Biology subjects

Wei, X.

Publications and source records attributed to Wei, X..

At least 73 records · Page 4Linked to original sources

bioRxiv2026

Asymmetric partition of the O-GlcNAcome in mitosis ensures binary cell fate decision

Many cellular components undergo biased segregation during stem cell division, but whether such order extends to the proteome has remained unknown. Using our real-time O-GlycoTracer, we find that O-GlcNAcylated proteins (the O-GlcNAcome) segregate asymmetrically during Drosophila neuroblast mitosis, predisposing the two daughters to distinct fates. The daughter that preserves stem cell identity inherits most of the O-GlcNAcome. This asymmetric partition requires putative O-GlcNAc readers, notably 14-3-3 proteins. We further identify the nuclear pore complex (NPC) as a major O-GlcNAc substrate in neuroblasts; Nup153, located in the nuclear basket, shows O-GlcNAc-dependent biased inheritance. Perturbing multiple steps in this segregation pathway disrupts neuroblast differentiation, reduces brain size, and causes adult learning deficits. These findings reveal a coordinated O-GlcNAc-driven mechanism for proteome-level asymmetric inheritance during neural stem cell division, with implications for neurodevelopmental disorders linked to OGT or 14-3-3 mutations.

cell biology↗

bioRxiv2026

GapR is a nucleoid-associated protein that stiffens and overtwists DNA to promote transcription and replication

GapR is an -proteobacterial nucleoid-associated protein reported to either directly regulate transcription of AT-rich DNA or to regulate transcription indirectly through sensing DNA topology and modulating topoisomerase activity. We use single-DNA micromechanics, biolayer interferometry (BLI), and computational analysis to study GapR transcriptional regulation. Micromechanics experiments show that GapR overtwists DNA and shortens its contour length. GapR binding also substantially increases DNA bending persistence length and twist stiffness, while promoting duplex DNA melting at higher forces. Nonequilibrium binding experiments show GapR-DNA complexes to be extremely stable, with essentially no dissociation on hour-long time scales. Strikingly, our BLI experiments show that GapR has high affinity for AT-rich DNA while our micromechanics show that GapR binding is enhanced by pre-twisting of DNA, validating GapR affinity for both forms. By analyzing published GapR binding data, we differentiate between AT-rich and overtwisted DNA binding, revealing that overtwisted DNA primarily determines GapR localization. Lastly, we show that these two binding modes exert opposite effects on transcription. AT-rich binding stimulates initiation, likely by promoting RNA polymerase progression, while overtwisted DNA binding represses transcription, possibly by stabilizing local overtwist at promoters. We propose that GapR binding promotes a conformation of DNA ideal for efficient replication and transcription progression. 3 Key pointsO_LIGapR is a DNA structuring protein that binds AT-rich and overtwisted, positively supercoiled DNA C_LIO_LIGapR binding stiffens DNA, limits plectoneme formation, and melts undertwisted DNA at higher force C_LIO_LIGapR binding modes exert opposing effects on transcription initiation while promoting elongation C_LI

molecular biology↗

bioRxiv2025

Transcriptome-wide mapping reveals an RNA-dependent mechanism of platinum cancer drugs.

Off-target interactions frequently compromise the clinical utility of anticancer agents by driving dose-limiting toxicity and therapeutic resistance. Although RNA has been predicted to be an off-target for numerous FDA-approved drugs, the extent and functional significance of RNA off-targeting among anticancer small molecules remain poorly understood. Using a systematic drug-binding screen, we identified cisplatin, a frontline chemotherapeutic that acts canonically through DNA adduct formation, as a prominent RNA binder. We employed cisplatin as a model compound to characterize the mechanistic basis and functional impact of RNA-small molecule off-targeting. To map transcriptome-wide cisplatin-RNA interactions, we developed PlatRNA-seq, a click-chemistry-enabled RNA-binding profiling platform. Genomic and functional analyses reveal that cisplatin preferentially accumulates at RNA G-quadruplex (rG4) structures near 5' transcript ends, inducing R-loop formation. Critically, we demonstrate that cisplatin cytotoxicity is partially mediated through RNA binding, revealing a noncanonical mechanism of action. Collectively, these findings illustrate the functional consequences of RNA-small-molecule off-targeting and provide a generalizable framework for investigating small-molecule-RNA interactions, opening new avenues for therapeutic innovation.

molecular biology↗

bioRxiv2025

Pulmonary mRNA-LNP Vaccines for Rapid and Durable Protection Against Bacterial Infection

Pulmonary bacterial infections remain a major clinical challenge. Although vaccination reduces infection rates and mortality, the vulnerable post-vaccination immunity gap can still result in infection and vaccine failure. In addition, effective vaccines are unavailable for many clinically important bacterial pathogens. Here, we report a pulmonary mRNA-lipid nanoparticle (mRNA-LNP) vaccine incorporating a novel ionizable lipid engineered for localized high-level expression, which elicits both rapid and durable protections against bacterial lung infections, effectively bridging this critical window of vulnerability. Intratracheal delivery of mRNA-LNP rapidly primes lung neutrophils and macrophages into a transcriptionally pre-activated state, enhancing their phagocytic activity and enabling rapid, antigen-independent bacterial clearance during the early post-vaccination period (approximately 1-7 days). Subsequently, vaccination induces potent antigen-specific adaptive responses, conferring sustained protection against both laboratory and clinical drug-resistant Pseudomonas aeruginosa strains. Single-cell transcriptomics and immune profiling reveal coordinated activation of innate and adaptive immune programs. This dual-phase immune response exemplifies a paradigm-shifting vaccine design that integrates innate and adaptive immunity to confer both immediate and long-term protection. Our findings establish a mechanistic basis for rapid antibacterial defense and highlight pulmonary mRNA-LNP vaccination as a promising strategy for combating respiratory infections.

bioengineering↗

bioRxiv2025

Identification of Vector Organisms and Their Carried Pathogens by Metagenomic Sequencing

Vector surveillance is vital for public health, yet traditional methods often face limitations. This study assessed the use of metagenomic sequencing for analyzing mosquitoes, cockroaches, flies, and rodents from Zhongshan, China. The results revealed a high concordance between metagenomic and Sanger sequencing for species identification (Kappa = 0.667, P = 0.011). Metagenomic analysis identified vector-specific dominant bacteria, including Wolbachia in mosquitoes, Blattabacterium sp. in cockroaches, Bartonella tribocorum and Leptospira interrogans in rodents, and Vagococcus teuberi in flies. It also detected numerous classical pathogens, such as Enterococcus faecalis, L. interrogans, and Klebsiella pneumoniae. Among the identified fungi and viruses, 19 were human pathogens, while seven posed a threat to agriculture, notably Pepper mild mottle virus and Tomato brown rugose fruit virus. These findings systematically characterize the microbial communities associated with various vectors, highlight the potential of metagenomics for broad pathogen screening, and underscore its utility in large-scale vector surveillance and public health protection. ImportanceVectors are major carriers of numerous diseases and present risks fot cross-border transmission. Existing detection methods for vectors and their pathogens remain inefficient, insensitive, and prone to difficulties in species identification. Despite extensive efforts in vector identification, the potential threats posed by their associated pathogens remain unclear, demonstrating the need for more effective diagnostic strategies. Metagenomic sequencing offers a promising solution to this challenge. In this study, we applied metagenomic sequencing to vectors collected from urban and port areas in Zhongshan, China, to evaluate its efficacy in pathogen detection. Overall, our work contributes to a better understanding of the public health risks posed by vector-borne pathogens.

microbiology↗

bioRxiv2025

Identification of SASP-associated biomarkers and regulatory mechanisms in diabetic foot ulcers based on transcriptomics and experimental validation

BackgroundDiabetic foot ulcers (DFU) constitute a major complication arising from diabetes mellitus. Emerging research findings have underscored the pivotal contribution of cellular senescence to the pathophysiological development of wounds exhibiting delayed healing; nevertheless, the molecular regulatory networks governing this process in DFU pathogenesis remain incompletely elucidated MethodsDFU-related transcriptomic datasets were acquired from public databases. Biomarkers were identified through machine learning algorithms combined with expression validation. To evaluate the diagnostic predictive capacity of identified biomarkers for DFU, a nomogram prediction model was developed and validated. Further analyses included Gene Set Enrichment Analysis (GSEA), molecular network regulation, immune infiltration profiling, and drug prediction. Subsequently, the expression profiles of these biomarkers were quantitatively assessed through reverse transcription quantitative PCR (RT-qPCR) methodology. ResultsFour feature genes were identified using machine learning. Among them, CXCR2 and SIGLEC7 passed expression validation and were significantly up-regulated in DFU groups across two datasets (p < 0.05), confirming their potential as biomarkers. A nomogram constructed based on these two biomarkers demonstrated high discriminative accuracy and good calibration (decision curve net benefit > 0, AUC = 0.99). GSEA indicated that CXCR2 and SIGLEC7 were both enriched in the cytokine-cytokine receptor interaction pathway as well as other functionally associated pathways. Molecular regulatory network analysis indicated that both CXCR2 and SIGLEC7 were predicted to be regulated by transcription factors (TFs) such as SOX2. Immune infiltration analysis showed a significant strong positive correlation between CXCR2 and activated mast cells (correlation coefficient (cor) = 0.72, p < 0.001), and between SIGLEC7 and macrophages M0 (cor = 0.71, p < 0.001). Drug prediction identified 12 drugs targeting CXCR2 (e.g., Indoprofen) and one drug targeting SIGLEC7 (N-acetyl-alpha-D-glucosamine). Validation through Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR) revealed that CXCR2 and SIGLEC7 expression levels were markedly increased in the DFU group, showing statistically significant differences (p<0.05), which aligned with the bioinformatics analysis outcomes ConclusionThis study identified CXCR2 and SIGLEC7 as SASPRG-related biomarkers in DFU through bioinformatic analysis, providing new theoretical support and a basis for early diagnosis and treatment of DFU.

genomics↗

bioRxiv2025

Disruption of small RNAs and mechanistic variation in Segregation Distorter, a sperm-killing drive system in Drosophila melanogaster

Meiotic drivers achieve biased transmission to the next generation, often at the expense of their host. Drive is widespread and can shape the evolution of proteins, chromosome structure, and karyotype. The sperm killer Segregation Distorter (SD) in Drosophila melanogaster is a well-studied driver but like most complex drivers its mechanism remains elusive. SD is a multigene complex, frequently associated with chromosomal inversions, where the main driver locus, a truncated duplication of the gene RanGAP kills wild-type sperm containing a satellite DNA called Responder (Rsp). Functional small RNAs are frequently implicated in the mechanisms of sperm killers, and we recently showed that Rsp is a source of these RNAs. Here we use transcriptomics in two SD haplotypes to link Rsp expression and/or RNAs to drive. We found that Rsp-derived small RNAs are underrepresented in driving testes of only one of the SD haplotypes. We show that over-expressing Rsp is sufficient to reduce drive strength in the haplotype with downregulated Rsp but not the other. We therefore shed light on the mechanism of SD by making a connection between the target and the drive phenotype. Additionally, our data imply that different haplotypes of complex drivers, like SD, can vary in their mechanism.

genomics↗

bioRxiv2025

ClsDiff-AMP30: Generating Antimicrobial Peptides by a Classifier Guidance Noise Predictor

Antimicrobial peptides (AMPs) represent a promising therapeutic strategy to combat the increasing challenge of multidrug-resistant pathogens, a crisis intensified by the overuse of conventional antibiotics. In addition to their broad-spectrum antimicrobial activity, low toxicity, and reduced propensity for resistance development, AMPs offer significant advantages over traditional antibiotic therapies. However, the discovery of novel AMPs through biological experiments remains constrained by high costs, labor-intensive workflows, and time-consuming procedures, underscoring the urgent need for in silico computational methods to design AMP sequences. Notably, shorter AMPs ([≤] 30 residues) demonstrate superior antimicrobial efficacy, improved structural stability, and minimal cytotoxicity toward human cells. To address these challenges, we present a classifier-guided diffusion framework specialized for generating AMPs shorter than 30 residues (ClsDiff-AMP30). The architecture integrates two interdependent submodels, including a noisy AMP classifier that evaluates AMP likelihood at intermediate denoising steps and a noise predictor guided by classifier-derived probability scores, dynamically adjusted via a self-optimized coefficient to modulate guidance strength. ClsDiff-AMP30 achieves a validation accuracy of 66% across 10,000 synthesized sequences by a self-developed AMP classifier. Furthermore, wet lab experiments demonstrated that all 11 selected sequences exhibited high antimicrobial activity against at least one of the three tested bacterial strains and low hemolytic activity.

bioinformatics↗

bioRxiv2025

Mutational divergence in local populations of the selfing nematode Caenorhabditis elegans

Laboratory mutation accumulation experiments allow the assessment of spontaneous mutation rates and patterns with minimal selection. Here, we aimed to follow the accumulation and fate of mutations in natural populations, in a spatial context. The nematode Caenorhabditis elegans is particularly suited for such endeavor, as it reproduces almost exclusively by selfing. We analyzed the evolution of clonal C. elegans genotypes along a 300-m long stream bank in the Santeuil wood (France), based on short-read whole-genome sequencing of individuals collected between 2009 and 2022. We followed along years two of these quasi-clones composed of individuals only differing by recent mutations. Recombination was scarce. A temporal signal was detected: strains from earlier years were found close to inner nodes of the tree, while recent ones were found on outer tips. This signal allowed us to estimate a substitution rate of 4 to 5x10-8 mutations per base pair per year, which can be used to calibrate divergence times among and within species. Mutation densities were higher on the X chromosome, on chromosome arms, and in non-exonic regions. We detected a high transition-to-transversion ratio, not observed in C. elegans laboratory mutation accumulation lines. Based on the spontaneous mutation rate per generation in laboratory lines of intergenic regions under minimal purifying selection, we estimated that C. elegans locally undergoes around 25 effective generations per year. Finally, using these recent mutations, we detected a spatio-temporal pattern within the field site, indicating limited dispersal at the scale of 100 meters within 10 years.

evolutionary biology↗

bioRxiv2025

Old-New Recognition Memory Revisited

Identifying old items during recognition memory tasks elicits widespread cortical responses. Recent results suggest that a distributed network, labeled the Parietal Memory Network (PMN), responds to old items potentially reflecting mnemonic familiarity. An independent, parallel line of evidence suggests that the same network responds to salient stimuli in non-mnemonic contexts, referring to the network as the Salience Network (SAL). Here we examined responses during old-new recognition and non-mnemonic detection tasks in intensively scanned individuals to directly contrast component processes related to mnemonic familiarity with more general processes associated with target detection. In an oddball detection paradigm devoid of traditional memory demands, SAL/PMN was robustly activated. SAL/PMN was also activated by old words in a standard old-new recognition task, thus reproducing both previously reported effects. In a critical oppositional contrast, recognition was examined when the task was to detect uncommon old words versus when the task was to detect uncommon new words. SAL/PMN was activated during the detection of targets even when those targets were new items, consistent with a response to the task relevance of the targets not mnemonic familiarity. Exploratory analyses further suggested that a right-lateralized network, referred to as Frontoparietal Network B (FPN-B), responds to mnemonic familiarity. These results revise our understanding of brain networks contributing to old-new recognition separating component processes common to many forms of target detection task from processes preferentially associated with mnemonic history.

neuroscience↗

bioRxiv2025

Characterization of zebrafish nucleus pulposus in development and aging: A fish model for probing human intervertebral disc degeneration and regeneration

Intervertebral disc (IVD) degeneration (IDD) contributes to low back and neck pain. The nucleus pulposus (NP) of the IVD is usually degenerated first in age-related IDD. Addressing NP degeneration remains challenging due to insufficient understanding of its etiologies. Here, we characterize zebrafish IVD, particularly the NP, and note several characteristics that are common to or different from those of mammals. The adult zebrafish NP can be partitioned into three sections: the NP envelope with a monolayer of basal cells, the peripheral region with clustered vacuolated and non-vacuolated NP cells, and the central fibrotic region with low cellularity. With age, the percentage of vacuolated NP cells decreases, whereas that of non-vacuolated NP cells increases, as in mammals. However, a cluster of vacuolated cells persists in the NP periphery even in aged zebrafish. Notably, the ratio of the span of the fibrotic NP center to NP diameter remains largely constant at 50% regardless of age, suggesting that the zebrafish NP can better maintain its overall shape and structure, compared to mammalian NP. The fibrotic NP center and cell-populated NP periphery serve as internal controls to each other for studying NP maintenance and fibrosis. With external development, high fecundity, and great regenerative capabilities, zebrafish may be a unique NP model to complement mammalian models for studying how the NP degenerates and how the dormant capability of NP regeneration may be revitalized in humans.

developmental biology↗

bioRxiv2025

Mechanical control of histone serotonylation initiates neural crest migration in vivo

Collective cell migration (CCM) is pivotal in several biological contexts, and posttranslational modifications of histones are essential to initiate this process1-3. Here, we show that a recently discovered chromatin mark, termed histone serotonylation4, is involved in the collective migration of cranial neural crest cells - an embryonic multipotent stem cell population5. Our in vivo data reveal that histone serotonylation appears in neural crest cells just before they start migrating and that its occurrence is essential to initiate their CCM. Surprisingly, we found that stiffening of the neural crest migratory substrate, the mesoderm, induces histone serotonylation by promoting nuclear translocation of transglutaminase 2 (Tgm2), the enzyme responsible for adding serotonin to histones4. Moreover, mechanical and molecular perturbations demonstrate that mechanical shuttling of Tgm2 into the nucleus, with concomitant increases in histone serotonylation, are both required and sufficient to allow CCM in vivo. Furthermore, integrated chromatin immunoprecipitation and RNA sequencing analyses uncover a transcriptional module, which is enabled by histone serotonylation in response to mesoderm stiffening. Altogether, our results provide in vivo evidence showing that tissue stiffening leads to increased levels of histone serotonylation to reinforce permissive patterns of gene expression, supporting the switch from non-migratory to migratory cell states.

developmental biology↗

bioRxiv2025

Targeting the host factor HGS-viral membrane protein interaction in coronavirus infection

While current antivirals primarily target viral proteins, host-directed strategies remain underexplored. Here, we performed a genome-wide CRISPRi screening to identify the host protein, Hepatocyte Growth Factor-Regulated Tyrosine Kinase Substrate (HGS), as essential for the pan-coronaviruses infection both in vitro and in vivo. Mechanistically, HGS directly interacts with the viral membrane (M) protein, facilitating its trafficking to the ER-Golgi intermediate compartment (ERGIC) for virion assembly. Conversely, HGS deficiency caused M retention in the ER, blocking assembly. Leveraging this interaction, we designed M-derived peptides and screened over 5,000 FDA-approved drugs, identifying riboflavin tetrabutyrate (RTB). Both the peptides and RTB bind HGS and disrupt its interaction with the M protein, leading to M retention in the ER and subsequent blockade of virion assembly. These agents demonstrated broad anti-pan-coronavirus activity in vitro and in vivo. Collectively, our findings establish HGS as a druggable host target and identify RTB as a promising broad-spectrum antiviral candidate.

microbiology↗

bioRxiv2025

ctsGRN: Inferring cell type-specific gene regulatory networks in Arabidopsis

Gene regulatory networks (GRNs) govern life processes. And exploring GRNs is of great value for understanding biological functions and developing species resources. At present, studies on plant GRNs are limited, mostly focusing on the tissue level, which hinders the discovery of cell type-specific regulatory networks and mechanisms. To address this, we have proposed a GRN research framework named ctsGRN (CellTypeSpecGRN) for conducting cell-specific GRN research at the cell type resolution. In this framework, we employed three representative algorithms from statistics (e.g., correlation analysis), traditional machine learning (e.g., random forest) and deep learning (e.g., neural networks) to construct GRNs for different cell types in Arabidopsis roots. Among these, traditional machine learning methods showed the best performance. Experimental validation through yeast one-hybrid (Y1H) assays and ChIP-seq data confirmed the high reliability and predictive power of these inferred GRNs. By analyzing these networks, we mined novel cell type-specific regulatory relationships, identified core transcription factor (TF) AT5G08790 involved in root development, discovered 249 cell-type-specific hub TFs, and 136 key functional modules were found to be enriched in biological processes. Additionally, we revealed significant heterogeneity of TF-target gene interactions across various cell types. This comprehensive analysis offers a detailed overview of TF-mediated cell type-specific transcriptional regulation in Arabidopsis roots, providing new insights into the molecular mechanisms of root development and establishing a guideline framework for GRN research at the cell-type level in plants.

bioinformatics↗

bioRxiv2025

FlexRibbon: Joint Sequence and Structure Pretraining for Protein Modeling

AO_SCPLOWBSTRACTC_SCPLOWProtein foundation models have advanced rapidly, with most approaches falling into two dominant paradigms. Sequence-based language models (e.g., ESM-2) capture sequence semantics at scale, and a number of recent works incorporate structural signals into sequence encoders. MSA-based predictors (e.g., AlphaFold 2/3) achieve accurate folding by exploiting evolutionary couplings, but their reliance on homologous sequences makes them less reliable in highly mutated or alignment-sparse regimes. We present FlexRibbon{ddagger}, a pretrained protein model that jointly learns from amino acid sequences and three-dimensional structures. Our pretraining strategy combines masked language modeling with diffusion-based denoising, enabling bidirectional sequence-structure learning without requiring MSAs. Trained on both experimentally resolved structures and AlphaFold 2 predictions, FlexRibbon captures global folds as well as flexible conformations critical for biological function. Evaluated across diverse tasks spanning interface design, intermolecular interaction prediction, and protein function prediction, FlexRibbon establishes new state-of-the-art performance on 12 different tasks, with particularly strong gains in mutation-rich settings where MSA-based methods often struggle.

molecular biology↗

bioRxiv2025

Inference of complex demographic history using composite likelihood based on whole-genome genealogies

Accurate parametric inference on complex demographic models is a continuing challenge in population genetics. Ancestral recombination graphs (ARGs) provide richer information than simple population genetic summary statistics and can potentially improve the power and accuracy of such inference. We present mrpast, a tool for inferring complex demographic history from ARGs. mrpast uses a composite likelihood formulation based on the pairwise sample coalescence time, observable in an ARG, and the coalescence probabilities from a continuous-time Markov process. We have evaluated mrpasts accuracy on a variety of models, including stepping-stone models with asymmetric migrations, changes in effective population sizes, out-of-Africa, and American admixture. We demonstrated mrpasts accuracy using simulated ARGs and inferred ARGs, and its high versatility in jointly inferring all parameters in complex models, including time of demographic events (e.g., population split, admixture), effective population size (e.g., constant, exponential growth), and gene flow (e.g., admixture proportion, migration rate). Extending the three-population out-of-Africa model with asymmetric migrations, we observed significantly more migrations from East Asians to Europeans than from Europeans to East Asians. Notably, mrpast can reliably recover all parameters in an American admixture model, when treating non-admixed Native Americans as an unsampled ("ghost") population. Applying this model to Mexican, Puerto Rican, and Colombian populations, we found that the reconstructed histories of Native and admixed Americans align closely with both historical records and genetic evidence. Lastly, mrpast provides a comprehensive pipeline to facilitate easier, more appropriate, and robust demographic inference, in which users can easily simulate, infer, and manipulate ARGs, and illustrate and test a demographic model.

genetics↗

bioRxiv2025

From Home to Transcriptome: Comparing the transcriptomic profile of induced immune response via lipopolysaccharide stimulation in homeRNA and venous blood

Remote blood sampling offers multiple advantages over traditional clinic-based blood sampling studies, including greater patient inclusion, more frequent sampling, and broader geographical reach. Combining remote blood sampling with transcriptomic analysis opens potential in translational applications for capturing acute and dynamic immune responses to various exposures. In this study, we establish the feasibility of homeRNA, a capillary blood collection and RNAlater-based stabilization kit, for use in downstream bulk RNA-sequencing applications via capturing a lipopolysaccharide (LPS)-induced inflammatory response. We also compared the baseline gene expression profiles and induced inflammatory response following LPS stimulation between homeRNA-stabilized samples and venous blood stabilized with RNAlater or PAXgene. We found that homeRNA was successfully able to capture an inflammatory response to LPS, specifically targeting various cytokines (e.g., IL6, IL12B, IL1B), chemokines (e.g., CCL3, CXCL10, CCL4), and other transcriptional factors in the toll-like receptor pathway, the primary pathway activated during LPS stimulation. Importantly, we also found that homeRNA captured a LPS-induced inflammatory response comparable to that of venous blood samples stabilized with either RNAlater or PAXgene. Overall, this work demonstrates that the homeRNA platform is compatible with downstream bulk RNA-sequencing analysis and can capture transcriptomic immune responses to a known stimulus which are analogous to results in traditional stabilized venous blood samples.

bioengineering↗

bioRxiv2025

A platform of robust patient-derived leukemia models covering subgroups for which no cell lines exist

Preclinical cancer research requires robust model systems, especially for poor prognosis entities like acute myeloid leukemia (AML), a highly aggressive blood cancer. Here, primary tumor cells from 137 AML patients of all age groups were transplanted into immune compromised mice to generate patient-derived xenografts (PDX). From these, 23 models enable robust, virtually endless serial re-transplantation and are amenable to lentiviral genetic engineering (*PDX AML models). These models primarily originate from patients with highly aggressive, relapsed disease. Comprehensive genomic, transcriptomic, and epigenomic analyses confirmed that they replicate primary AML biology more faithfully than conventional cell lines. Notably, *PDX AML models include AML subgroups that are underrepresented or absent in existing model systems, such as cytogenetically normal or IDH1/2-mutant AML. They withstand freeze-thaw cycles, making them suitable for broad distribution and reproducibility across research institutions. Luciferase-based in vivo imaging enables real-time monitoring of tumor progression and treatment responses in preclinical trials. Surprisingly, long-term treatment, including repeated cytarabine therapy over a period of one year, showed a gradual reduction in leukemia cell proliferation, which decreased continuously after each treatment block. Collectively, our *PDX models represent a robust, versatile, and relevant platform that holds great promise to accelerate translational research for the benefit of cancer patients. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=123 SRC="FIGDIR/small/677299v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1ead31eorg.highwire.dtl.DTLVardef@1ca702corg.highwire.dtl.DTLVardef@18a7137org.highwire.dtl.DTLVardef@edbe6_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LIWe present new robust AML PDX models covering subgroups for which no cell lines exist for use in various ex vivo and in vivo applications. C_LIO_LI*PDX models enable serial transplantation, genetic engineering and better representation of primary AML biology than cell lines. C_LIO_LIOne-year in vivo trials mimicking clinical chemotherapy showed surprising gradual decline in leukemia growth after each treatment block. C_LI

cancer biology↗