Biology subjects

Cui, Y.

Publications and source records attributed to Cui, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Particle Biology: A Perspective on a First-Principles Theory of Life

This Perspective formally proposes Particle Biology as a unifying theoretical framework to address the critical bottleneck in current life science research. Current life science research has reached a critical bottleneck. While the field has advanced to the study of 3D genomic spatial configurations and chromosomal organization, it remains largely descriptive and confined to the macromolecular level. This approach lacks a first-principles understanding of the underlying physical forces that drive biological processes. This Perspective formally proposes Particle Biology as a unifying theoretical framework. We establish an axiomatic system positing that life phenomena are fundamentally emergent spatiotemporal patterns of electromagnetic forces among atoms, electrons, and nuclei operating far from thermodynamic equilibrium. By defining biological states through the Biological Hamiltonian and mapping biochemical pathways to multidimensional Potential Energy Surfaces (PES), we bridge the gap between descriptive biology and predictive physics. We categorize core research technologies into three modalities--seeing, computing, and controlling particles--facilitated by advancements in Cryo-EM, AlphaFold 3, and Boron Neutron Capture Therapy (BNCT). Ultimately, the trajectory of molecular biology has evolved from cells to DNA and onto the 3D spatial genome, yet it cannot go deeper within current paradigms. The next logical evolution is to move beyond the macromolecular bottleneck to focus on the electromagnetic interactions between atoms and ions--the true Particle Biology level--to redefine disease and intervention.

biophysics↗

bioRxiv2026

H2O: A Foundation Model Bridging Histopathology to Spatial Multi-Omics Profiling

Spatial omics technologies have revolutionized the molecular profiling of tissues but remain constrained by high costs and limited scalability. While hematoxylin and eosin (H&E) staining is ubiquitous, it lacks molecular specificity. Here, we present H2O (Histopathology to Omics), a generalist AI framework that bridges the modality gap between histopathology and spatial multi-omics, enabling the direct inference of spatial transcriptomics (ST) and proteomics (SP) landscapes from routine H&E images. H2O integrates Vision Transformers (ViT) with Large Language Models (LLM) via contrastive learning to align histological morphology with semantic molecular knowledge. This cross-modal approach allows the model to incorporate spatial expression profiles into histological pattern recognition, effectively decoding the molecular heterogeneity underlying tissue morphology. Trained on a pan-tissue dataset of 1.3 million paired H&E-spatial patches across 25 organs and cancer types, H2O predicts spatial omics expression from histology with high concordance to sequenced measurements and consistently outperforms state-of-the-art models across three cancer benchmarks. Notably, H2O recovers the MIF-CD74/CD44 signaling axis directly from H&E images, highlighting its capacity to infer biologically meaningful cell-cell communication without molecular profiling. Applying on three additional public cohorts covering fetal and paediatric thymus tissues, human metastatic lymph node, and breast cancer, encompassing human development, 3D spatial frameworks, and integrative multi-omics, H2O yields biologically concordant insights, demonstrating superior accuracy, robustness, and generalizability across real-world applications in diverse scenarios. H2O converts routine histopathology into a portal for spatially resolved multi-omics profiling by computationally generating transcriptomic and proteomic landscapes, thereby enhancing tissue phenotyping and enabling scalable, integrative tissue-atlas construction.

bioinformatics↗

bioRxiv2026

Emotion regulation or dual task? Dissociation of neural and behavioral measures

Cognitive reappraisal, the deliberate reinterpretation of emotional events, is widely considered an effective emotion regulation strategy, and modulation of the late positive potential (LPP) during negative affect reduction has become the primary electrophysiological evidence for volitional emotional control. Experimental instructions, however, impose dual-task demands that free viewing does not, confounding reappraisal with cognitive load. By including instructions to increase emotional responses to pictures ("enhance") as well as instructions to decrease ("suppress"), different predictions are generated. If the LPP reflects regulation, then, compared to free viewing, suppress instructions should decrease LPP amplitude, and enhance instructions should increase LPP amplitude. If modulation instead reflects cognitive load, both instructions should reduce the LPP, as both impose an additional cognitive task. In a sample of 107 participants, evaluative ratings confirmed that regulation instructions modulated reported emotional intensity in the expected directions (Enhance > View > Suppress), but that both enhance and suppress instructions reduced LPP amplitude compared to free viewing, with Bayesian model comparisons providing strong evidence against direction-specific regulation and in favor of cognitive load. Whole-scalp multivariate pattern analysis confirmed that no instruction-related neural signal exists at any scalp location or latency within the first second after stimulus onset. These data indicate that LPP modulation following both instruction types reflects dual-task cognitive load rather than volitional emotional control. Significance StatementCognitive reappraisal is considered the gold standard of emotion regulation, and reduced late positive potential (LPP) amplitude during negative emotion suppression is the primary neural evidence that humans can voluntarily control emotional responses. The current data are inconsistent with this regulatory account and instead support a cognitive load interpretation. Whether instructed to enhance or suppress emotional responses, LPP amplitude was reduced in both conditions relative to free viewing, consistent with attentional resource competition rather than directional regulatory control. The same participants reported successfully regulating emotional experience in opposite directions, producing a clear dissociation between neural and behavioral measures. These findings challenge a basic tenet of emotional regulation and raise questions concerning LPP modulation as a biomarker of regulatory capacity.

neuroscience↗

bioRxiv2026

A Hierarchy-aware Gene Exploration Platform for Multi-layered Toxicogenomic Analysis: A Case Study on Acetaminophen-induced Hepatotoxicity

BackgroundThe interpretation of high-dimensional transcriptomic data remains a major challenge in mechanistic toxicology and drug safety assessment. Conventional clustering approaches based solely on expression profiles often fail to capture intrinsic biological relationships among genes, limiting interpretability and downstream analysis. MethodsWe developed a hierarchy-aware gene exploration platform that integrates structured biological knowledge from the HUGO Gene Nomenclature Committee (HGNC). The core of the framework is a similarity kernel based on a single-step hyperdiffusion formulation (HKH{top}), which embeds gene family hierarchy into the similarity space. The platform is implemented as an interactive web application supporting Uniform Manifold Approximation and Projection (UMAP) visualization, Leiden clustering, functional enrichment analysis, and hierarchy-based gene recommendation. ResultsApplied to a transcriptomic dataset of acetaminophen-induced acute liver failure (APAP-ALF), the proposed approach achieved a 33.8-fold improvement in functional coherence compared to an expression-only baseline. The hierarchy-aware embedding produced compact and biologically consistent clusters, enabling identification of key toxicological modules, including disruption of RNA processing, extracellular matrix remodeling, and impairment of lipid transport. In addition, the framework detected small but highly significant regulatory modules associated with epigenetic reprogramming. ConclusionBy incorporating biological hierarchy into gene similarity, the proposed platform enhances the interpretability of transcriptomic analysis and enables structured exploration of functional relationships. This approach provides a practical framework for mechanistic insight generation and supports more transparent and reproducible analysis in toxicogenomics. AvailabilityThe web application is freely available at https://hgncgeneexplorer.streamlit.app/.

bioinformatics↗

bioRxiv2026

Skin DNA Methylation Encodes Multidimensional Facial Aging Phenotypes with Distinct Biological Architectures

Whether distinct visible aging traits, e.g., wrinkling, pigmentation, and inflammation, reflect shared or independent epigenetic programs remains unknown; existing clocks compress aging into a single chronological axis, leaving the phenotype-specific architecture of cutaneous aging uncharacterized. Here, we integrate AI-derived facial phenotypes with skin DNA methylation profiles from 706 individuals to develop EpiVision, a panel of 21 epigenetic predictors spanning structural, pigmentary, inflammatory, and textural aging traits. Predictors reveal shared and trait-specific pathways, including developmental patterning, epithelial remodeling, hormonal signaling, and UV damage responses, and capture environmentally induced acceleration in sun-exposed skin alongside lifestyle and topical treatment-associated variation. These findings establish that visible skin aging comprises molecularly distinct axes with shared regulatory substrates and trait-specific drivers, providing a scalable epigenetic framework for intervention evaluation and aging biology research.

genomics↗

bioRxiv2026

Analysis of tumor-derived and cross-presented peptide antigens defines improved immunotherapeutic strategies

BackgroundCross-presentation of tumor antigens by antigen-presenting cells (APCs) is essential for initiating effective anti-tumor T cell immunity. The presence of cross-presenting immune cells across multiple solid tumors correlates with improved clinical outcomes. Despite the importance of this process, the identities and characteristics of tumor-derived MHC-I antigens that are cross-presented by APCs remain largely undefined, limiting rational design of targeted immunotherapies. MethodsWe performed an immunopeptidomic analysis of cross-presented glioblastoma (GBM) antigens on APCs, including bone marrow-derived macrophages, bone marrow-derived dendritic cells, and splenic dendritic cells, using SILAC labeling and in vitro co-culture systems. Additionally, we also profiled endogenous APC and tumor antigen repertoires. We made selected cross-presented antigen targets into mRNA vaccines and evaluated their immunogenicity in comparison to tumor endogenous antigens in vivo. ResultsWe identified over one thousand putative cross-presented GBM antigens. Comparative analysis of endogenous APC and tumor antigen repertoires revealed that cross-presented antigens possess distinct features and are predominantly shaped by intrinsic antigen processing and presentation pathways within APCs, resulting in limited cross-presentation of tumor-specific epitopes. Two doses of mRNA encoding cross-presented tumor-specific epitopes delayed tumor growth and elicited robust antigen-specific T cell responses. ConclusionOur findings define the landscape and constraints of tumor antigen cross-presentation in GBM and establish a framework for improved antigen selection in the development of next-generation GBM immunotherapies.

immunology↗

bioRxiv2026

CASTLE: Cell-type Aware SpaTial domain detection via contrastive Learning Embedding

With advances in spatially resolved transcriptomics across platforms and resolutions, it is now possible to measure gene-expression profiles while preserving the tissue microenvironment. Spatial clustering is central to these analyses, and recent graph neural network (GNN)-based approaches have greatly improved their accuracy. Nevertheless, precisely delineating spatial domain boundaries remains a significant challenge. Here we present a novel method CASTLE (Cell-type Aware SpaTial domain detection via contrastive Learning Embedding) by leveraging cell type information for improved spatial domain detection. CASTLE first integrates spatial proximity to construct a spatial graph, refined from cell-type similarity or expression similarity when cell type information is unavailable. Then, a self-supervised, local-context contrastive objective aligns embeddings with their immediate microenvironments. Evaluated across multiple tissues and technology resolutions, CASTLE outperforms state-of-the-art methods and detects more accurate and stable spatial domains. In downstream analyses, it resolves fine-grained tissue structures and differentiates functional subtypes, supporting more nuanced biological interpretation.

bioinformatics↗

bioRxiv2026

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

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

bioinformatics↗

bioRxiv2026

Borrelia Burgdorferi binds Serum Amyloid A and Modulates Subcutaneous Adipose Tissue Immune Signaling

Borrelia burgdorferi causes over 470,000 infections annually [1]. Following a tick bite, Borrelia spirochetes replicate in human skin and disseminate through tissues, including subcutaneous adipose tissue. Adipose tissue is a complex organ containing non-immune and immune cells that play a significant role in the immune response [2], yet little is known about adipose tissue signaling after Borrelia infection. We investigated the landscape of immune signaling within adipose tissue-resident cells during Borrelia infection in human tissue (skin and adipose tissue) ex vivo. Immune pathways overall were downregulated in adipose tissue during Borrelia infection. Despite this, adipose stem/progenitor cells exhibited increased pro-inflammatory and extracellular matrix (ECM)-related signaling (IL-6, MIF, collagen, laminin, fibronectin), positioning them as key hubs of intercellular communication during infection. Myeloid lineage cells showed cluster-specific upregulation of immune genes such as IL1B and TNF. Network analyses highlighted laminin, and strong outgoing MIF signals in all adipose-resident cell clusters. Among the identified genes modulated after Borrelia infection, we observed reduced expression levels of serum amyloid A in adipose tissue-resident cells, a marker typically elevated in Lyme disease patient serum during acute infection. Here, we show that human serum amyloid A1 and serum amyloid A2 directly bind B. burgdorferi spirochetes, using flow cytometry. Functionally, serum amyloid A2 reduces spirochete viability in vitro in a dose-dependent manner and enhances opsonization and phagocytosis by macrophages. Together, these findings show that adipose tissue is a site of active immune signaling in the setting of Borrelia infection and identifies SAA as a host defense factor against Borrelia.

immunology↗

bioRxiv2026

Endothelin 2-Mediated Neuroinflammation Drives Hypertension-Associated Retinal Dysfunction

Systemic hypertension is a significant risk factor for glaucoma, a leading cause of irreversible blindness, yet the mechanistic basis connecting the two conditions remains elusive. Here, we demonstrate that hypertensive rats develop accelerated retinal degeneration characterized by retinal thinning, functional impairment, reduced perfusion, and progressive neuroinflammation. Critically, these changes occur despite persistently lower intraocular pressure (IOP), indicating an IOP-independent mechanism of pathology. Transcriptomic and biochemical analyses identified a robust activation of the retinal endothelin system, with endothelin-2 (EDN2) being the earliest and most significantly upregulated component. AAV-delivered retinal Edn2 knockdown completely restored retinal function, ameliorated neuroinflammation, and enhanced retinal blood flow in hypertensive rats. Pharmacological blockade of endothelin receptor type A (EDNRA), but not type B, replicated these protective effects without altering IOP. Our findings establish EDN2 as a central, IOP-independent driver of hypertension-associated retinal neurodegeneration and identify the endothelin-EDNRA axis as a potential therapeutic target to prevent vision loss in hypertensive patients.

neuroscience↗

bioRxiv2026

Co-option of ILF2/3 in primates restrains Alu hyper-editing to enable cell fate transitions

Understanding human specific mechanisms of cell fate control is essential for advancing developmental biology and regenerative medicine. Here, we identify the ILF2/3 complex as a critical regulator of primate cell fate transitions. Using genetically and epigenetically engineered gastruloids and adult stem cells, we show that ILF2/3 is required for gastrulation in primates but not mice, and for differentiation of adult progenitor cells. Mechanistically, ILF2/3 directly binds Alu elements in chromatin-associated RNAs and shields them from ADAR1-mediated adenosine-to-inosine (A-to-I) editing. Acute ILF2/3 degradation increases A-to-I editing at Alu elements, but not murine retrotransposons, leading to aberrant splicing and nonsense-mediated decay of transcripts encoding key chromatin regulators in primate cells. This in turn destabilizes the epigenetic landscape and blocks lineage commitment across all three germ layers. Re-expression of correctly spliced chromatin regulators rescues differentiation defects in ILF2/3-deficient cells, functionally linking Alu editing control to chromatin regulation and cell fate. These findings define an evolutionary mechanism that restrains retrotransposon-associated RNA editing to preserve proteome integrity and enable primate-specific developmental programs.

genetics↗

bioRxiv2026

RodZ acts through MreBCD to activate the elongasome in Escherichia coli

Most bacteria are surrounded by a peptidoglycan (PG) matrix that maintains cell shape and provides protection against turgor pressure. In many rod-shaped bacteria, synthesis of PG along the cell cylinder is organized by the elongasome, also called the Rod complex, which consists of six highly conserved proteins, including the actin-like MreB, the PG synthase complex RodA-PBP2 and three regulatory membrane proteins, MreC, MreD and RodZ. However, how these proteins interact with each other to form the elongasome and synthesize lateral PG remains elusive. In this study, by characterizing MreC mutations affecting elongasome activity in Escherichia coli, we provide evidence that MreC alternates between an active and an inactive state and these mutations affect MreC interaction with PBP2. We also find that RodZ interacts with MreC via a periplasmic region and disruption of this interaction compromises elongasome function. Additionally, we show that the cytoplasmic region of RodZ, which interacts with MreB, work synergistically with activating MreC mutations to promote rod shape formation. These results indicate that RodZ activates the elongasome by initiating two signaling cascades, one in the periplasm via MreCD, and another in the cytoplasm through MreB. This role of RodZ in regulating elongasome activity is analogous to that of FtsN in activating the divisome, suggesting that the mechanisms governing the activation of PG synthesis in cell elongation and division are similar in E. coli. ImportanceThe elongasome, or Rod complex, mediates lateral peptidoglycan synthesis during cell elongation in many rod-shaped bacteria. It consists of the cytoskeletal protein MreB, the peptidoglycan synthase RodA-PBP2, and three regulatory proteins MreCD and RodZ. Although it has been extensively studied, how its activity is controlled remains incompletely understood. Here, we reveal the roles of MreCD and RodZ in regulating elongasome activity in Escherichia coli. Our results indicate that RodZ triggers lateral peptidoglycan synthesis by interacting with MreB in the cytoplasm and MreCD in the periplasm. These interactions likely switch MreBCD to the active state such that they can stimulate the activity of the RodA-PBP2 complex. This regulatory mechanism resembles the activation mechanism of the divisome that mediates septal peptidoglycan synthesis.

microbiology↗

bioRxiv2025

Breast cancer through the lens of whole transcriptome spatial imaging

Using the worlds first transcriptome-scale spatial imaging of a breast tumor, we assessed what could be learned about one patients disease. We cataloged heterogeneity across 3 morphological regions, 9 spatial domains, 37 cell types, and 1692 pathways. Then, employing a new algorithm for spatially stratified differential expression, we tested >2 million hypotheses about cell types behavior across space. We measured how CD8+ T cells change upon entering the tumor, how cancer cells adapt to nutrient-poor microenvironments, how tumor glycolysis impacts nearby healthy cells, and how low-proliferation zones of the tumor are distinct. We found several instances of druggable biology: the tumors heterogeneity and invasion programs suggested aggressiveness independent of traditional grading; PDCD1 expression and exhaustion limited T-cell activity; cancer cells engaged angiogenic signaling but struggled to proliferate in hypoxic regions; and a tumor subcluster up-regulated lipid metabolism. These findings demonstrate the insights obtainable through spatial profiling of individual tumors.

cancer biology↗

bioRxiv2025

Temporal structure of chemical stress controls single-cell inhibition and recovery in photosynthetic microorganisms

Although fluctuating conditions are a hallmark of microbial habitats, how the timing of chemical stress influences single-cell inhibition and recovery is not well resolved. Using a microfluidic platform, we continuously tracked photosynthetic performance in thousands of individual cells of a coral-associated microalga (Symbiodiniaceae sp.) exposed to identical cumulative doses of the photosystem II inhibitor diuron delivered either as constant or fluctuating profiles. Fluctuating exposure produced stronger early inhibition than constant exposure, but as concentrations declined it enabled partial recovery that did not occur under time-averaged constant conditions. These dynamics revealed distinct response subpopulations that differed in both the magnitude and timing of inhibition, including groups that regained activity exclusively under fluctuating stress. Quantifying per-cell decline kinetics showed that fluctuating exposure synchronizes the onset of inhibition across cells, creating a narrow temporal window in which a subset of cells can recover once stress levels fall, whereas constant exposure yields more heterogeneous but uniformly declining trajectories. These results demonstrate that stress timing, not cumulative dose alone, governs whether photosynthetic inhibition is reversible at the single-cell level. More broadly, our findings illustrate how temporal variability and intrinsic phenotypic heterogeneity jointly govern cellular function, highlighting stress timing as an important and often overlooked axis shaping microbial performance in dynamic environments. Significance statementEnvironmental stress in nature is rarely constant; instead, it fluctuates over minutes to days. Yet most laboratory assays rely on static exposures, leaving the role of stress timing poorly understood. Using microfluidics, we imposed precisely timed chemical stress on individual cells of a coral-associated microalga and monitored their photosynthetic performance continuously. Fluctuating exposure caused strong initial inhibition but later allowed partial recovery in a subset of cells, revealing functional heterogeneity that constant stress concealed. These results show that when stress occurs, its temporal structure can be as important as how much stress is delivered. By uncovering mechanisms that govern reversibility and heterogeneity in a key coral symbiont, this work provides a mechanistic basis for understanding microbial responses in dynamic environments.

ecology↗

bioRxiv2025

Targeting Putrescine Homeostasis with Lactiplantibacillus plantarum PS128 to Mitigate Autism-like Behaviors in Fmr1 KO Mice

BackgroundAutism spectrum disorder (ASD) is biologically heterogeneous and has limited mechanism-informed interventions targeting core behavioral symptoms. Emerging evidence suggests that gut microbial metabolism shapes neurobehavioral outcomes, yet the specific metabolic pathways linking gut ecology to brain function remain incompletely understood. One candidate pathway is polyamine metabolism, which links microbial amino acid metabolism to host regulation and represents a plausible but underexplored contributor to ASD-related phenotypes. Notably, the psychobiotic Lactiplantibacillus plantarum PS128 has been reported to improve social behaviors in individuals with ASD, although the molecular basis for these effects is unclear. In this study, we used Fragile X mental retardation 1 knockout (Fmr1 KO) mice, a well-established ASD model, to investigate microbiota-dependent metabolic mechanisms underlying autism-like behaviors. ResultsWe found that autism-like behaviors in Fmr1 KO mice were associated with gut dysbiosis, impaired intestinal barrier integrity, disruption of the arginine-ornithine-polyamine pathway, and elevated putrescine in the prefrontal cortex (PFC). Supplementation with PS128 remodeled the gut microbiota, reduced inflammation- and disease-associated taxa, improved intestinal structure and permeability, and restored polyamine homeostasis. Targeted metabolomics revealed an increased PFC-to-serum putrescine ratio in KO mice, which was normalized following PS128 intervention. This correction was accompanied by reduced expression of polyamine transporters in the PFC, including ATP13A family members. Causal experiments supported a functional role for putrescine, as peripheral elevation of putrescine induced autism-like behaviors in wild-type mice, whereas pharmacological inhibition of putrescine synthesis ameliorated behavioral deficits in Fmr1 KO mice. ConclusionsThese findings identify putrescine metabolic dysregulation as a key contributor to autism-like phenotypes and support the existence of an ASD subtype defined by disruption of the arginine-ornithine-polyamine axis. Integrated multi-omics and causal perturbation analyses support a model in which microbiota-targeted intervention rebalances systemic polyamine regulation along the gut-brain axis, thereby improving ASD-relevant behaviors. Our work provides mechanistic evidence linking microbial metabolism to neurochemical homeostasis and highlights the translational potential of metabolic stratification in ASD. Graphical AbstractWe identify dysregulated microbiota-driven polyamine homeostasis as a causal contributor to autism-like behaviors in Fmr1 knockout mice. A psychobiotic intervention restores gut-brain metabolic balance and rescues social and repetitive behavioral deficits through coordinated microbial and host transport regulation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/694366v2_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@e1e780org.highwire.dtl.DTLVardef@125a3baorg.highwire.dtl.DTLVardef@201228org.highwire.dtl.DTLVardef@1880650_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

Haruka Resolves Perturbation Response Heterogeneity in Spatial Cell Niches

Understanding how tissues remodel in response to perturbations requires computational tools that can untangle condition-specific changes from the conserved tissue architecture. We present Haruka, a spatially aware contrastive learning framework that identifies salient (condition-specific) and background (shared) spatial domains across tissue slices and experimental conditions. Haruka integrates contrastive variational inference with an auxiliary microenvironment reconstruction task, enabling the model to learn spatial-context-informed embeddings that capture both perturbation effects and local neighborhood context. Benchmarking on simulated and real datasets demonstrates superior detection of spatially heterogeneous perturbation responses compared to existing methods. Applied to diverse spatial omics platforms, Haruka distinguished immunotherapy responders in melanoma, traced fibrosis progression in human lung tissue, and mapped treatment-resistant microenvironments in KRASG12D-mutated lung cancer. Extended to spatially resolved multiomics data co-profiling transcriptomics and chromatin accessibility from an LPC-induced neuroinflammation mouse model, using a modality-parallel design that independently models each omics layer before integration, Haruka resolved condition-specific regulatory programs encoded across chromatin and transcriptome layers, linking spatial domain identity to transcription factor network architecture and human psychiatric disease genetics. Thus, Haruka provides a generalizable framework for spatial contrastive analysis, enabling systematic dissection of tissue organization, cellular plasticity, and microenvironmental remodeling across disease contexts and therapeutic perturbations.

bioinformatics↗

bioRxiv2025

An Expectation and Maximization Algorithm for Multivariate Genome-wide Association Studies (EMmvGWAS)

Genome-wide association studies (GWAS) commonly focus on one quantitative trait at a time, even when multiple traits are collected. However, joint analysis of multiple traits can increase the power to detect genetic associations by leveraging trait correlations. Multivariate GWAS is particularly important for identifying pleiotropic effects and uncovering shared genetic architecture underlying complex traits. Despite its great potential, multivariate GWAS methods face substantial computational challenges due to the high dimensionality of polygenic covariance structures and the cost of scanning genome-wide markers. We present EMmvGWAS, an efficient computational framework implemented as an R, which dramatically reduces computation time for multivariate GWAS involving a moderate number of traits. The algorithm estimates the genetic and environmental covariance matrices from a pure polygenic model using an Expectation-Maximization (EM) algorithm. To improve computational efficiency during genome-wide scanning, we use a semi-exact method that fixes the estimated covariance matrices and treats their ratio, defined as the genetic covariance matrix multiplied by the inverse of the environmental covariance matrix, as a known constant across all markers. This approach enables closed-form solutions for marker effects at each locus, dramatically reducing the computation time without compromising statistical power. We demonstrate the scalability and performance of our method through both simulation studies and real dataset analyses from rice, mouse and human populations. The method is implemented in R and is designed to support multivariate GWAS involving a moderate number of traits, while also accommodating univariate analyses as a special case. The R package is available at https://github.com/Jason-Teng/EMmvGWAS.

genomics↗

bioRxiv2025

Reprogramming the Rossmann Fold Signature Motif Creates Orthogonal Redox Biocatalysts

Biological reducing power is carried by nicotinamide adenine dinucleotide (phosphate) (NAD(P)/H), which supports cellular functions and cannot be specifically directed to engineered metabolic pathways. Nicotinamide mononucleotide (NMN(H)) has emerged as an orthogonal redox cofactor to address this challenge, yet strategies remain elusive for creating NMN(H)-specific enzymes that no longer interact with the cellular NAD(P)/H pools. Previous designs avoided perturbing the most ancient and conserved GxGxxG motif in Rossmann fold enzymes, the root cause of persistent NAD(P)/H recognition. Herein, we demonstrated that this motif, though long considered essential, is in fact mutable to yield active NMN+-specific enzymes. This is implemented on two unrelated model enzymes chosen to garner orthogonal reducing power from either the cheap electron source phosphite or glycolysis. On phosphite dehydrogenase (PTDH), variants NRC-01 and NRC-02 eliminated electron leaking to numerous NAD(P)H-dependent side reactions in whole cells and crude cell lysates while driving NMNH-dependent biotransformation with [~]240-fold higher productivity than existing catalysts. On glyceraldehyde-3-phosphate dehydrogenase (GapA), variant RSQ featured a [~]2.9x104-fold cofactor specificity switch to NMN+ from NAD+. Combined Rosetta modeling, systematic structural alignment, and experimental results revealed that Rossmann fold reprogramming, paired with engineered structural reinforcement, may offer a general route to orthogonal redox biocatalysts.

molecular biology↗