Biology subjects

Fishel, M. L.

Publications and source records attributed to Fishel, M. L..

5 recordsLinked to original sources

bioRxiv2026

A kinetic-aware approach to infer metabolic variations and flux using transcriptomics and metabolomics data

Assessing metabolic variations and flux quantities enable systematic understandings of metabolic shifts, reprogramming, adaptation and interactions in human diseases. However, omics-based estimation of metabolic flux and its variation remains challenging due to several fundamental limitations: the need for disease and tissue context specific metabolic model; nonlinear enzyme kinetic model that links enzyme and substrate changes to reaction flux; partial, unpaired, and snap-shot measurements across omics modalities; and uncertainty in computational prediction. Here, we present Michaelis-Menten model-based Flux Estimation Analysis (mmFEA), a Monte Carlo framework for estimating condition-specific flux changes by integrating paired or unpaired metabolomics and transcriptomics (or proteomics) data. mmFEA separates each reaction-rate change into enzyme- and substrate-associated components and assess reaction rate using Michaelis-Menten kinetics equation. A baseline metabolite saturation rate is introduced by integrating protein language model predicted kinetic parameters and human baseline level metabolic concentration to enable kinetic-aware integration of unpaired substrate and enzyme level measurements. Distribution of metabolic flux and variations between conditions are further computed using MCMC sampling by treating Michaelis-Menten-derived marginal flux distribution as prior and coherency in flux balance as likelihood. To benchmark mmFEA, we generated an in-house multi-omics data set including transcriptomics, metabolomics, metabolic activity functional assay, and CRISPR screening data using pancreatic cancer cell line system treated by APEX1 inhibitors. We demonstrated that mmFEA could accurately capture experimentally observed metabolic changes and achieved a better performance than all baseline methods. Our analysis revealed the necessity in using both substrate and enzyme modality and kinetic aware model in metabolic flux assessment. Further analysis using independent pancreatic cancer cohorts further validated the robustness of mmFEA, supporting integration of condition-linked unpaired data. Pan-cancer and spatial multi-omic applications demonstrated the use of mmFEA for resolving context-dependent metabolic variation when direct flux measurements are unavailable. Together, mmFEA provides a mechanistically grounded framework for estimating relative metabolic flux changes and their uncertainty from heterogeneous omics data.

systems biology↗

bioRxiv2026

Integrated Post-Translational Modification (PTM) Proteomics Reveals Early Redox and Mitochondrial Remodeling Following Ref-1 Inhibition in Pancreatic Cancer

BackgroundApurinic/apyrimidinic endonuclease 1/redox factor-1 (Ref-1/APE1) is a central regulator of redox-dependent transcriptional signaling that promotes pancreatic ductal adenocarcinoma (PDAC) progression, therapeutic resistance, and metabolic adaptation. While pharmacologic inhibition of Ref-1 suppresses tumor growth and alters cellular metabolism, immediate molecular events linking Ref-1 inhibition to downstream cellular adaptation remain poorly understood. We therefore sought to characterize proteome-wide signaling responses induced by second-generation Ref-1 redox inhibitor, APX2014. MethodsWe applied an integrated multiplexed proteomics workflow to simultaneously quantify global protein abundance together with cysteine oxidation, phosphorylation, and lysine acetylation in Pa03C PDAC cells following acute treatment (30-120 min) with selective Ref-1 redox inhibitor APX2014. Differential post-translational modification (PTM) analysis, pathway enrichment, structural mapping of regulated sites, and functional mitochondrial substrate utilization assays were performed to define early signaling responses. ResultsAPX2014 induced rapid and extensive remodeling of PTM landscape while producing minimal changes in global protein abundance. Cysteine oxidation represented the earliest and most sustained response, accompanied by widespread phosphorylation and delayed lysine acetylation. Integrated pathway analyses identified mitochondrial translation, respiratory electron transport, TCA cycle metabolism, and mitochondrial redox homeostasis as the earliest and most consistently regulated processes. Functional mitochondrial assays confirmed impaired utilization of TCA cycle substrates following APX2014 treatment. Coordinated PTM remodeling was observed on Ref-1-associated signaling proteins, including NF-{kappa}B1 and p53, revealing simultaneous regulation of oxidation, phosphorylation, and acetylation within functionally important domains. Early redox-sensitive protein networks were also associated with subsequent disruption of mitotic organization. ConclusionsIntegrated multi-PTM proteomics reveals that pharmacologic Ref-1 redox inhibition rapidly rewires regulatory signaling networks before detectable changes in protein abundance. Our findings identify mitochondrial redox remodeling as an early consequence of Ref-1 inhibition, providing systems-level insight into how Ref-1-targeted therapies disrupt metabolic and stress-adaptive programs in pancreatic cancer. This work establishes a framework for understanding the molecular basis of Ref-1-directed therapeutics and highlights integrated PTM profiling as a powerful strategy for defining early drug response mechanisms. These findings provide a strong translational rationale for advancing next-generation Ref-1 redox inhibitors such as APX2014, developed from the first-in-class inhibitor APX3330 currently in clinical trials, and underscore the broader therapeutic potential of targeting Ref-1 redox signaling in pancreatic cancer.

cancer biology↗

bioRxiv2026

Extravascular coagulation stabilizes pro-fibrotic stromal states via tumor-intrinsic PAR1 signaling in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) exhibits a desmoplastic stroma with context-dependent tumor-restraining and tumor-promoting functions, highlighting the need to selectively reprogram stromal states. Extravascular coagulation is a prominent feature of the PDAC tumor microenvironment, yet whether it functions as an upstream regulator of fibrotic stromal states, rather than merely a byproduct of tumor-associated vascular dysfunction, has remained unclear. Here, we identify extravascular coagulation as a tumor-amplified regulatory module that stabilizes pro-fibrotic stromal states via tumor-intrinsic protease-activated receptor-1 (PAR1) signaling. To interrogate this axis mechanistically, we integrated human tumor bioinformatics with microphysiological tumor-stroma (MPTS) models that reconstruct tumor-stroma interactions under controlled coagulation exposure, followed by cross-scale validation in vivo. Analysis of The Cancer Genome Atlas (TCGA) revealed heterogeneous F2R (PAR1) expression across tumors, with elevated expression associated with fibrotic transcriptional programs and reduced survival. Consistently, thrombin induced coordinated pro-fibrotic programs in tumor cells and cancer-associated fibroblasts (CAFs), which were recapitulated in MPTS where tumor-intrinsic PAR1 was required for amplification of extracellular matrix deposition and CAF activation. Mechanistically, PAR1 signaling amplified tumor-stroma communication, in part through induction of TGF-{beta}1-dependent pathways, establishing a reinforcing feedback loop that stabilizes fibrotic remodeling. Pharmacologic inhibition of PAR1 selectively suppressed the fibrotic transcriptional program within myofibroblastic CAFs while reducing the abundance of other CAF subtypes, reprogramming stromal states and attenuating tumor progression across MPTS and in vivo models. These findings establish a coagulation-PAR1 axis as an upstream organizer of PDAC stromal architecture and identify pharmacologic PAR1 inhibition as a mechanistically grounded strategy for selectively reprogramming the tumor-promoting stroma.

cancer biology↗

bioRxiv2020

scFEA: A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data

The metabolic heterogeneity, and metabolic interplay between cells and their microenvironment have been known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single cell metabolomics technology, we are yet to establish systematic understanding of intra-tissue metabolic heterogeneity and cooperation phenomena among cell populations. To mitigate this knowledge gap, we developed a novel computational method, namely scFEA (single cell Flux Estimation Analysis), to infer single cell fluxome from single cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a comprehensively reconstructed human metabolic map into a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multi-layer neural networks to fully capitulate the non-linear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq dataset with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this dataset demonstrated the consistency between predicted flux and metabolic imbalance with the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics datasets and identified context and cell group specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analysis including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.

bioinformatics↗

bioRxiv2018

Ref-1/APE1 inhibition with novel small molecules blocks ocular neovascularization

Ocular neovascular diseases like wet age-related macular degeneration are a major cause of blindness. Novel therapies are greatly needed for these diseases. One appealing antiangiogenic target is reduction-oxidation factor 1-apurinic/apyrimidinic endonuclease 1 (Ref-1/APE1). This protein can act as a redox-sensitive transcriptional activator for NF-{kappa}B and other pro-angiogenic transcription factors. An existing inhibitor of Ref-1s function, APX3330, previously showed antiangiogenic effects. Here, we developed improved APX3330 derivatives and assessed their antiangiogenic activity. We synthesized APX2009 and APX2014 and demonstrated enhanced inhibition of Ref-1 function in a DNA-binding assay compared to APX3330. Both compounds were antiproliferative against human retinal microvascular endothelial cells (HRECs; GI50 APX2009: 1.1 M, APX2014: 110 nM) and macaque choroidal endothelial cells (Rf/6a GI50 APX2009: 26 M, APX2014: 5.0 M). Both compounds significantly reduced the ability of HRECs and Rf/6a cells to form tubes at mid nanomolar concentrations compared to control, and both significantly inhibited HREC and Rf/6a cell migration in a scratch wound assay, reducing NF-{kappa}B activation and downstream targets. Ex vivo, both APX2009 and APX2014 inhibited choroidal sprouting at low micromolar and high nanomolar concentrations respectively. In the laser-induced choroidal neovascularization mouse model, intraperitoneal APX2009 treatment significantly decreased lesion volume by 4-fold compared to vehicle (p < 0.0001, ANOVA with Dunnetts post hoc tests), without obvious intraocular or systemic toxicity. Thus, Ref-1 inhibition with APX2009 and APX2014 blocks ocular angiogenesis in vitro and ex vivo, and APX2009 is an effective systemic therapy for CNV in vivo, establishing Ref-1 inhibition as a promising therapeutic approach for ocular neovascularization.

pharmacology and toxicology↗