Biology subjects

Shen, B.

Publications and source records attributed to Shen, B..

At least 19 recordsLinked to original sources

bioRxiv2026

NEIL2 regulates RNA polymerase II dynamics to suppress transcription replication conflicts in pancreatic cancer

Transcription replication conflicts (TRCs) arise from spatiotemporal interference between replication and transcription machinery and contribute to genome instability in cancer cells. In pancreatic ductal adenocarcinoma (PDAC) cells, oncogenic KRAS drives TRC formation, and base excision repair (BER) contributes to TRC resolution as an adaptive response. Here, we show that TRCs are enriched in pancreatic tumor sections from patients and are associated with malignant progression. We identify NEIL2, a bifunctional DNA glycosylase in the BER pathway, as a critical regulator of TRCs in PDAC cells. Mechanistically, NEIL2 loss causes aberrant RNA polymerase II (RNAPII) occupancy at transcription start sites and downstream of transcription end sites. Dysregulated RNAPII dynamics promote R-loop-dependent and -independent TRCs contributing to replication stress, DNA damage, and impaired cancer cell proliferation. Notably, the DNA-binding motif of NEIL2 necessary for canonical BER function is dispensable for TRC regulation. Together, these findings reveal an unrecognized role for NEIL2 in regulating RNAPII dynamics and mitigating TRCs in pancreatic cancer cells.

molecular biology↗

bioRxiv2026

Stochastic Decision Policy Is Optimal Under Costly Control

Human and animal decisions are often noisy. This variability is usually attributed to noise that corrupts the decision evidence, a defect that an ideal decision maker would eliminate. Here we ask whether stochastic choice can instead be optimal once the cost of implementing prescribed policies is counted. We treat the decision maker as an ideal observer that maximizes expected gain but pays costs to sharpen its policy away from a random default. Using sequential sampling, a setting in which the optimal policy is precisely defined, we show that this trade-off makes a stochastic policy optimal, replacing the hard decision boundaries of classical theory with soft stopping zones whose softness is set by the price of control. This soft policy aligns with the strategy used in cutting-edge artificial learning agents for robustness. Noise that corrupts the agent's belief, an important confound of noisy behavior, produces superficially similar yet distinctive signatures in behavioral policies. Leveraging this, we recovered the policies of two macaques with low-dimensional recurrent neural networks and found that their behavior carries both a stochastic policy and belief noise. By separating variability in constructing a decision belief from stochasticity in selecting actions, these results establish that stochastic decision policies can themselves be optimal, offering a common principle for understanding behavioral variability in biological and artificial decision makers.

animal behavior and cognition↗

bioRxiv2026

Stem cell factor acts locally in the bone marrow to promote stem and progenitor cell maintenance

Stem cell factor (SCF) from Leptin Receptor-expressing (LepR+) mesenchymal stromal cells and endothelial cells is required for hematopoietic stem and progenitor cell maintenance in bone marrow. Consistent with this, Scf is highly expressed by LepR+ cells and at lower levels by endothelial cells in bone marrow. However, endothelial cells outside the bone marrow produce circulating SCF, raising the question of whether the SCF that promotes stem and progenitor cell maintenance is produced inside or outside bone marrow. We found that Scf deletion in spleen and liver endothelial cells reduces SCF levels in the blood without affecting stem or progenitor numbers in the bone marrow. Moreover, deletion of Scf from mesenchymal cells in the long bones, but not vertebrae, of Prx1Cre; Scf fl/fl mice depletes stem and progenitor cells in long bones but not vertebrae. SCF thus acts locally within the bones in which it is made to promote stem and progenitor cell maintenance.

cell biology↗

bioRxiv2026

Large Language Models Predict Human Social Behavior via Interpretable Mechanisms

The development of large language models (LLMs) offers promising opportunities for predicting human behavior across diverse contexts. However, most prior work has emphasized behavioral imitation, with limited attention to transparent or interpretable models of the cognitive mechanisms underlying human decisions. In this study, we introduce MindEvolve, an autonomous workflow designed to predict behavior in social interactions by generating interpretable symbolic models of cognition. We systematically evaluate the modeling capabilities of multiple LLMs across a battery of socioeconomic games covering four core domains of social cognition: economic preferences, social preferences, social reasoning (theory of mind), and recursive planning. The symbolic models proposed by the LLMs are assessed by expert human evaluators for interpretability and theoretical coherence. Our results show that while most LLMs robustly capture economic and social preferences in relatively simple strategic settings, and in some cases generate novel models that integrate broader knowledge than those proposed by human experts, their capacity to model more complex psychological processes remains limited. However, a subset of state-of-the-art models demonstrates promising performance in capturing higher-order reasoning processes such as theory of mind and recursive planning. Together, these findings highlight the emerging potential of LLMs not only to imitate behavior, but to generate interpretable, mechanistic accounts of human social decision-making. Our results provide a roadmap for advancing LLM-based cognitive modeling toward human-expert-level theory construction.

neuroscience↗

bioRxiv2026

The function of human PIF1 in G quadruplex formation and replication stress response at ALT telomeres

Cancers maintain their telomeres through two telomere maintenance mechanisms: 85-90% of cancers rely on telomerase (TEL+), while 10-15% of cancers adopt the Alternative Lengthening of Telomeres (ALT) pathway. The Break-Induced Replication (BIR) pathway plays a critical role in maintaining telomere length in the ALT+ cells. In both yeast and human, PIF1, a 5' to 3' helicase, is required for the robust activity of BIR. However, the extent of human PIF1 (hPIF1) involvement in the ALT pathway remains unknown. Here we showed that hPIF1 can be recruited to damaged telomeres in ALT+ cells. In addition, we demonstrated that inhibition of hPIF1 induced DNA damage and G quadruplex (G4) accumulation at ALT telomeres, leading to a moderate reduction of the mean telomere length. Most interestingly, we demonstrated that inhibition of hPIF1 also attenuates checkpoint activation, BLM recruitment, single-stranded DNA (ssDNA) formation, DNA damage, and G4s at telomeres in the FANCM deficient ALT+ cells. Finally, we showed that inactivation of hPIF1 affects the viability of both ALT+ and TEL+ cancers, suggesting that hPIF1 is a potential drug target for cancer therapy.

molecular biology↗

bioRxiv2026

Electron counting enables cryo-electron ptychography for near-atomic-resolution cryo-electron microscopy

Cryo-electron ptychography is an emerging technique for studying radiation-sensitive biological specimens, developed from four-dimensional transmission electron microscopy (4D-STEM). Although ptychography has achieved ultrahigh resolution beyond conventional transmission electron microscopy limits for radiation-resistant samples, its application to frozen hydrated biological specimens currently remains at sub-nanometer resolution. Here we overcome this limitation by implementing electron counting with a hybrid-pixel detector, establishing key technical foundations for near-atomic-resolution cryo-ptychography. This counting approach significantly improves weak signal detection in convergent-beam electron diffraction, enabling ptychographic reconstruction at doses below 1 e-/[A]{superscript 2}. Additionally, we found beam-induced motion is effectively eliminated within single scans, suggesting conventional cryoEMs dose-fractionation approach may need reevaluation. Demonstrating high contrast under both low-dose and tilted conditions, along with achieving 3.59 [A] resolution for the [~]700 kDa T20S proteasome, we validate cryo-electron ptychography as a viable general imaging modality that could complement or surpass conventional phase-contrast cryoEM methods.

biophysics↗

bioRxiv2026

DPCGS: a computational framework for linking GWAS to single-cell transcriptomics in complex traits and diseases

Complex traits and diseases arise from the interplay between genetic variation and cellular heterogeneity, making it essential to understand how genetic risk manifests at the cellular level. However, connecting genome-wide association studies (GWAS) to specific cell populations remains challenging due to cellular complexity and the prevalence of noncoding variants. Here, we present DPCGS, a computational framework that systematically integrates GWAS summary statistics with single-cell RNA-sequencing (scRNA-seq) data to identify trait-associated cell subpopulations, genes, and regulatory programs. Unlike existing approaches that primarily evaluate pathway enrichment or cell-type-level associations, DPCGS quantifies the enrichment of genetically prioritized genes within individual cells through a statistically calibrated gene-set scoring strategy, enabling high-resolution mapping of genetic risk to cellular states. Benchmarking across simulated and diverse human single-cell datasets demonstrates that DPCGS achieves superior accuracy, sensitivity, and robustness compared with existing methods, including scDRS and scPagwas. Applying DPCGS to Alzheimers disease and asthma reveals disease-associated cellular populations and uncovers potential molecular drivers, including CD74, FOS, and AP-1 family regulatory programs, providing insights into disease-specific immune and cellular mechanisms. By bridging genetic discoveries from GWAS with functional interpretation at single-cell resolution, DPCGS establishes a generalizable framework for dissecting the cellular architecture of complex traits and diseases. This approach enables systematic discovery of disease-relevant cell subpopulations, regulatory networks, and potential therapeutic targets, offering broad applications in human genetics, single-cell biology, and precision medicine.

bioinformatics↗

bioRxiv2026

A combinatorial DNA origami platform for biologically replicable, thermostable data storage and molecular authentication

DNA origami is becoming an attractive platform for data storage, yet current approaches rely on the limited stability of DNA hybridization, preventing them from fully utilizing the stability and cost-effective copying inherent to classical sequence-based storage. Here we introduce DNA Origami for Combinatorial data Storage (DOCS), where we encode information into the scaffold molecule using a combinatorial enzymatic approach. This enables text encoding that is biologically cloneable, stable at high temperatures, and randomly accessible. We further demonstrate the DOCS platforms combinatorial power by creating a stochastic molecular authentication system. Finally, we show using simulations that expanding the information capacity of data carriers allows for the storage and recovery of large files up to several hundred kilobytes in size. DOCS provides a robust, scalable strategy for molecular data storage and security that bridges the gap between classical DNA data storage strategies and DNA nanostructure-based methods.

bioengineering↗

bioRxiv2026

EXO1 Facilitates MiDAS and Prevents Genome Instability and Cell Death in Ewing Sarcoma

Ewing sarcoma (EwS) is an aggressive malignancy driven by EWSR1::ETS fusions, predominantly EWSR1::FLI1. Previous efforts using both direct and indirect approaches to target these chimeric oncoproteins have yielded limited clinical benefit. Although EWSR1::FLI1 is a well-known source of replication stress and genome instability, targeting DNA damage response (DDR) factors that mitigate these effects remain poorly understood. Here, we identified a marked dependency of EwS cells on exonuclease 1 (EXO1). We demonstrate that EXO1 is essential for EwS cell survival and tumor growth, highlighting its potential as a novel therapeutic target. Intriguingly, we unveil that EXO1 loss impairs mitotic DNA synthesis (MiDAS), promoting EWSR1::FLI1-associated genome instability and cell death. Collectively, our results support the idea that targeting DDR factors, which counteract replication stress and/or DNA damage induced by fusion oncoproteins, represents a promising therapeutic option for EwS.

cancer biology↗

bioRxiv2026

PARP1-mediated 5' flap dynamics facilitate Okazaki fragment maturation

Abstract/SummaryPrecise regulation of enzyme recruitment during Okazaki fragment maturation (OFM) is essential for faithful and efficient lagging-strand DNA synthesis. Emerging evidence suggests that PARP1 contributes to OFM yet its specific functions remain unclear. Here, we define context-dependent functions of PARP1 during OFM. Under physiological conditions, PARP1 co-localizes with PCNA in early S phase and restrains Pol {delta}-PCNA- mediated strand-displacement DNA synthesis, thereby preventing the formation of long 5' flaps, which is refractory to FEN1 cleavage. On the other hand, in LIG1-deficient cells, in which DNA nicks and unexpectedly long 5' flaps accumulate, PARP1 promotes the recruitment of LIG3 to catalyze OF ligation and DNA2 to facilitate long 5' flap processing. Collectively, our findings uncover previously unrecognized roles of PARP1 in regulating 5' flap dynamics to ensure efficient OFM and cell viability. HighlightsO_LIPARP1 plays context-dependent regulatory functions in Okazaki fragment maturation (OFM). C_LIO_LIPARP1 controls strand displacement DNA synthesis by the PCNA-Pol{delta} complex to dictate generation of short over long RNA-DNA flaps during canonic OFM. C_LIO_LIPARP1 senses unligated Okazaki fragments in DNA Ligase 1 deficient cells and suppresses unwanted conversion of DNA nicks into 5 flaps. C_LIO_LIProcessing of unligated nicks or flaps by DNA ligase 3 or DNA2, respectively in LIG1 deficient cells depends on PARP1. C_LIO_LIPARP1 inhibitors induce synthetic lethality with DNA ligase 1 or DNA2 inhibition. C_LI

molecular biology↗

bioRxiv2026

Predicting Pre-treatment Resistance or Post-treatment Effect? A Systematic Benchmarking of Single-Cell Drug Response Models

Intratumoral heterogeneity drives variable drug responses in cancer. Single-cell RNA sequencing (scRNA-seq) enables characterization of such heterogeneity and prediction of drug response at single-cell resolution. Accordingly, various computational models have been developed to infer drug response from scRNA-seq data. However, their performance, robustness, and generalizability across different biological contexts remain insufficiently evaluated. To address this gap, we benchmarked representative single-cell drug response prediction models using 26 curated datasets comprising over 760,000 cells across 12 cancer types and 21 therapeutic agents. We constructed balanced and imbalanced scenarios to reflect realistic drug-response label distributions. To address the lack of ground-truth labels in conventional scRNA-seq datasets, we incorporated lineage-tracing data with experimentally validated drug-response annotations, enabling evaluation in a clinically relevant pre-treatment prediction setting. Our results show that prediction performance was markedly higher in cell lines than in tissue samples. Under imbalanced conditions, most methods exhibited sharp performance declines, whereas scDEAL demonstrated the highest robustness. Independent validation using an in-house pancreatic ductal adenocarcinoma dataset further confirmed scDEALs robustness and ability to capture biologically meaningful state transitions. Label-substitution experiment revealed that this robustness was partially driven by the models specific training-label construction. However, benchmarking with lineage-tracing data revealed a fundamental limitation: most models capture drug-induced transcriptional changes but struggled to predict intrinsic resistance before treatment. In summary, our study defines the performance boundaries of current approaches and highlights their limitations in addressing intratumoral heterogeneity, class imbalance, and intrinsic resistance prediction, emphasizing the need for the next-generation single-cell drug response models with stronger clinical relevance.

bioinformatics↗

bioRxiv2026

Decomposing multi-scale dynamic regulation from single-cell multiomics with scMagnify

Deciphering the highly coupled regulatory circuits that drive cellular dynamics remains a fundamental goal in biology. However, capturing the multi-scale time-lagged dynamics and combinatorial regulatory logic of gene regulation remains computationally challenging. Here we present scMagnify, a deep-learning-based framework that leverages multiomic single-cell assays of chromatin accessibility and gene expression via nonlinear Granger causality to reconstruct and decompose multi-scale gene regulatory networks (GRNs). Benchmarking on both simulated and real datasets demonstrates that scMagnify achieves superior performance. scMagnify employs tensor decomposition to systematically identify combinatorial TF modules and their activation profiles across different time-lags. It enables a hierarchical dissection of the regulatory landscape-- from the activity of individual regulator to the combinatorial logic of regulatory modules and intercellular communications. We applied scMagnify to human hematopoiesis and mouse pancreas development, where it successfully recovered known lineage-driving regulators and provided novel insights into the combinatorial logic that governs cell fate decisions. Furthermore, in the context of kidney injury, scMagnifys intracellular communication module mapped key signaling-to-transcription cascades linking microenvironment cues to pathological epithelial cell changes. In summary, scMagnify provides a powerful and versatile computational framework for dissecting the multi-scale regulatory logic that governs complex biological processes in development and disease.

bioinformatics↗

bioRxiv2025

DNA replication errors drive genome-wide small inverted triplication dynamics

Structural variants (SVs) have a profound impact on phenotype and diversity and are associated with human diseases. To explore the origination of SVs, we have analyzed 1,340 cancer genomes with annotation of 4,608 novel small inverted triplication (SIT) events and found that FEN1 is strongly associated with SIT incidence. Then, we performed long-read sequencing and developed PacBioR to annotate SITs in yeast FEN1 mutant cells. We found that SIT structures mimic classic inverted triplications but with a smaller DUP/IN/DUP structure of 184/160/184 bp on average, with a spacer sequence of 30 bp and breakpoint junction of 6 bp. We further showed that breakpoints of SITs preferentially occurred at nucleosome midpoints, aligned with Okazaki fragment termini, and those harbored in plasmids were precisely eliminated via DNA polymerase slippage over SIT-derived hairpin structures. This study provides mechanistic insight into SIT origination and offers practical tools for future studies on genome rearrangements.

genetics↗

bioRxiv2025

Adaptive regulation of dNTP homeostasis confers osimertinib resistance in EGFR mutant non-small cell lung carcinoma

Maintaining sustained deoxyribonucleotide triphosphate (dNTP) pools is essential for DNA replication fidelity and genome stability. In EGFR-mutant non-small cell lung carcinoma (NSCLC), we find that disruption of dNTP homeostasis plays a critical role in determining sensitivity to the EGFR inhibitor osimertinib and in shaping mechanisms of acquired resistance. Transcriptomic and biochemical analyses revealed that osimertinib suppresses RRM2 expression, a key regulator of dNTP synthesis, through the downregulation of the transcription factor MYBL2. In response to osimertinib-mediated replication stress and dNTP depletion, cells activated a compensatory pathway involving the stress-inducible ribonucleotide reductase subunit RRM2B via a novel transcriptional regulator, TNNT3. We further identified CHK2 signaling as essential for TNNT3 nuclear translocation and RRM2B transcriptional activation. Inhibition of CHK2 or combined CHK1/2 blockade impaired RRM2B induction, exacerbated replication stress, and delayed the development of osimertinib resistance both in vitro and in xenograft models. Collectively, our findings reveal that EGFR-mutant NSCLC cells rely on a dynamic EGFR-MYBL2-RRM2 and CHK2-TNNT3-RRM2B regulatory axis to maintain dNTP pool balance under therapeutic pressure. Disruption of this axis sensitizes tumors to osimertinib and impairs the acquisition of resistance, highlighting dNTP metabolism as a critical vulnerability and actionable target in EGFR-driven lung cancer. STATEMENT OF SIGNIFICANCETargeting dNTP metabolism via the EGFR-MYBL2-RRM2 and CHK2-TNNT3-RRM2B regulatory axis sensitizes EGFR-mutant NSCLC to osimertinib treatment and delays drug resistance, revealing a novel and actionable therapeutic vulnerability in EGFR-driven lung cancer.

cancer biology↗

bioRxiv2025

MSICKB: A Curated Knowledgebase for Exploring Molecular Heterogeneity and Biomarker Discovery in Microsatellite Instability Cancers

BackgroundMicrosatellite instability (MSI) is a critical form of genomic instability and a key biomarker for predicting patient prognosis and response to immunotherapy in cancer. However, significant heterogeneity in clinical outcomes exists even among patients with MSI cancers of the same type, suggesting the presence of distinct molecular subtypes and the need for more personalized biomarkers. The systematic exploration of this heterogeneity is currently hindered by the lack of a specialized, multi-dimensional data resource. This raises a critical scientific question: building upon the established link between MSI status and therapeutic efficacy, how can we leverage a multi-dimensional, deeply curated data resource to precisely characterize the molecular heterogeneity of MSI cancers and discover personalized biomarkers for precision oncology? MethodsTo address this question, we developed the Microsatellite Instability Cancer Knowledgebase (MSICKB). We systematically curated MSI-related data from 492 publications spanning from 1997 to 2024, covering 31 cancer types. The curated data encompasses four key dimensions: Genetic & Molecular Features, Clinicopathological Features, Therapeutic Response, and Prognostic Factors. Furthermore, we constructed and analyzed a comprehensive gene-disease network to systematically characterize the molecular landscape of MSI cancers. ResultsMSICKB(http://www.sysbio.org.cn/MSICKB/) curates a total of 1,382 MSI-related features from 492 publications, covering 31 cancer types. The knowledgebase includes four major categories: Genetic & Molecular Features, Clinicopathological Features, Prognostic Factors, and Therapeutic Response. An application analysis of the curated data revealed that the constructed gene-disease network exhibits a scale-free-like topology, indicating a hierarchical organization governed by a few highly connected hub nodes. Further investigation of the 14 identified hub genes showed significant functional enrichment in core MSI-related pathways, including DNA mismatch repair, immune checkpoint regulation, and key oncogenic signaling pathways. ConclusionMSICKB provides an integrated, multi-dimensional data resource that facilitates the exploration of the molecular heterogeneity landscape and common regulatory principles within MSI cancers. It serves as a valuable platform for identifying novel molecular subtypes and biomarkers. In the future, the continuous evolution of MSICKB will support the development of clinical prediction models, assist in the advancement of intelligent medicine, and ultimately promote the progress of precision diagnostics and therapeutics for MSI cancers.

bioinformatics↗

bioRxiv2025

MitoEM 2.0 - A Benchmark for Challenging 3D MitochondriaInstance Segmentation from EM Images

We present MitoEM 2.0, a curated data resource for training and evaluating three-dimensional (3D) mitochondria instance segmentation in volume electron microscopy. The collection assembles multiscale vEM datasets (FIB-SEM, SBF-SEM, ssSEM) spanning diverse tissues and species, with expert-verified instance labels emphasizing biologically difficult scenarios, including dense mitochondrial packing, hyperfused networks, and thin filamentous connections with ambiguous boundaries. All releases include native-resolution volumes and standardized processed versions, per-volume metadata (voxel size, modality, tissue, splits), and official train/validation/test partitions to enable reproducible benchmarking. Annotations follow a consistent protocol with quality checks and instance reindexing. Data are provided in NIfTI with nnU-Net-compatible layout, alongside machine-readable split files and checksums. Baseline scripts support common training pipelines and size-stratified evaluation. By consolidating challenging volumes and harmonized labels, MitoEM 2.0 facilitates robust model development and fair comparison across methods while supporting reuse in bioimage analysis, algorithm benchmarking, and teaching.

neuroscience↗

bioRxiv2025

XPF mediates 3' flap processing for FEN1-independent Okazaki fragment maturation

Okazaki fragment maturation (OFM), the process that removes RNA-DNA primers, is a major source of DNA replication stress and mutations. It involves Pol{delta}-mediated DNA strand displacement synthesis that produces 5 flaps, FEN1-mediated 5 flap cleavage, and LIG1-catalyzed nick ligation. Recently, we discovered that under stress conditions, yeast cells convert 5 flaps into 3 flaps, which are degraded by 3 flap nucleases to produce ligatable DNA nicks. However, little is known about this stress-induced, 3 flap-based OFM in human cells. Here, we report that 3 flaps frequently form in various human cancer cells, and that FEN1 deficiency significantly enhances 3 flap levels. XPF1 is recruited to the replication forks in FEN1 mutant or FEN1-chemically inhibited cells. Notably, XPF deficiency or inhibition in those defective cells leads to accumulation of 3 flaps, replication-related DNA strand breaks, and unique mutation signatures. Furthermore, XPF and FEN1 inhibitors show synergistic effects in killing human cancer cells. In summary, we demonstrate that 3 flap-based OFM is an important alternative of 5 flap-based OFM in mammalian cells. XPF is a key nuclease to degrade 3 flaps and complete OFM for survival. Targeting this compensatory mechanism could provide new therapeutic strategies to selectively impair cancer cell survival under pre-existed replication stress. HighlightsO_LIXPF is recruited to DNA replication forks in mouse or human cells of functional deficiency of FEN1 due to genetic mutations or chemical inhibition. C_LIO_LIXPF gene deficiency or chemical inhibition results in accumulation of 3 flaps and replicative DNA single-stranded breaks. C_LIO_LIFunctional deficiency of XPF and FEN1 displays a synergy in inducing genome instability and cell death in human cancer cells. C_LI

cell biology↗

bioRxiv2025

JMJD1B-mediated FEN1 demethylation allows timely switching of Okazaki fragment maturation core enzymes to avoid mutagenic flap ligation by PARP1-LIG3

Efficient and faithful Okazaki fragment maturation (OFM) depends on the PCNA-coordinated actions of core enzymes Pol{delta}, FEN1, and LIG1. We demonstrate that Pol{delta}, FEN1, and LIG1 sequentially but not simultaneously bind to PCNA in mammalian cells. The association of FEN1 with PCNA, which lies at the center of this orderly program, is mediated by FEN1 R192 methylation, which is also crucial for preventing premature loading of LIG1. Conversely, FEN1 demethylation by the recently identified arginine demethylase JMJD1B promotes FEN1 dissociation from PCNA and LIG1 recruitment. Disruption of the sequential PCNA binding program in R192Q or Jmjd1b-/- cells results in unprocessed 5 flaps that prevent OFM and induction of PARP1-dependent recruitment of LIG3, which has flap ligation activity to join incompletely processed OFs. This alternative OFM process supports cell survival but causes duplications and other DNA mutations. Our findings define fundamental and alternative DNA replication processes underlying mutagenesis and cell survival. HighlightsO_LIThe core enzymes involved in Okazaki fragment maturation (OFM), DNA polymerase {delta} (Pol{delta}), flap endonuclease 1 (FEN1), and ligase 1 (LIG1), sequentially bind to the scaffold protein PCNA for their concerted actions C_LIO_LIFEN1 R192 methylation enables FEN1 binding to PCNA; JMJD1B-mediated FEN1 arginine demethylation provokes FEN1 exit from PCNA and LIG1 recruitment C_LIO_LIFEN1 R192Q mutation and JMJD1B deficiency oppositely alter dynamic PCNA interactions with FEN1 and LIG1 and result in distinct OFM DNA intermediate structures C_LIO_LIActivation of PARP1 at replication forks due to disruption of the sequential PCNA binding program induces LIG3-mediated alternative mutagenic 5 flap ligation for survival. C_LI

cell biology↗