Biology subjects

Liu, X.

Publications and source records attributed to Liu, X..

At least 415 records · Page 23Linked to original sources

bioRxiv2025

A machine-learning-guided hydrogen-bonded organic framework for long-term, ultrasound-triggered pain therapy

Effective treatment of chronic pain remains hindered by the lack of drug delivery systems that simultaneously achieve long-term stability, high spatial precision, and non-invasiveness1,2,3. Here, we utilize a programmable, ultrasound-responsive drug delivery platform based on hydrogen-bonded organic framework (HOF) nanoparticles, enabling on-demand anesthetic release with long-term and durable analgesic efficacy. A machine learning (ML)-guided screening pipeline was developed to evaluate about 250 FDA-approved drugs, spanning both hydrophilic and lipophilic agents, and identified bupivacaine (lipophilic) and lidocaine hydrochloride (hydrophilic) as optimal candidates. These agents were efficiently encapsulated into HOF nanoparticles via diffusion and double-solvent methods. Ultrasound-triggered drug release in vitro transiently suppressed calcium signaling in adeno-associated virus (AAV)-transfected neurons. In vivo, ultrasound-activated release of bupivacaine or lidocaine HCl at the sciatic nerve site significantly elevated mechanical nociceptive thresholds for up to seven days, reduced autotomy (self-mutilaion) behavior, and improved motor function in a rat model of chronic pain. Notably, the machine learning-identified top candidates not only exhibited high loading efficiency, but also demonstrated superior therapeutic outcomes in vivo, establishing a direct link between computational prediction and biological efficacy. This non-invasive, programmable HOF-based system provides a clinically translatable platform for on-demand, spatiotemporally precise pain management.

bioengineering↗

bioRxiv2025

Development of a genetically encoded melanocortin sensor for high sensitivity intravital imaging

The central melanocortin system, composed of peptides derived from pro-opiomelanocortin (POMC) such as the melanocyte-stimulating hormones (-, {beta}-, {gamma}-MSH) and melanocortin 4 receptors (MC4R), along with the agouti-related protein (AgRP), plays a pivotal role in controlling energy balance. To elucidate the dynamic role of -MSH release in regulating appetite, specific, sensitive, and spatiotemporally resolved genetic sensors are required. The melanocortin 1 receptor (MC1R) scaffold was leveraged for its robust plasma membrane expression, high affinity for melanocortin peptides and low affinity for AgRP to design a -MSH selective sensor for in vivo use. This was achieved by integrating circularly permuted green fluorescent protein (cpGFP) into the receptor, which we named Fluorescence Amplified Receptor sensor for Melanocortin (FLAREMC). The FLAREMC sensor shows high potency and selectivity in heterologous and homologous expressing cells for -MSH and the synthetic melanocortin agonist MTII but not to the inverse agonist AgRP. The sensor exhibited impaired signaling, with reduced G protein activation, no {beta}-arrestin coupling, and failed to internalize upon agonist stimulation. In vitro, FLAREMC displayed high photostability and reversible photoactivation. These properties suggest that the FLAREMC is suitable for long-term activity recording in the brain without desensitizing or interfering with endogenous melanocortin receptor signaling. When expressed in the paraventricular nucleus (PVN) of the mouse hypothalamus, the primary site of anorexigenic -MSH signaling, FLAREMC demonstrated its effectiveness in detecting changes associated with melanocortin responses in vivo. FLAREMC enables the study of melanocortin system in cultured cells and intravitally. This first of its class highly sensitive melanocortin sensor will serve as a valuable tool to advance our understanding of the complex dynamics governing melanocortin-dependent appetite regulation and related processes in the brain.

neuroscience↗

bioRxiv2025

Basal ganglia-independent thalamic bursts do not wake cortex during sleep

The thalamus is a key forebrain structure that gates peripheral, subcortical, and cortico-cortical communication1,2. Awake thalamic bursts provide the cortex with a "wake-up" signal2-4. Paradoxically, thalamic neurons discharge tonically during cellular depolarization and activated brain states (wakefulness, REM sleep) but burst during hyperpolarization and NREM sleep5-9. It has been proposed that NREM thalamic bursts do not awaken the cortex because of their periodic and synchronized nature2-4; however, this has never been tested in vivo during natural sleep. We simultaneously recorded polysomnographic signals, local field potentials, and spiking activity from multiple thalamic neurons in the ventral anterior and centromedian nuclei of two female non-human primates during naturally occurring vigilance states. These nuclei receive GABAergic output from the basal ganglia10,11, with discharge rate and GABA outflow decreasing during NREM sleep12. We found that despite the expected thalamic depolarization, bursting increased significantly. NREM bursts were neither periodic nor highly synchronized. However, EEG activity time-locked to burst onset during NREM sleep differed markedly from that observed during wakefulness and REM sleep. These results support a modulatory, rather than a driving, relationship between the basal ganglia and thalamus. NREM thalamic bursts do not awaken the cortex, probably due to unique state-dependent thalamocortical dynamics.

neuroscience↗

bioRxiv2025

Nuclear polysaccharides maintain H3K9me3-heterochromatin and genomic stability

Polysaccharides are known to be synthesized by enzymes in the endoplasmic reticulum and Golgi apparatus and transported through the secretory pathway to the cell surface or extracellular space1, where they mediate essential biological processes2-6. While classical localization and functions of polysaccharides are well established, their presence and potential roles in the nucleus remain unclear. Here, we demonstrate that N-glycans, a type of polysaccharides, are present in the cell nucleus and modify inner nuclear membrane (INM) proteins across diverse cell types--a modification referred to as N-linked glycosylation (N-glycosylation). N-glycosylation is enriched in chromatin regions marked by H3K9me3 and long interspersed nuclear element-1 (LINE-1) retrotransposons. N-glycosylation inhibition and INM protein N-glycosylation site mutation both downregulate H3K9me3 within lamina-associated domains (LADs) and lead to genomic instability. Mechanistically, N-glycosylation regulates the interaction between the histone H3K9 methyltransferase SETDB1 and INM proteins, promotes the association of SETDB1 with the INM, and maintains H3K9me3. Moreover, we reveal that canonical N-glycan biosynthetic machinery in ER contributes to the N-glycosylation of INM proteins. These findings uncover a previously unrecognized nuclear role for polysaccharides, broadening our understanding beyond their traditional subcellular distributions and functional profiles.

molecular biology↗

bioRxiv2025

Aging-Associated Microbiota Drives Treg Dysfunction via TNF Signaling

Aging is associated with a chronic, low-grade inflammatory state referred to as inflammaging, which contributes to impaired immune regulation and increased susceptibility to disease. While regulatory T (Treg) cells are key mediators of immune homeostasis, their role in the context of age-related inflammation remains poorly understood. Here we demonstrate that age-related changes in the microbiota promote impaired Treg cell function, resulting in the differentiation of inflammatory T cells. In agreement, we find that aged germ-free (GF) mice exhibited a more balanced immune profile, where the Treg cells are functional and pro-inflammatory mediators are reduced, suggesting that microbial exposure is essential for the establishment of inflammaging. Furthermore, we show that the use of old microbiota in young animals was sufficient to induce pro-inflammatory T cell responses and impaired mucosal Treg cell proliferation, while young microbiota restored Treg cell function in old animals. Mechanistically, we show that exposure to aged microbiota was associated with sustained TNF signaling, elevated oxidative stress, DNA damage, and increased expression of senescence markers such as {gamma}H2AX and p16 in Treg cells. These findings uncover a microbiota-TNF-dependent mechanism by which age-associated microbial dysbiosis drives Treg cell dysfunction and promotes immune aging, highlighting the therapeutic potential of microbiota-targeted strategies to restore immune homeostasis in the elderly.

immunology↗

bioRxiv2025

macpie: scalable workflow for high-throughput transcriptomic profiling

High-throughput transcriptomic profiling (HTTr) enables scalable characterisation of transcriptional responses to chemical and genetic perturbations. While plate-based technologies such as MAC-Seq, TempO-seq and PLATE-seq have made HTTr more accessible, they pose unique computational challenges in modelling data and integration across modalities. We present macpie, an R package designed to streamline the analysis of HTTr data from plate-based screens. Built on the tidySeurat framework, macpie streamlines the entire analytical pipeline from preprocessing and quality control to pathway enrichment, chemical feature extraction, and multimodal data integration. The package incorporates multiple statistical frameworks and leverages parallelisation for scalability. By leveraging Docker and Nextflow, macpie ensures reproducibility and ease of use for transcriptome-wide screening. AvailabilityThe R package macpie is freely available at https://github.com/PMCC-BioinformaticsCore/macpie, with images of the working environment hosted at Docker Hub: xliu81/macpie. A companion Nextflow pipeline for preprocessing from FASTQ files is available at https://github.com/PMCC-BioinformaticsCore/dinoflow. Contactnenad.bartonicek@petermac.org Supplementary informationPackage vignettes with the full analytical workflow available at https://pmcc-bioinformaticscore.github.io/macpie/articles/macpie.html

bioinformatics↗

bioRxiv2025

Rationally Engineered Coronavirus-mimicking Protein Nanocage Platform for Glioblastoma Targeted Delivery and Immunity Regulation

Glioblastoma (GBM) is one of the most aggressive and treatment-resistant brain tumors, owing to the dual challenges of the blood-brain barrier (BBB) and a profoundly immunosuppressive tumor microenvironment. However, few existing strategies are capable of simultaneously overcoming these two barriers, underscoring a critical scientific gap and the need for innovative therapeutic platforms that enable both effective BBB traversal and tumor immune reprogramming. Here, we present a coronavirus-mimicking protein nanocage platform (EcomPC) derived from Thermotoga maritima encapsulin, rationally engineered for the targeted and sustained delivery of interferon- (IFN-) to GBM lesions. Through strategic insertion of flexible linkers, cysteine-to-serine mutations, and modular surface functionalization via SpyTag/SpyCatcher chemistry, EcomPC enables intracellular self-assembly of IFN- and glioma-specific targeting. In orthotopic GBM models, EcomPC-IFN demonstrates efficient BBB translocation, selective tumor accumulation, and potent anti-tumor efficacy. Mechanistically, localized IFN- release induces tumor cell apoptosis and reprograms the immune microenvironment--characterized by increased CD8 T cell infiltration, decreased Foxp3 regulatory T cells, and a favorable chemokine shift. These therapeutic effects are not recapitulated by free IFN- or untargeted nanocages, underscoring the essential role of both structural mimicry and ligand-guided delivery. Collectively, this work establishes EcomPC as a programmable, virus-inspired protein delivery platform capable of overcoming key physiological barriers in brain tumor immunotherapy, and lays the foundation for its broader application in CNS-targeted biologic delivery.

bioengineering↗

bioRxiv2025

YY1 relieves p300 autoinhibition to promote histone acetylation in advanced prostate cancer, enhancing the oncogenic signaling of Androgen Receptor Splice Variant 7

Genetic and epigenetic aberrations often act in concert to establish oncogenic transcriptomic programs in aggressive cancers. For example, the development of castration-resistant prostate cancer (CRPC), an advanced prostate cancer form, is closely associated with over-expression and/or hyper-activation of transcription factors (TFs) such as Androgen Receptor (AR) and Yin Yang 1 (YY1), as well as p300, a prominent histone acetyltransferase. How exactly these cancer-related lesions are coordinated to generate a malignant cell state remains elusive. Here, we demonstrate that YY1, which is frequently over-expressed in advanced prostate cancers, allosterically stimulates the acetyltransferase activity of p300 in cis, leading to the globally elevated acetylation of histone H3 lysine 18 and 27 (H3K18ac and H3K27ac). Mechanistically, YY1s N-terminal activation domain (AD) directly interacts with p300s TAZ2 domain, relieving the autoinhibition of p300 to facilitate substrate acetylation. Our integrated genome-wide mapping and transcriptomic studies reveal significant co-localization of genomic binding sites of YY1, androgen receptor splice variant 7 (AR-V7, a constitutively active form of AR) and p300 in CRPC cells, where the YY1-mediated p300 activation and resultant histone acetylation increases promote the oncogenic gene-expression programs downstream of YY1 and AR/AR-V7. Both in vitro and in vivo functional assays demonstrate a critical requirement of the above signaling for the advanced disease progression and drug resistance seen in CRPC. Altogether, this study uncovers that YY1 acts to alleviate p300s autoinhibition at target genes co-bound by oncogenic TFs (YY1 and/or AR/AR-V7) in CRPC, thereby sustaining tumorigenicity. Additionally, the blockade of YY1-mediated gene activation resensitizes CRPC to treatment to the clinic anti-AR agent (enzalutamide), which provides a rationale for overcoming the therapeutic resistance often seen in advanced prostate cancers.

cancer biology↗

bioRxiv2025

Population genomics of the endangered scaly-foot snail defines conservation units amid deep-sea mining threats in Indian Ocean vents

Hydrothermal vents in the Indian Ocean face imminent deep-sea mining, yet lack baseline high-resolution connectivity data for their endemic fauna. We present the first population genomic study of the endangered scaly-foot snail (Chrysomallon squamiferum), an iconic species distributed across three vent biogeographic provinces. Analysing 125 individuals from eight vent fields using 14 million single-nucleotide polymorphisms, we identify five genetic groups that warrant recognition as evolutionarily significant units. Demographic modelling reveals critical contributions from extinct or unsampled phantom populations to the contemporary genetic structure. Together with physical ocean modelling, we show that the deep currents drive asymmetric south-to-north gene flows, while transform faults act as dispersal barriers. We propose Longqi-Duanqiao fields on the Southwest Indian Ridge and Wocan field on the Carlsberg Ridge as two isolated populations prioritised for protection. Given its exceptional adaptations, wide distribution, and public recognition, the scaly-foot snail is well-positioned to serve as both a flagship and umbrella species to ensure the survival of a broader suite of vent biodiversity. As the International Seabed Authority finalises its Mining Code in 2025, our findings provide essential genomic evidence to mitigate the impacts of mining on genetic diversity and inform transboundary conservation strategies for this vulnerable ecosystem.

ecology↗

bioRxiv2025

Genetic Insights of Image-Based Traits: Analysis Pipeline for AI-based Phenotyping, Combined-GWAS, and Federated Learning with Application to the Human Face

Biological images capture rich morphological information and understanding their genetic basis is crucial for elucidating underlying molecular mechanisms, relevant in biomedical, evolutionary, and forensic research and applications. However, progress in understanding the genetic basis of image-based complex traits is hindered by key limitations in the current methodology, regarding both, the degree of which the large image complexity is captured with the phenotyping methods and how the analysis methods deal with the underlying large genetic complexity. Here, we present a robust, scalable, privacy-preserving computer pipeline for analysing image-based complex traits and unveiling their genetic basis by integrating (i) AI-based phenotyping for large-scale extraction of image-based traits across cohorts in a federated learning framework, without sharing individual images; (ii) Combined-GWAS (C-GWAS) for identifying genetic variants underlying the numerous AI-derived image-based traits; and (iii) Explainable AI (XAI)-based image visualization of the identified genetic association effects. In its first application to the example of the human face, we analysed 3D facial images and genomic data from two European cohorts (N=7,309), extracted 200 AI-derived facial traits, identified 43 significantly face associated genetic loci, including 12 novel ones, and replicated 70% of them in an independent European dataset (N=8,246). XAI-based visualization of the identified genetic effects shows the involvement of many of these loci in different parts of the face. Our study provides a privacy-aware and extensible pipeline for investigating the genetic basis of image-based complex traits implemented in a computationally efficient python package available at GitLab and its first application yielded new insights into the genetic architecture of facial shape variation.

genetics↗

bioRxiv2025

Heuristic Multi-site Optimization for Protein Sequence Design using Masked Protein Language Models

Protein sequence design for tailored functional properties is a fundamental task in protein engineering, with critical applications in drug discovery and therapeutic development. Efficient navigation of the combinatorial vastness of protein sequence space to identify functional variants remains a formidable challenge. Conventional approaches, which predominantly rely on template-based local search or single-residue mutagenesis, are constrained by their susceptibility to local optima and their potential risk of destabilizing native structural stability. In this study, we introduce ProtHMSO, a heuristic multi-site optimization framework leveraging masked protein language models (ProtLMs) for context-aware sequence exploration. ProtHMSO mimics natural evolutionary mechanisms by employing ProtLM-derived substitution probabilities to guide heuristic searches for synergistic mutations, thereby constraining combinatorial search spaces through evolutionary and biophysical priors. Given a starting sequence and a predictive fitness model, ProtHMSO heuristically generates high-fitness multi-site variants under evolutionary constraints. Besides, ProtHMSO can also be as a modular plugin that enhances the convergence efficiency of genetic algorithms (GAs) and Monte Carlo tree search (MCTS) by integrating evolutionary priors into exploration strategies. Benchmark experiments demonstrate that protein sequences generated by ProtHMSO exhibit superior functional performance and closer alignment with natural sequence distribution, compared with state-of-the-art methods. These advancements highlight that ProtHMSO has strong potential and compatibility to accelerate functional protein discovery, offering a robust framework for efficient and context-aware exploration of protein sequence space. Author summaryTo address the challenge of efficiently discovering functional new proteins in protein engineering due to the vast sequence space, and to overcome the limitations of traditional evolutionary algorithms that rely on blind random mutagenesis, resulting in inefficiency and prone to structural destabilization, we proposed a heuristic multi-site optimization framework, ProtHMSO. Its core concept is to leverage the powerful contextual prediction capabilities of masked protein language models (such as ESM-2) to guide sequence mutagenesis. By predicting amino acid substitutions at specific sites that are consistent with evolutionary laws and biophysical priors, ProtHMSO narrows the exploration scope from the vast combinatorial space to a small number of high-potential candidate sequences, achieving intelligent and efficient optimization of protein sequences. Furthermore, ProtHMSO is not just a standalone algorithm, but also a plug-and-play "enhancement module". By embedding it into a genetic algorithm (GA) and a Monte Carlo tree search (MCTS), it replaces the random mutation operator in the former with its intelligent mutation and guides the tree expansion process in the latter. This enables these classic optimization algorithms to break free from the blindness of exploration and achieve faster convergence and better results, demonstrating the wide applicability and great potential of this framework in improving the performance of tools in the entire field of computational protein design.

bioinformatics↗

bioRxiv2025

Quality assessment of RNA 3D structure models using deep learning and intermediate 2D maps

Accurate quality assessment is critical for computational prediction and design of RNA three- dimensional (3D) structures. In this work, we introduce RNArank, a novel deep learning-based approach to both local and global quality assessment of predicted RNA 3D structure models. For a given structure model, RNArank extracts a comprehensive set of multi-modal features and processes them with a Y-shaped residual neural network. This network is trained to predict two intermediate 2D maps, including the inter-nucleotide contact map and the distance deviation map. These maps are then used to estimate the local and global accuracy (i.e., lDDT). Extensive benchmark tests indicate that RNArank consistently outperforms traditional methods and other deep learning-based methods. Moreover, RNArank demonstrates promising performance in identifying high-quality structure models for targets from the recent CASP15 and CASP16 experiments. We anticipate that RNArank will serve as a valuable tool for the RNA biology community, improving the reliability of RNA structure modeling and thereby contributing to a deeper understanding of RNA function.

bioinformatics↗

bioRxiv2025

Efficient Data Collection for Establishing Practical Identifiability via Active Learning

Practical identifiability analysis (PIA) plays a crucial role in model development by determining whether available data are sufficient to yield reliable parameter estimates. In bioengineering applications, identifying the minimal experimental design that ensures parameter identifiability is essential in order to reduce cost, time, and resource consumption. In this paper, we introduce E-ALPIPE, a sequential active learning algorithm that recommends new data collection points most likely to establish practical identifiability given the current data, mathematical model and noise assumptions. We empirically evaluate E-ALPIPE against both a benchmark algorithm from the literature and random sampling over three synthetic experiments. Our results show that E-ALPIPE requires up to 50% fewer observations on average to achieve practical identifiability, compared to the strongest competitor, while producing comparable or narrower confidence intervals and more accurate point estimates of system dynamics. Author summaryScientists and engineers often use computer models to understand complex biological systems, such as how cells grow or how chemicals behave in a reactor. To make these models useful, they need to determine the correct values for various model parameters, based on data from experiments. However, experiments are usually expensive, time-consuming, and often produce noisy data. If the available data is insufficient, our confidence in the fitted model parameters may be low and thus the models predictions might be unreliable. This is known as a problem of practical identifiability. This paper introduces a new method called E-ALPIPE, which helps scientists decide which experiment is likely to produce the most useful information for improving their model. Using E-ALPIPE they can determine the model parameter values more efficiently, saving time and resources. We tested E-ALPIPE on several example problems and found that it could reach the same level of reliability using up to 50% fewer experiments than standard methods. It also produced more precise and accurate results. Overall, E-ALPIPE is a smarter, more efficient way to design experiments for tuning the parameters of computer models.

systems biology↗

bioRxiv2025

A trade-off mechanism underpins the evolution of a young two-gene sex-determining system in plants

Sexual systems in animals and plants are remarkably diverse, with dioecy having evolved independently in numerous lineages. In plants, dioecy often evolved more recently than in the best-studied animal systems, making plants especially important for understanding how separate sexes evolved independently from functionally hermaphrodite ancestors. Despite long-standing theories of developmental trade-offs in sex allocation, the underlying genetic mechanisms remain elusive. Here, we show that the XY sex determination system in the dioecious plant species Eurycorymbus cavaleriei in Sapindaceae involves two Y-linked mutations that act jointly within the developmental male-female trade-off: YUN{Delta}, a truncated allele that lowers the dosage of the D-class MADS-box gene YUN, and SUNMAO, a novel sRNA locus that silences the X-linked SUN allele. In females, SUN stabilizes the HD-ZIP transcription factor KUN, which is a known sex determinant in another dioecious plant, thereby promoting femaleness by increasing YUN expression; loss of SUN expression, together with the effect of YUN{Delta}, shifts development toward males. Two interlocking regulatory loops in this "SKY" module (SUN-KUN-YUN) fine-tunes YUN dosage. This dioecious system in E. cavaleriei likely evolved by sequential mutations in genes acting in the predicted male-female trade-off system, with their close linkage reflecting a translocation, and later recombination-suppressing inversions.

plant biology↗

bioRxiv2025

A high-quality bread wheat genome unravels adaptive evolution of wheat end-use quality

Understanding the genetics of superior dough performance is essential for improving wheat end-use quality. Here, we presented a de novo assembly of the genome of JM44, a Chinese wheat cultivar known for its exceptional end-use quality. The JM44 genome achieved reference-level quality (QV = 66.74), depicting a complete picture of complex regions containing gluten genes. Our microsynteny analysis across the Triticum-Aegilops complex showed that high-molecular-weight glutenin subunits (HMW-GSs) loci are highly conserved, while low-molecular-weight glutenin subunits (LMW-GSs) and -/{beta}-gliadins exhibited greater structural variation. These variable loci appear to have been preferentially selected by humans and contributed substantially to the evolution of wheat quality traits. Moreover, we observed that epistatic interactions between gluten genes are strong in modern cultivars, but markedly weaker in landraces, indicating the importance of epistatic selection during modern breeding. Our findings shed light on the genomics and evolution of wheat quality traits, providing valuable guidance for future breeding efforts.

genomics↗

bioRxiv2025

Methionine γ-lyase Traps Cofactor Pyridoxal-5'-phosphate through Specific Serine-Mediated Affinity

Pyridoxal-5'-phosphate (PLP), the ubiquitous and ancient cofactor, plays important roles in enzymatic elimination, transamination and other reactions. The catalytic efficiency of PLP-dependent enzymes is significantly higher than that of free PLP. The recruitment of PLP from the environment by the enzymes, particularly through interactions outside the active site, is the key step determining the occurrence of PLP-mediated catalysis. However, the precise mechanism by which enzymes recruit PLP remains elusive. Methionine {gamma}-lyase (MGL), a PLP-dependent enzyme, catalyzes the degradation of L-methionine, thereby suppressing cancer cell proliferation through serum or dietary methionine depletion. Here, we report the crystal structure of yMGL, which belongs to a newly identified subgroup of cystathionine {gamma}-lyases, in complex with L-methionine and PLP. Through truncating the C-terminal domain of yMGL both in vitro and in vivo, we demonstrated that this domain, outside the canonical PLP-binding domain, is essential for the specific interaction between yMGL and PLP, as well as for efficient L-methionine catabolism. A conserved Ser in the C-terminal domain, positioned at the active-site entrance of MGLs, confers specificity for PLP. These findings elucidate a previously uncharacterized mechanism of PLP recruitment by MGLs and promote rational design of MGLs with tailored cofactor selectivity and catalytic performance.

biochemistry↗

bioRxiv2025

Integrative genomic and transcriptomic analyses uncover regulatory landscape of symbiotic nitrogen fixation in soybean natural population

Symbiotic nitrogen fixation (SNF) is a key trait in legume productivity, yet the genetic and regulatory basis underlying its natural variation remains poorly understood. Here, we integrated genome, transcriptome, and chromatin accessibility data from a soybean diversity panel comprising 380 accessions, including 108 wild and 272 cultivated lines. Genome-wide association studies (GWAS) detected multiple loci for SNF traits but with limited resolution due to polygenic architecture and environmental influences. Independent component analysis (ICA) identified 136 co-expression modules; ten ICs were strongly correlated with SNF phenotypes and enriched in circadian clock components (e.g., GmLHY1a/b), lipid metabolism, or defense signaling pathways. Transcriptome-wide association studies (TWAS) linked 1,453, 806, and 178 genes to NFP, NW, and NFE traits, respectively. Among TWAS hits, 185 transcription factors were identified, with 39.0% overlapping selective sweeps, suggesting regulatory evolution under domestication. To further dissect expression regulation, we performed eQTL mapping and detected 4,654 significant eQTLs, including 1,241 local (cis), 2,505 distal (trans), and 908 mixed. By integrating ATAC-seq data from sorted nodule nuclei, we found that eQTLs, particularly local eQTLs, are significantly enriched within open chromatin regions, indicating their regulatory potential. Notably, we validated the circadian clock gene GmLHY1b as a negative regulator of nodulation using CRISPR mutagenesis and CUT&Tag. Our integrative study provides comprehensive genomic and transcriptomic resources from a diverse soybean population, offering novel insights into SNF regulatory networks and a valuable foundation for future SNF research and soybean improvement.

plant biology↗

bioRxiv2025

Overcoming breeding constraints in polyploid oat from evolutionary insights

Polyploidy provides adaptive advantages in plants by buffering deleterious mutations1,2. While polyploidization can enhance agronomic traits such as increased biomass, known as the gigas effect1-4, increasing genetic gain in polyploid crops remains a critical but difficult goal due to the difficulty in dissecting complex trait inheritance5,6. Here we present chromosome-scale genome assemblies for 26 Avena taxa, spanning diploid, tetraploid, and hexaploid lineages. We traced four independent polyploidization events across the genus, including the formation of A. agadiriana as an allotetraploid (AgAgAg'Ag'), and revealed a reticulate evolutionary history shaped by gene flow involving four subgenomes (A, B, C, D), for example, the hybrid speciation of A. hirtula. Transcriptomic analysis of 286 samples across 11 tissues, combined with deleterious mutation analysis from a cultivated population of 112 accessions, showed that polyploidization led to widespread functional redundancy among homoeologs, supporting a genome-wide buffering effect. However, derived allele frequency analysis revealed that, while disrupting functional genes may yield desirable traits, the buffering effect impedes the fixation of beneficial loss-of-function mutations and thereby limits breeding efficiency. Based on these integrative analyses, we propose breeding strategies to circumvent these limitations by targeting beneficial loss-of-function alleles within the complex polyploid background of oat. Our study highlights broader challenges in the improvement of polyploid crops and provides a foundation for future breeding strategies.

evolutionary biology↗