Biology subjects

Zhang, H.

Publications and source records attributed to Zhang, H..

At least 145 records · Page 8Linked to original sources

bioRxiv2026

Distinct HPO axis responses and ovarian aging trajectories to chronic unpredictable mild stress in reproductively young versus middle-aged female mice

Psychosocial stressors are key contributors to ovarian functional decline. Chronic unpredictable mild stress (CUMS) is widely used to model stress-induced premature ovarian insufficiency (POI) in mice; however, current animal models do not adequately reflect middle-aged women, who represent a key population exposed to chronic psychosocial stress, nor do they capture the dynamic progression toward POI. Here, female C57BL/6 mice aged 2 or 6 months were subjected to CUMS for 8 or 12 weeks. Estrous cyclicity, endocrine profiles, ovarian histology, and transcriptomic changes in HPO axis-related tissues were systematically analyzed. After 8 weeks of exposure, 2-month-old mice exhibited impaired pituitary responsiveness to estradiol negative feedback, as evidenced by dysregulated FSH secretion, indicating reduced stress tolerance compared with 6-month-old mice. Following 12 weeks of CUMS exposure, both age groups showed significant reductions in ovarian size and follicle numbers across all developmental stages. These findings demonstrate that CUMS induces an age-dependent progression toward POI, with short-term exposure eliciting compensatory phases preceding overt ovarian insufficiency, accompanied by distinct endocrine and reproductive alterations and differential responsiveness of the HPO axis. Transcriptomic analyses revealed age-dependent stress responses: ovaries of 2-month-old mice displayed marked activation of inflammatory and immune-related pathways, whereas 6-month-old mice showed sustained upregulation of protein kinase-related signaling networks. Notably, the 6-month-old CUMS model more closely recapitulates stress-associated reproductive aging in adult women. In briefCUMS has been widely used to establish mouse models of psychosocial stress-induced POI. However, current animal models do not adequately reflect middle-aged women, who represent a key population exposed to chronic psychosocial stress, nor do they capture the dynamic progression toward premature ovarian insufficiency (POI). In this study, we demonstrate that different durations of CUMS exposure induce distinct stages of ovarian dysfunction in both young and middle-aged mice, with short-term exposure driving age-dependent compensatory phases and prolonged exposure leading to overt POI, both accompanied by divergent endocrine and reproductive alterations, alongside age-dependent changes in HPO axis responsiveness to CUMS. Notably, the 6-month-old CUMS model shows greater clinical relevance in recapitulating chronic psychosocial stress and stress-related reproductive aging in adult women.

physiology↗

bioRxiv2026

Inhibition of CKAMP44 attenuated seizure activity via protein phosphatase 3 regulatory subunit B-mediated GluA1 phosphorylation and synaptic transmission

Temporal lobe epilepsy (TLE) is a complex neurological disorder characterized by spontaneous recurrent seizures and its underlying mechanism remains elusive. This study aimed to investigate the role of cystine-knot AMPAR modulating protein 44 (CKAMP44) in the pathological process of TLE and its potential as a therapeutic target using kainic acid (KA)-induced epilepsy mouse model of TLE. Our results showed that CKAMP44 protein and mRNA expression was significantly increased and primarily localized to neurons during the chronic phase of TLE. Nkx2-1 regulated the transcription of CKAMP44 in the hippocampus brain tissues of KA-induced TLE mice. Inhibition of CKAMP44 suppressed seizure susceptibility and severity in the KA-induced epilepsy mice via behavioral and local field potential monitoring. Furthermore, inhibition of CKAMP44 decreased frequency and amplitudes of spontaneous excitatory postsynaptic currents indicating that the excitatory synaptic transmission was reduced in an in vitro epilepsy model. Mechanistically, inhibition of CKAMP44 specifically upregulated the membrane surface expression of GluA1 and the phosphorylation level of GluA1-ser831 by downregulating protein phosphatase 3 regulatory subunit B(PPP3r2) expression. Overexpression of PPP3r2 downregulated the phosphorylation level and surface expression of GluA1, which ultimately exacerbated the seizure activity suppressed by CKAMP44 knockdown. Collectively, our results indicate that CKAMP44 may be a potential therapeutic target for the treatment of TLE.

pathology↗

bioRxiv2026

OneGenomeRice (OGR): A Genomic Foundation Model for Rice

The transition of genomics to a predictive intelligence discipline is driven by the advent of genomic foundation models. While substantial progress has been observed in all-life and human-centric models, plant species, particularly for the staple crops, remains hindered by a lack of models. Here we introduce OneGenome-Rice (OGR), a rice (Oryza sativa) genomic foundation model pre-trained on a genomic dataset comprising 422 high-quality genomes of cultivated and wild rice. OGR is engineered upon a Mixture of Experts (MoE) transformer architecture with 1.25-billion parameters and supports an ultra-long context window of up to 1 million base (Mb) pairs at single-nucleotide resolution. A comprehensive benchmark demonstrated that OGR significantly outperforms existing state-of-the-art multi-plant or all-life genome models in 11 categories (e.g. motif identification, sweep detection, etc). We further demonstrated the utility of OGR in several downstream applications, such as indica-japonica subspecies introgression analysis, identification of agronomy trait-associated functional loci and prediction of gene expression from DNA sequences. These results establish OGR as a promising foundational computational infrastructure for rice functional genomics and precision breeding. The OGR and its fine-tuned models, including pretrained weights, training code and the rice genomic benchmark suit, have been fully opened.

genomics↗

bioRxiv2026

Dual Pathways of Extracellular ATP Action in Cancer Cells: Purinergic Signaling Driven Senescence and Macropinocytic ATP Internalization

BackgroundOpportunistic nutrient uptake is a hallmark of cancer metabolism. Cancer cells upregulate macropinocytosis to acquire extracellular nutrients to support growth and stress adaptation. We previously showed that extracellular ATP (eATP) is internalized by macropinocytosis and promotes multiple cancer phenotypes. Here, we tested whether eATP uptake is prevalent across cancers and whether eATP also induces senescence through purinergic receptor (PR) signaling. MethodsIntracellular ATP (iATP) levels were measured following eATP exposure across multiple cancer cell lines. eATP internalization was visualized in vitro and in vivo using a non-hydrolyzable fluorescent ATP analog together with high-molecular-weight dextran as a macropinocytosis marker. Senescence was quantified using three SA-{beta}-galactosidase assays and flow cytometry. Pharmacologic inhibitors of macropinocytosis and purinergic receptors were used to define pathway dependence. Combination treatments with the glucose transporter inhibitor DRB18 and the senolytic navitoclax were evaluated for antiproliferative effects. ResultseATP produced dose- and time-dependent increases in iATP across diverse cancer cell types. Imaging demonstrated widespread macropinocytic internalization of ATP in vitro and in tumor xenografts. eATP induced senescence in NSCLC cells, confirmed by multiple {beta}-gal assays and flow cytometry. PR inhibition significantly reduced senescence, whereas macropinocytosis inhibition had minimal effect on senescence induction. ConclusionseATP acts through dual pathways in cancer cells: macropinocytic internalization that elevates iATP and PR signaling that drives senescence. Targeting metabolic uptake together with senolytic therapy may offer a novel anticancer strategy.

cancer biology↗

bioRxiv2026

BioEngine: scalable execution and adaptation of bioimage AI through agent-readable interfaces

Foundation models and curated repositories have transformed bioimage AI, yet most biologists cannot readily run, adapt, or extend them on available hardware. BioEngine lls this gap as the execution and adaptation layer between curated AI and scalable compute, deployable on a laptop, workstation, or cluster. Scientists then screen models, ne-tune from the browser, enable real-time smart microscopy, and deploy analysis applications, all by describing their goal to an AI agent.

bioinformatics↗

bioRxiv2026

Genomic diversification underlies the broad ecological range of Salmonella enterica serotype Typhimurium

Salmonella Typhimurium is a versatile foodborne pathogen with a broad ecological range, making it an ideal model to better understand pathogen adaptations that allow them to infect multiple hosts and persist across environments. Here, we analyzed 595 genomes of S. Typhimurium representing three food animal sources (bovine, swine, and poultry) and one non-food animal source (wild birds). We found that S. Typhimurium from food animal sources generally had a more open pangenome and harbored more antimicrobial resistance genes (ARGs). Notably, swine isolates exhibited the most open pangenome and the highest prevalence of ARGs, patterns that were associated with a greater presence of mobile genetic elements, particularly plasmids. Despite similar core genome sizes, S. Typhimurium from different sources displayed distinct patterns of positive selection in the core genome that varied in frequency and targeted functional categories. In contrast, although accessory genome sizes varied substantially across sources, the frequency of positive selection remained similar. Using machine learning, we identified source-predictive genetic variants, many of which are associated with stress-response functions. These findings suggest that gain and loss of accessory genes and positive selection acting on core genes support differential adaptation in S. Typhimurium, potentially contributing to its broad ecological range.

genomics↗

bioRxiv2026

Single-crypt evolutionary trajectories of human sporadic and hereditary colorectal precancers

Our understanding of the earliest genomic processes and evolutionary trajectories that distinguish sporadic from hereditary human cancers remains limited, impeding early diagnosis and risk stratification. Here, we performed whole-genome sequencing of 319 single crypts dissected from normal colorectal mucosa, premalignant polyps and malignant colorectal cancers (CRCs) from five sporadic patients and three familial adenomatous polyposis (FAP) patients. We found 66% of premalignant crypts from sporadic polyps did not harbor APC mutations, but frequently accumulated non-canonical but positively selected driver mutations including KMT2C, CACNA1A and FBLN2. In contrast, most premalignant crypts (71%) in FAP indeed acquired one or more somatic APC mutations or 5q loss of heterogeneity (LOH). Sporadic polyps were dominated by clock-like mutational processes at single-crypt level. In contrast, FAP crypts evolved more diverse genomics processes, including reactive oxygen species-associated damages, APOBEC-associated mutagenesis and pks+ Escherichia coli-associated mutagenesis. Premalignant crypts in FAP also exhibited a 2-fold higher burden of LINE-1 retrotranspositions than sporadic ones, despite arising at a substantially younger age. Phylogenetic timing estimations showed that the first somatic APC mutation in FAP often occurs prenatally. Phylogeographic analysis further revealed spatial lineage segregation in early polyclonal polyps, followed by Big-Bang lineage intermixing in later monoclonal ones. Together, these data demonstrate that early colorectal tumorigenesis does not obey a universal APC-first model, where sporadic and hereditary precancers follow distinct evolutionary trajectories. Our study also highlights that polyclonal-to-monoclonal transition in premalignant stages represents the Big-Bang growth of colorectal tumors, offering crucial insight into early diagnosis and risk stratification.

cancer biology↗

bioRxiv2026

Protenix-v2: Broadening the Reach of Structure Prediction and Biomolecular Design

Advances in biomolecular modeling have broadened the range of problems addressable by structure prediction and design models. Here, we present results from Protenix-v2, a system spanning high-accuracy structure prediction and biomolecular design. On the structure prediction side, Protenix-v2 achieves antibody-antigen success rates with up to 13-point gains over Protenix-v1, while 5-seed performance surpasses previous 1000-seed results. On the design side, Protenix-v2 demonstrates a 100% target-level success rate in novelty-controlled VHH-Fc campaigns, reaching hit rates up to 48%. Crucially, the model enables hit discovery on difficult GPCR targets with hit rates of 16%-88% (VHH-Fc) and up to 50% (mAb) under 16-30 testing budgets per target. Resulting hits show high developability and diversity. Beyond antibody tasks, we report improved ligand-related plausibility and successful cross-variant SARS-CoV-2 spike RBD mini-binder design. These results establish Protenix-v2 as a robust and powerful model for accelerated drug discovery.

molecular biology↗

bioRxiv2026

Connectome-based spatial statistics enabling large-scale population analyses of human connectome across cohorts

Large-scale population analyses of structural connectome organization remain challenging because of cross-subject alignment, pathway interpretability and computational burden. No widely adopted standard exists for systematic evaluation across processing methods. We developed connectome-based spatial statistics (CBSS), a scalable framework for anatomically aligned and functionally informed quantification of white-matter microstructure that yields atlas-defined pathways organized into 13 functional networks. Using data from 56,510 UK Biobank participants together with five independent lifespan cohorts, we evaluated the streamline-, voxel- and network-level measures in the aspects of reliability, heritability, structure-function coupling, cognitive and behavioral prediction, brain aging patterns and lifespan trajectories across cohorts. The systematic evaluation workflow compares population-level white-matter representations across methods, spatial scales, tasks and datasets. The results support CBSS as a common connectome reference for large-scale, cross-cohort diffusion MRI studies.

neuroscience↗

bioRxiv2026

Spectral Graph Features for Reference-free RNA 3D Quality Assessment

MotivationExisting RNA 3D structure quality assessment (QA) methods rely on local geometric descriptors or statistical potentials that evaluate atomic-level contacts but are blind to global topological coherence. This creates a critical failure mode--structures that are "locally correct but globally wrong"--where well-formed local helices mask misplaced domains and incorrect overall packing. ResultsWe introduce SpecRNA-QA, a lightweight RNA QA method based on multi-scale graph-Laplacian features of inter-nucleotide contact networks. In CASP16 leave-one-out cross-validation, it achieves median per-target Spearman{rho} = 0.69 (target-clustered bootstrap 95% CI [0.64, 0.73]) versus 0.47 for an internal geometry baseline--a +0.22 gap that is significant at p = 1.2 x 10-10 (one-sided Wilcoxon signed-rank) and reflects a per-target win rate of 93%. The gain is concentrated on large, multi-domain RNAs, where global coherence is poorly captured by local descriptors. In a contextual comparison with established statistical potentials, local energy-based scores remain strongest on compact RNAs, while SpecRNA-QA yields the strongest signal we observed on targets longer than 200 nt; within the single-threaded runtime budget used here, the strongest local-energy comparator, rsRNASP, timed out on 22 of 26 large targets, and we report an explicit paired head-to-head on the four commonly scored targets in Section 4.2. A training-free heuristic variant further shows that the spectral prior carries intrinsic quality information even in the absence of labeled QA data. AvailabilitySpecRNA-QA is available as a Python package at https://github.com/yudabitrends/specrnaq. Contactybi3@gsu.edu Supplementary informationSupplementary data are available online. Key PointsO_LISpecRNA-QA uses multi-scale graph-Laplacian spectra to score global RNA fold coherence that local geometric descriptors and local statistical potentials can miss. C_LIO_LIThe method uncovers a size-dependent division of labor: on compact RNAs that can be scored exhaustively, atom-level statistical potentials such as rsRNASP remain strongest, whereas on >200 nt RNAs--where the strongest local comparator times out on most targets under the single-threaded runtime budget used here--SpecRNA-QA provides the strongest signal we observed. C_LIO_LIHeat-kernel traces at intermediate diffusion times emerge as the most discriminative spectral features and form an interpretable bridge between local packing and long-range tertiary organization. C_LIO_LIA training-free heuristic variant of SpecRNA-QA retains informative spectral signal without any labeled QA data, supporting the interpretation of the learned model as amplifying a real structural signal rather than overfitting one. C_LI

bioinformatics↗

bioRxiv2026

Dimerisation and twist reversal of the Lewy fold in α-synuclein mutants with Parkinson's disease and dementia

Dominantly inherited missense and gene dosage mutations in SNCA, the -synuclein gene, cause familial forms of Parkinsons disease and dementia. Here we report the structures of -synuclein filaments from the brains of such individuals. Pathogenic mutations A53T and G51D in SNCA give rise to singlets and doublets of the Lewy fold with a left-handed helical twist in the absence of a peptide-like density for island A. By contrast, filaments from the non-pathogenic variant H50Q consist of singlets of the right-handed Lewy fold with a density for island A, like filaments of wild-type -synuclein. The structures of filaments from homozygous mice transgenic for human mutant A53T -synuclein (line M83) are unlike those from human brains. They are more similar to the multiple system atrophy folds than to the Lewy fold of Parkinsons disease, Parkinsons disease dementia and dementia with Lewy bodies

biophysics↗

bioRxiv2026

Volumetric Scattering Microscopy

Optical scattering in biological tissues fundamentally limits fluorescence imaging by disrupting spatial and angular information, thereby restricting volumetric visualization. Although hardware-intensive and computational approaches have advanced scattering microscopy, practical three-dimensional imaging through tissue remains constrained by instrumental complexity and axial ambiguity. Here, we present volumetric scattering microscopy (VSM), a scan-free, optical-computational framework for three-dimensional fluorescence imaging in scattering biological media. VSM captures angularly resolved speckle-encoded fluorescence using an aperture-segmented Fourier light-field configuration and reconstructs volumetric structure through adaptive feature-based descattering and joint sub-pupil alignment. This hybrid strategy preserves angular information embedded in scattered light without wavefront measurement or mechanical scanning, while maintaining the simplicity of a standard epi-fluorescence architecture. We demonstrate high-fidelity volumetric reconstruction across phantoms, engineered cellular systems, ex vivo tissues with volumetric muscle loss, and intact Xenopus embryos, achieving preserved spatial resolution, enhanced optical sectioning, and quantitative accuracy under strong scattering conditions. VSM supports large-field, robust volumetric imaging in both layered and fully embedded scattering environments. By transforming scattered light into a structured encoding resource, VSM establishes a scalable pathway toward routine three-dimensional fluorescence imaging in complex biological systems.

bioengineering↗

bioRxiv2026

Deep Learning Reveals Persistent Individual Signatures in Bat Echolocation Calls of the Greater Leaf-nosed Bat

Intraspecific variation is a prerequisite for natural selection and can manifest in various phenotypic traits, including vocal signals. However, classifying individuals based on their vocalizations, or acoustic individual identification (AIID), remains a significant challenge. This is particularly true for species that use rapidly varying echolocation calls for orientation. Here, we demonstrate that deep learning can overcome the limitation of traditional methods and reveal persistent individual signatures within bat echolocation calls. We recorded echolocation calls from 34 individuals of the greater leaf-nosed bat (Hipposideros armiger) under controlled laboratory conditions, with 19 individuals recorded repeatedly over three months. We show that a convolutional neural network (CNN) dramatically outperforms a traditional method, achieving an average identification accuracy of 84% for single calls and 91% for call sequences. In contrast, the traditional Discriminant Functional Analysis method achieved accuracies of only 39% and 47%, respectively. Through systematically altering the temporal structure of echolocation calls in input sequences, we found that temporal patterning enhances individual classification accuracy, suggesting it contributes to the encoding of individual-specific information. This study revealed that echolocation calls of H. armiger can contain stable, individual identity that were previously undetectable. Our findings highlight the potential of deep learning for non-invasive AIID and provide a methodological basis for future studies aiming to monitor animals in more dynamic environments.

zoology↗

bioRxiv2026

Mechanical confinement drives monocyte-to-macrophage differentiation

Cells in vivo experience mechanically diverse microenvironments in which physical confinement is a pervasive but poorly understood regulator of their behavior and fate. Whether and how mechanical confinement governs immune cell differentiation remains unknown. Here, we reveal that a mechanical cue -- long-term confinement is sufficient to drive monocyte-to-macrophage differentiation through a mechanoepigenetic pathway. In vivo, differentiating monocytes exhibited flattened nuclei in the liver capsule, indicative of confinement by surrounding stromal and parenchymal structures. Using a custom cell confiner to recapitulate this confined niche, we found that confinement induces macrophage-like protrusive architectures, enhances motility, and upregulates macrophage-associated genes in RAW264.7 and THP-1 monocyte-lineage cells. Notably, extending this paradigm to primary murine bone-marrow, human umbilical-cord, and tissue-derived hepatic-associated monocytes yielded similar outcomes, thus enhancing phagocytic capacity, directly demonstrating that mechanical confinement can program monocytes into macrophages. Mechanistically, we found that confinement activates KDM6B, leading to H3K27me3 demethylation, which derepresses macrophage-specific transcriptional programs. Pharmacological inhibition of KDM6B with GSK-J4 restored H3K27me3 and blocked macrophage differentiation both in vitro and in vivo. These findings define a KDM6B-H3K27me3 axis that links nuclear mechanics to transcriptional reprogramming, positioning mechanical confinement as a "super-enhancer-like" cue for engineer macrophage function in therapeutic and bioengineering contexts.

cell biology↗

bioRxiv2026

LATTE for locus-specific quantification of transposable element expression across species

Transposable elements (TEs) are pivotal drivers of eukaryotic genome evolution and phenotypic diversity. However, their functional contributions to complex traits remain largely obscured by expression quantification challenges arising from high sequence homology and multi-mapping ambiguities. Here, we present LATTE, an efficient computational framework for defining and quantifying TE expression at locus-specific resolution by leveraging an innovative multi-indicator Expectation-Maximization (EM) algorithm. Extensive benchmarking against simulated datasets demonstrated that LATTE significantly outperformed existing state-of-the-art tools, achieving an accuracy of 0.998 at the subfamily level and 0.839 at the locus-specific level. Applying LATTE to 813 RNA-seq datasets across humans, cattle, and chickens, we quantified expression profiles of 2,703 TEs, followed by TE-expression quantitative trait loci (TE-eQTL) mapping. The colocalization rates between TE-eQTL and host gene-eQTL was low, revealing a distinct regulatory landscape of TE expression. This decoupled correlation between TEs and host genes are likely mediated by the differential expression of alternative transcripts. Through integrated TE-eQTL and genome-wide association studies on 3,746 complex traits across three species, we demonstrated that TEs constitute 204 (8.7%) additional associations with complex traits beyond gene-eQTL. More specifically, the Sjogrens syndrome-associated variant rs10954213 acts as a TE-eQTL that shifts the splicing landscape of IRF5, upregulating TE-containing transcripts while simultaneously suppressing canonical ones. Collectively, LATTE provides an efficient framework for studying TE expression across species, and our findings highlight the key role of TEs in understanding the genetic architecture of complex phenotypes.

bioinformatics↗

bioRxiv2026

eBiota: Designing microbial communities from large seed pools with desired function using rapid optimization and deep learning

Designing microbial communities to generate target products is crucial for biotechnology, agriculture, and disease treatment. However, rationally designing such communities from large seed pools has become a major challenge, as the rapidly expanding number of complete microbial genomes greatly expands the search space and sharply increases the required screening time and computational cost. Here, we introduce eBiota, a platform for ab initio design of microbial communities from a pool of 21,514 strains to generate target products. eBiota not only identifies optimal strain combinations but also simulates community behaviors, including microbial interactions and relative abundances. eBiota integrates three modules: CoreBFS, a graph-based search algorithm that rapidly screens for bacteria with complete metabolic pathways related to the target product; ProdFBA, an extended flux balance analysis that identifies microbial consortia with maximal production efficiency; and DeepCooc, a deep learning model trained on 23,323 microbiome samples across various environments to infer co-occurrence patterns. We validated eBiotas capabilities in microbial community design and production efficiency calculation using public microbiome datasets, ranging from single strains to six-member consortia. Further in vitro experiments involving 94 strains confirmed eBiotas ability to identify species that inhibit pathogen growth and to accurately model the relative abundances within complex microbial communities. As an initial digital twin, eBiota provides a powerful platform for the rational design of functional microbial communities, offering new opportunities for metabolic engineering and synthetic biology.

bioengineering↗

bioRxiv2026

KuafuPrimer: Machine learning empowers the design of 16S amplicon sequencing primers toward minimal bias for bacterial communities

Amplicon sequencing protocol targeting the 16S rRNA gene is a widely used and cost-effective method for exploring bacterial communities. However, its performance is often limited by primer bias arising from the arbitrary use of universal primers across diverse microbial communities and habitats. We propose KuafuPrimer to design the optimal 16S rRNA gene primers toward minimal bias for targeted bacterial communities, using few-shot machine learning to guide the primer design procedure based on a small number of samples. Simulations on 809 samples across 26 representative environments and habitats showed that KuafuPrimer-designed primers outperformed the universal primers in taxonomic accuracy, achieving an averaged 16.31% relative reduction in primer bias, with reductions up to 46.08% in plant samples. Notably, KuafuPrimer detected 29 rare and key taxa undetectable by the universal primers. Validation with 317 longitudinal gut microbiota samples demonstrated that KuafuPrimer-designed primers consistently outperformed the universal primers across temporal, individual, and cohort levels, with relative bias reductions of 5.03%, 3.53%, and 3.10%, respectively. Finally, in real PCR experiments on human gut samples from Clostridioides difficile-infected and healthy groups showed that polymerase chain reaction products using KuafuPrimer-designed primers correlated better with metagenomic data compared to the universal primers. More importantly, KuafuPrimer successfully detected Clostridioides difficile, the key pathogen missed by the universal primers, highlighting its potential for improving clinical diagnostics. In summary, KuafuPrimer provides a machine learning-based primer design strategy for targeted bacterial communities, with demonstrated utility in large-scale microbiome initiatives, longitudinal surveys and clinical diagnostics.

bioinformatics↗

bioRxiv2026

Single-cell full-length transcriptome of human lung reveals genetic effects on isoform regulation beyond gene-level expression

Genetic regulation of splicing uniquely contributes to trait-associated genome-wide association studies (GWAS) signals. However, quantitative trait loci (QTL) analysis using short-read sequencing of bulk tissues fails to capture full-length and cell-type-specific isoforms. Here, we present an isoform-level lung cell atlas from 129 never-smoking Korean women using single-cell long-read RNA-sequencing, identifying abundant unannotated and cell-type-specific isoforms. Isoform-level signatures of 37 lung cell types display a larger difference and therefore improve cell-type classification compared to gene-level expression. Notably, isoform-QTLs (isoQTLs) detect unannotated and/or cell-type-specific isoforms with independent genetic regulation from expression-QTL (eQTL), supported by enriched splicing functional elements. IsoQTLs nominate susceptibility isoforms from previously unexplained lung function and cancer GWAS loci, via eQTL-independent signals. We highlight a potentially functional novel variant of PPIL6 in multiciliated cells underlying lung cancer risk through alternative splicing. This isoform-level resource advances our understanding of cell-type-specific isoform regulation and its contribution to lung traits and diseases.

genetics↗