Biology subjects

Kang, Z.

Publications and source records attributed to Kang, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Haplotype-resolved genome assembly of barberry reveals structural divergence and allele-specific expression during infection by wheat stripe rust pathogen

Barberry is an ecologically and medicinally important perennial shrub and an alternate host of Puccinia striiformis f. sp. tritici (Pst), the causal agent of wheat stripe rust. However, its genome architecture and molecular responses to Pst infection remain poorly understood. Here, we generated a haplotype-resolved, chromosome-level genome assembly of Berberis aggregata using PacBio HiFi and Hi-C sequencing. The two haplotypes, Ba5A and Ba5B, were assembled into 14 pseudochromosomes each, with genome sizes of approximately 1.13 and 1.16 Gb, respectively, and showed high continuity and completeness. Comparative analyses revealed extensive divergence between the haplotypes, including widespread structural variation, substantial hemizygosity, and TE-rich, gene-poor non-alignable regions. Haplotype-specific hemizygous genes also exhibited distinct functional biases, with Ba5A enriched in immune-related processes and Ba5B associated with transport and cell-envelope functions. Dual RNA-seq across infection stages revealed stage-dependent host transcriptional reprogramming, characterized by early pathogen perception followed by stronger defense activation and photosynthesis repression, whereas Pst displayed a coordinated colonization program consistent with its biotrophic lifestyle. Allele-specific expression analysis showed that ~25-28% of genes exhibited significant haplotype-biased expression, and a subset transitioned from unbiased expression at 0 dpi to Ba5A- or Ba5B-biased expression during infection. Integration with differential expression analysis further identified infection-responsive induced-biased genes associated with defense-related processes, including responses to fungal pathogens. These findings show that haplotype divergence and dynamic allele-specific regulation jointly contribute to transcriptional responses during Pst infection, providing new insights into the genomic basis of barberry-rust pathogen interactions.

genomics↗

bioRxiv2026

Apical3DTip: Elliptic Cross-section-based Reconstruction for the Embryo Initial Cell of Arabidopsis

BackgroundCell geometry plays a central role in determining division orientation and body axis formation during early embryogenesis in Arabidopsis thaliana. However, quantitative analysis of dynamic three-dimensional (3D) morphology remains challenging because live-imaging studies often rely on two-dimensional (2D) projections, while existing 3D reconstruction approaches, including mesh-based methods, often lose the original orientation information relative to the ovule and require labor-intensive mesh correction. In addition, embryo positional fluctuation caused by floating in liquid medium and continuous growth makes it difficult to analyze temporal morphological changes within a common coordinate system. ResultsWe developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology. The method first establishes a standardized 3D coordinate system by normalizing cell orientation based on the bottom plane and the optical axis of the observation. Cell morphology is then reconstructed through ellipse-based approximation of serial cross-sections extracted from stacked imaging data, enabling accurate geometric characterization without the need for complex surface mesh reconstruction. To evaluate shape anisotropy, we quantified the apical cell shape in 3D. The framework further supports the characterization of volumetric features of subsequent division, providing a basis for quantifying 3D embryogenesis. ConclusionOur framework provides a simple and noise-reduced approach for quantitative analysis of living cell morphology in 3D. We named the integrated method of combining coordinate normalization with elliptical cross-section-based reconstruction Apical3DTip. This method enables consistent comparison of cell shapes without extensive manual corrections. The method overcomes key limitations of 2D projection-based and mesh-dependent analyses and offers a practical platform for quantifying cell shape and daughter cell shapes in 3D. More broadly, it provides a quantitative foundation for exploring the relationship between cell geometry, morphodynamics, and developmental patterning in living plant embryos.

plant biology↗

bioRxiv2026

Gut bacterial Infection drives Parkinsonian pathology in LRRK2 G2019S Knock-in Mice

The LRRK2 G2019S mutation is one of the most common genetic risk factors for Parkinsons disease (PD), yet LRRK2 G2019S knock-in (KI) mice rarely develop robust neurodegeneration under basal conditions, suggesting that additional environmental triggers are required for disease progression. Here, we established a clinically relevant gene-environment interaction mouse model of PD by subjecting LRRK2 G2019S KI mice to recurrent Citrobacter (C.) rodentium infection, a murine model of enteric bacterial inflammation. Repeated infection induced progressive PD-like phenotypes selectively in KI mice, including motor impairment, reduced locomotor activity, impaired motor coordination, selective nigrostriatal dopaminergic neurodegeneration, enhanced neuroinflammation, and pathological phosphorylated -synuclein (p-Syn) accumulation, whereas wild-type (WT) mice remained largely resistant. Mechanistically, infected KI mice developed markedly exacerbated colonic inflammation, epithelial barrier dysfunction, increased intestinal permeability, and enhanced inflammasome activation despite normal bacterial clearance, indicating that pathogenic LRRK2 signaling amplifies inflammatory responses rather than impairing antimicrobial defense. In parallel, recurrent infection induced pronounced intestinal p-Syn accumulation and expansion of pathology beyond the epithelial layer in KI mice, supporting a gut-brain axis mechanism linking intestinal inflammation to neurodegeneration. Collectively, these findings demonstrate that the LRRK2 G2019S mutation functions as a sensitizing factor that cooperates with recurrent enteric inflammation to drive PD- related pathology. This study establishes a physiologically relevant LRRK2 G2019S gene- environment interaction mouse model that recapitulates key behavioral, neuropathological, and inflammatory features of PD.

immunology↗

bioRxiv2026

Deformation Gradient Tensor Model of Roll-Spiral Transformation for Protein Assembly Refractile Body

Spiral geometries commonly occur in natural and engineered systems and are fundamentally described by curvature and torsion. In deformation-dominated systems, these variables evolve dynamically, requiring a continuum mechanical framework to link geometry and deformation. This study focused on refractile bodies (R-bodies), protein supramolecular assemblies that undergo reversible roll-spiral transformations in response to stimuli such as pH changes. Although multiple R-body types with distinct morphologies and unrolling behaviours were experimentally identified, their deformation mechanisms lack quantitative theoretical descriptions. We proposed a deformation-gradient-tensor-based continuum model incorporating geometrical mapping from the rolled to spiral state within a unified framework. The model successfully reconstructed macroscopic deformation behaviours of types 51, 7, and Pa R-bodies by capturing differences in unrolling behaviours, tapered geometry, and spatio-temporal evolution. The analysis revealed that deformation proceeds through a coupled process in which the curvature decreases via straightening, while the torsion increases by twisting. Importantly, the framework connected the macroscopic morphology with microscopic lattice deformation, enabling quantitative inference of lattice intervals and angles. The proposed comprehensive geometric model of the R-body roll-spiral transformation offers a general mathematical foundation for understanding deformation-driven spiral transformations in soft matter systems.

biophysics↗

bioRxiv2026

Two ancient nuclear lineages and divergent reproductive strategies drive global success of the wheat stripe rust pathogen

Understanding the mechanisms for generating and maintaining plant pathogen genetic diversity is essential for predicting disease emergence and safeguarding the food supply. Here, using population-genomic analyses of 507 global isolates of the wheat stripe rust fungus Puccinia striiformis f. sp. tritici, we uncover two nuclear lineages that diverged before modern agriculture and reinforced during wheat domestication. Distinct lineage combinations underpin three major global populations: two predominantly sympatric, homozygous lineages (nuclA-nuclA and nuclB-nuclB) and a globally dominant heterokaryotic lineage (nuclA-nuclB) of hybrid origin. This hybrid population evolves largely clonally, accompanied by recurrent somatic nuclear exchange. Identical virulence traits can arise independently via somatic nuclear exchange in clonal populations or through sexual recombination in recombining populations, revealing convergent evolutionary routes to adaptation. Together, our results provide a transformative nuclear-level perspective linking deep evolutionary history and reproductive strategy to the global success of cereal rust fungi.

genetics↗

bioRxiv2026

Decoding social integration in schooling fish using closed-loop real-virtual interactions

Collective motion in animal groups emerges from local interactions, yet how individuals select and integrate social information remains poorly understood. In schooling fish, inferring which neighbors drive an individuals behavior at any given moment is extremely challenging because the analysis of trajectories alone does not unambiguously reveal the underlying causal interactions that generate the observed motion. Here, we used a closed-loop virtual-reality system in which a single real rummy-nose tetra Hemigrammus rhodostomus interacted in real time with four model-driven virtual conspecifics whose interaction rules were precisely controlled. By systematically varying the number of influential neighbors of the virtual fish and combining experiments with simulations, we quantified how social information filtering shapes individual and collective dynamics. Collective coordination increased with stronger social coupling, but analyses of kinematics, spatial organization, correlations, and turning dynamics consistently showed that the real fish behaved as if it were responding primarily to the single most influential neighbor, whose identity could change over time. These results demonstrate that selective interaction with the most influential neighbor is sufficient to sustain coordinated group motion and highlight the power of bio-hybrid closed-loop experiments to reveal causal mechanisms of collective behavior.

animal behavior and cognition↗

bioRxiv2026

Enhancing the detection of HTT1a with neoepitope antibodies in mouse models of Huntington's disease

Huntingtons disease is an inherited neurodegenerative disorder caused by a CAG repeat expansion in exon 1 of the Huntingtin (HTT) gene, encoding an expanded polyglutamine tract in the huntingtin (HTT) protein. The pathogenic CAG repeat of HTT is unstable and undergoes progressive somatic expansion in specific brain cells and peripheral tissues throughout life. Genes involved in DNA mismatch repair pathways, which promote repeat expansion, have been identified as genetic modifiers of the disease. Consequently, the rate of CAG repeat expansion is a key determinant driving the age of onset and disease progression. As the CAG repeat expands, alternative processing of HTT pre-mRNA increasingly favours production of the HTT1a transcript, which encodes the highly pathogenic and aggregation-prone HTT1a protein. This process provides a mechanistic link between CAG repeat expansion and disease pathogenesis, as increased HTT1a production accelerates HTT aggregation and neuronal dysfunction. HTT1a has previously been detected in Huntingtons disease mouse models by using immunoprecipitation coupled with western blotting, homogeneous time-resolved fluorescence (HTRF) and Meso Scale Discovery (MSD) bioassays, and immunohistochemistry. These approaches were developed using MW8, a neoepitope antibody that specifically recognizes the C-terminus of HTT1a. MW8 is a relatively weak antibody with limited detection sensitivity. To generate more robust HTT1a-specific reagents, two novel recombinant antibodies, 1B12 and 11G2, have been developed for evaluation. Using an allelic series of knock-in (HdhQ20, HdhQ50, HdhQ80, HdhQ111, CAG140 and zQ175) mice, alongside transgenic YAC128 and N171-82Q models, we extensively evaluated and compared the performance of MW8, 1B12 and 11G2. We demonstrate that 1B12 and 11G2 function as HTT1a-specific neoepitope antibodies by immunoprecipitation with western blotting, and by immunohistochemistry. To enhance HTT1a detection using HTRF and MSD technology platforms, we further evaluated the performance of 1B12 and 11G2 in HTT bioassays using cortical lysates from zQ175 and YAC128 mice. In zQ175 mice, enhanced detection of aggregated HTT1a by HTRF and MSD revealed that HTT fragments longer than HTT1a can be incorporated into HTT1a-containing aggregates. The most sensitive assays were subsequently applied across the allelic series of knock-in mice to assess the effect of polyglutamine length on bioassay performance. For optimal sensitivity, we recommend the preferential use of 1B12 for HTRF assays and 11G2 for MSD assays. Collectively, these findings establish 1B12 and 11G2 as robust antibodies to reliably detect and track HTT1a pathology in vivo and promotes the replacement of previously used MW8-based experimental approaches. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=191 HEIGHT=200 SRC="FIGDIR/small/708805v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@16d10faorg.highwire.dtl.DTLVardef@1759f41org.highwire.dtl.DTLVardef@12a8c21org.highwire.dtl.DTLVardef@55fe6a_HPS_FORMAT_FIGEXP M_FIG C_FIG Osborne et al. used Huntingtons disease mouse models to evaluate and compare the performance of HTT1a-specific neoepitope antibodies by using immunoprecipitation with western blotting, bioassays, and immunohistochemistry. In contrast to MW8, they establish that 1B12 and 11G2 are robust antibodies to reliably detect and track HTT1a pathology in vivo.

neuroscience↗

bioRxiv2026

Ca2+ oscillations promote microtubule-band turnover and support tip growth in Arabidopsis zygotes

The zygote is the origin of development, and in most angiosperms, it divides asymmetrically to establish the apical-basal axis. In Arabidopsis thaliana, the zygote undergoes tip growth-like polar elongation, using a subapical transverse microtubule band (MT band). Because canonical tip-growing cells rely on longitudinal actin filaments (F-actin), it remains unclear whether and how the zygote employs conserved tip-growth mechanisms. Here, using quantitative live-cell imaging, pharmacological perturbations, and mechanical simulations, we found that oscillatory Ca{superscript 2} waves, a hallmark of tip growth, are coupled to zygote elongation through a bidirectional feedback loop, as in other tip-growing cells. However, Ca{superscript 2} waves were dispensable for overall F-actin alignment but promoted MT band turnover. Our study provides a model showing that the zygote uses a conserved tip-growth module, in which Ca{superscript 2} oscillations and cell elongation reinforce each other, but redirects the target cytoskeleton to the MT band, enabling zygote-specific tip growth for axis formation.

plant biology↗

bioRxiv2026

A Beneficial Endornavirus Enhances the Fitness of Phytopathogenic Fungus Rhizotonia solani

Although viruses are primarily characterized as pathogenic agents, certain viruses confer advantages to their hosts. The extent to which a virus can improve host biological performance, however, remains a fascinating topic in virology. An endornavirus, Rhizoctonia solani endornavirus IM (RsEV-IM), was identified as prevalent in Rhizoctonia solani isolates obtained from potato plants. Comparative analysis with a virus-free isogenic strain demonstrated that RsEV-IM infection enhances mycelial growth, sclerotium formation, stress tolerance, and fungal virulence across multiple plant species. Inoculation tests involving numerous R. solani strains confirmed that only RsEV-IM-infected strains exhibited high pathogenicity, independent of other mycovirus infections. Additionally, its secreted protein fraction of RsEV-IM-infected fungus contained elevated levels of various proteins, including those involved in cell wall degradation. This fraction not only facilitated R. solani infection but also suppressed the growth of other fungi and bacteria. These findings position RsEV-IM as a beneficial virus that widely enhances its hosts biological fitness. From both pathological and ecological perspectives, these observations are significant, as they reveal that a mycovirus can serve as a key virulence determinant in fungal populations and potentially shape microbial community dynamics in natural environments. ImportanceFungal pathogenicity and ecological traits have long been thought to be primarily governed by endogenous genetic factors. However, this study reveals a mutualistic relationship between an endornavirus (RsEV-IM) and Rhizoctonia solani, demonstrating that viral infection enhances fungal virulence and ecological fitness. RsEV-IM stimulates fungal growth and the secretion of cell wall-degrading enzymes, resulting in a severe disease phenotype. Ecologically, RsEV-IM-infected fungi potentially gain a competitive advantage over soil microbiota. These findings present a key example of a virus acting as an essential extrachromosomal determinant of fungal pathogenicity and ecosystem interactions. Our results advance the understanding of fungal virulence mechanisms and underscore the broader significance of beneficial virus-fungus associations in agriculture and microbial ecology.

microbiology↗

bioRxiv2026

Two-step polar plastid migration via F-actin and microtubules ensures unequal inheritance during asymmetric division of Arabidopsis zygote

The zygote is the origin of development, and in most angiosperms, it divides asymmetrically to establish the apical-basal axis. In Arabidopsis thaliana, various organelles in the zygote undergo polar migration along actin filaments (F-actin), resulting in unequal inheritance, but the behavior of plastids, essential precursors of chloroplasts, has remained unclear. Here, using quantitative live-cell imaging, we reveal that plastids undergo two-step polar migration: they first move apically together with the nucleus along F-actin, and when nuclear migration slows, they switch to microtubule (MT)-dependent migration to move further apically. This results in unequal plastid inheritance by the apical cell. Although these plastids are amyloplasts containing starch granules, starch is dispensable for migration, unlike the gravity response. Instead, a fertilization-activated MAP kinase pathway is required for polar plastid migration. Our results demonstrate that the zygote possesses a spatiotemporal regulatory mechanism that ensures unequal plastid inheritance at the onset of plant ontogeny.

plant biology↗

bioRxiv2025

Discovery and optimization of antimicrobial peptides from extreme environments on global scale

The escalating crisis of global antimicrobial resistance (AMR) necessitates the discovery of novel antibiotics. Antimicrobial peptides (AMPs), particularly those from under-explored extreme environments, represent a promising therapeutic class. Here, we introduce SEGMA (Structure-aware Extremophile Genome Mining for Antimicrobial peptides), a computational framework that integrates structure information to systematically mine AMPs from extremophile genomes on a global scale. By analyzing 60,461 extremophile metagenome-assembled genomes (MAGs) from diverse habitats, SEGMA identified 3,298 novel AMPs (termed "extremocins"), which exhibit unique amino acid profiles and physicochemical properties. Leveraging a beam search-guided optimization strategy, we further enhanced selected extremocins to achieve broad-spectrum antimicrobial activity. Experimental validation confirmed potent in vitro efficacy against clinically relevant pathogens. This study underscores the value of structure-aware mining and extremophile microbiomes in expanding the antibiotic arsenal against AMR. HighlightsO_LISEGMA, a structure-aware deep learning framework, mines 3,298 novel antimicrobial peptides (extremocins) from 60,461 extremophile genomes on global scale. C_LIO_LIExtremocins exhibit unique sequence features, and expand known antibiotic space, few of which shows homology to existing AMP databases. C_LIO_LIA beam search-guided optimization strategy enhanced selected extremocins to achieve broad-spectrum activity against clinically relevant pathogens. C_LIO_LIExperimental validation confirmed that candidate extremocins exhibit potent in vitro and in vivo antimicrobial activity, highlighting their therapeutic potential. C_LI

bioinformatics↗

bioRxiv2025

Title: KymoTip: High-throughput Characterization of Tip-growth Dynamics in Plant Cells

Live imaging data analysis often requires an objective, local, and accurate way of quantification of cell dynamics. In the research field of polarized tip-growth, the cell fluctuations and/or fluctuations in tip position and growth direction hampers automated analyses of huge amounts of imaging sequences. The fluctuated nature in data makes it unclear how cell shape and growth are linked to intracellular events that could be the actual driving force of cell growth. To overcome these difficulties, we developed a powerful and user-friendly tool called KymoTip with an available format. In this software, novel functions such as coordinate normalization, tip-bottom detection, and signal kymograph were implemented. We confirmed that not only plasma membrane-labeled fluorescent images, but also images such as bright-field and cortical microtubule markers --so long as the cell contours can be identified-- are amenable to KymoTip. Furthermore, by combining markers for cell contours with those that visualize intracellular structures, it becomes possible to quantitatively analyze various intracellular events, such as nuclear migration and calcium wave, in conjunction with cellular growth dynamics. Since KymoTip can be handled by non-specialist, it is expected to promote understanding of what happens at the sub- and cellular level with high throughput outcomes. Significance statementFaced with fluctuations in cell coordinates and cell tip positions, position correction of live imaging data and accurate detection of tip position are key challenges in plant developmental biology. We solved them with a powerful and user-friendly tool, KymoTip, that can realize cell position correction, cell tip detection with cell centerline, and quantification of intracellular events.

bioinformatics↗

bioRxiv2025

Development, validation, and evidence of frequent exchange of mating type HD alleles of Puccinia striiformis f. sp. tritici provide an insight into highly genetic diversity

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a worldwide destructive wheat disease. It possesses the potential to generate novel virulence races through sexual reproduction, which is the primary cause of its extensive epidemics. Mating type (MAT) of Pst lacks direct experimental evidence to account for the role of sexual recombination on high genetic diversity in evolution. Herein, we developed 11 pairs of HD alleles of Pst MAT genes, and 2 pairs HD alleles of P. striiformis f. sp. hordei (Psh, the causal agent of barley stripe rust) MAT genes. HD alleles in Pst are different from those in Psh. We tested that mating system of Pst is tetrapolar responsible for sexual genetic recombination via HD genes by experiments of selfing and sexual hybridization on alternate (secondary) hosts. Multiple HD gene types and HD gene recombination types were detected in Chinese natural Pst populations of 200 Pst isolates and 13 Psh isolates from 92 sampling sites in 11 provinces, supporting that sexual reproduction accounts for frequent emergence of new races and high genetic diversity of Pst population in northwestern oversummering variable region (hotspot) than other stripe rust-occurring regions in China. Overall, these results provide an insight into diverse types and frequent exchange of Ps MAT alleles, revealing the important role of sexual recombination via MAT alleles in virulence variation, high genetic diversity and epidemics of Pst, and also provide a basis for wheat stripe rust control in those regions where sexual reproduction occurs. Author summaryHeteroecism of the wheat stripe rust pathogen Puccinia striiformis f. sp. tritici (Pst) was discovered a decade ago. The significance of sexual reproduction in maintaining genetic diversity has emerged as a pressing concern in contemporary scientific discourse. In this study, we elucidated the presence of multiple HD gene types (HD1 linked to HD2) in Puccinia striiformis f. sp. tritici (Pst), distinguishing it from P. striiformis f. sp. hordei. Through biological experiments on sexual reproduction, we demonstrated that the mating system of Pst is tetrapolar. Furthermore, we observed that sexual reproduction, particularly selfing (individual race) and crossing (between two races), facilitates sexual recombination via HD genes, leading to pathogenic variation and a high level of genetic diversity. We established a model illustrating sexual reproduction of Pst by selfing and crossing, respectively. In China, we have observed that the Pst population exhibits a diverse range of HD gene types and HD gene recombination types. Notably, northwestern oversummering variable regions (hotspot) have a higher number of HD genes compared to other epidemic region. Population structure and cluster analyses based on HD genes indicate that Pst populations in Tibet and Xinjiang are distinct from those in other inland regions. These findings provide valuable insights into comprehending the intricate interplay between sexual reproduction and genetic diversity of Pst, thereby elucidating the reasons behind the high genetic diversity of the Chinese Pst population.

microbiology↗

bioRxiv2025

Deciphering Biosynthetic Gene Clusters with a Context-aware Protein Language Model

Microbial secondary metabolites, synthesized by biosynthetic gene clusters (BGCs), offer vast potential for biotechnological applications. Among BGC profiling techniques, computational detection methods face challenges, including time-consuming alignment and reliance on predefined profiles. To address these, we present BGC-Finder, an end-to-end pipeline utilizing protein language models for BGC detection and annotation from microbial genomes and metagenomes. This approach achieves remarkable increase in profiling speed of up to 100-fold, and employs genomic context-aware modeling to facilitate interpretable genetic essentiality assessment and large-scale BGC clustering. BGC-Finder outperformed traditional methods, successfully detecting 9.49% more biosynthetic-core genes and 27.70% more cytochrome P450s in 742 experimentally-validated BGCs. Notably, it retrieved 31 remote biosynthetic homologs from 210 polar marine metagenomes and identified 4,585 BGCs with 6,388 core genes from 256 fungal genomes. These findings highlight BGC-Finders capability to illuminate "microbial biosynthesis dark matter" (sequence-unrelated, function-similar biosynthetic enzymes) and expedite natural product discovery. HighlightsO_LIBGC-Finder is an accurate and ultrafast pipeline leveraging protein language models (pLMs) to predict and annotate biosynthetic gene clusters (BGCs) from microbial genomes and metagenomes. C_LIO_LIThe genomic context-aware model enables interpretable analysis: attention-driven identification of essential biosynthetic genes and embedding-guided BGC clustering. C_LIO_LIBGC-Finder sensitively retrieves remote homologous BGCs from both bacteria and fungi genomes, uncovering hidden microbial biosynthesis dark matter. C_LIO_LIWe discovered a non-ribosomal peptide synthetase (NRPS) family, which involved into function-specific BGCs in two evolutionarily distant fungi. C_LI

bioinformatics↗

bioRxiv2025

The membrane skeleton density of red blood cells in MYH9-related disease patients is decreased

MYH9-related disease (MYH9-RD) is a rare autosomal dominant disorder caused by mutations in MYH9 gene, which encodes the heavy chain of nonmuscle myosin IIA. Nearly all MYH9-RD patients present with macrothrombocytopenia, characterized by decreased platelet count and increased platelet size. In this study, we collected blood samples from three MYH9-RD patients (R702S, D1424N, and R1464C) and unexpectedly found that the actin levels in the red blood cells (RBCs) from all three MYH9-RD patients are substantially lower than the healthy controls. We further revealed that the levels of two RBC membrane skeleton proteins, -spectrin and tropomodulin, are also reduced in MYH9-RD RBCs. We showed that the membrane skeleton of MYH9-RD RBCs was more porous and that MYH9-RD RBCs produced more severe deformation under hyperosmotic pressure compared to healthy controls. We conclude that MYH9-RD mutations reduce the RBC membrane skeleton density and impair its mechanical properties, and propose that defects in the membrane skeleton network in RBCs may be a common symptom in MYH9-RD. Key pointsThe levels of membrane skeleton proteins and the density of membrane skeleton network in RBCs of MYH9-RD patients are reduced. MYH9-RD RBCs produce greater deformation under hypertonic conditions compared to healthy controls.

pathology↗

bioRxiv2025

Estimation of total mediation effect for a binary trait in a case-control study for high-dimensional omics mediators

Mediation analysis helps uncover how exposures impact outcomes through intermediate variables. Traditional mean-based total mediation effect measures may suffer from the cancellation of opposite component-wise effects, and existing methods often lack the power to capture weak effects in high-dimensional mediators. Additionally, most high-dimensional mediation analysis methods have focused on continuous outcomes, with limited attention to binary outcomes, particularly in case-control studies. To fill this gap, we propose an R2 total mediation effect measure within the liability framework that offers a clear and intuitive causal interpretation, provides additional insights beyond the mean-based measures, and is invariant to disease prevalence. We develop a cross-fitted, modified Haseman-Elston regression-based estimation procedure tailored for mediation analysis in case-control studies, which can also be applied to cohort studies. Our estimator remains consistent in the presence of non-mediators and weak effects, as demonstrated in extensive simulations. Theoretical justification for consistency is provided under mild conditions and without requiring exact mediator selection. In a case-control substudy of the Womens Health Initiative involving 2150 individuals, we found that many metabolites were mediators with weak effects in the path from BMI to coronary heart disease, and we estimated that 89% (95% CI: 57%-100%) of the BMI-explained variation in underlying CHD liability is mediated by the measured metabolomics. The proposed estimation procedure is implemented in the R package "r2MedCausal", available on GitHub.

genomics↗

bioRxiv2025

Towards a Generative Paradigm for Large-scale Microbiome Analysis by Generative Language Model

Microbiome analysis has traditionally relied on taxonomic abundance tables, which, while effective, often constrain the exploration of deeper contextual relationships. In this study, we present MGM 2.0, a novel framework that applies advanced natural language processing (NLP) techniques to microbiome research. By reimagining microbiome samples as sentences and microbial species as words, MGM 2.0 enabled the extraction of nuanced patterns and relationships. The model demonstrated robust predictive performance in identifying exogenous species colonization (AUROC = 0.86). Additionally, through prompt-guided microbiome data generation, MGM 2.0 produced realistic microbial profiles conditioned on disease labels. The framework further revolutionized donor selection in fecal microbiota transplantation (FMT) by framing it as a sequence-to-sequence prediction task, enabling the prediction of post-transplantation community compositions and the identification of super donors for personalized treatments (average increase in C2R = 0.52). This innovative integration of NLP and microbiome science provides a versatile toolkit for predictive modeling, data generation, and personalized medicine. HighlightsO_LIIntroduced MGM 2.0, a generative language model utilizing sentence-like representation for microbiome analysis and generation. C_LIO_LIDemonstrated that sentence-like representation preserves sample distinctions, enabling accurate microbial sample classification tasks, such as colonization prediction. C_LIO_LIGenerated realistic, disease-specific microbiome profiles using a prompt-guided approach, validated by a novel "Microbiome Turing Test." C_LIO_LIApplied MGM 2.0 to fecal microbiota transplantation (FMT) donor selection, accurately predicting post-transplant community compositions and identifying potential "super donors" for personalized treatment strategies. C_LI

bioinformatics↗

bioRxiv2025

Microbial general model: Leveraging large language model for contextualized microbiome analysis

Microbial communities significantly impact medicine, biotechnology, and agriculture. Advanced sequencing technologies have generated extensive microbiome data, enabling the discovery of substantial evolutionary and ecological patterns. However, traditional supervised learning methods struggle to capture universal patterns in microbial community data, largely due to the large data heterogeneity and profound batch effects among samples, rendering it difficult to classify samples as well as detect biomarkers from millions of samples, not to say the intricate but important dynamic patterns from a variety of contextualized sceneries. In this study, we introduce the Microbial General Model (MGM), the first microbiome community foundation model pre-trained on a dataset of 263,302 microbiome samples using language modeling techniques. MGM demonstrated significant improvements in microbial community classification compared to traditional machine learning methods. Additionally, MGM has enabled contextualized classification, effectively overcomes cross-regional limitations, showing enhanced performance on intercontinental datasets through transfer learning. Furthermore, fine-tuning MGM on a longitudinal infant dataset revealed distinct keystone genera during development, with Bacteroides and Bifidobacterium exhibiting higher attention weights in vaginal deliveries, and Haemophilus in cesarean deliveries. Finally, through in silico modeling, the model also uncovered novel microbial dynamic patterns in a Crohns disease cohort following antibiotic treatment. In conclusion, by leveraging self-attention and autoregressive pre-training, MGM serves as a versatile model for various downstream microbiome tasks and holds significant potential for achieving contextualized aims. Key pointsO_LIThe Microbial General Model (MGM) is a foundation model with millions of parameters pre-trained on sub-million microbial community data. C_LIO_LIMGM outperforms traditional methods in various microbiome classification and prediction tasks, such as microbial community classification. C_LIO_LIMGM effectively captures the spatial and temporal dynamics of microbial communities. C_LIO_LIMGM could detect the effects of perturbation on microbial community through in silico experiments. C_LI

bioinformatics↗