1, a novel GWAS locus associated with LDL-cholesterol, and that the EHBP1 rs10496099 T>C variant in NASH patients is associated with decreased hepatic EHBP1 expression and augmented NASH fibrosis. Congruent with the human data, EHBP1 loss- and gain-of-function increases and decreases NASH fibrosis in mice, respectively. Mechanistic studies reveal that EHBP1 promotes sortilin (SORT1)-mediated PCSK9 secretion, leading to LDLR degradation, decreased LDL uptake, and reduced TAZ, a fibrogenic effector. Moreover, we show that the TNF/PPAR pathway suppresses EHBP1 in NASH. These data not only provide new mechanistic insight into the role of EHBP1 in cholesterol metabolism and NASH fibrosis by uncovering the interaction between EHBP1 and other cholesterol-related loci, including SORT1, PCSK9, and LDLR, but also elucidate a novel interplay between inflammation and EHBP1-mediated cholesterol metabolism. HighlightsHepatic EHBP1 is reduced in human and mouse fibrotic NASH EHBP1 rs10496099 T>C is associated with decreased hepatic EHBP1 and augmented NASH EHBP1 reduces LDLR, cholesterol uptake, and TAZ in hepatocytes EHBP1 promotes sortilin-mediated PCSK9 secretion in hepatocytes TNF suppresses EHBP1 expression in hepatocytes

pathology↗

bioRxiv2023

The memory of pathogenic IgE is contained within CD23+IgG1+ memory B cells poised to switch to IgE in food allergy

Food allergy is caused by allergen-specific IgE antibodies but little is known about the B cell memory of persistent IgE responses. Here we describe in human pediatric peanut allergy CD23+IgG1+ memory B cells arising in type 2 responses that contain peanut specific clones and generate IgE cells on activation. These type2-marked IgG1+ memory B cells differentially express IL-4/IL-13 regulated genes FCER2/CD23, IL4R, and germline IGHE and carry highly mutated B cell receptors (BCRs). Further, high affinity memory B cells specific for the main peanut allergen Ara h 2 mapped to the population of type2-marked IgG1+ memory B cells and included convergent BCRs across different individuals. Our findings indicate that CD23+IgG1+ memory B cells transcribing germline IGHE are a unique memory population containing precursors of pathogenic IgE. One-Sentence SummaryWe describe a unique population of IgG+ memory B cells poised to switch to IgE that contains high affinity allergen-specific clones in peanut allergy.

immunology↗

bioRxiv2019

An exposition of ejaculate senescence and its inhibition in Drosophila

Declining ejaculate performance with male age is taxonomically widespread and has broad ramifications for fertility and fitness. However, we have a poor understanding of age-related changes to specific ejaculate components, how they cause reduced performance, and whether the decline is ameliorable. Here, we show that, in Drosophila, sperm production chronologically declines with age, invariant to mating activity, while repeated mating causes infertility via reduced sperm stores and viability. However, changes to sperm do not fully explain ejaculate deterioration: impacts on seminal fluid contribute to aspects of reduced ejaculate performance, associated with shifts in proteome abundance and quality. We show that ablation of insulin-like peptide-producing cells in males ameliorates aspects of ejaculate performance loss, suggesting that anti-ageing interventions can be coopted to benefit male reproductive health.\n\nOne Sentence SummaryEjaculate performance declines with male age via mating-dependent sperm and seminal protein deterioration, but it can be ameliorated.

evolutionary biology↗

bioRxiv2017

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

BackgroundCancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host system, sometimes with unexpected results. Systematic investigation of mechanistic computational models can augment traditional laboratory and clinical studies, helping identify the factors driving a treatments success or failure. However, given the uncertainties regarding the underlying biology, these multiscale computational models can take many potential forms, in addition to encompassing high-dimensional parameter spaces. Therefore, the exploration of these models is computationally challenging. We propose that integrating two existing technologies--one to aid the construction of multiscale agent-based models, the other developed to enhance model exploration and optimization--can provide a computational means for high-throughput hypothesis testing, and eventually, optimization.\n\nResultsIn this paper, we introduce a high throughput computing (HTC) framework that integrates a mechanistic 3-D multicellular simulator (PhysiCell) with an extreme-scale model exploration platform (EMEWS) to investigate high-dimensional parameter spaces. We show early results in applying PhysiCell-EMEWS to 3-D cancer immunotherapy and show insights on therapeutic failure. We describe a generalized PhysiCell-EMEWS workflow for high-throughput cancer hypothesis testing, where hundreds or thousands of mechanistic simulations are compared against data-driven error metrics to perform hypothesis optimization.\n\nConclusionsWhile key notational and computational challenges remain, mechanistic agent-based models and high-throughput model exploration environments can be combined to systematically and rapidly explore key problems in cancer. These high-throughput computational experiments can improve our understanding of the underlying biology, drive future experiments, and ultimately inform clinical practice.

systems biology↗