Biology subjects

Yang, Y.

Publications and source records attributed to Yang, Y..

At least 361 records · Page 20Linked to original sources

bioRxiv2025

Early antifungal resistance prediction based on MALDI-TOF mass spectrometry and machine learning

Antimicrobial resistance (AMR) is a significant global health threat. Recent studies have shown that combining MALDI-TOF mass spectrometry with machine learning (ML) algorithms can accelerate AMR determination. However, these efforts have predominantly focused on bacterial pathogens. Given the significant morbidity, mortality and healthcare costs associated with fungal infections and their evolving antifungal resistance to treatment, there is a need for precision medicine approaches that enable early and accurate detection of antifungal resistance. We developed a machine-learning pipeline that integrates MALDI-TOF mass spectrometry data and drug features to predict antifungal resistance and identify spectral biomarkers for antifungal resistance. By leveraging the DRIAMS dataset, we included 658 pathogen spectra linked to 3,046 phenotypic antifungal resistance results. This dataset covered three drug classes and seven yeast species. The model was trained using categorical, phenotypic antifungal susceptibility testing results as ground truth. Our study systematically investigated the influence of various dimensionality reduction methods on MALDI-TOF mass spectra, antifungal encodings, and machine learning models using nested cross-validation to evaluate predictive performance. We identified that applying principal component analysis to MALDI-TOF mass spectra for dimensionality reduction, and training a multi-layer perceptron yielded the highest and most stable performance for the prediction of antifungal resistance. Our method achieved an AUPRC of 0.77 across the 10 highest-performing species-drug pairs and corresponding Matthews correlation coefficient of 0.69. The model demonstrated the best performance for the species-drug combinations of Candida albicans with micafungin, Candida parapsilosis with fluconazole, and Saccharomyces cerevisiae with itraconazole and fluconazole. By comparing established species-based guidelines, susceptibility test results, and machine learning predictions, we estimated that integrating our algorithm into antifungal selection could help avoid prescriptions to likely resistant pathogens in approximately 3 out of 10 patients for whom standard guidelines recommend such treatments.

bioinformatics↗

bioRxiv2025

AAV6-HCN4t-mediated biological pacing as a potential life-saving therapy for congenital complete heart block

Congenital complete heart block (CCHB) is a life-threatening condition in fetuses due to severe bradycardia. Maternal administration of {beta}-adrenergic agonists is used to increase fetal heart rates, but its effectiveness is limited and lost over time most likely due to insufficient expression of HCN channels in some individuals. We report the development of an injectable gene therapy that produces reliable cardiac pacemaker function in the presence of {beta}-adrenergic stimulation. Intramyocardial injection of adeno-associated viral serotype 6 vectors expressing HCN4t (AAV6-HCN4t) into the left ventricular apex significantly increased ectopic pacing frequency and heart rate in response to isoproterenol in rats with complete heart block, and this effect remained stable throughout the 4 weeks of follow-up. Injection of AAV6-HCN4t showed similar reliable biological pacing in complete heart block pigs. These results suggest that AAV6-HCN4t generates robust biological pacing in the presence of isoproterenol, providing the foundation for a potentially life-saving therapy for in utero CCHB.

physiology↗

bioRxiv2025

Machine Learning Resolves Functional Phenotypes and Therapeutic Responses in KCNQ2 Developmental Epileptic Encephalopathy iPSC Models

Pathogenic KCNQ2 variants are associated with developmental and epileptic encephalopathy (KCNQ2-DEE), a devastating disorder characterized by neonatal-onset seizures and impaired neurodevelopment with no effective treatments. KCNQ2 encodes the voltage-gated potassium channel KV7.2, which regulates action potential threshold and repolarization. However, the relationship between KV7.2 dysfunction and abnormal neuronal activity remains unclear. Here, we use human induced pluripotent stem (iPSC)-derived neurons from 5 KCNQ2-DEE patients with pathogenic variants and CRISPR/Cas9-corrected isogenic controls to investigate pathophysiological mechanisms. We identify a common dyshomeostatic enhancement of Ca2+-activated small conductance potassium (SK) channels, which drives larger post-burst afterhyperpolarizations in KCNQ2-DEE neurons. Using microelectrode arrays (MEAs), we recorded over 18 million extracellular spikes from >8,000 neurons during 5 weeks in culture and then applied supervised and unsupervised machine learning algorithms to dissect time-dependent functional neuronal phenotypes that defined both patient-specific and shared firing features among KCNQ2-DEE patients. Our analysis identified irregular spike timing and enhanced bursting as functional biomarkers of KCNQ2-DEE and demonstrated the significant influence of genetic background on phenotypic diversity. Importantly, using unbiased machine learning models, we showed that chronic treatment with the KV7 activator retigabine rescues the disease-associated functional phenotypes with variable efficacy. Our findings highlight SK channel upregulation as a critical pathophysiological mechanism underlying KCNQ2-DEE and provide a robust MEA-based machine learning platform useful for deciphering phenotypic diversity amongst patients, discovering functional disease biomarkers, and evaluating precision medicine interventions in personalized iPSC neuronal models.

neuroscience↗

bioRxiv2025

Representation Transfer via Invariant Input-driven Neural Manifolds for Brain-inspired Computations

Domain adaptation is a core challenge for embodied AI deployed in unpredictable, noisy environments. Conventional deep models degrade under domain shifts and require costly retraining. Inspired by biological brains, we propose a modular framework where each module is a recurrent neural network pretrained via a simple, task-agnostic protocol to learn robust, transferable features. This shapes stable yet flexible representations as invariant input-driven continuous attractor manifolds embedded in high-dimensional latent space, supporting robust transfer and resilience to noise. At deployment, only a lightweight adapter needs training, enabling rapid few-shot adaptation. Evaluated on the DVS Gesture benchmark and a custom RGB rehabilitation dataset, our framework matches or surpasses leading C3D and ViViT models while using ten times fewer parameters and only one training epoch. By unifying biologically inspired attractor dynamics with cortical-like modular composition, our approach offers a practical path toward robust, continual adaptation for real-world embodied AI. TeaserInvariant input-driven attractors enable representation transfer and domain adaptation in brain-inspired AI.

neuroscience↗

bioRxiv2025

MTARC1 Regulates Lipid Droplet Degradation via Phospholipid Remodeling in Metabolic Fatty Liver Disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a spectrum of liver disease, including simple fatty liver, hepatic steatosis, fibrosis, cirrhosis, and hepatocellular carcinoma, with limited treatment options. MTARC1 p.A165T variant is associated with reduced risk of MASLD. We previously showed that this variant promotes MTARC1 protein degradation, suggesting MTARC1 inactivation may represent a potential therapeutic strategy. Here, we report that global or liver specific Mtarc1 knockout markedly suppresses diet-induced hepatic TG accumulation, liver injury, inflammation, and fibrosis in a manner dependently on lipolysis and lipophagy. MTARC1 inactivation post-transcriptionally upregulates PEMT and CEPT1 expression, consequently elevating GPL levels, including PC, PE, and lyso-PE, three major GPLs of hepatic LDs. Furthermore, we observed that MTARC1-deficient hepatocytes exhibited smaller but more numerous LDs compared to controls, under comparable cellular TG levels during lipolysis/lipophagy inhibition. Importantly, inhibition of CEPT1 or PEMT could attenuated the hepatoprotective effect of MTARC1 deficiency. Mechanistically, subcellular GPL remodeling induced by MTARC1 deficiency reduce LD size while increase their surface-to-volume ratio, which in turn promote TG degradation through lipolysis and lipophagy. Collectively, our findings identify MTARC1-GPL biosynthesis-LD degradation axis as a key regulator of fatty liver disease and highlight MTARC1 inhibition as a promising therapeutic strategy for MASLD.

cell biology↗

bioRxiv2025

Maturation of Dorsal Association Tracts during Preadolescence Links to Concurrent and Future Cognitive Performance and Transdiagnostic Psychopathology

Many psychiatric disorders begin during adolescence, coinciding with the rapid development of brain white matter (WM). However, it remains unclear whether deviations from normal WM maturation during this age period contribute to the development of psychopathology. In this study, we developed and validated normative models of brain age based on specific WM tracts using three large-scale developmental datasets (a total of [~]10,000 subjects). We found that tract-specific deviations in WM development of association and limbic/subcortical systems were linked to concurrent cognition and psychopathology. The spatial pattern of the association system aligned closely with distributions of high-order brain networks, and with mitochondrial content and respiratory capacity. The maturation of the association system contributed significantly to better cognitive performance assessed two or three years later. Importantly, delayed WM development especially in dorsal association tracts predicted psychiatric disorders across diagnoses and disorder onset over a 2-year follow-up. By identifying tract-specific WM development during preadolescence as a predictor of cognitive capacity and psychiatric disorder risks, this study provides a valuable framework for tracking individualized brain maturation and understanding the neurobiological underpinnings of cognitive performance and transdiagnostic psychopathology.

neuroscience↗

bioRxiv2025

Linking power, efficiency, and bifurcations in ecological systems

Three hypotheses help organize energetic thinking about living systems: Lotkas Maximum Power Principle, Odum-Pinkertons Intermediate Efficiency Principle, and Morowitzs Biological Cycling Principle. Here we show how these hypotheses fit together in consumer-resource systems, moving from qualitative principles to formal, testable statements. Using the Rosen-zweig-MacArthur model, we prove that the consumers maximum output power lies exactly on the Hopf boundary that separates stable points from cycles; at that point, the resulting power efficiency is intermediate. In the Rosenzweig-MacArthur model the Hopf is supercritical, so a stable limit cycle appears smoothly as the equilibrium loses stability. We treat the Hopf onset of time-periodic population oscillations as a population-level analogue of sustained cycling under energy flux. We then embed these energetic statements in adaptive dynamics: with convex trait costs, evolutionary singular strategies exist and are locally stable, but they coincide with the maximum-power state only under explicit marginal-cost conditions. Together, these results unify classic ideas in the concrete setting of consumer-resource systems and suggest measurements to evaluate when bifurcations, energetics, and evolution can converge.

ecology↗

bioRxiv2025

NAD⁺ Replenishment Mitigates Cardiomyocyte Senescence and Corrects Heart Failure with Preserved Ejection Fraction in Aged Mice

Cardiomyocyte senescence, characterized by elevated cell cycle inhibitor expression, persistent DNA damage response, and mitochondrial dysfunction, contributes to myocardial stiffness and the progression of heart failure with preserved ejection fraction (HFpEF), the most common form of heart failure affecting individuals over 65. In this study, we investigated the role of NAD metabolism in cardiomyocyte senescence and cardiac function. Aged mice exhibited reduced cardiac NAD levels, impaired NAD biosynthesis and mobilization, and increased consumption, leading to suppressed SIRT1/6 activity and accumulation of senescent cardiomyocytes. This was accompanied by diastolic dysfunction consistent with HFpEF. In senescent AC16 cardiomyocytes, NAD depletion promoted senescence, which was reversed by the NAD precursors nicotinamide riboside (NR) and dihydronicotinamide riboside (NRH). In aged mice, two months of NR or NRH treatment improved diastolic function and reduced cardiomyocyte senescence. While NR primarily activated SIRT1 to suppress cell cycle arrest markers, NRH more robustly activated both SIRT1 and SIRT6, enhancing DNA damage repair. Acetylated H2AX, a SIRT6 substrate elevated in aged hearts and senescent cells, was selectively deacetylated by NRH. These findings identify NAD availability as a critical regulator of cardiac senescence and support NAD precursors, particularly NRH, as promising senescence-reducing therapies for treating aging-associated HFpEF.

pharmacology and toxicology↗

bioRxiv2025

Improving causal effect estimation in multi-ancestry multivariable Mendelian randomization with transfer learning

Multivariable Mendelian randomization (MVMR) has been largely limited to individuals of European ancestry, due to the larger sample sizes available in European genome-wide association studies (GWAS). We introduce MRBEE-TL, one of the first multi-ancestry MVMR methods, which combines transfer learning with bias-corrected estimating equations to improve power in underpowered ancestries and to assess cross-ancestry heterogeneity of disease risk factors. In simulations, MRBEE-TL consistently outperformed MR methods that relied solely on ancestry-specific GWAS data. In real data analyses, MRBEE-TL not only identified ancestry-consistent and ancestry-specific causal effects missed by conventional methods, but also improved power in African and East Asian ancestries. MRBEE-TL is available through the R package MRBEEX at https://github.com/harryyiheyang/MRBEEX.

bioinformatics↗

bioRxiv2025

A Paracrine-to-Autocrine Shunt of GREM1 Fuels Colorectal Cancer Metastasis via ACVR1C

Tumor cells typically rely on paracrine stromal signals to guide malignant behaviors, but whether they acquire signaling autonomy to support metastasis remains unclear. We elucidate this in colorectal cancer (CRC) by uncovering a paracrine-to-autocrine shunt of Gremlin1 (GREM1), a canonical stromal-secreted antagonist of bone morphogenetic proteins (BMPs). We demonstrate that while GREM1 remains restricted to stromal cells in earlier-stage (I-III) CRC, its ectopic expression in tumor epithelium increases markedly in stage IV. Mechanistically, we identify ACVR1C as a novel, high-affinity epithelial receptor for GREM1. Their interaction activates SMAD2/3 signaling, which upregulates SNAI1 and GREM1, establishing a feedback loop that amplifies epithelial-mesenchymal transition (EMT). Disrupting this loop impairs CRC metastasis in vivo. Clinically, epithelial GREM1 or ACVR1C expression predicts metastasis and poor survival. These findings define a paradigm in which tumor cells hijack stromal GREM1 to establish a GREM1-ACVR1C autocrine loop that sustains EMT and metastasis, marking a shift toward signaling autonomy and revealing a targetable vulnerability in advanced CRC.

cancer biology↗

bioRxiv2025

Conformations and sequence determinants in the lipid binding of an adhesive peptide derived from Vibrio cholerae biofilm

Surface adhesion is critical to the survival of pathogenic bacteria both in natural niches and during infections, often via forming matrix-embedded communities called biofilms. We previously identified a 57-amino acid peptide (Bap1-57aa) as a key contributor to biofilm adhesion of the pandemic pathogen Vibrio cholerae to various surfaces including lipid membranes. Here, we combine biophysical, computational, and genetic approaches to elucidate the molecular mechanism. A central aromatic-rich motif anchors the peptide to lipid bilayers while peripheral pseudo repeats enhance binding through avidity. Surprisingly, the core motif undergoes a lipid-induced conformational transition into a {beta}-hairpin, enabling robust membrane insertion. Moreover, the biofilm-derived peptide, conserved in several other Vibrio species, can adhere to model host surfaces and is sensitive to membrane curvature. Our results provide molecular insight into biofilm adhesion and may lead to new strategies for targeted biofilm removal and the design of bioinspired underwater adhesives. TeaserA short peptide from Vibrio cholerae binds lipids using a unique {beta}-hairpin motif and contributes to host colonization.

microbiology↗

bioRxiv2025

BrainBeacon: A Cross-Species Foundation Model for Single-cell Resolved Brain Spatial Transcriptomics

The brains functional complexity emerges from spatiotemporal heterogeneity in molecular, cellular, and circuit organization, shaped by evolution, aging, and disease. Single-cell spatial transcriptomics provides a transformative framework to decode this complexity, resolving cell-type-specific expression patterns, region-specific regulation, evolutionary divergence, and disease-associated disruptions. Despite recent advances in spatial transcriptomics, a unified foundation model capturing cross-species, whole-brain cellular architecture remains lacking. Here, we introduce BrainBeacon, the first cross-species foundation model for whole-brain spatial transcriptomics. Trained on 133 million spatially resolved cells covering a total area of 210194 mm2 from human, macaque, marmoset and mouse brains profiled across five major platforms, BrainBeacon integrates gene expression rankings, spatial cellular organization, and evolutionarily conserved genetic relationships into a unified transformer-based architecture. A two-stage training strategy captures both intra-cellular transcriptional dependencies and inter-cellular spatial interactions, yielding biologically grounded and interpretable representations of brain structure. Subsequently, BrainBeacon enables cross-species alignment of brain cell types and anatomical regions, facilitating integrative analyses of conserved and divergent cellular architectures. Moreover, it uncovers spatial regulatory mechanisms of aging by predicting bidirectional perturbation responses between cells and their local niche. Together, BrainBeacon serves as a foundational cross-species model for whole-brain spatial transcriptomics, enabling virtual brain atlas construction, systematic in silico perturbation studies, and mechanistic insights into brain disease pathogenesis.

bioinformatics↗

bioRxiv2025

DIFFERENTIAL ROLES OF RAD18 IN REPRESSING CARCINOGEN- AND ONCOGENE-DRIVEN MUTAGENESIS IN VIVO

The DNA repair protein RAD18 activates Y-family Trans-Lesion Synthesis (TLS) DNA polymerases that are DNA damage-tolerant and potentially error-prone. RAD18 is also frequently overexpressed and pathologically activated in cancer cells. However, the extent to which RAD18 shapes cancer genomes and impacts tumorigenesis is unclear. Therefore, we tested the effect of Rad18 status on chemically-induced and oncogene-driven tumorigenesis. In a chemically-induced oral carcinogenesis model, acute (2-16 days) 4NQO-treatment induces expression of Rad18 and TLS polymerase mRNAs in mouse oral epithelial cells prior to emergence of oral squamous cell carcinomas (OSCCs). Chronic (8 week) 4NQO-treatment leads to onset of oral tumors that is accelerated in Rad18-/- mice when compared with Rad18+/+ animals. Analysis of OSCC exomes reveals increased levels of G(C)>T(A) transversions in Rad18-/- tumors when compared with Rad18+/+. Therefore, Rad18 promotes error-free bypass of 4NQO-induced DNA lesions and suppresses 4NQO-induced oral carcinogenesis. In a KrasG12D-induced lung carcinogenesis model, Rad18-deficiency did not affect rates or incidence of oncogene-induced lung tumors or mutations. Taken together, we demonstrate that Rad18 has context-specific tumor-suppressive activity. Given the prevalence of 4NQO-like environmental exposures, RAD18 is highly likely to shape human cancer genomes and perhaps influence other aspects of the tumorigenic process.

cancer biology↗

bioRxiv2025

Accurate Protein-Protein Interactions Modeling through Physics-informed Geometric Invariant Learning

AlphaFold has set a new standard for predicting protein structures from primary sequences; however, it faces challenges with protein complexes across species, engineered proteins, and antigen-antibody, where co-evolutionary signals may be sparse or missing. Herein, we present ProTact, a SE(3)-invariant geometric graph neural network that integrates physics-informed geometric complementarity and trigonometric constraints as robust inductive biases to enhance protein-protein contact predictions. ProTact is applicable to both experimental and predicted monomer structures and utilizes a modulated key point matching algorithm to approximate accurate docking poses. Experimental evaluations demonstrate that ProTact consistently outperforms state-of-the-art sequence-based and structure-based methods on benchmark datasets, achieving notable relative improvements of 31.63% in Precision@10 for CASP 13 and 14 targets, and 31.94% for DIPS-Plus datasets. Moreover, when combined with AlphaFold3 as re-scoring functions, ProTact surpasses its default confidence scores, offering over 30.48% improvements in the low-MSA contexts. We anticipate that the proposed framework will advance our understanding of protein interactions, functions, and design.

bioinformatics↗

bioRxiv2025

Construction of recombinant Bacillus subtilis spores surface-displaying PRRSV ORF1b, GP5, M, and N proteins in tandem and evaluation of their immunogenicity in mice

Porcine reproductive and respiratory syndrome virus (PRRSV), a single-stranded RNA virus, is a highly contagious pathogen that causes severe reproductive and respiratory disorders in pigs, leading to significant economic losses in the swine industry worldwide. However, current commercial vaccines provide only limited protection against circulating PRRSV strains, highlighting the urgent need for novel vaccine strategies. B. Bacillus subtilis, a well-characterized probiotic, has emerged as a promising platform for mucosal vaccine delivery due to its safety profile and ability to induce robust immune responses. In this study, we screened B cell and T cell linear epitopes from PRRSV-ORF1b, GP5, M, and N proteins to construct a recombinant tandem antigen (ER), which was displayed on the surface of B. subtilis 168 spores. The resulting recombinant strain, designated B. subtilis BE, was evaluated for immunogenicity in mice via oral administration. The results showed that Oral immunization with B. subtilis BE significantly elevated antigen-specific secretory IgA levels in intestinal contents and IgG levels in serum, indicating potent mucosal and systemic humoral responses. Moreover, B. subtilis BE induced measurable neutralizing antibody titers and enhanced cellular immunity, as evidenced by increased frequencies of CD3+ and CD8+ T cells in mesenteric lymph nodes and upregulation of IFN-, TNF-, and IFN-{gamma} expression in intestinal tissues. Collectively, these findings demonstrate that B. subtilis BE elicits both humoral and cellular immune responses and may serve as a novel oral vaccine candidate against PRRSV, offering a promising alternative for controlling the ongoing epidemic.

immunology↗

bioRxiv2025

Synergizing Bayesian and Heuristic Approaches: D-BPP Uncovers Ghost Introgression in Panthera and Thuja

Hybridization involving extinct or unsampled ("ghost") lineages profoundly influences species evolutionary histories, but detecting such introgression remains methodologically challenging. We introduce D-BPP, a novel framework that integrates the heuristic D-statistic (or ABBA-BABA test) with Bayesian phylogenomic inference (implemented in BPP) to efficiently infer phylogenetic networks. In D-BPP, we first employ the D-statistic to rapidly identify candidate introgression events on a predefined binary species tree; then we leverage the Bayesian test in BPP to rigorously validate these candidates and sequentially add them to the species tree, retaining only those events with strong statistical support. If the species tree is ambiguous, D-BPP identifies the most probable tree by evaluating competing topologies through Bayesian model comparison of their corresponding introgression models. Critically, our framework excels at detecting ghost introgression, which is often unidentifiable or overlooked by existing methods-- whether heuristic or full-likelihood. Applied to genomic datasets from Panthera (big cats) and Thuja (conifers), D-BPP uncovered previously undetected ghost introgression events in both clades, underscoring the pervasive role ghost lineages have played across diverse taxa. By uniquely combining the computational efficiency of heuristic D-statistics with the robust statistical rigor of full-likelihood Bayesian inference, D-BPP deciphers complex hybridization patterns obscured by conventional methods, providing a powerful tool for accurately reconstructing phylogenetic networks.

evolutionary biology↗

bioRxiv2025

Metabolic control of enteroendocrine cell fate through a redox state sensor CtBP

Enteroendocrine (EE) cells monitor the intestinal nutrient composition and consequently control organismal physiology through hormonal signaling. In addition to the immediate effects on hormone secretion, nutrients influence EE cell abundance by affecting the determination and maintenance of cell fate. EE cells are known to import and respond to dietary sugars, but how the sugar-induced changes in the intracellular metabolic state are sensed to control the immediate and long-term responses of EE cells, remains poorly understood. We report that the NADH binding transcriptional cofactor C-terminal binding protein (CtBP) acts at the interface between nutrient sensing and fate regulation of Drosophila larval EE cells, thus controlling organismal energy metabolism and survival on a high sugar diet. CtBP dimerization in EE cells is regulated through the redox balance of nicotinamide cofactors controlled by glycolysis and pentose phosphate pathway, allowing EE cells sense their internal metabolic state in response to sugar catabolism. CtBP interacts with the EE cell fate determining transcription factor Prospero through a conserved binding motif and binds to genomic targets controlling EE cell fate and size, such as components of Notch and insulin/mTOR pathways. Collectively, our findings uncover a modality where changes in intracellular redox state serve as an instructive signal to control EE cell function to globally control organismal homeostasis. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/662346v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@3baaeorg.highwire.dtl.DTLVardef@d505edorg.highwire.dtl.DTLVardef@178361dorg.highwire.dtl.DTLVardef@128e210_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2025

Paclitaxel-induced mitotic arrest results in a convergence of apoptotic dependencies that can be safely exploited by BCL-XL degradation to overcome cancer chemoresistance

Paclitaxel and other microtubule-targeting agents are cornerstone therapies for diverse cancers, including lung, breast, cervical, pancreatic, and ovarian malignancies. Paclitaxel induces tumor cell apoptosis during mitosis by disrupting microtubule dynamics required for chromosome segregation. However, despite initial responsiveness, many tumors develop resistance, limiting therapeutic durability. Here, we used high-grade serous ovarian carcinoma (HGSOC), the most common and lethal subtype of ovarian cancer, as a model to dissect the mechanisms underlying this resistance. We find that paclitaxel-induced mitotic arrest triggers degradation of the pro-survival protein MCL-1 and upregulation of BCL-XL, followed by inactivating phosphorylation of BCL-XL at Ser62 to promote apoptosis. In resistant cells, this MCL-1 downregulation is insufficient to commit cells to apoptosis but instead results in a transient convergence of apoptotic dependencies by forcing BCL-XL to sequester the pro-apoptotic proteins BIM, BAX, and BAK. During this state, BCL-XL inhibition induces synergistic apoptosis, even in chemoresistant cells. Surprisingly, we also discover that loss of substrate attachment recapitulates this apoptotic convergence both in vitro and in vivo, with HGSOC cells growing in metastasis-promoting malignant ascites displaying heightened apoptotic priming and dependence on BCL-XL relative to solid tumors. In HGSOC xenografts, targeted degradation of BCL-XL using the platelet-sparing proteolysis-targeting chimera (PROTAC) DT2216 matches the efficacy of paclitaxel monotherapy while avoiding the chronic thrombocytopenia induced by BCL-XL inhibitors such as navitoclax (ABT-263). Strikingly, combination therapy leveraging the synergy between paclitaxel and DT2216 leads to complete eradication of HGSOC cell line and patient-derived xenografts. Moreover, DT2216 treatment blunts the rapid apoptotic adaptation caused by other BCL-XL inhibitors, indicating that targeted degradation of pro-survival proteins may yield more durable responses than inhibition alone. These findings uncover a mechanistic framework for safely exploiting the apoptotic dependency convergence caused by mitotic arrest and substrate detachment and support the clinical development of BCL-XL-targeting PROTACs to overcome chemoresistance in ovarian cancer and other solid tumors.

cancer biology↗