Biology subjects

Qin, T.

Publications and source records attributed to Qin, T..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

HPV integration in head and neck cancer: downstream splicing events and expression ratios linked with poor outcomes

HPV integration (HPVint) is associated with carcinogenesis and tumor progression in HPV-associated cancers, including head and neck squamous cell carcinomas (HNSCC). While its impact on human DNA has been well characterized, its relationship with clinical outcomes remains unconfirmed. Here we investigate the consequences of HPVint both with respect to human and HPV characteristics by analyzing 261 HPV-associated HNSCC bulk and single-cell RNA-seq samples from five cohorts, and DNA HPVint events from 102 HPV+ participants in two of the cohorts. By leveraging this large meta-cohort, we first reveal an oncogenic network based on the recurrent HPV integration locations in HNSCC. We then classify HPVint-positive (HPVint(+)) participants by HPV RNA features, specifically based on spliced HPV-human fusion transcripts and ratios of HPV gene transcripts, showing that subsets of participants have worse clinical outcomes. Our analyses, focused mainly on RNA instead of DNA, expand our understanding of the carcinogenic mechanisms of HPVint, partially addressing the conflicting findings of whether HPVint is associated with aggressive phenotypes and worse clinical consequences, and provide potential biomarkers to advance precision oncology in HPV-associated HNSCC.

cancer biology↗

bioRxiv2024

Irx3/5 define the cochlear sensory domain and regulate vestibular and cochlear sensory patterning in the mammalian inner ear

The mammalian inner ear houses the vestibular and cochlear sensory organs dedicated to sensing balance and sound, respectively. These distinct sensory organs arise from a common prosensory region, but the mechanisms underlying their divergence remain elusive. Here, we showed that two evolutionarily conserved homeobox genes, Irx3 and Irx5, are required for the patterning and segregation of the saccular and cochlear sensory domains, as well as for the formation of auditory sensory cells. Irx3/5 were highly expressed in the cochlea, their deletion resulted in a significantly shortened cochlea with a loss of the ductus reuniens that bridged the vestibule and cochlea. Remarkably, ectopic vestibular hair cells replaced the cochlear non-sensory structure, the Greater Epithelial Ridge. Moreover, most auditory sensory cells in the cochlea were transformed into hair cells of vestibular identity, with only a residual organ of Corti remaining in the mid-apical region of Irx3/5 double knockout mice. Conditional temporal knockouts further revealed that Irx3/5 are essential for controlling cochlear sensory domain formation before embryonic day 14. Our findings demonstrate that Irx3/5 regulate the patterning of vestibular and cochlear sensory cells, providing insights into the separation of vestibular and cochlear sensory organs during mammalian inner ear development.

developmental biology↗

bioRxiv2024

Targetable Treatment Resistance in Thyroid Cancer with Clonal Hematopoiesis

Anaplastic thyroid cancer (ATC) is a clinically aggressive malignancy with a dismal prognosis. Combined BRAF/MEK inhibition offers significant therapeutic benefit in patients with BRAFV600E-mutant ATCs. However, relapses are common and overall survival remains poor. Compared with differentiated thyroid cancer, a hallmark of ATCs is significant infiltration with myeloid cells, particularly macrophages. ATCs are most common in the aging population, which also has an increased incidence of TET2-mutant clonal hematopoiesis (CH). CH-mutant macrophages have been shown to accelerate CH-associated pathophysiology including atherosclerosis. However, the clinical and mechanistic contribution of CH-mutant clones to solid tumour biology, prognosis and therapeutic response has not been elucidated. Here we show that TET2-mutant CH is enriched in the tumour microenvironment of patients with solid tumours and associated with adverse prognosis in ATC patients. We find that Tet2-mutant macrophages selectively infiltrate mouse BrafV600E-mutant ATC and that their overexpression of Tgf{beta}-family ligands mediates resistance to BRAF/MEK inhibition. Importantly, inhibition of Tgf{beta} signaling restores sensitivity to MAPK pathway inhibition, opening a path for synergistic strategies to improve outcomes of patients with ATCs and concurrent CH.

cancer biology↗

bioRxiv2024

A 3D Image Segmentation Study of U-Net on CT Images of the Human Aorta with Morphologically Diverse Anatomy

For the machine learning -assisted diagnosis of cardiac diseases, such as thoracic aortic aneurysm, the geometries of the heart and blood vessels need to be reconstructed from medical images, which is usually done by image segmentation followed by meshing. In this study, we applied U-Net (2D and 3D versions), a deep neural network with a U-shaped architecture, to segment human aorta from CT images. From our experiments, we have the following observations: (1) 2D U-Net, which segments each of the 2D slices of a 3D CT image independently, produced erroneous fragments (e.g., missing part of the aorta) and boundaries (i.e., aortic walls) in 3D; (2) 3D U-Net, which does segmentation in 3D regions of a 3D CT image, performed much better than 2D U-Net. We also observe the major weakness of the 3D U-Net: the reconstructed geometries of the aortic wall had large errors (measured by HD95) for some cases. The 3D U-Net in this study serves as a baseline for developing more advanced architectures of deep neural networks for more accurate geometry reconstruction of human aorta.

bioengineering↗

bioRxiv2024

Compressed higher-order structures facilitate human knowledge network learning

Humans naturally seek knowledge, yet integrating vast, fragmented information remains challenging. Traditionally, knowledge acquisition has relied on random walks within network--an unguided and inefficient process. We introduce compressive learning, a framework that embeds higher-order structural features--specifically node-degree inhomogeneity--into pre-learning trajectories to scaffold more efficient learning. Across two large-scale experiments, we demonstrate that scale-free networks--due to their pronounced node-degree inhomogeneity--are more compressible and learnable than other network types, and confirm the efficacy of the compressive learning approach. Magnetoencephalography (MEG) recordings reveal that compressive pre-learning enhances structured neural representations in the dorsal anterior cingulate cortex (ACC). A hypergraph-based two-stage model further reveals that compressive learning constructs a network skeleton of hyperedge-defined substructures that more effectively accommodate new inputs. Together, our results highlight the central role of higher-order network structure in human learning and offer a strategic approach to effectively "connect the dots."

neuroscience↗

bioRxiv2024

Performance and Robustness of Parameter Estimation from Phylogenetic Trees Using Neural Networks

Species diversification is characterized by speciation and extinction, the rates of which can, under some assumptions, be estimated from time-calibrated phylogenies. However, maximum likelihood estimation methods (MLE) for inferring rates are limited to simpler models and can show bias, particularly in small phylogenies. Likelihood-free methods to estimate parameters of diversification models using deep learning have started to emerge, but how robust neural network methods are at handling the intricate nature of phylogenetic data remains an open question. Here we present a new ensemble neural network approach to estimate diversification parameters from phylogenetic trees that leverages different classes of neural networks (dense neural network, graph neural network, and long short-term memory recurrent network) and simultaneously learns from graph representations of phylogenies, their branching times and their summary statistics. Our best-performing ensemble neural network (which corrects graph neural network result using a recurrent neural network) can compute estimates faster than MLE and is less affected by tree size. Our analysis suggests that the primary limitation to accurate parameter estimation is the amount of information contained within a phylogeny, as indicated by its size and the strength of effects shaping it. In cases where MLE is unavailable, our neural network method provides a promising alternative for estimating phylogenetic tree parameters. If there are detectable phylogenetic signals present, our approach delivers results that are comparable to MLE but without inherent biases.

evolutionary biology↗

bioRxiv2024

Tumor Subtype Classification Tool for HPV-associated Head and Neck Cancers

ImportanceMolecular subtypes of HPV-associated Head and Neck Squamous Cell Carcinoma (HNSCC), named IMU (immune strong) and KRT (highly keratinized), are well-recognized and have been shown to have distinct mechanisms of carcinogenesis, clinical outcomes, and potentially differing optimal treatment strategies. Currently, no standardized method exists to subtype a new HPV+ HNSCC tumor. Our paper introduces a machine learning-based classifier and webtool to reliably subtype HPV+ HNSCC tumors using the IMU/KRT paradigm and highlights the importance of subtype in HPV+ HNSCC. ObjectiveTo develop a robust, accurate machine learning-based classification tool that standardizes the process of subtyping HPV+ HNSCC, and to investigate the clinical, demographic, and molecular features associated with subtype in a meta-analysis of four patient cohorts. Data SourcesWe conducted RNA-seq on 67 HNSCC FFPE blocks from University of Michigan hospital. Combining this with three publicly available datasets, we utilized a total of 229 HPV+ HNSCC RNA-seq samples. All participants were HPV+ according to RNA expression. An ensemble machine learning approach with five algorithms and three different input training gene sets were developed, with final subtype determined by majority vote. Several additional steps were taken to ensure rigor and reproducibility throughout. Study SelectionThe classifier was trained and tested using 84 subtype-labeled HPV+ RNA-seq samples from two cohorts: University of Michigan (UM; n=18) and TCGA-HNC (n=66). The classifier robustness was validated with two independent cohorts: 83 samples from the HPV Virome Consortium and 62 additional samples from UM. We revealed 24 of 39 tested clinicodemographic and molecular variables significantly associated with subtype. ResultsThe classifier achieved 100% accuracy in the test set. Validation on two additional cohorts demonstrated successful separation by known features of the subtypes. Investigating the relationship between subtype and 39 molecular and clinicodemographic variables revealed IMU is associated with epithelial-mesenchymal transition (p=2.25x10-04), various immune cell types, and lower radiation resistance (p=0.0050), while KRT is more highly keratinized (p=2.53x10-08), and more likely female than IMU (p=0.0082). Conclusions and RelevanceThis study provides a reliable classifier for subtyping HPV+ HNSCC tumors as either IMU or KRT based on bulk RNA-seq data, and additionally, improves our understanding of the HPV+ HNSCC subtypes.

bioinformatics↗

bioRxiv2024

ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations

With large amounts of unlabeled RNA sequences data produced by high-throughput sequencing technologies, pre-trained RNA language models have been developed to estimate semantic space of RNA molecules, which facilities the understanding of grammar of RNA language. However, existing RNA language models overlook the impact of structure when modeling the RNA semantic space, resulting in incomplete feature extraction and suboptimal performance across various downstream tasks. In this study, we developed a RNA pre-trained language model named ERNIE-RNA (Enhanced Representations with base-pairing restriction for RNA modeling) based on a modified BERT (Bidirectional Encoder Representations from Transformers) by incorporating base-pairing restriction with no MSA (Multiple Sequence Alignment) information. We found that the attention maps from ERNIE-RNA with no fine-tuning are able to capture RNA structure in the zero-shot experiment more precisely than conventional methods such as fine-tuned RNAfold and RNAstructure, suggesting that the ERNIE-RNA can provide comprehensive RNA structural representations. Furthermore, ERNIE-RNA achieved SOTA (state-of-the-art) performance after fine-tuning for various downstream tasks, including RNA structural and functional predictions. In summary, our ERNIE-RNA model provides general features which can be widely and effectively applied in various subsequent research tasks. Our results indicate that introducing key knowledge-based prior information in the BERT framework may be a useful strategy to enhance the performance of other language models.

bioinformatics↗

bioRxiv2024

Epigenetic Reprogramming of Autophagy Drives Mutant IDH1 Glioma Progression and Response to Radiation

Mutant isocitrate dehydrogenase 1 (mIDH1) exhibits a gain of function mutation enabling 2-hydroxyglutarate (2HG) production and epigenetic reprogramming. This leads to enhanced DNA-damage response and radioresistance in mIDH1 gliomas. RNA-seq and ChIP-seq data revealed that human and mouse mIDH1 glioma neurospheres have downregulated gene ontologies (GOs) related to mitochondrial metabolism and upregulated GOs related to autophagy. Decreased mitochondrial metabolism was accompanied by decreased glycolysis, rendering autophagy a source of energy in mIDH1 gliomas. Human and mouse mutant IDH1 glioma cells exhibited increased expression of pULK1-S555 and enhanced LC3 I/II conversion, indicating augmented autophagy. Additionally, scRNA-seq data from human mIDH1 astrocytoma patients samples showed decreased mitochondrial metabolism and increased autophagy. We further demonstrate that inhibiting autophagy in vivo by systemic administration of synthetic protein nanoparticles encapsulating siRNA targeting Atg7 sensitized mIDH1 glioma cells to radiation-induced cell death, resulting in tumor regression, long-term survival, and immunological memory. In summary, our work uncovered that autophagy is a critical pathway for survival in mIDH1 gliomas and by blocking this pathway we can elicit radiosensitivity in vitro in human and mouse mIDH1 glioma cells, and in vivo in genetically engineered mouse models. Our data also highlights that blocking autophagy has significant potential for clinical translation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/584091v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@111f4a9org.highwire.dtl.DTLVardef@1d5bb30org.highwire.dtl.DTLVardef@f467ecorg.highwire.dtl.DTLVardef@1d2e9b5_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO Our genetically engineered mIDH1 mouse glioma model harbors IDH1R132H in the context of ATRX and TP53 knockdown. The production of 2-HG elicited an epigenetic reprogramming associated with a disruption in mitochondrial activity and an enhancement of autophagy in mIDH1 glioma cells. Autophagy is a mechanism involved in cell homeostasis related with cell survival under energetic stress and DNA damage protection. Autophagy has been associated with radio resistance. The inhibition of autophagy thus radio sensitizes mIDH1 glioma cells and enhances survival of mIDH1 glioma-bearing mice, representing a novel therapeutic target for this glioma subtype with potential applicability in combined clinical strategies. C_FIG

cancer biology↗

bioRxiv2024

Target-aware Molecule Generation for Drug Design Using a Chemical Language Model

Generative drug design facilitates the creation of compounds effective against pathogenic target proteins. This opens up the potential to discover novel compounds within the vast chemical space and fosters the development of innovative therapeutic strategies. However, the practicality of generated molecules is often limited, as many designs focus on a narrow set of drug-related properties, failing to improve the success rate of subsequent drug discovery process. To overcome these challenges, we develop TamGen, a method that employs a GPT-like chemical language model and enables target-aware molecule generation and compound refinement. We demonstrate that the compounds generated by TamGen have improved molecular quality and viability. Additionally, we have integrated TamGen into a drug discovery pipeline and identified 7 compounds showing compelling inhibitory activity against the Tuberculosis ClpP protease, with the most effective compound exhibiting a half maximal inhibitory concentration (IC50) of 1.9 M. Our findings underscore the practical potential and real-world applicability of generative drug design approaches, paving the way for future advancements in the field.

biochemistry↗

bioRxiv2023

Byproducts of inflammatory radical metabolism provide transient nutrient niches for microbes in the inflamed gut

Louis Pasteurs experiments on tartaric acid laid the foundation for our understanding of molecular chirality, but major questions remain. By comparing the optical activity of naturally-occurring tartaric acid with chemically-synthesized paratartaric acid, Pasteur realized that naturally-occurring tartaric acid contained only L-tartaric acid while paratartaric acid consisted of a racemic mixture of D- and L-tartaric acid. Curiously, D-tartaric acid has no known natural source, yet several gut bacteria specifically degrade D-tartaric acid. Here, we investigated the oxidation of monosaccharides by inflammatory reactive oxygen and nitrogen species. We found that this reaction yields an array of alpha hydroxy carboxylic acids, including tartaric acid isomers. Utilization of inflammation- derived D- and L-tartaric acid enhanced colonization by Salmonella Typhimurium and E. coli in murine models of gut inflammation. Our findings suggest that byproducts of inflammatory radical metabolism, such as tartrate and other alpha hydroxy carboxylic acids, create transient nutrient niches for enteric pathogens and other potentially harmful bacteria. Furthermore, this work illustrates that inflammatory radicals generate a zoo of molecules, some of which may erroneously presumed to be xenobiotics.

microbiology↗

bioRxiv2023

Accelerating protein engineering with fitness landscape modeling and reinforcement learning

Protein engineering holds significant promise for designing proteins with customized functions, yet the vast landscape of potential mutations versus limited lab capacity constrains the discovery of optimal sequences. To address this, we present the {micro}Protein framework, which accelerates protein engineering by combining {micro}Former, a deep learning model for accurate mutational effect prediction, with {micro}Search, a reinforcement learning algorithm designed to efficiently navigate the protein fitness landscape using {micro}Former as an oracle. {micro}Protein leverages single mutation data to predict optimal sequences with complex, multi-amino acid mutations through its modeling of epistatic interactions and a multi-step search strategy. Except from state-of-the-art performance on benchmark datasets, {micro}Protein identified high-gain-of-function multi-point mutants for the enzyme {beta}-lactamase, surpassing the highest known activity level, in wet-lab, trained solely on single mutation data. These results demonstrate {micro}Proteins capability to discover impactful mutations across vast protein sequence space, offering a robust, efficient approach for protein optimization.

synthetic biology↗

bioRxiv2023

Impact of Evolutionary Relatedness on Species Diversification: A New Birth-Death Model

Slowdowns in lineage accumulation are often observed in phylogenies of extant species. One explanation is the presence of ecological limits to diversity and hence to diversification. Previous research has examined whether and how species richness (SR) impacts diversification rates, but rarely considered the evolutionary relatedness (ER) between species, although ER can affect the degree of interaction between species, which likely sets these limits. To understand the influences of ER on species diversification and the interplay between SR and ER, we present a simple birth-death model in which the speciation rate depends on the ER. We use different metrics of ER that operate at different scales, ranging from branch/lineage-specific to clade-wide scales. We find that the scales at which an effect of ER operates yield distinct patterns in various tree statistics. When ER operates across the whole tree, we observe smaller and more balanced trees, with speciation rates distributed more evenly across the tips than in scenarios with lineage-specific ER effects. Importantly, we find that negative SR dependence of speciation masks the impact of ER on some of the tree statistics. Our model allows diverse evolutionary trajectories for producing imbalanced trees, which are commonly observed in empirical phylogenies but have been challenging to replicate with earlier models.

evolutionary biology↗

bioRxiv2023

Biomechanical stress analysis of Type-A aortic dissection at pre-dissection, post-dissection, and post-repair states

Acute type A aortic dissection remains a deadly and elusive condition, with risk factors such as hypertension, bicuspid aortic valves, and genetic predispositions. As existing guidelines for surgical intervention based solely on aneurysm diameter face scrutiny, there is a growing need to consider other predictors and parameters, including wall stress, in assessing dissection risk. Through our research, we aim to elucidate the biomechanical underpinnings of aortic dissection and provide valuable insights into its prediction and prevention. We applied finite element analysis (FEA) to assess stress distribution on a rare dataset comprising computed tomography (CT) images obtained from eight patients at three stages of aortic dissection: pre-dissection (preD), post-dissection (postD), and post-repair (postR). Our findings reveal significant increases in both mean and peak aortic wall stresses during the transition from the preD state to the postD state, reflecting the mechanical impact of dissection. Surgical repair effectively restores aortic wall diameter to pre-dissection levels, documenting its effectiveness in mitigating further complications. Furthermore, we identified stress concentration regions within the aortic wall that closely correlated with observed dissection borders, offering insights into high-risk areas. This study demonstrates the importance of considering biomechanical factors when assessing aortic dissection risk. Despite some limitations, such as uniform wall thickness assumptions and the absence of dynamic blood flow considerations, our patient-specific FEA approach provides valuable mechanistic insights into aortic dissection. These findings hold promise for improving predictive models and informing clinical decisions to enhance patient care.

bioengineering↗

bioRxiv2023

Insights from the Early Cretaceous: The promise of Lycoptera aDNA sequencing

Obtaining high-quality ancient DNA (aDNA) is a crucial process for advancements in molecular paleontology. However, current research is restricted to a limited range of fossil types due to DNA degradation and contamination from environmental DNA (eDNA). Utilizing the technology of nanoparticle affinity beads, we successfully extracted DNA from Lycoptera davidi fossils and obtained 1,258,901 DNA sequences. To ascertain their origins, we developed and implemented a novel protocol, the "mega screen method", designed to categorize aDNA fragments. This approach successfully identified 243 Actinopterygii species-specific fragments (SSFs), which subsequent analysis confirmed as original in situ DNA (oriDNA) sequences. Lycoptera davidi, a ray-finned fish from the Early Cretaceous Jehol Biota, dates back approximately 120 million years (Ma). Notably, the absence of deamination in certain oriDNA and paleoenvironmental DNA (paeDNA) fragments calls into question its reliability as a definitive marker for aDNA identification, a phenomenon potentially attributable to the protective effects of fossil diagenesis. Additionally, we identified a novel mechanism, "coding region sliding replication and recombination", responsible for generating new transposase. This mechanism, distinct from conventional processes such as horizontal gene transfer or vertical inheritance from parents, can facilitate a rapid genomic diversification. The transposase CDS insertion into the GAS2 gene of Anabarilius grahami offers molecular insights into the genetic interactions between ancestral carp and Lycoptera and the swift emergence of the expansion of early fish species starting from the early Cretaceous. These findings enhance our understanding of fossil DNA preservation, lay the groundwork for an ancient species DNA database, and introduce innovative tools for exploring evolutionary relationships across geological timescales.

paleontology↗

bioRxiv2023

H3.3-G34R Mutation-Mediated Epigenetic Reprogramming Leads to Enhanced Efficacy of Immune Stimulatory Gene Therapy in Pediatric High-Grade Gliomas

Diffuse hemispheric glioma (DHG), H3 G34-mutant, representing 9-15% of cases, are aggressive Central Nervous System (CNS) tumors with poor prognosis. This study examines the role of epigenetic reprogramming of the immune microenvironment and the response to immune-mediated therapies in G34-mutant DHG. To this end, we utilized human G34-mutant DHG biopsies, primary G34-mutant DHG cultures, and genetically engineered G34-mutant mouse models (GEMMs). Our findings show that the G34 mutation alters histone marks deposition at promoter and enhancer regions, leading to the activation of the JAK/STAT pathway, which in turn results in an immune-permissive tumor microenvironment. The implementation of Ad-TK/Ad-Flt3L immunostimulatory gene therapy significantly improved median survival, and lead to over 50% long term survivors. Upon tumor rechallenge in the contralateral hemisphere without any additional treatment, the long-term survivors exhibited robust anti-tumor immunity and immunological memory. These results indicate that immune-mediated therapies hold significant potential for clinical translation in treating patients harboring H3.3-G34 mutant DHGs, offering a promising strategy for improving outcomes in this challenging cancer subtype affecting adolescents and young adults (AYA). STATEMENT OF SIGNIFICANCEThis study uncovers the role of the H3.3-G34 mutation in reprogramming the tumor immune microenvironment in diffuse hemispheric gliomas. Our findings support the implementation of precision medicine informed immunotherapies, aiming at improving enhanced therapeutic outcomes in adolescents and young adults harboring H3.3-G34 mutant DHGs.

cancer biology↗

bioRxiv2022

Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design

Antibodies are versatile proteins that can bind to pathogens and provide effective protection for human body. Recently, deep learning-based computational antibody design has attracted popular attention since it automatically mines the antibody patterns from data that could be complementary to human experiences. However, the computational methods heavily rely on the high-quality antibody structure data, which is quite limited. Besides, the complementarity-determining region (CDR), which is the key component of an antibody that determines the specificity and binding affinity, is highly variable and hard to predict. Therefore, data limitation issue further raises the difficulty of CDR generation for antibodies. Fortunately, there exists a large amount of sequence data of antibodies that can help model the CDR and alleviate the reliance on structured data. By witnessing the success of pre-training models for protein modeling, in this paper, we develop an antibody pre-trained language model and incorporate it into the (antigen-specific) antibody design model in a systemic way. Specifically, we first pre-train an antibody language model based on the sequence data, then propose a one-shot way for sequence and structure generation of CDR to avoid the heavy cost and error propagation from an autoregressive manner, and finally leverage the pre-trained antibody model for the antigen-specific antibody generation model with some carefully designed modules. Through various experiments, we show that our method achieves superior performance over previous baselines on different tasks, such as sequence and structure generation, antigen-binding CDR-H3 design.

molecular biology↗

bioRxiv2022

Nimbolide Targets RNF114 to Induce the Trapping of PARP1 and Poly-ADP-Ribosylation-Dependent DNA Repair Factors

PARP1 is an abundant nuclear enzyme that is critically involved in DNA damage response. Its main enzymatic function is to catalyze a protein post-translational modification known as poly-ADP-ribosylation (PARylation). Despite the tremendous progresses of PARP1 inhibitors (PARPi) in the clinic, the basic mechanism of action of PARPi is poorly understood. Recent studies point to PARP1 trapping as a key factor driving the cytotoxic and immunomodulatory functions of PARPi. However, the molecular underpinnings of PARP1 trapping remain elusive. Here, using an unbiased, quantitative proteomic screen, we identified RING finger protein 114 (RNF114), as a PARylation-dependent, ubiquitin E3 ligase involved in DNA damage response. Upon sensing DNA damage, RNF114 was recruited, in a PAR-dependent manner, to the DNA lesions, where it specifically targeted PARylated-PARP1 for ubiquitin-proteasomal degradation. The blockade of this pathway interfered with the removal of PARP1 from the DNA damage site, leading to profound PARP1 trapping. We show that a natural product, Nimbolide, targeted RNF114 to block the degradation and removal of PARylated-PARP1 from DNA lesions. Unlike conventional PARPi, nimbolide treatment induced the trapping of both PARP1 and PARylation-dependent DNA repair factors. This unique mechanism of action of nimbolide translated into its superior cytotoxic effects against BRCA-deficient cancers. Furthermore, we demonstrated that, as a super trapper of both PARylated-PARP1 and PARylation-dependent DNA repair factors, nimbolide was able to overcome the intrinsic and acquired resistance to PARPi. Finally, we showed that nimbolide treatment activated innate immune signaling and was able to synergize with various cytotoxic agents. These results point to the exciting possibility of targeting homologous recombination-deficient cancers using nimbolide and its analogs.

cancer biology↗