Biology subjects

Zhong, J.

Publications and source records attributed to Zhong, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Competition among freshwater clades explains cave colonization in blind cavefishes

Flatfishes display the most dramatic asymmetric body plan in vertebrates, yet whether this rare innovation evolved once (flatfish monophyly, FM) or multiple times (flatfish polyphyly, FP) has remained contentious. A recent genome-wide study supported FP by placing Psettodes, the earliest-diverging flatfish lineage, among symmetric relatives within Carangaria, the clade that also includes billfishes, jacks, mahi-mahi, and barracudas. Subsequent work traced this to base-composition artifacts and inadequate substitution modeling. Here we revisit the question using whole-genome phylogenomic and synteny data from 17 carangarian species spanning flatfishes and carangarian relatives. We contribute three new chromosome-level assemblies, including the first for Psettodes. Nucleotide-based coalescent analyses (e.g., ROADIES, CASTER) yield strong support for FM, with Psettodes sister to all other flatfishes. Microsynteny analyses built from conserved gene-order blocks corroborate this result: topology tests, cluster-profile counts, and rearrangement-based trees favor FM over two competing FP topologies. Macrosynteny, based on chromosome-scale rearrangements, yields a more mixed signal, with support for FM depending on the metric and taxon-sampling scheme. We interpret this scale-dependent pattern in the context of the explosive post-Cretaceous radiation of Carangaria. The short intervals between speciation events that characterize rapid radiations appear to have left sufficient signal in fine-grained microsyntenic rearrangements, while chromosome-scale rearrangements were too rare to consistently resolve these closely spaced splits. When integrated with evidence from conserved developmental mechanisms active during metamorphosis, the stage at which flatfish asymmetry first emerges, and from the exceptionally complete fossil record, our multi-scale genomic evidence supports a single evolutionary origin of flatfish asymmetry.

evolutionary biology↗

bioRxiv2026

Coronavirus envelope protein drives iron sensing disorder by hijacking the TAp73-FDXR axis

Iron overload is increasingly recognized as a critical contributor to coronavirus pathogenesis1, yet the underlying induction mechanisms remain unclear. Here, we uncover a fundamental pathway by which coronavirus drives IRP1 RNA-binding activity to induce iron accumulation2 via targeting the TAp73-FDXR axis. Specifically, coronavirus infection represses transcription of FDXR (encoding the key rate-limiting enzyme in host iron-sulfur cluster synthesis3), thereby impairing host iron-sulfur cluster generation to trigger the functional conversion of the cytosolic aconitase 1 (ACO1) into iron-regulatory protein 1 (IRP1)4, ultimately leading to the hosts persistently false perception of iron deficiency. We identify TAp73 as the primary transcription factor governing FDXR expression, and demonstrate that the coronavirus envelope protein (CoV-E) orchestrates TAp73 nuclear export. Subsequently, CoV-E binds TAp73 through a critical valine residue within its C-terminal PBM domain, inducing the K48-linked ubiquitination and proteasomal degradation of TAp73. Furthermore, we developed a CoV-E-targeting molecule, DPTP-FC, which blocks CoV-E-TAp73 interaction via forming steric hindrance and effectively alleviates iron accumulation and tissue damage caused by PEDV, PDCoV, and SARS-CoV-2 infection. Our study reveals the central role of the TAp73-FDXR axis in CoV-induced iron accumulation, highlighting CoV-E as an attractive antiviral target and DPTP-FC as a promising therapeutic candidate.

microbiology↗

bioRxiv2026

A circRNA-based uricase replacement therapy for sustained treatment of hyperuricemia

Hyperuricemia, a major risk factor for gout and kidney disease, arises from the evolutionary loss of human uricase and remains a significant medical challenge due to its high prevalence. However, limited therapeutic options are available for refractory hyperuricemia that typically require long-term treatment. Here we developed a circRNA-based uricase replacement strategy and evaluated its efficacy in uricase-knockout mice as a model for severe hyperuricemia. Lipid nanoparticle-mediated delivery of circRNA enabled efficient in vivo expression of an engineered human-like uricase, which rapidly reduced serum urate levels after a single injection and maintained the urate-lowering effect for up to 10 days. Repeated administration led to sustained urate reduction for 10 weeks, mitigated renal injury, and exhibited favorable biosafety. These findings highlight the therapeutic potential of circRNA-based uricase replacement for the long-term treatment of hyperuricemia and its associated complications.

molecular biology↗

bioRxiv2026

Large-scale quantum computing framework enhances drug discovery in multiple stages

Coherent Ising machines (CIMs) excel at solving large-scale combinational optimization problems (COPs), but their insufficient long-term stability has hindered their applications in compute-intensive tasks like computer-aided drug discovery (CADD). By improving fiber vibration isolation and temperature control system, we have implemented a 2000-node CIM named QBoson-CPQC-3Gen achieving stable solutions over one hour on large-scale COPs. Graph-based encoding schemes were further introduced to realize a CIM-based CADD workflow including allosteric site detection, protein-peptide docking and intermolecular similarity calculation. CIM-based methods demonstrated superior speed and accuracy than heuristic algorithms. Especially, QBoson-CPQC-3Gen identified 2 novel druggable sites and bioactive compounds for 6 targets, which were further validated in vitro, in-cell and by crystal structures. Our contributions established a quantum-computing framework for multi-stage drug discovery, representing a significant advancement in both quantum computing applications and pharmaceutical research.

bioinformatics↗

bioRxiv2026

In vivo aberration measurement and correction for ultrafast FACED two-photon fluorescence microscopy of the brain

Ultrafast two-photon fluorescence microscopy (2PFM) based on free-space angular-chirp-enhanced delay (FACED) enables megahertz line scanning and kilohertz frame rates for in vivo brain imaging. However, optical aberrations from the imaging system and brain tissue degrade spatial resolution, signal, and contrast at depth. Here we integrate adaptive optics (AO) with FACED 2PFM to achieve synapse-resolving ultrafast imaging in the living mouse brain. Because FACED generates a one-dimensional array of temporally delayed, spatially separated excitation foci at 1 gigahertz, we developed a focus-averaging, frequency-multiplexed aberration measurement method that simultaneously measures and corrects the average aberration across all FACED foci using a segmented deformable mirror. We validated the accuracy of our method in correcting both system and artificial aberrations. When applied to in vivo morphological imaging of the mouse brain, AO enhances resolution, signal, contrast of dendritic shafts, spines, and boutons. Functionally, AO improves cerebral blood flow imaging by increasing plasma signal and kymograph contrast over large fields of view; when used for glutamate imaging, it amplifies transient amplitudes and reveals visually evoked glutamate release that were undetectable without correction. Together, these results establish AO-FACED 2PFM as a powerful approach that combines ultrafast imaging with high spatial resolution for structural and functional imaging in the living mouse brain.

neuroscience↗

bioRxiv2025

Ferritin Nanoparticle-Based Indirect ELISA for Immunodominant Region Screening and B-cell Epitope Validation of African Swine Fever Virus

African swine fever virus (ASFV) poses a severe threat to the global pig industry, creating an urgent need for effective vaccines and reliable diagnostic tools. While numerous ASFV B-cell epitopes have been reported based on murine-derived monoclonal antibodies (mAbs), such epitopes may not fully represent the antibody targets recognized in naturally infected pigs. Therefore, we need an approach to screen immunodominant regions by evaluating the reactivity of pre-defined epitopes with ASFV-infected pigs sera. In this study, we developed an indirect enzyme-linked immunosorbent assay (iELISA) based on recombinant epitope-ferritin nanoparticles (EFNs), as a platform for immunodominant region screening and epitope validation. This assay, hereafter referred to as EFNs-iELISA, shows an approximately twofold improvement in sensitivity compared with synthetic peptide-based ELISA for linear B-cell epitopes, and improved discrimination over GST-His-peptide and BSA-conjugated peptide formats. Using EFNs-iELISA, we evaluated 22 known murine-derived B-cell epitopes and 17 predicted epitopes from ASFV proteins CD2v, P30, P54, and P72 with ASFV-positive and negative porcine sera. According to the calculated cut-off threshold, tested epitopes were classified into different groups with positive or negative reactivity. Our analysis confirmed that among 22 known murine-derived ASFV B-cell epitopes, 16 (72%) also showed positive reactions with porcine-derived sera. Whats more, we also screened 7 new porcine-specific immunoreactive regions on these proteins. The EFNs-iELISA method provides a practical tool for screening immunodominant regions of ASFV, with potential applications in vaccine and diagnostic research.

immunology↗

bioRxiv2025

Sampling Function-Related Metastable States of Proteins With DASH

Rational discovery of function-specific protein modulators as well as activity-enhanced engineering proteins underscore the need to identify function-related metastable states (FMSs) of proteins. However, current experimental and computational methods struggle to generate these states directly from their native state (NS), likely because the NS [->] FMS transition is non-spontaneous. To address this challenge, we introduce Deep learning guided Adaptive sampling with seed Selection and Hopping (DASH), integrating both deep learning and physical functions to guide molecular dynamics (MD) simulation towards FMS. DASH successfully sampled NS [->] FMS transitions in 18 cases across two tasks: protein activation and cryptic allosteric site opening. DASH combined with secondary-structure collective variables is further able to sample folding of disordered regions. Compared to existing methods, DASH demonstrates superior performance while requiring comparable simulation time. Crucially, we applied DASH to sample new conformations of proteins, by which revealing new folding states, previously unknown allosteric sites, and potential activators. These predictions have been further verified in wet-lab experiments and crystal structures determinations. Collectively, our framework provides a robust strategy for function-specific pharmacy research and could accelerate future drug discovery efforts.

bioinformatics↗

bioRxiv2025

Large generative mRNA language foundation model for efficient coding sequence generation and design with mRNA-GPT

mRNA design plays a central role in synthetic biology, nucleic acid therapeutics, and vaccine development. Although large language models are applied in many biological fields, generative language models for de novo mRNA design remains largely unexplored. Here, we introduce mRNA-GPT, a series of generative mRNA language models which for the first time covers the three domains of life as pretraining datasets. Based on a GPT-2 transformer architecture with 302 million parameters, we pre-trained three separate models on 19,676 bacterial, 4,688 eukaryotic, and 702 archaeal species, leveraging 80 million, 83 million, and 2 million mRNA coding sequences, respectively. Distinct clustering of mRNA coding sequence embeddings from animals, plants, and fungi in the pretrained mRNA-GPT-eukaryote indicates that the model captures organism-specific sequence features. Following unsupervised pre-training, we fine-tuned mRNA-GPT on a translation-efficiency dataset to generate high-performance mRNA sequence. Compared to the pretrained model, the fine-tuned mRNA-GPT produced mRNA sequences with significantly higher translation efficiency scores, demonstrating the ability of mRNA-GPT to capture sequence features underlying high translation efficiency. We further fine-tuned mRNA-GPT on datasets for mRNA stability and mRNA expression, where it likewise produced high-performance mRNA sequences. Our pretrained models are publicly available, enabling other researchers to adapt mRNA-GPT to specialized tasks such as tissue-specific mRNA expression or stability by fine-tuning on their own data. Together, our study demonstrates that generative mRNA language modeling as a promising approach for accelerating mRNA design across diverse biological fields.

bioinformatics↗

bioRxiv2025

Quantitative mapping of antigen specificity in adaptive immune repertoire embedding spaces

The adaptive immune receptor repertoire (AIRR) encompasses an immense diversity of antibody and T-cell receptor sequences, whose collective organization - how receptors are distributed, clustered, and interrelated across sequence and functional (e.g., antigen-binding) dimensions - remains poorly characterized. Representing AIRRs in continuous representation spaces that capture sequence, biochemical, and structural similarity between receptors may enable comparisons beyond discrete sequence features. Using both one-hot encodings and protein language model (PLM) embeddings, we developed a quantitative framework to map immune receptor organization at global (sequence-set-level) and local (single-sequence-level) scales. Applying the geometry-aware Wasserstein-2 distance, we show that the global structure of the AIRR space can be recovered from as few as [~]105 sequence embeddings, at least 10 orders of magnitude smaller than the theoretical immune receptor diversity. We found that immune receptor sequences annotated with different antigen specificities occupy distinct regions of representation space. To resolve local relationships, we introduce a spatial homogeneity metric that quantifies the extent of functional clustering. We found higher spatial homogeneity in embedding spaces than in sequence space for diverse antigen-specific datasets. Our framework establishes a foundation for quantitative mapping of adaptive immune repertoire organization.

immunology↗

bioRxiv2025

mIDEA: An Interpretable Structure-Sequence Model for Methylation-Dependent Protein-DNA Binding Sensitivity

DNA methylation dynamically reshapes protein-DNA interaction landscapes, yet the mechanistic principles governing methylation-dependent recognition remain incompletely understood. Currently, there is no predictive framework that integrates methylation-aware binding specificity with 3D structure-based residue interactions for quantitatively assessing the impact of DNA methylation on protein-DNA interactions. To bridge this gap, we introduce mIDEA (Methylation-informed Interpretable protein-DNA Energy Associative model), a structure-aware, residue-level biophysical framework that predicts and interprets protein-methylated-DNA interactions by optimizing an amino acid-nucleotide energy matrix explicitly accounting for the modulatory effect of 5-methylcytosine. We validate mIDEA across diverse human transcription factors with distinct CpG methylation preferences. Using only structural templates and prior biochemical knowledge, mIDEA accurately recapitulates both "methyl-plus" and "methyl-minus" effects of DNA methylation on protein-DNA binding. Incorporating quantitative methylation-sensitive binding data further enables mIDEA to capture position-specific methylation effects. When integrated with whole-genome methylation profiles, the model enhances in vivo binding-site prediction by reducing false positives while preserving sensitivity compared to models trained on unmethylated sequences. Overall, mIDEA provides a principled framework for elucidating the molecular mechanisms underlying methylation-modulated protein-DNA recognition. The mIDEA source code is available at: https://github.com/LinResearchGroup-NCSU/IDEA_DNA_Methylation.

bioinformatics↗

bioRxiv2025

Time-resolved phylogenomics analysis reveals patterns in biosphere nutrient limitation through Earth history

The co-evolution of life and Earth has profoundly transformed global biogeochemical cycles over the past 3.5 billion years. These cycles, in turn, have dictated the availability of essential nutrients like phosphorus, nitrogen, and iron, thereby affecting primary productivity and the scale of the Earths biosphere. Despite the critical role of nutrient limitation in shaping the size and scope of the biosphere, significant uncertainties persist about which nutrients were globally limiting at various points in Earth history. Here, we use a phylogenomic approach to trace the origin and spread of genes associated with nutrient limitation over time. We show that genes associated with phosphorus limitation emerged relatively early in lifes history, whereas genes associated with nitrogen limitation emerged later, closer to the Great Oxidation Event. In terms of iron limitation, we present novel evidence that siderophores, compounds that facilitate iron uptake, may have arisen as early as the Archean. Overall, our results have important implications for understanding how the geosphere has influenced the scale and extent of life on Earth for the past 4 billion years.

bioinformatics↗

bioRxiv2025

TDP-43 dysfunction leads to impaired proteostasis and predisposes mice to worse neurological outcomes after brain injury

BackgroundPathological TAR DNA-binding protein 43 (TDP-43) dysfunction is associated with multiple neurodegenerative disorders. However, the mechanistic link between TDP-43 dysfunction and neurodegeneration is poorly understood and likely involves a combination of genetic and environmental risk factors. A major risk factor for neurodegenerative disease is exposure to traumatic brain injury (TBI). Here, we investigated the synergistic interplay between TDP-43 dysfunction and TBI in a murine model of amyotrophic lateral sclerosis (ALS)/frontotemporal dementia (FTD). MethodsA model of TDP-43 dysfunction caused by a knock-in Q331K mutation in Tardbp was combined with a mild model of TBI. Control conditions included both WT mice and mice with sham surgery. Animals were evaluated for behavioral deficits at timepoints pre- and post-surgery. Additionally, post-mortem brain tissues were examined using RNA sequencing and mass spectrometry-based quantitative proteomics together with histological and biochemical analyses. ResultsExpression of dysfunctional TDP-43 in vivo caused deficits in multiple branches of the proteostasis network, including protein folding, protein synthesis, and protein turnover. Examples include mis-expression of chaperones and genes within the ubiquitin-proteosome pathway in mutant TDP-43 versus WT mice. Further, mutant TDP-43 expression correlated with reduced thermostability of proteins associated with the ribosome and the chaperonin containing TCP-1 complex. In response to TBI, mutant TDP-43 mice exhibited significantly worse neurological outcomes relative to WT animals. Heightened neurological deficits in mutant TDP-43 mice following TBI coincided with a robust upregulation of proteostasis- and stress-related genes at the transcript level. However, this upregulation was not detected at the protein level. ConclusionsOur data demonstrate that expression of dysfunctional TDP-43 leads to deficits within the proteostasis network in vivo at baseline. Despite an upregulation of proteostasis-related genes at the transcript level in mutant TDP-43 mice after TBI, mutant TDP-43 mice exhibit an impaired response to, and recovery from, brain trauma relative to their WT counterparts. Restoring proteostasis is expected to protect against the detrimental effects of TDP-43 dysfunction, especially under stress conditions that promote neurodegenerative disease.

neuroscience↗

bioRxiv2025

PEPE: Scalable extraction of multi-modal protein language model representations

MotivationProtein language models (PLMs) capture intricate amino-acid dependencies, producing embeddings that encode rich structural, functional, and evolutionary information. Despite their potential, current extraction workflows rely on arbitrary choices, with respect to embedding layer, pooling, and padding, that frequently yield suboptimal representations for feature extraction and downstream analyses. Large-scale embedding generation is further limited by inefficiencies in computation and memory: (i) accumulating all model outputs in memory before writing to disk causes severe bottlenecks, and (ii) repeatedly embedding identical sequences to extract different modes introduces redundant computation and drastically reduces throughput and scalability. ResultsWe introduce PEPE (Parallel Extraction for Protein Embeddings), a command-line tool and Python library that enables efficient, high-throughput, and multimodal extraction from protein language models. PEPEs parallelized and streaming-based architecture achieves runtimes several orders of magnitude faster than sequential approaches. Unlike conventional methods--whose peak memory usage scales linearly with output size and fails when memory capacity is exceeded--PEPE maintains stable, low memory consumption, enabling multimodal embedding extraction even beyond available RAM. PEPE supports a wide range of state-of-the-art and custom PLMs through a simple, flexible interface. By combining scalability, robustness, and ease of use, PEPE allows researchers to generate massive, information-rich embedding datasets efficiently, and facilitate the discovery of optimal representations for structural, functional, and evolutionary downstream tasks. By streamlining the generation of diverse embedding configurations, PEPE provides researchers with the necessary data to identify high-performing latent states for specific biological contexts without requiring additional computational resources. Availability and ImplementationPEPE is a command-line tool written in Python and published under MIT license. The source code and documentation are available at https://github.com/csi-greifflab/pepe-cli. PEPE is also available for installation from PyPI under https://pypi.org/project/pepe-cli and deposited on Zenodo at https://zenodo.org/records/15912054.

bioinformatics↗

bioRxiv2025

A Novel 3D Visualization Method in Mice Identifies the Periportal Lamellar Complex (PLC) as a Key Regulator of Hepatic Ductal and Neuronal Branching Morphogenesis

The liver is a complex organ responsible for multiple functions, including metabolism, energy storage, detoxification, bile secretion, and immune regulation. Its highly organized vascular system plays a crucial role in maintaining functional zonation and tissue homeostasis. Within the liver, the hepatic artery, portal vein, hepatic vein, bile duct, and nerve networks intertwine to form an intricate three-dimensional architecture; however, traditional two-dimensional imaging fails to reveal their true spatial relationships, and current three-dimensional imaging methods remain insufficient to capture fine structural details. To achieve comprehensive visualization of these multi-ductal systems, we established a high-resolution three-dimensional imaging platform that combines multicolor perfusion of metallic compound nanoparticles (MCNPs) with an optimized tissue-clearing protocol (Liver-CUBIC), enabling simultaneous 3D reconstruction of the portal vein, hepatic artery, bile duct, and hepatic vein in mouse livers. Based on these data, we identified and defined a previously unrecognized structure located in the outer layer of the portal vein, termed the Periportal Lamellar Complex (PLC). The PLC encircles the portal vein between the vascular endothelium and the perisinusoidal region, exhibits low-permeability barrier characteristics, and contains a distinctive population of CD34Sca-1 endothelial cells. During liver fibrosis, the PLC extends from the portal vein toward the hepatic lobule, forming a structural scaffold that guides bile duct and nerve migration.

developmental biology↗

bioRxiv2025

Development of Degraders and 2-pyridinecarboxyaldehyde (2-PCA) as a recruitment Ligand for FBXO22

Targeted protein degradation (TPD) is a promising therapeutic strategy that requires the discovery of small molecules that induce proximity between E3 ubiquitin ligases and proteins of interest. FBXO22 is an E3 ligase that is overexpressed in many cancers and implicated in tumorigenesis. While FBXO22 was previously identified as capable of recognizing ligands bearing a primary amine degron, further investigation and development of recruitment ligands is required to enable its broader utility for TPD. Here, we describe the discovery of chemical probes that can either selectively degrade FBXO22 or recruit this ligase for TPD applications. First, we describe AHPC(Me)-C6-NH2 as a potent and selective FBXO22 degrader (DC50 = 77 nM, Dmax = 99%) that is suitable for interrogating the effects of FBXO22 loss of function. Further, we discovered that the simple hexane-1,6-diamine acts as a minimal FBXO22 self-degrader, whereas shorter C4 (putrescine) to C5 (cadaverine) analogs, found in mammalian cells, do not induce degradation. Finally, we found that 2-pyridinecarboxaldehyde (2-PCA) functions as a novel electrophilic degron capable of forming a reversible thioketal with cysteine 326 for recruiting FBXO22. Conjugating 2-PCA to various ligands successfully induced FBXO22-dependent degradation of BRD4 and CDK12. Collectively, these chemical probes will facilitate the study of FBXO22 biology and broaden its applicability in TPD.

biochemistry↗

bioRxiv2025

Helicobacter pylori genome aggregation database reveals complex evolutionary forces shaping its genomic landscape and clinical impact in East Asia

We assembled HPgnomAD, a comprehensive global genome aggregation database of Helicobacter pylori (H. pylori) featuring 7,544 high-quality genomes from 278 populations, along with the first species-wide haplotype reference panel for genotype imputation. The panel provides high accuracy across diverse H. pylori populations, including those from low-coverage data, and is openly accessible with integrated analysis tools (https://www.hpgnomad.top/). Variant discovery revealed 1.82 million SNPs and 0.65 million InDels, with African strains exhibiting the most tremendous diversity and East Asian strains showing unexpectedly high novel variation. Fine-scale analysis of the hpEAsia lineage revealed six new sublineages shaped by altitude-, latitude-related divergence and region-specific admixture. Phylogenomic dating revealed two divergence waves in East Asia, paralleling Upper Paleolithic settlement and Neolithic human expansions, with highland-lowland separation at [~]18.8 kya, followed by the formation of complex geography-related population substructures. Genome-wide scans revealed adaptive loci related to metal acquisition, nitrogen metabolism, surface adhesion, and membrane transport, including altitude-associated highly differentiated variants linked to antibiotic resistance and gastric disease. By integrating genomic, evolutionary, environmental, and clinical data, HPgnomAD offers a framework for understanding H. pylori evolution, host-pathogen adaptation, and precision medicine worldwide.

evolutionary biology↗

bioRxiv2025

Computationally Driven Top-Down Mass Spectrometry of Ubiquitinated Proteins

Ubiquitination regulates numerous cellular processes through the attachment of polyubiquitin (Ub) chains that vary in linkage type, length, and branching topology. However, current mass spectrometry approaches cannot simultaneously define both the site of ubiquitination and the topology of the attached Ub chain on intact protein substrates. Here, we present the first integrated strategy that enables simultaneous determination of ubiquitin site and chain architecture using top-down mass spectrometry (TD-MS). Central to this approach is UbqTop, a custom computational platform that predicts Ub chain topology from tandem MS (MS{superscript 2}) fragmentation data by utilizing Bayesian-like scoring algorithm. To address the challenge of analyzing complex substrates, we combine this with selective Asp-N proteolysis, which digests the substrate while preserving intact Ub chains. This enables direct, site-resolved mapping of Ub chain topology on proteins. We demonstrate the broad utility of this method on both free Ub chains and multiply ubiquitinated protein substrates, including the resolution of isomeric chains and branched architectures. Together, this work establishes a powerful new framework for proteoform-level analysis of ubiquitin signaling with unprecedented structural resolution.

bioinformatics↗

bioRxiv2025

A Thiopurine-like Mutagenic Process Defines TGCT Subtypes

Testicular germ cell tumors (TGCTs) are the most common malignancy in young men, exhibit a unique developmental origin and exceptional chemosensitivity. However, the molecular distinctions between TGCT subtypes remain poorly understood. Here we present a comprehensive genomic analysis of 252 treatment-naive primary TGCTs, integrating deep whole-genome sequencing with matched transcriptomic and epigenomic data. We identify new driver genes and uncover defining features of TGCTs, including pervasive chromosome X amplification with subtype-specific X chromosome inactivation, and a germ cell-like transcriptional program. Although previously reported, whole genome doubling (WGD) in TGCTs is further characterized here as ubiquitous, developmentally early, and associated with age at onset. Seminomas are enriched for early driver mutations, secondary WGD events, sustained XIST expression and replication stress-associated indel mutational signatures, while non-seminomas show greater structural complexity, subclonal diversity, relatively earlier-onset WGD, extended tumor latency, and telomere elongation. Moreover, we identify a mutational signature, SBS87, that is exceptionally rare across cancers with exception of thiopurine-treated leukemia, but strikingly prevalent in TGCT, especially non-seminomas. SBS87 is linked to extended tumor latency and telomere elongation, implicating possible environmental or endogenous processes that mimic thiopurine-induced DNA damage in TGCT pathogenesis. Collectively, our findings define TGCTs as molecularly distinct tumors shaped by early genomic instability and highlight SBS87 as a novel mutational footprint with potential etiologic and clinical relevance.

genomics↗