Biology subjects

Gu, B.

Publications and source records attributed to Gu, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Grow2Fold: mapping heterogeneous developmental growth to human brain folding

Cortical folding emerges through complex developmental processes, yet what organizes these processes into the characteristic folding patterns of the human brain remains unclear. Here, we developed an imaging-informed whole-brain modeling framework, Grow2Fold, that integrates developmental cortical regionalization, quantitative growth characterization, and mechanics-guided simulation to investigate how cortical growth shapes brain folding patterns. Using 836 fetal and neonatal MRI sessions from 747 participants spanning 21-45 postmenstrual weeks, we identified a reproducible and hierarchical spatiotemporal organization of cortical expansion characterized by coordinated regional growth trajectories. Rapid expansion occurs earlier around peri-central territories and progressively shifts toward frontal, parietal, and temporal association cortices. We encoded these imaging-derived trajectories into region- and age-specific growth laws and incorporated them into anatomically realistic whole-brain mechanical models. The resulting simulations recapitulate major sulcal landmarks and characteristic folding development across global and regional morphological measures. Comparing regional and uniform growth, we find that differential growth drives cortical instability, while heterogeneous developmental growth organizes this instability into anatomically recognizable folding patterns. Combined with subject-specific anatomy, these population-informed growth laws reproduce individualized longitudinal folding trajectories. Together, our results identify spatiotemporally patterned developmental growth as a key spatial constraint on cortical morphogenesis and establish a quantitative bridge from developmental imaging, through mechanical instability, to anatomically organized folding.

biophysics↗

bioRxiv2026

Macroalgal fucoidan can activate the biological carbon pump

Macroalgae secrete complex carbohydrate polymers, their extracellular matrix, as protection against microbial degradation. By resisting breakdown, these carbohydrates can contribute to marine carbon sequestration, though mechanisms, extent, and timescales remain unknown. Using ship-based sampling and experiments, we found that brown macroalgae release 1.7-4.2% of carbon fixation as fucoidan, equivalent to 0.32-0.88 mg fucoidan per gram of dry seaweed tissue per day. A Bayesian model trained on our empirical data, coupled with Monte Carlo simulations suggests an annual global release of 13-37 megatons fucoidan carbon. Moreover, degradation resistance combined with surface-activity enabled fucoidan to act as glue that cross-linked allochthonous organic carbon including microbes and proteins into marine snow. Notably, substantial fucoidan exudation was universally conserved across all tested species and regions. Thus, any brown macroalgal species can be used e.g. via aquafarming to enhance the formation of marine snow.

ecology↗

bioRxiv2026

Long-read sequencing maps transposable element variation and its regulatory and epigenetic effects in the human brain

Transposable elements (TEs) are mobile DNA sequences that shape genome architecture and gene regulation, yet their roles in the human brain remain largely unresolved. Short-read sequencing lacks the resolution to accurately map TE insertions, detect associated structural variants, and resolve highly repetitive regions. Here, we leverage long-read whole-genome sequencing to profile germline TE insertions in postmortem brain tissue from two ancestrally diverse cohorts: the North American Brain Expression Consortium (NABEC; European ancestry, n = 205) and the Human Brain Collection Core (HBCC; African and African-admixed ancestry, n = 146). We identified 2,842 and 1,660 high-confidence non-reference insertions in HBCC and NABEC, respectively, spanning Alu, LINE-1, and SVA elements. We then also further characterized complex short tandem repeat and variable number tandem repeat variation within reference SVA and Alu loci. Reference TEs were also found to mediate complex structural variants at loci implicated in brain development and neurodegenerative disease, with several showing ancestry-specific patterns. Integration of bulk RNA-sequencing data identified TE expression quantitative trait loci, including insertions that modulate neuronal gene expression. Single-nucleus RNA sequencing revealed cell-type-specific effects of TE regulation across cortical populations. Long-read methylation profiling further demonstrated age-associated epigenetic regulation of both reference and non-reference Alu elements. As a community resource, we release a catalog of TE insertions, allele frequencies, and ancestry-specific distributions to enable future functional and disease-focused investigations. Together, these findings highlight the widespread regulatory and epigenetic influence of TEs in the human brain and establish long-read sequencing as a powerful approach for uncovering cell-type- and population-specific TE dynamics.

genomics↗

bioRxiv2026

Unbiased Long-Read Whole-Genome Sequencing Enables High-Resolution Mapping of Transgene Concatenation and Off-target Genomic Disruption in a Mouse Model

Transgenic mouse models are indispensable for dissecting disease mechanisms; yet, their interpretability is frequently compromised by cryptic genomic alterations introduced during transgenesis. Thus, robust quality control strategies are needed to elucidate integration architecture and evaluate model performance when such unintended events occur. Here, we applied unbiased whole-genome long-read sequencing using the PacBio Revio to investigate a mouse model exhibiting unexpected transgene silencing, originally designed to recapitulate autosomal-dominant hereditary macular dystrophy driven by upregulation of a ZZEF1-ALOX15 fusion gene. Long-read sequencing analysis revealed a [≥]29-kb head-to-tail concatemer containing more than three copies of the transgene vector. Reconstruction of transgene-genome junctions revealed off-target integration of the concatemer into the calcium-sensing receptor gene (Casr), along with exogenous E. coli DNA, that together defined final transgene architecture. 5-methylcytosine profiling identified hypermethylation of the transgene promoter and additional phenotyping indicated disruption of endogenous Casr function resulting from the rearrangement. Our workflow enabled direct detection of transgene concatenation and off-target mapping. These findings establish long-read sequencing as a powerful and scalable quality control standard for genetically engineered animal models, uniquely capable of uncovering hidden genomic complexity, resolving aberrant phenotypes, and enhancing the reliability of in vivo disease modelling.

genomics↗

bioRxiv2026

A comprehensive assessment of tandem repeat genotyping methods for Nanopore long-read genomes

BackgroundTandem repeats (TRs) play critical roles in human disease and phenotypic diversity but are among the most challenging classes of genomic variation to measure accurately. While it is possible to identify TR expansions using short-read sequencing, these methods are limited because they often cannot accurately determine repeat length or sequence composition. Long-read sequencing (LRS) has the potential to accurately characterize long TRs, including the identification of non-canonical motifs and complex structures. However, while there are an increasing number of genotyping methods available, no systematic effort has been undertaken to evaluate their length and sequence-level accuracy, performance across motifs from STRs to VNTRs and across allele lengths, and, critically, how usable these tools are in practice. ResultsWe reviewed 25 available bioinformatic tools, and selected seven that are actively maintained for benchmarking using publicly available Oxford Nanopore genome sequencing data from more than 100 individuals. Our benchmarking catalog included [~]43k TR loci genome-wide, selected to represent a range of simple and challenging TR loci. As no "truth" exists for this purpose, we used four complementary strategies to assess accuracy: concordance with high-quality haplotype-resolved Human Pangenome Reference Consortium (HPRC) assemblies, Mendelian consistency in Genome in a Bottle trios, cross-tool consistency, and sensitivity in individuals with pathogenic TR expansions confirmed by molecular methods. For all comparisons, we assess both total allele length and full sequence similarity using the Levenshtein distance. We also evaluated installation, documentation, computational requirements, and output characteristics to reflect real-world use. We provide a complete analysis workflow for all tools to support community reuse. Tool performance varied substantially across both accuracy and usability. Most methods achieved high concordance with HPRC assemblies, with higher accuracy when using the R10 ONT pore chemistry. Accuracy generally declined with increasing allele length, and most tools performed worse on homopolymers, likely reflecting underlying sequencing accuracy. Tools generally performed worse at heterozygous loci and at alleles that differed from the reference genome. Interestingly, concordance with assembly in population samples did not predict sensitivity to pathogenic expansions, with different genotypers performing best in each category. Similarly, Mendelian consistency was highest in the tool that performed worst in assembly concordance. ConclusionsNo single genotyper emerged as consistently best across all assessments, but strong contenders emerged in each. Our results demonstrate that length accuracy (a typical benchmarking approach) alone overestimates TR genotyping performance. Sequence-level benchmarking is essential for selecting tools best-suited for population studies and clinical diagnostics. This work provides practical guidance for tool selection and highlights key priorities for future long-read TR genotyping method development.

bioinformatics↗

bioRxiv2026

GmEBS7a-E2 complex dissociation hyperactivates ERAD to impair plant immunity against Phytophthora pathogens

Endoplasmic reticulum-associated degradation (ERAD) pathways are crucial for maintaining plant development and stress responses. While ERAD has been extensively studied for its role in regulating responses to abiotic stress, its specific manipulation by eukaryotic pathogens stress remains unclear. Here, we reported that soybean GmEBS7a, a plant-specific ERAD component, is targeted and stabilized by a transmembrane domain-containing effector Avh131 from Phytophthora sojae. GmEBS7a enhances plant disease susceptibility by increasing ERAD efficiency and ER stress sensitivity. Mechanistically, Avh131 competitively disrupts the interaction with GmEBS7a and the ubiquitin-conjugating enzyme GmUBC32, reducing ubiquitination of GmEBS7a and subsequent inhibition of its degradation. These findings demonstrate that excessive ERAD triggered by stabilized GmEBS7a and its partnering E3 ligase impairs the plants response to biotic stress. Our study uncovers a fine-tuning of the ER protein-folding capacity to promote infection, thereby expanding our understanding of ER-centered immunity during plant-pathogen interactions.

immunology↗

bioRxiv2025

Resolving Heterogeneity in Major Depression: Overcoupling and Undercoupling Subtypes Exhibit Differential Treatment Response and Molecular Pathways

Major depressive disorder (MDD) exhibits significant heterogeneity whose neurobiological mechanisms remain elusive. Alterations in morphological-functional coupling (MFC) have been observed in MDD. This study aims to investigate MDD subtypes based on MFC alterations and their associations with clinical symptoms and molecular basis. We identified two clinically distinct MDD subtypes in multi-center neuroimaging data (Discovery: 828 MDD/776 healthy controls; Validation: 236 MDD/86 healthy controls) by using a semi-supervised machine learning approach based on MFC changes, which were validated and robustly repeated in the validation dataset. Differences among subtypes were then examined in relation to clinical assessments, gene expression patterns, neurotransmitter and cell density, and treatment response. Subtype I (overcoupling) showed elevated MFC in high-order association cortices, linking to synaptic transmission activity and severe symptoms. Subtype II (undercoupling) demonstrated reduced MFC in primary cortices, associated with cell cycle regulation and treatment resistance. Both subtypes shared molecular signatures (cell types: astrocytes and oligodendrocytes; neurotransmitter systems: serotonergic and GABAergic receptors). Crucially, longitudinal data (33 MDD undergoing Escitalopram monotherapy) revealed that undercoupling subtype exhibited better response, implying a potential mechanism of the drug via increasing the MFC. Our work deciphers MFC-driven MDD phenotypes with distinct molecular profiles and differential treatment outcomes, suggesting potential pathways for personalized diagnosis and treatment strategies in MDD, advancing precision psychiatry.

neuroscience↗

bioRxiv2025

Automatic Classification of Circulating Blood Cell Clusters based on Multi-channel Flow Cytometry Imaging

Circulating blood cell clusters (CCCs) containing red blood cells (RBCs), white blood cells (WBCs), and platelets are significant biomarkers linked to conditions like thrombosis, infection, and inflammation. Flow cytometry, paired with fluorescence staining, is commonly used to analyze these cell clusters, revealing cell morphology and protein profiles. While computational approaches based on machine learning have advanced the automatic analysis of single-cell flow cytometry images, there is a lack of effort to build tools to automatically analyze images containing CCCs. Unlike single cells, cell clusters often exhibit irregular shapes and sizes. In addition, these cell clusters often consist of heterogeneous cell types, which require multi-channel staining to identify the specific cell types within the clusters. This study introduces a new computational framework for analyzing CCC images and identifying cell types within clusters. Our framework uses a two-step analysis strategy. First, it categorizes images into cell cluster and non-cluster groups by fine-tuning the You Only Look Once(YOLOv11) model, which outperforms traditional convolutional neural networks (CNNs), Vision Transformers (ViT). Then, it identifies cell types by overlaying cluster contours with regions from multi-channel fluorescence stains, enhancing accuracy despite cell debris and staining artifacts. This approach achieved over 95% accuracy in both cluster classification and phenotype identification. In summary, our automated framework effectively analyzes CCC images from flow cytometry, leveraging both bright-field and fluorescence data. Initially tested on blood cells, it holds potential for broader applications, such as analyzing immune and tumor cell clusters, supporting cellular research across various diseases.

bioinformatics↗

bioRxiv2025

Stem-like Prostate Remodeling in Obesity Mediates Resistance to 5α-Reductase Inhibition Therapy in BPH

Benign prostatic hyperplasia (BPH) is closely associated with obesity and metabolic syndrome. Although 5-reductase inhibitors (5ARIs) are widely used to treat BPH, their effectiveness varies significantly, particularly in obese patients. However, the mechanisms by which obesity affects prostate growth and modulates therapeutic responses remain poorly understood. In mice, highfat diet (HFD) increased prostate weight and induced SRD5A2-independent epithelial remodeling with expansion of proximal urethral luminal epithelial cells. ScRNA-seq of prostate tissues from Srd5a2null and control mice on HFD showed enrichment of proximal stem-like populations, reduced androgen signaling, and activation of WNT and NOTCH pathways via heightened stromal-epithelial crosstalk. Clinically, BMI gain, regardless of 5ARI use, correlated with transition zone growth, stronger activity of stem-cell signatures, reduced SRD5A2, and androgen signaling downregulation. Xenium spatial profiling indicated that BMI gain expands stem-like epithelial populations and a NOTCHenriched periepithelial stromal niche surrounding epithelial compartments, supporting stemness. Patients with substantial BMI gain were less responsive to 5ARI, whereas weight control plus 5ARI therapy synergistically improved outcomes. In conclusion, BMI gain promotes proximal prostate enlargement through an SRD5A2-independent stem-like cell mediated pathway that blunts 5ARI efficacy. Combining pharmacotherapy with weight control yields superior efficacy and supports individualized management of BPH.

pathology↗

bioRxiv2025

SOX2 is a dispensable modulator of NUT carcinoma oncogenesis in mouse

NUT carcinoma (NC) is a highly aggressive malignancy driven by BRD4::NUTM1 and other NUTM1 fusion oncogenes. BRD4::NUTM1 aberrantly activates transcription factors (TFs) linked to basal progenitor identity, producing poorly differentiated squamous phenotypes. Among these TFs, SOX2 has been proposed as a critical oncogenic driver, but its functional requirement in NC has not been tested in vivo. Using a genetically engineered mouse model that faithfully recapitulates human NC, we performed lineage-specific conditional deletion of Sox2 in both squamous and non-squamous tissues. We found that Sox2 is dispensable for NC initiation and progression, with tumors retaining characteristic histology and expression of key drivers including BRD4::NUTM1, MYC, and TP63. Transcriptomic profiling revealed only modest changes in Sox2-deficient tumors, mainly affecting metabolic and biosynthetic pathways, without disrupting core oncogenic programs. These findings challenge the assumption that SOX2 is universally required in NC and suggest that SOX2-targeted therapies may have limited utility, refining the framework for therapeutic prioritization. Summary blurbThis study shows that SOX2 is not required for NUT carcinoma initiation or maintenance in vivo, challenging its assumed oncogenic role and refining therapeutic target prioritization.

cancer biology↗

bioRxiv2025

Tissue-specific consequences of tag fusions on protein expression in transgenic mice

Genetic fusion of protein tags is widely used to study protein functions in vivo. It is well known that tag fusion can cause unwanted changes in protein stability, but whether this is an inherent property of the tagged protein, or can be influenced by the cell and tissue environment, is unclear. Using a series of genome edited mouse models, we show that tag-dependent changes in protein expression can vary across different primary cell and tissue contexts. In one case (Ncaph2), a C-terminal auxin-inducible degron fusion strongly increased protein stability in some tissues but decreased it in others. Destabilisation resulted from tissue-specific leakage of the auxin-inducible degron, which depended on TIR1 expression, and occurred selectively in the small intestine where basal concentrations of auxin / indole-3-acetic acid can reach levels that are sufficient to trigger protein degradation in cultured cells. Stabilisation occurred in post-mitotic cells via an endogenous degradation signal situated at the Ncaph2 C-terminus, which normally undergoes activation upon cell cycle exit, but is inactivated by C-terminal tag fusion. Our results highlight the underappreciated importance of cell and tissue environment in determining the consequences of tag fusions on protein expression, which may be particularly important in animal models that contain diverse cell types.

molecular biology↗

bioRxiv2025

Pathway Sculptor for Compact and Versatile Combinatorial Genetic Perturbation

The Transforming Growth Factor beta (TGF-{beta}) superfamily, like other biological pathways, relies on families of co-expressed, partially redundant protein components, such as receptor subunits. The inability to systematically modulate multi-gene component expression profiles has made it difficult to understand how components and sets of components collectively process information. To overcome this, we developed Pathway Sculptor, a dCas12a-based epigenetic editing system that achieves simultaneous same-cell knockdown of at least twelve target genes. Programming TGF-{beta} receptor profiles, by knocking down different receptor subsets, revealed functional interactions between the canonical BMP and TGF-{beta} pathway branches. Unexpectedly, signaling within each branch depended on receptors in the opposite branch. Further, different receptor subsets played distinct roles: ACVR-class receptors modulated signaling magnitude, whereas BMPRs and TGFBRs discriminated among ligand variants. These results show how the two branches of the TGF-{beta} superfamily collaboratively process signals, and establish Pathway Sculptor as a general platform for high-order combinatorial perturbation.

synthetic biology↗

bioRxiv2025

Stromal SRD5A2 promotes prostate growth through WNT5A-LEF1-IGF1 signaling in benign prostatic hyperplasia

Steroid 5-reductase type 2 (SRD5A2) is a key enzyme in androgen metabolism and a pharmacologic target in benign prostatic hyperplasia (BPH). While SRD5A2 is known to mediate stromal-epithelial interactions that influence prostate growth, the relationship between baseline SRD5A2 expression and prostate volume remains unclear. In this study, we analyzed SRD5A2 expression in human prostate tissues from the Medical Therapy of Prostatic Symptoms (MTOPS) trial and institutional biorepository cohorts. Quantitative assessments were performed and correlations were evaluated between expression level of SRD5A2, WNT5A, prostate volume, and tissue signaling profiles. SRD5A2 expression was significantly associated with total prostate and transition zone volume in both human cohorts. Stromal-specific WNT5A expression showed a strong positive correlation with SRD5A2, while neither serum nor tissue dihydrotestosterone levels correlated with SRD5A2 expression. In Srd5a2-null mice, Wnt5a expression in the prostate stroma was dependent on Srd5a2 and showed region-specific regulation. Mechanistically, SRD5A2 overexpression in human prostate stromal cells upregulated WNT5A and Lymphoid Enhancer-Binding Factor 1 (LEF1), activated insulin-like growth factor 1 (IGF1) signaling, increased proliferation, and reduced apoptosis. Conditioned media from these cells enhanced epithelial proliferation through paracrine IGF1 activity, independent of epithelial WNT signaling. This study provides the first evidence that SRD5A2 promotes prostate growth through a stromal WNT5A-LEF1-IGF1 paracrine signaling axis, functioning independently of androgen levels. These findings suggest a novel therapeutic mechanism relevant for BPH patients with resistance to conventional 5-reductase inhibitor therapy.

pathology↗

bioRxiv2025

Development of Surface Enhanced Raman Spectra coupled with Machine Learning Analysis for Differentiation of Closely Related Species within Enterobacter cloacae Complex

BackgroundEnterobacter cloacae complex (ECC) is an important nosocomial pathogen and consists of multiple similar species. The taxonomy of ECC has been consecutively updated, adding to its identification difficulty. MethodsA total of 92 ECC strains isolated from bloodstream infections during 2015-2020 were collected from a tertiary hospital in China. All the strains were identified by Vitek 2 Compact and Vitek MS and then subjected to whole genome sequencing (WGS) for average nucleotide identity (ANI) analysis. Surfaced-enhanced Raman spectroscopy (SERS) with a set of machine learning algorithms was applied in identifying species within ECC. ResultsSeven species were identified through ANI, including 28 E. hormaechei subsp. steigerwaltii, 17 E. hormaechei subsp. xiangfangensis, 12 E. cloacae, 11 each of E. hormaechei subsp. hoffmannii and E. bugandensis, seven E. kobei and six E. roggenkampii. The Vitek 2 compact indistinguishably identified all the strains as ECC and Vitek MS correctly identified one strain of E. kobei while achieving ambiguous results for all the other isolates. SERS combined with XGBoost model achieved 97.75% accuracy with an area under the ROC curve value of 0.9982 in the identification of ECC. ConclusionSERS coupled with machine learning algorithms holds a promising potential to acquire early prediction of ECC, outperforming the capabilities of other methods.

microbiology↗

bioRxiv2025

Engineered protein circuits for cancer therapy

A central challenge in cancer therapy is selectively killing cancer cells while minimizing resistance. Here, we engineer modular protease-based protein circuits that sense mutant RAS, the most frequently mutated oncogene in cancer, and conditionally activate cell death. Delivered transiently as mRNA in lipid nanoparticles (LNPs), circuits selectively eliminated RAS-mutant cancer cells in culture and suppressed aggressive, multifocal RAS-driven liver tumors. Compared to RAS inhibitors, circuits killed cancer cells independently of oncogene addiction, achieved potent cytotoxicity rather than cytostasis, and functioned at low RAS occupancy through catalysis rather than stoichiometric inhibition. Critically, circuits acquired minimal to no resistance under prolonged selection, while remaining potent against prevalent drug-resistance mechanisms including RAS amplification and bypass signaling. Together, these results establish design principles for engineering therapeutic protein circuits and highlight their potential to overcome longstanding limitations of existing therapeutic modalities.

synthetic biology↗

bioRxiv2025

Behavior decoding delineates seizure microfeatures and associated sudden death risks in mice

Behavior and motor manifestations are distinctive yet often overlooked features of epileptic seizures. Seizures can result in transient disruptions in motor control, often organized into specific behavioral patterns that can inform seizure types, onset zones, and outcomes. However, refined analysis of behaviors in epilepsy remains challenging in both clinical and preclinical settings. Current manual video inspection approaches are subjective, time-consuming, and often focus on gross and ambiguous descriptions of seizure behaviors, overlooking much of the intricate behavioral dynamics and action kinematics. Here, we utilized two machine learning-aided tools, DeepLabCut (DLC) and Behavior Segmentation of Open Field in DLC (B-SOiD), to decode previously underexplored behavior and action domains of epilepsy. We identified 63 interpretable behavior groups during seizures in a population of 32 genetically diverse mouse strains. Analysis of these behavior groups demonstrates significant differential behavior expression and complexity that can delineate distinct seizure states, unravel intrinsic seizure progression over time, and inform mouse strain backgrounds and genotypes. We also identified seizure behavior patterns and action/subaction kinematics that determine the risks of sudden unexpected death in epilepsy (SUDEP). These findings underscore the significant potential for translation into inpatient settings for video analysis in epilepsy monitoring units and outpatient settings via home surveillance devices and smartphones. One Sentence SummaryAI-aided behavior decoding delineates intricate seizure microfeatures in mice.

neuroscience↗

bioRxiv2024

Neural correlates differ between crystallized and fluid intelligence in adolescents

Fluid and crystallized intelligence are acknowledged as distinct facets of cognitive ability during brain development, but the specific neural substrates and molecular mechanisms underlying them remain unclear. This study used a sample comprising 7471 young adolescents (mean age 9.87 {+/-} 0.62 years) from the ABCD cohort to elucidate the differential neural correlates of fluid and crystallized intelligence. Our findings indicated that micro-level brain MRI phenotypes such as water diffusivity were closely associated with fluid intelligence, whereas macro-level brain MRI phenotypes such as gray matter cortical thickness were indicative of crystallized intelligence. We further investigated the molecular mechanisms underlying fluid and crystallized intelligence by correlating the characteristic MRI markers with spatial transcriptome profiles and PET imaging. Results showed that fluid intelligence had significant associations with serotonin and glutamate system, while crystallized intelligence was related to serotonin, dopamine and acetylcholine system. Furthermore, we examined the impacts of lifestyle factors on these two forms of intelligence and how the molecular pathways mediated these impacts. Our investigation suggested that physical activities, screen use and sleep duration influenced fluid intelligence mainly through mGlu5 receptors and crystallized intelligence through 5HT1a and D2 receptors. In conclusion, these findings illustrated a distinct neural basis between fluid and crystallized intelligence from the perspectives of neuroimaging, neurotransmitters, and lifestyles in young adolescents.

neuroscience↗

bioRxiv2024

Complex genetic variation in nearly complete human genomes

Diverse sets of complete human genomes are required to construct a pangenome reference and to understand the extent of complex structural variation. Here, we sequence 65 diverse human genomes and build 130 haplotype-resolved assemblies (130 Mbp median continuity), closing 92% of all previous assembly gaps1,2 and reaching telomere-to-telomere (T2T) status for 39% of the chromosomes. We highlight complete sequence continuity of complex loci, including the major histocompatibility complex (MHC), SMN1/SMN2, NBPF8, and AMY1/AMY2, and fully resolve 1,852 complex structural variants (SVs). In addition, we completely assemble and validate 1,246 human centromeres. We find up to 30-fold variation in -satellite high-order repeat (HOR) array length and characterize the pattern of mobile element insertions into -satellite HOR arrays. While most centromeres predict a single site of kinetochore attachment, epigenetic analysis suggests the presence of two hypomethylated regions for 7% of centromeres. Combining our data with the draft pangenome reference1 significantly enhances genotyping accuracy from short-read data, enabling whole-genome inference3 to a median quality value (QV) of 45. Using this approach, 26,115 SVs per sample are detected, substantially increasing the number of SVs now amenable to downstream disease association studies.

genomics↗