Biology subjects

Liao, W.

Publications and source records attributed to Liao, W..

At least 19 recordsLinked to original sources

bioRxiv2026

High-resolution analysis of T cell subsets, clonality, and trajectories in psoriasis

Psoriasis is sustained by type 17 immunity, yet how transcriptionally defined T-cell states are clonally organized across lesions, clinically uninvolved skin, and the circulation remains incompletely understood. We performed paired single-cell RNA and T-cell receptor (TCR) sequencing of T cells isolated from two spatially separated lesions, non-lesional skin, and peripheral blood from 23 participants with psoriasis, together with healthy skin and blood comparators. We resolved 19 cutaneous T-cell populations and found that lesions were enriched for IL-17-associated CD4+ and CD8+ states and activated regulatory T-cell states. CD8+ Tc17 cells showed prominent clonal expansion, and expanded clonotypes contributed more prominently to skin than to blood. Clonotype sharing was greatest between paired lesions, while clonotypes detected in cutaneous IL-17-associated cells mapped to multiple circulating phenotypes, indicating that blood and skin capture complementary features of the disease-associated repertoire. At the sample level, T-cell composition separated 57 lesional samples into two recurrent compositional states that were largely concordant across paired body sites. HLA-C*06:02 status was associated with differences in pretreatment lesional CD8+ T-cell composition, clonotype overlap, and clonal-size distributions. Exploratory longitudinal analyses in eight treated participants showed heterogeneous remodeling of T-cell composition and clonotype detection after therapy. Together, these findings define tissue-restricted and tissue-shared features of the psoriatic T-cell repertoire and identify clonal architecture across skin sites and blood as an organizing feature of lesion heterogeneity.

immunology↗

bioRxiv2026

High-throughput in vivo DNA data encoding by multiplexed conjugationand orthogonal base editing

Bacterial conjugation and CRISPR base editing offer complementary routes for programmable DNA transfer and heritable genomic writing, but their combined potential for information encoding in living cells remains unexplored. Here, we introduce CONCORD, a bacterial encoding platform that couples multiplexed bacterial conjugation to genomic base editing for synthesis-free in vivo DNA data writing. A recipient Writer strain expresses the base-editing machinery and is equipped with a writing template, whereas reusable donor Guide strains deliver the encoding sgRNA signals by conjugation. Using this setup, we encode binary information into genomic DNA in a high-throughput manner. Moreover, we systematically evaluated multiple orthogonal base editors for an expanded encoding repertoire and improved data recovery through redundancy encoding. We deploy the system to encode 45,689 bits into DNA including two culturally and historically significant artifacts: a greeting from NASA's Voyager Golden Record and a photograph of Navajo petroglyphs and recovered these datasets with 100% and 99.5% accuracy, respectively, without computational error correction. Connecting and engineering the unique mating and immune system of microbial cells we have established a framework for programmable information writing in bacterial populations, with applications for biological event recording and sustainable DNA data storage.

synthetic biology↗

bioRxiv2026

Hippocampal Glucuronyl C5-epimerase promotes stress resilience by directly engaging PI3K through a non-enzymatic mechanism

Major depressive disorder (MDD) seriously affects human physical and mental health and causes global socioeconomic burdens. Stress-induced depressive etiology is linked to dendritic atrophy in the hippocampus, however, the underlying mechanism was poorly understood. Here, we identify that glucuronyl C5-epimerase (Glce), a heparan sulfae-modifying enzyme, as a critical regulator of hippocampal dendritic integrity and stress resilience. Genetic ablation of Glce in the hippocampal excitatory neurons is sufficient to induce dendritic atrophy and depressive-like behaviors, whereas its restoration rescues these deficits. Notably, Glce levels are reduced in the plasma of depressed individuals. We further find that Glce functions independently of its enzymatic activity, instead binding directly to and activating PI3K (p85/p110), thereby triggering an Akt/CREB/BDNF signaling cascade. Together, our findings uncover a non-canonical role for Glce and establish that Glce-PI3K-BDNF axis is essential for maintaining hippocampal structure and behavioral resilience, thereby highlighting a critical role of Glce in the pathophysiology of depression.

neuroscience↗

bioRxiv2026

An explainable boosting machine model for identifying artifacts caused by formalin-fixed paraffin embedding

BackgroundFormalin-fixed paraffin-embedding (FFPE) is a widely used, cost-effective method for long-term storage of clinical samples. However, fixation is known to introduce damage to nucleic acids that can present as artifactual bases in sequencing otherwise absent from higher fidelity storage methods such as fresh freezing (FF). Various machine learning methods exist for filtering these variant artifacts, but benchmarking performance can be difficult without reliable truth sets. In this study, we employ a collection of 90 paired fresh-frozen and formalin-fixed paraffin embedded samples from the same tumor to robustly define real and FFPE-derived, artifactual variation and enable objective evaluation of filtering methods. To address existing shortcomings, we propose a novel explainable boosting machine (EBM) model that improves performance, can be easily updated with new data, requires modest computational resources, and is analysis pipeline agnostic, making it broadly accessible. ResultsWe evaluated several methods for limiting FFPE-derived variant artifacts using cohorts of B-cell lymphoma samples. We found capturing local context around variants to be a highly informative, under-utilized feature set not commonly incorporated into many existing machine learning methods. Consequently, we developed a novel algorithm, FIFA, for filtering FFPE artifacts, which uses an EBM model, an interpretable decision-tree-based learning algorithm, to address some of the existing shortcomings. We used four independent cohorts composed of paired lymphoma and cervical cancer samples and a breast cancer cell line with both FF and FFPE samples to define clearly annotated training and test sets and demonstrated improved performance over existing methods. Additionally, FIFA filtering increased relevant biological signals in FFPE breast cancer datasets distinct from the training and testing sets. The EBM framework employed by FIFA is computationally efficient and easily amenable to incorporation of additional datasets due to its generalized additive modeling of features making it straightforward to incorporate new data into existing models dynamically over time. ConclusionsOur novel FFPE variant artifact filtering tool, FIFA, is a marked improvement over existing methods. It can be easily implemented, post hoc, to supplement existing somatic calling pipelines, training and inference can be run quickly across most compute environments, and it can be easily updated online as new training data becomes available. Accordingly, FIFA represents an important advance in retrospective cancer genomics research by further enhancing access to the vast stores of FFPE-archived tumor samples currently in existence.

bioinformatics↗

bioRxiv2026

Metabolic reprogramming and stress mitigation of Chlamydomonas reinhardtii using protective metal-phenolic networks

Single-cell encapsulation is widely used in biotechnology to protect highly sensitive living cells from environmental stress. However, the impact of encapsulation on biological function and metabolism remains rarely explored despite its importance for understanding stress response and modulating the production of valuable metabolites. Herein, we show that coating individual cells with protective metal-phenolic networks not only improves survival against stress, but also modifies cellular metabolism. Importantly, this encapsulation technique induces a reversible state of quiescence, which enables accumulation of high-energy-density compounds through selective nutrient diffusion and the resulting carbon flux redistribution. Specifically, light exposure favors the accumulation of nearly eightfold higher starches, while incubation in darkness leads to twofold higher lipid accumulation compared to native cells. This metabolic engineering approach via individual cell encapsulation expands the cell editing toolbox and will facilitate applications in synthetic biology, bioengineering, and cell-based therapeutics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=184 SRC="FIGDIR/small/711231v2_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@d27478org.highwire.dtl.DTLVardef@dc73baorg.highwire.dtl.DTLVardef@131dcaeorg.highwire.dtl.DTLVardef@8f3dc4_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2026

Metabolic control of mitochondrial plasticity and extracellular vesicle biology drives Cryptococcus neoformans virulence

Metabolic adaptation to nutrient stress is a key but poorly understood driver of fungal virulence. Here, we show that a dominant East Asian lineage of Cryptococcus neoformans (VNIa-5), which disproportionately infects immunocompetent hosts, has undergone lineage-specific rewiring of glucose-responsive stress pathways. Integrating population genomics, transcriptomics, and experimental infection models, we demonstrate that VNIa-5s clinical dominance is not explained by environmental prevalence. Instead, selective activation of Snf1 signalling links glucose limitation to mitochondrial tubularisation, extracellular vesicle remodelling, and enhanced melanization. Under low-glucose conditions, VNIa-5 exhibits marked mitochondrial plasticity and extracellular vesicle compositional shifts resembling hypervirulent outbreak lineages of Cryptococcus gattii. Following experimental induction of dormancy, VNIa-5 shows significantly increased virulence in vivo compared with the closely related but clinically rare VNIa-31 subclade, with host survival tightly correlated with mitochondrial morphology. These findings identify metabolic stress integration as a central mechanism shaping cryptococcal virulence and disease in immunocompetent human hosts.

microbiology↗

bioRxiv2026

The structure and catalytic mechanism of new cellular and viral HDV ribozymes

We have determined the molecular structure and investigated the catalytic mechanism of two new ribozymes of the Hepatitis delta virus family, found in the nematode Caenorhabditis briggsae and virus Ackermannviridae. Crystal structures of both conform to the double-pseudoknot architecture adopted by the viral HDV ribozyme. The C. briggsae ribozyme has been determined both pre- and post-cleavage. In the former both nucleotides flanking the scissile phosphate are observed, along with a metal ion, and cytosine 75 N3 bound to the O5 leaving group. The pH dependence of cleavage rate reveals a pKa of 6.6 and together with the inactivity of a C75U mutant provides evidence for its role as general acid. In contrast to other nucleolytic ribozymes that use catalytic metal ions, reaction rate does not depend on the pKa of the divalent metal ion. Limited adjustment of structure of the active center is consistent with direct bonding of the metal ion to the O2 and non-bridging O, suggesting that the ion acts as a Lewis acid to activate nucleophilic attack. This mechanism appears to be general for the HDV ribozyme class, and distinguishes it from the majority of nucleolytic ribozymes that use general base catalysis.

biochemistry↗

bioRxiv2026

TRPV4 Inhibition by a Natural Product Mediates Analgesia

The transient receptor potential vanilloid 4 (TRPV4) channel, a polymodal calcium-permeable cation channel, is activated by diverse stimuli, including hyposmolarity, moderate heat, mechanical stress, and acidic pH. TRPV4 plays a critical role in the onset and progression of pain and is implicated in multiple pathological conditions, positioning it as an attractive therapeutic target. However, the development of TRPV4-targeted therapeutics has been hindered by limited mechanistic understanding of its inhibition. Here, the natural product (R)-1-(3-ethylphenyl)ethane-1,2-diol (AH001) is identified as a potent, subtype-selective TRPV4 inhibitor. A potential gating mechanism of TRPV4 inhibition is proposed through a combination of structural analyses and site-directed mutagenesis. Electrophysiological studies and cryo-electron microscopy (cryo-EM) further reveal that AH001s glucuronide metabolite, AHP, retains robust TRPV4 inhibitory activity, providing a critical structural basis for rational optimization of AH001 as a lead compound. Notably, AH001 demonstrated significant efficacy in reducing neuronal hyperexcitability in dorsal root ganglia (DRGs) and produced pronounced analgesic effects across multiple pain models, highlighting its therapeutic potential in pain relief. This study offers critical structural and mechanistic insights into TRPV4 inhibition and lays the groundwork for the structure-based design of TRPV4-targeted therapeutics.

biochemistry↗

bioRxiv2025

Immunological Differences in Atopic Dermatitis Across Age Groups: Insights from Single-Cell Multi-Omics

BackgroundAtopic dermatitis (AD) occurs across all ages but presents distinct clinical and immunologic features between children, adults, and older adults. The molecular programs underlying these age-specific immune differences remain poorly understood. MethodsWe performed single-cell multi-omics profiling of peripheral blood mononuclear cells (PBMCs) from 29 AD patients and 29 matched healthy controls (HC), spanning pediatric (0-17 years), adult (18-59 years), and geriatric ([≥]60 years) groups. Using Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), we simultaneously quantified transcriptomic (RNA) and surface proteomic (ADT) profiles across [~]280,000 immune cells. Integrated analyses identified 30 immune subsets for cell-type proportion and differential expression analyses. Machine-learning classifiers were trained on significant gene and protein features to distinguish AD subgroups by age. ResultsCompared with HC, AD blood showed enrichment of CD14+ monocytes, plasmacytoid dendritic cells, and CD4+ proliferating T cells. Within AD, pediatric patients had increased {gamma}{delta} T cells, naive CD4+, and naive CD8+ T cells, while geriatric patients exhibited more CD4+ cytotoxic and CD8+ central memory T cells, indicating a shift from naive to effector predominance with aging. Transcriptomic and proteomic analyses revealed distinct programs: pediatric AD was enriched for IL-10 and cytokine-cytokine receptor signaling; adult AD demonstrated activation of metabolic and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-{kappa}B)/Th1/Th17 pathways; and geriatric AD exhibited reduced adaptive immune activity but increased innate signaling. Machine-learning models based on differentially expressed genes and proteins accurately classified AD age groups (transcript-based F1 = 0.70, AUC = 0.79), identifying stable markers such as IRF2, PDK4, ZFP90, CD21, CD94, and CD122. ConclusionsSingle-cell multi-omics profiling revealed immune differences across the AD lifespan, transitioning from developmental tolerance in children to inflammatory and metabolic activation in adults to enhanced innate signaling in geriatric individuals. These findings highlight molecular signatures that could support age-specific diagnostics and therapeutic strategies for AD across the lifespan.

immunology↗

bioRxiv2025

Physical, chemical, and structural properties of human gastric organoid-derived mucus

The gastric mucus layer protects the epithelium from gastric acid and ingested pathogens. However, studies of human gastric mucus have been limited due to poor accessibility of native human mucus and the abundance of contaminants in these samples. Here, we explored the potential of human gastric organoids as models for mucus production. Immunofluorescence staining confirmed that the organoids produced mucus containing MUC5AC and MUC6. The luminal mucus had viscoelastic properties similar to those of native human gastric mucus, as determined by particle tracking microrheology. To collect organoid-produced gastric mucus, termed bioengineered gastric mucus (BGM), organoids were cultured as monolayers at the air-liquid interface (ALI), and apically-secreted mucus was harvested and analyzed by MUC5AC ELISA, proteomics, CryoFE-SEM, and bulk rheometry. BGM contained high-molecular weight molecules also found in native gastric mucus, including MUC5AC. Proteomic analysis confirmed that BGM contained MUC5AC, MUC6, MUC1, and other stomach-specific molecules such as gastricsin, olfactomedin 4, and gastrokine. CryoFE-SEM showed that both BGM and native mucus had a porous structure and a characteristic honeycomb scaffold. Bulk rheometry confirmed that BGM exhibited shear thinning and predominantly elastic behavior, consistent with native mucus. Collectively, these findings indicate that BGM is an accessible alternative to native gastric mucus that can be produced on-demand for in vitro studies. New and NoteworthyWe demonstrate the structural and functional similarities of organoid-derived gastric mucus and native mucus collected from human patients. The bioengineered gastric mucus mimics its native counterpart in its proteomic profile, physical architecture, and viscoelasticity. This work highlights the translational potential of organoid-derived mucus for functional investigations of the human gastric mucus layer.

cell biology↗

bioRxiv2025

StrainOptimizer empowers rational cell factory design through multi-scale metabolic models with expression and proteome constraints

The rational design of microbial cell factories for high bioproduction remains a key challenge in metabolic engineering. While advanced modelling frameworks incorporating protein resource allocation, such as enzyme-constrained models (ecGEMs) and Expression and Thermodynamic Flux (ETFL), provide superior predictive power, their application is limited by a lack of user-friendly computational tools. Here, we present strainOptimizer, a comprehensive computational platform for rational strain design that systematically evaluates key resource allocation principles: the coupling of gene expression with metabolism, subcellular compartmentalization, and enzyme capacity limitations. Our benchmark analyses demonstrate that each principle offers distinct advantages: models coupling metabolism and expression (like ETFL) enable the identification of non-metabolic targets, organelle-level proteomic constraints improve precision for high-protein-cost products, and protein-usage-based objectives consistently outperformed traditional flux-based approaches. To demonstrate its practical utility, we applied strainOptimizer to an engineered sclareol-overproducing Saccharomyces cerevisiae strain. The platform identified novel targets, and experimental validation confirmed a 67% success rate, increasing the final sclareol titer by 14-26% and productivity by up to 45%. StrainOptimizer bridges the gap between resource allocation theory and applied engineering, providing a powerful, validated tool to accelerate the development of high-performance cell factories.

bioengineering↗

bioRxiv2025

scYeast: a Biological-knowledge-guided Foundation Model on Yeast Single-Cell Transcriptomics

Though large-scale pre-trained models are vital for foundational cell modeling, most focus on human or mouse systems, neglecting model organisms like yeast (Saccharomyces cerevisiae) and often failing to use existing biological prior knowledge effectively. To address this, we present scYeast, the first foundational cell model tailored for yeast that seamlessly embeds biological priors. scYeast employs a novel asymmetric parallel architecture to infuse transcriptional regulatory information directly into the Transformers attention mechanism, leveraging established biological knowledge during training. Pre-trained on large-scale yeast single-cell transcriptomics data, scYeast demonstrates strong generalization and biological interpretability. It excels in zero-shot tasks, such as inferring regulatory relationships and identifying critical cell states. After fine-tuning, scYeast performs exceptionally across a diverse set of tasks from cell type classification to predicting growth doubling time and gene perturbation response. Additionally, using transfer learning, scYeast can be adapted to other omics datasets, such as proteomics, thus broadening its utility in big data analysis. Overall, scYeast is a powerful tool for yeast single-cell biology research and sets a new standard for integrating foundational models with biological prior knowledge, dramatically accelerating the pace of discovery in yeast synthetic and systems biology and providing a replicable framework for other organisms.

bioinformatics↗

bioRxiv2025

Target-Stabilized Base Editors Enable Robust High-Fidelity RNA Editing

RNA base editing using engineered deaminases expands target compatibility, offering a powerful tool to correct mutations at the RNA level. However, widespread off-target effects, primarily arising from dissociated free deaminases, remain a significant challenge. Here, we devised the RECODE (RNA editing with conditionally stable and enhanced ADAR1 deaminase variants) system, which employs designer degron-tagged ADAR1 deaminase (ADAR1d) with guide RNA (gRNA)-regulated stability. By promoting degradation of gRNA-unbound ADAR1d, RECODE markedly reduced transcriptome-wide edits while maintaining high on-target efficacy. Engineering gRNA for target RNA-induced conformational switching restricts ADAR1d stabilization, further enhancing editing precision. With structure-guided rational engineering of ADAR1d, RECODE achieved editing efficiencies of 90.3% and 58.8% for an Amyotrophic Lateral Sclerosis-relevant FUS mutation in vitro and in vivo, respectively. These findings establish RECODE as a highly stringent and efficient RNA editing technology and underscore a general principle for enhancing the specificity of RNA-guided protein effectors.

molecular biology↗

bioRxiv2025

Description of three new predator species of Litostomatea (Alveolata, Ciliophora), including sequencing and annotation of their mitochondrial genomes

Three new species of haptorian ciliates Lacrymaria venatrix sp. nov., Phialina famelica sp. nov., and Chanaea vermicularis sp. nov., recovered from water bodies of Tuscany, Italy, are described according to the standards of the Next Generation Taxonomy, which include morphology, ultrastructure, phylogeny, and associated microbial consortium analysis plus mitochondrial genome sequencing and annotation. Lacrymaria venatrix is a species with a small spindle-shaped trunk, a highly contractile neck that equals the trunk in length at its full extension, and a macronucleus consisting of two nodules; it is mostly similar to L. songi but diverges in the denser ciliature, the closer macronuclear nodules, and the single micronucleus. Phialina famelica has a blunter spindle-shaped trunk with a shallow oral bulge and an elongated macronucleus; it is mostly similar to P. salinarum and P. serranoi, but differs in the freshwater habitat, and by having fewer but denser kineties. Chaenea vermicularis differs from its congenerics by a modest contractility combined with a greater elongation ability, the mucocysts organized into multiple rows forming stripes, and the several hundreds of macronuclear nodules. The 18S rDNA-based phylogenetic analysis supports previous observations of the paraphyly of the genera Lacrymaria and Phialina, that need a deep systematics revision. The genus Chaenea seems not to be challenged, though its position within Haptoria remains not fully clear, and C. vermicularis sp. nov. appears rather divergent from its congeners.

zoology↗

bioRxiv2025

Spleen-derived Small Extracellular Vesicles Protect against Myocardial Infarction via Mediating Spleen-heart Crosstalk

Background and AimsAcute myocardial infarction (AMI) triggers systemic responses that influence cardiac injury and repair, but protective mediators within the cardiosplenic axis remain incompletely understood. This study aimed to investigate whether spleen-derived small extracellular vesicles (sEVs) exert cardioprotection after AMI, identify critical cargo, and evaluate their clinical relevance. MethodsThe effects of splenectomy, spleen-derived sEVs and pharmacological inhibition of sEVs biogenesis on cardiac injury were evaluated in mice with AMI. Parabiosis, donor splenectomy, and fluorescent labelling traced the origin and myocardial recruitment of circulating sEVs. 4D-proteomics profiled sEVs cargo with a focus on pyruvate carrier-1 (MPC1). ResultsSplenectomy worsened survival, ventricular function, infarct size, and fibrosis in mice with AMI. AMI upregulated splenic extracellular-vesicle pathways, with sEVs release peaking at day 3 (M3D-sEVs). Labelled splenic sEVs preferentially accumulated in ischemic myocardium, confirmed by parabiosis experiments. M3D-sEVs improved survival and cardiac function and reduced infarct size, fibrosis, apoptosis, inflammation, and hypertrophy. In addition, anti-apoptotic effects were reproduced in vitro. Inhibition of sEVs biogenesis decreased circulating sEVs, aggravated injury, and was rescued by M3D-sEVs. M3D-sEVs were enriched in MPC1, and MPC1 neutralization or pharmacological blockade abrogated sEVs-mediated restoration of respiration, ATP generation, and reduction of reactive oxygen species. Plasma sEVs-associated MPC1 was highest in patients with AMI, intermediate in coronary heart disease, and lowest in controls. ConclusionsThe spleen responds adaptively to AMI by releasing MPC1lenriched sEVs that travel to injured myocardium, preserve mitochondrial energetics, and reduce damage, supporting cargolspecific sEVs augmentation and sEVslMPC1 as a potential therapeutic target and biomarker in ischemic heart disease. Structured graphical abstractAcute myocardial infarction activates a spleen-heart axis in which the spleen releases mitochondrial pyruvate carrier 1 (MPC1) enriched small extracellular vesicles that home to the infarcted myocardium, preserve mitochondrial oxidative phosphorylation, reduce ROS, and limit infarct size, apoptosis, fibrosis, and hypertrophy. Circulating sEVs-associated MPC1 might serve as a potential biomarker for estimating risk of ischemic heart disease. Translational perspectiveO_LIOur findings reveal a novel heart-spleen communication axis mediated by splenic small extracellular vesicles (sEVs) following AMI. These vesicular messengers predominantly deliver mitochondrial pyruvate carrier 1 (MPC1), enhancing cardiac mitochondrial energy metabolism in the injured myocardium, ultimately improving post-AMI functional recovery in experimental models. C_LIO_LIA higher plasma sEVs-associated MPC1 level was observed in AMI patients when compared with non-AMI patients. These findings support the clinical relevance of the spleen-heart axis, introduce sEVs-associated MPC1 as a potential circulating biomarker for early myocardial injury and therapeutic target. C_LI

molecular biology↗

bioRxiv2025

Population-specific brain charts reveal Chinese-Western differences in neurodevelopmental trajectories

Human brain charts provide unprecedented opportunities for decoding neurodevelopmental milestones and establishing clinical benchmarks for precision brain medicine 1-7. However, current lifespan brain charts are primarily derived from European and North American cohorts, with Asian populations severely underrepresented. Here, we present the first population-specific brain charts for China, developed through the Chinese Lifespan Brain Mapping Consortium (Phase I) using neuroimaging data from 43,037 participants (aged 0-100 years) across 384 sites nationwide. We establish the lifespan normative trajectories for 296 structural brain phenotypes, encompassing global, subcortical, and cortical measures. Cross-population comparisons with Western brain charts (based on data from 56,339 participants aged 0-100 years) reveal distinct neurodevelopmental patterns in the Chinese population, including prolonged cortical and subcortical maturation, accelerated cerebellar growth, and earlier development of sensorimotor regions relative to paralimbic regions. Crucially, these Chinese-specific charts outperform Western-derived models in predicting healthy brain phenotypes and detecting pathological deviations in Chinese clinical cohorts. These findings highlight the urgent need for diverse, population-representative brain charts to advance equitable precision neuroscience and improve clinical validity across populations.

neuroscience↗

bioRxiv2025

Efficient Cell Factory Design by Combining Meta-Heuristic Algorithm with Enzyme Constrained Metabolic Models

The rational design of high-performance microbial cell factories remains a central challenge in sustainable biomanufacturing due to the complexity of metabolic networks and the difficulty of predicting synergistic genetic interventions. Despite recent advances in strain design algorithms, predicting combinatorial targets remains computationally prohibitive due to the combinatorial explosion. Here, we present MetaStrain, a unified computational framework that integrates enzyme-constrained models (ecModels) with meta-heuristic algorithms to identify non-intuitive combinatorial gene targets for improving product yields. MetaStrain first performs pre-screening through a modified enforced objective flux scanning algorithm to reduce the dimensionality of candidate genes and annotate editing strategies. The subsequent search module employs flexible individual encodings compatible with diverse meta-heuristic algorithms and adopts a bottom-up phenotype evaluation strategy enabling efficient exploration of the combinatorial design space. Integrated redundancy analysis tools further identify single and fixed-size combinatorial strategies, facilitating direct experimental implementation. Computational simulation in Saccharomyces cerevisiae reveals significant enhancements in 2-phenylethanol and spermidine biosynthesis, while controlling target count and covering experimentally validated targets. Experimental validation in Escherichia coli further confirmed the algorithms predictive power, achieving up to a 61.25% increase in L-tryptophan titer of the five-target combination strain. Overall, MetaStrain achieves high computational efficiency, stable convergence, and broad adaptability across diverse metabolic targets. This study provides a powerful tool for metabolic engineering, bridging computational prediction and experimental realization, and highlighting the potential of meta-heuristic optimization in synthetic biology.

bioinformatics↗

bioRxiv2025

Robust inference and widespread genetic correlates from a large-scale genetic association study of human personality

Personality traits describe stable differences in how individuals think, feel, and behave and how they interact with and experience their social and physical environments. We assemble data from 46 cohorts including 611K-1.14M participants with European-like and African-like genomes for genome-wide association studies (GWAS) of the Big Five personality traits (extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience), and data from 51K participants for within-family GWAS. We identify 1,257 lead genetic variants associated with personality, including 823 novel variants. Common genetic variants explain 4.8%-9.3% of the variance in each trait, and 10.5%-16.2% accounting for measurement unreliability. Genetic effects on personality are highly consistent across geography, reporter (self vs. close other), age group, and measurement instrument, and we find minimal spousal assortment for personality in recent history. In stark contrast to many other social and behavioral traits, within-family GWAS and polygenic index analyses indicate little to no shared environmental confounding in genetic associations with personality. Polygenic prediction, genetic correlation, and Mendelian randomization analyses indicate that personality genetics have widespread, potentially causal associations with a wide range of consequential behaviors and life outcomes. The genetic architecture of personality is robust and fundamental to being a human.

genomics↗