Biology subjects

Yang, X.

Publications and source records attributed to Yang, X..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Autoantibodies from recovered Guillain-Barre syndrome patients exhibit altered effector functions that prevent subsequent neuron degeneration

Guillain-Barre syndrome (GBS) is an autoimmune polyneuropathy that is the leading cause of nonpoliovirus-associated acute flaccid paralysis worldwide. In most cases, GBS occurs following an infection, most commonly Campylobacter jejuni, through the induction of antibodies recognizing bacterial ganglioside-mimicking lipooligosaccharides that cross-react with human neuronal gangliosides. This post-infectious autoimmune cascade causes neuropathy, from which patients can recover as their anti-ganglioside antibody titers diminish and immunostimulation decreases. In this study, we find 10% of clinically confirmed GBS patients maintain high titers of circulating anti-ganglioside antibodies more than one decade after recovery. These antibodies no longer cause neuropathy compared to acute sera from the same patients using a human pluripotent stem cell-derived sensory neuron model with human complement. We found both IgG subclass and glycoform differences between paired acute and recovered GBS patient sera, including anti-inflammatory modifications on isolated anti-GM1 ganglioside antibodies. Together, these data suggest that patients with GBS select for non-pathogenic variants of autoantibodies that are no longer capable of damaging their neurons, but may still protect against C. jejuni infection.

immunology↗

bioRxiv2026

Unifying physical and molecular coordinate systems across modalities in spatial biology

Establishing a unified physical and molecular coordinate system from fragmented multi-modal data is a longstanding challenge in biology. Here, we present MAPS, a modality-agnostic platform for spatial biology comprising (1) MAPS-alignment for ultrafast alignment of any modality, (2) MAPS-integration for both anchored and unanchored integration across orthogonal modalities for 3D multi-modal reconstruction, and (3) MAPS-Explorer for large-scale interactive 3D analysis. MAPS outperformed existing methods across extensive benchmarks on 34 datasets spanning 16 technology platforms and 6 modalities, while delineating fine-grained multi-modal tissue architectures across diverse biological systems in mouse and human. At cross-consortium scale, MAPS integrated 434 slices comprising 21 million cells from 18 atlases and 5 modalities to construct the most comprehensive 3D multi-modal mouse brain atlas. At individual laboratory scale, MAPS empowered routine 2D spatial assays to reconstruct continuous 3D multi-modal landscapes of human hepatocellular carcinoma, revealing the limitations of 2D spatial relationships and uncovering depth-dependent immune-state transitions.

bioinformatics↗

bioRxiv2026

β4-integrins safeguard nuclear mechanics to suppress prostate cancer progression

Prostate cancer (PCa) progression is accompanied by profound alterations in cell-extracellular matrix (ECM) adhesion, nuclear architecture and mechanical adaptability, yet the molecular mechanisms linking these processes remain poorly understood. Hemidesmosomes (HDs), formed by 6{beta}4-integrins, anchor epithelial cells to the basement membrane and couple extracellular forces to the intermediate filament (IF) cytoskeleton. Here, we identify a previously unrecognized tumor-suppressive function of {beta}4-integrins in preserving nuclear integrity in prostate epithelial cells. Loss of {beta}4-integrins disrupted the cytokeratin-5 network and its coupling to the nucleus, leading to nuclear softening, lamin remodeling, reduced heterochromatin content and enhanced confined migration. Unexpectedly, proximity-labeling proteomics revealed that {beta}4-integrins engage nuclear pore complex (NPC) components in an 6-independent manner, particularly upon HD disassembly. Selected interactions were validated using proximity ligation and co-immunoprecipitation assays. {beta}4-integrin loss was associated with enlarged nuclear pores and aberrant nucleocytoplasmic transport, including nuclear accumulation of YAP1. Consistent with these findings, reduced {beta}4-integrin expression in a large PCa tissue cohort correlated with altered nuclear morphology, adverse clinicopathological features, metastatic progression, and poor patient survival. Collectively, our study establishes {beta}4-integrins as a critical molecular link between cell-ECM adhesion, nuclear mechanics and genome integrity.

cancer biology↗

bioRxiv2026

AmPair: automating housekeeping-gene primer design for species-level metataxonomics

Amplicon sequencing of the 16S rRNA gene is the most widely used approach for profiling bacterial communities, but its taxonomic resolution is typically limited to the genus level. Many species carry multiple divergent 16S rRNA alleles that overlap across species boundaries, an ambiguity that even full-length, long-read sequencing cannot fully resolve. Shotgun metagenomics achieves species-level resolution but remains costly, particularly when only a single genus is of interest. Amplicon sequencing of rapidly evolving, protein-coding housekeeping genes offers a cost-effective alternative, yet no tool exists to identify suitable primer sets for a given target taxon. Here we present AmPair, a Snakemake pipeline that, given a target genus and one or more candidate housekeeping genes, designs and ranks primer pairs binding conserved regions while flanking a variable region capable of species-level discrimination, and validates them in silico across all available genomes. Using the genus Bacillus and the housekeeping gene tuf as a case study, the primer set recommended by AmPair amplified 99% of 2,392 genomes; only 0.04% carried multiple alleles and none showed inter-species allele overlap, compared with 91.41% and 69.49%, respectively, for the standard 16S rRNA V1-V9 region. Applied to a Bacillus community profiled by Nanopore sequencing, the same primers resolved closely related species. AmPair thus offers a generalizable and accessible route to species-level community profiling.

bioinformatics↗

bioRxiv2026

RECON infers regions of interest from H&E images and reconstructs whole-slide molecular profiles at single-cell resolution

Spatial omics technologies resolve molecular expression and spatial architecture at single-cell resolution, but profiling whole slides remains costly. In practice, only a few regions of interest (ROIs) are profiled, leaving the rest of the tissue unmeasured. S2-omics was the first framework to unify ROI selection with out-of-ROI prediction, but it operates on superpixels rather than individual cells and predicts discrete cell types rather than continuous molecular profiles. Superpixel-based representations do not explicitly preserve cell boundaries, while categorical cell-type labels cannot quantify molecular expression within cells. Here we present RECON, a two-stage framework that performs ROI inference and whole-slide molecular reconstruction at single-cell resolution, predicting both continuous molecular profiles and discrete cell-type labels. In the first stage, RECON extracts morphological and microenvironmental features from individual cells to identify a representative ROI for spatially resolved single-cell molecular profiling. In the second stage, RECON trains deep learning models on molecular measurements acquired within the selected ROI and reconstructs transcriptomic or proteomic profiles for all remaining cells on the slide. Benchmarked against pathologist annotations, RECONs ROI selection outperforms the superpixel-based S2-omics approaches (IoU: 0.75 versus 0.64). For transcriptomics, refining the modeling unit from superpixels to single cells improves per-gene Pearson correlation by 22%. For proteomics, RECON surpasses the current state-of-the-art method, ROSIE, across all 16 markers, with a median per-cell Pearson correlation of 0.91 versus 0.84. Moreover, RECON delineates tumour boundaries and regions with distinct immune-cell densities, and highlights candidate tertiary lymphoid structures. Together, these results demonstrate that RECON enables informative ROI selection and whole-slide molecular reconstruction at single-cell resolution for both spatial transcriptomics and spatial proteomics.

bioinformatics↗

bioRxiv2026

TigerAI: An AI-powered genetic evidence platform to support clinical development

Genetic evidence is a major determinant of clinical success in drug development, yet its aggregation has long relied on laborious human curation. Large language models (LLMs) have the potential to rapidly synthesize knowledge across biomedical resources, providing a route to scalable AI-driven genetic evidence generation. Here we develop a novel domain-grounded instruction framework to systematically evaluate GPT-5 for producing genetic evidence relevant to clinical trial success. Using 13,022 target-indication pairs from a comprehensive drug development database, we benchmark LLM-derived evidence against a recent exhaustive human expert-curated study. We find that GPT-5 yields genetic evidence that is at least as informative as expert curation for inferring clinical success, while substantially expanding coverage relative to traditional curation resources. Building on these results, we introduce TigerAI (https://tigerai.bio/), a dual-purpose platform for AI-powered genetic evidence that (i) benchmarks emerging state-of-the-art LLMs and (ii) provides an accessible service for querying reliable AI-generated genetic evidence. These contributions outline a practical, domain-grounded pathway for integrating AI-powered genetic evidence into drug development pipelines and for realizing the potential of LLMs to inform clinical success.

genetics↗

bioRxiv2026

Structural basis for catalytic and inhibitory divergence between archaeal and bacterial ammonia monooxygenases

Ammonia oxidation initiates nitrification and is closely linked to microbial N2O production. Ammonia monooxygenase (AMO) catalyzes the first and rate-limiting step of nitrification and is widespread across evolutionarily distinct ammonia-oxidizing archaea (AOA) and bacteria (AOB). The ocean is the largest biome for AOA and AOB, which have distinct ecological niches and markedly different sensitivities to nitrification inhibitors. However, the lack of archaeal AMO structures and inhibitor-bound AMO complexes has hindered mechanistic understanding of the architectural, catalytic, and inhibitory divergence between these two enzyme systems. Here, we report high-resolution cryo-electron microscopy (cryo-EM) structures of marine archaeal AMO captured in active and inactivated states within its native membrane environment, together with inhibitor-bound structures of estuarine bacterial AMO. Archaeal AMO forms an unexpected cup-shaped homotrimer composed of eight subunits per protomer and exhibits substantial architectural divergence from bacterial AMO. Integrated structural, biochemical, kinetic, and computational analyses reveal distinct periplasmic architectures, copper-center organization, and hydrophobic channels between archaeal and bacterial AMOs for ammonium acquisition, catalysis and inhibitor response. These findings provide a structural and mechanistic framework for understanding how archaeal and bacterial AMOs have diverged to distinct ammonia-oxidizing strategies and inhibitor susceptibilities across environmentally important ammonia oxidizers.

molecular biology↗

bioRxiv2026

Single-Cell Profiling of Dynamic Epicardial Cell States During Myocardial Infarction

Background: The epicardium is reactivated after myocardial infarction (MI); however, the gene expression profiles of post-MI adult epicardial subpopulations remain incompletely defined. Methods: Single-cell RNA sequencing was performed on lineage-traced Wt1+ epicardial cells from Wt1CreERT2/+; R26tdT/+; PdgfranGFP/+ adult mice after sham surgery or at 7 and 14 days after permanent artery ligation to induce MI. Immunostaining was performed on Wt1-lineage-traced cardiac tissue to validate spatial expression after ischemic injury. Results: Unbiased clustering identified nine transcriptionally distinct epicardial populations, encompassing mesothelial, fibroblast/mesenchymal, transitional, and proliferative phenotypes. Fibroblast-like epicardial cells (Wt1+/Pdgfra+) showed time-dependent expression profiles associated with upregulation of epithelial-to-mesenchymal transition (EMT) and extracellular matrix (ECM) gene programs. At 7 days post-MI, there was notable enrichment of genes related to chemokines and Wnt components. By 14 days post-MI, the expression profile shifted toward immune regulation. In contrast, a Wt1high/Msln+ population showed minimal upregulation of EMT gene programs but enhanced paracrine signaling related to wound healing and semaphorins, suggesting reactivation of reparative and angiogenic functions akin to those of the epicardium during embryonic development. Immunostaining and in situ hybridization fluorescence analyses validated laminar epicardial cell placement after MI, comprising a surface Msln+ sheet, an overlapping Wt1-lineage band, and a subadjacent PDGFR+ and Periostin+ compartment that expands 7-14 days after MI and regresses by day 28 post-ischemia. Conclusions: Our data define epicardial gene programs in which a signaling epithelial cell surface overlays an effector mesenchymal cell stroma to coordinate angiogenesis, leukocyte recruitment, and ECM remodeling. This study presents the first integrated single-cell atlas of epicardial-derived cells across multiple post-ischemic timepoints, offering new insights into their reparative potential and dynamic signaling diversity in the injured adult heart.

molecular biology↗

bioRxiv2026

A virus co-opts vector's m⁶A machinery for adaptive compensation

Insect vectors harbor diverse viruses that threaten human health and agricultural production, yet the genetic basis of mutualistic virus-vector interactions remain elusive. This underscores a long-standing paradox in pest management: viruses that cause destructive diseases also facilitate vector adaptation in ways that benefit both parties. Here we show that the globally devastating tomato yellow leaf curl virus (TYLCV) acts as a Cooperative Partner, co-opting its vector's m6A epitranscriptomic machinery to offset the reproductive fitness costs of neonicotinoid resistance in the whitefly. In resistant vectors, TYLCV infection alleviates reproductive deficits via the m6A-dependent pathway, where the methyltransferase METTL14 and demethylase ALKBH4 coordinately stabilize vitellogenin (Vg) transcripts to boost female fecundity. Mechanistically, the viral C2 and CP proteins directly interact with METTL14 and ALKBH4, respectively, remodeling their RNA binding affinity to facilitate mA modification on Vg. Disrupting either of these viral-vector protein interactions restores a reproductive cost phenotype in viruliferous resistant vectors, offering novel targets for sustainable pest control. Our findings uncover a fundamental role of epigenetic marks in adaptive trait, reshaping beneficial virus-vector relationships and providing new insights for innovative public health and crop protection strategies to address global challenges.

microbiology↗

bioRxiv2026

Programmable Recruitment of RNA-Binding Proteins Enables Small Molecule-Directed Destabilization of Nuclear Pre-mRNA

Chemically induced proximity has not been systematically applied to control RNA fate. Here, a programmable platform was developed to identify RNA-binding proteins (RBPs) that can be recruited by small molecules to destabilize RNA. Using microtubule-associated protein Tau (MAPT) pre-mRNA as a model target, a heterobifunctional molecule was designed to bind both a ligandable structure in MAPT pre-mRNA and FKBP12F36V-tagged RBPs. Screening of a library of tagged RBPs identified several proteins that reduced MAPT RNA levels, including zinc finger protein 36 (ZFP36) and nanos C2HC-type zinc finger 3 (NANOS3). The approach was then extended from engineered proteins to an endogenous RBP. Using small molecule ligandability maps, a cysteine-reactive ligand for ZFP36 was identified. When this ligand was linked to the MAPT-binding small molecule, endogenous ZFP36 was recruited to MAPT mRNA, reducing its abundance in cells. Genetic and chemical controls demonstrated that activity was dependent on both RNA binding and ZFP36 recruitment, supporting an induced-proximity mechanism. These studies establish a general strategy for identifying new recruitable RBP effectors and should advance ribonuclease-targeting chimera (RiboTAC) technology by expanding the repertoire of effector proteins that can be harnessed for RNA degradation. More broadly, new effectors can be discovered through model reporter-based screens and translated to endogenous systems by mining known protein binders and ligandability maps, providing a systematic path to develop small molecules that control RNA stability, including RNAs targeted through structured regions of nuclear pre-mRNAs.

biochemistry↗

bioRxiv2026

Plant Bioengineering Atlas: A Knowledge Graph of Genes, DNA Constructs, and Plant Traits.

Plant bioengineering has generated tens of thousands of genotype-to-phenotype relationships, but this knowledge remains fragmented across narrative literature and difficult to use computationally. Inconsistent descriptions of DNA constructs, host species, and traits, including variable species names, omitted regulatory elements, and inconsistent gene symbols, impede data reuse, comparative analysis, and design-build-test-learn cycles. Here, we present the Plant Bioengineering Atlas, a literature-mined, ontology-grounded knowledge base assembled using an artificial intelligence (AI)-aided extraction pipeline. A large language model parsed open-access primary research articles to generate structured, provenance-anchored records of engineered genes, modification types, promoter-gene-terminator constructs, host species, target traits, and reported phenotypes, with every record traceable to its source. The current release contains 14,358 curated records encompassing 6,998 distinct genes across 436 plant species from 6,452 papers published between 2000 and 2026. Corpus analysis reveals that experiments are concentrated in a small group of model and crop species, disease and pathogen resistance is the most frequently engineered trait class, and constitutive regulatory parts (particularly the CaMV 35S promoter and NOS terminator) remain pervasive. Two in five records omit one or both flanking regulatory elements (i.e., promoter and terminator), while only 23.4% describe cassettes in which both elements resolve to named part classes, exposing a systematic reproducibility gap. We organize these data into a knowledge graph linking genes, constructs, species, and traits; provide access through an interactive web portal; and propose an AI-compatible documentation standard for AI-ready reporting. The Plant Bioengineering Atlas provides a foundation for data-driven hypothesis generation and AI-aided plant biodesign.

synthetic biology↗

bioRxiv2026

Brain State Dynamics and Developmental Differences in Reading Comprehension

Reading comprehension is a complex cognitive task that involves dynamic interactions between the brain and external information. Previous studies on reading development primarily focused on localized or static brain activities. However, it remains an enigma how brain state dynamics evolve with development underlying reading comprehension. This study aims to address this issue by combining functional magnetic resonance imaging (fMRI) with Hidden Markov Model (HMM) to explore brain state dynamics. A total of 35 typically developing children and 31 adults were scanned while reading a story. Our results demonstrated a tripartite brain state organization, characterized respectively by high activities in the visual (State #1), language (State #2), and default mode network (DMN, State #3) regions. Children exhibited significantly longer dwell time in the DMN state (State #3) compared to adults, along with a higher probability of transitioning from the language state (State #2) to the DMN state (State #3). In addition, adults exhibited greater flexibility in state transitions during reading comprehension. Finally, the alignment between the dynamic states of children and the average states of adults was a significant positive predictor of their reading comprehension performance. This study provides a novel, intuitive perspective on how brain state dynamics evolve during the development of reading comprehension.

neuroscience↗

bioRxiv2026

BCIJelly: An integrated ecosystem for brain-computer interface research

Brain-computer interface (BCI) research relies on multistage computational pipelines, but progress has been slowed by fragmented data formats, heterogeneous decoder implementations and hardware-specific deployment toolchains. Here, we introduce BCIJelly, a unified ecosystem that standardizes 18 BCI datasets into AI-ready inputs and integrates 15 benchmark decoders, 80 reusable modules, automated architecture search (AAS) and hardware-aware neuromorphic deployment. Our AAS constructs task-specific decoders without manual design and extends into a large language model (LLM)-driven closed-loop mode supporting single-task, multitask and cross-species decoder design. A single-command pipeline compiles trained decoders for neuromorphic hardware, reducing power consumption by 30 to 50 times while preserving decoding performance. An interactive visualization software enables code-free exploration of neural recordings and decoding outputs. BCIJelly is validated across five BCI paradigms (motor, visual, speech, emotion and auditory) in humans, macaques and mice, providing an extensible ecosystem connecting data standardization, decoder development, systematic evaluation and hardware-aware deployment for BCI research.

neuroscience↗

bioRxiv2026

ETAP-CLF: an ESM3-based transformer attention framework for binary protein classification

Binary protein classification supports diverse tasks in computational biology, including pathway-membership inference and sequence-based candidate prioritization. Protein language models generate information-rich residue-level representations, but downstream classifiers commonly compress them using fixed pooling operations that may discard task-relevant sequence context. We present ETAP-CLF, a compact framework that combines pretrained per-residue ESM3 embeddings with lightweight transformer contextualization and learned attention pooling to classify variable-length proteins and generate residue-level attention scores. The ESM3 parameters remained frozen, and the same ETAP-CLF architecture and hyperparameter configuration were used across ferroptosis-, senescence-, and pyroptosis-associated protein prediction. ETAP-CLF achieved AUROCs of 0.98, 0.95 and 0.91 for these tasks, respectively. In the ferroptosis benchmark, ETAP-CLF outperformed the evaluated published models. These results demonstrate that a common downstream design can adapt to multiple process-associated classification tasks without fine-tuning the task-specific model architecture. ETAP-CLF provides a generalizable approach for sequence-based protein prioritization and a basis for broader evaluation across binary protein-classification problems.

bioinformatics↗

bioRxiv2026

A modular architecture of human cellular aging across organs and diseases

Aging is a heterogeneous biological process in which different cellular systems undergo molecular remodeling at distinct rates, yet whether human cellular aging follows an organized architecture across organs remains unclear. Here, we integrate a multi-organ human single-cell transcriptomic atlas with plasma proteomic profiles from approximately 50,000 participants to reconstruct cellular aging states at population scale. By projecting cell-type-enriched molecular signatures onto circulating proteins, we characterize aging patterns across 128 organ-cell type pairs and identify 14 cellular aging modules comprising conserved cross-organ programs and organ-specific aging states. These modules reveal cellular identity as a dominant organizing axis of human aging that transcends anatomical boundaries. Module-level aging states uncover substantial inter-individual heterogeneity, with 34% of individuals exhibiting extreme aging deviation in at least one cellular module. Cellular aging modules exhibit distinct temporal trajectories, with structural and tissue-resident modules showing earlier remodeling than immune lineages. The modular organization of cellular aging is reflected in disease susceptibility, with accelerated aging of specific modules, particularly epithelial aging, showing broad associations with disease burden and mortality. Longitudinal analyses further demonstrate the stability and clinical relevance of cellular aging states, whereas lifestyle, metabolic and pharmacological factors show selective relationships with individual aging programs. Together, our study establishes a modular framework for understanding human cellular aging and reveals an organization of biological aging that may help explain individual differences in healthspan.

cell biology↗

bioRxiv2026

Serial Immunohistochemistry for High-Dimensional Single-Cell Spatial Analysis of Human Kidney Biopsies

BackgroundTraditional immunohistochemistry (IHC) with chromogen detection has limited multiplex capacity, detecting at most 4 protein markers per tissue section simultaneously, thereby restricting comprehensive spatial analysis of valuable human biopsies. We developed and validated a robust serial IHC (sIHC) staining method to detect multiple antigens on a single kidney biopsy slide, maximizing data yield for diagnosing and studying complex kidney diseases. MethodsFormalin-fixed, paraffin-embedded kidney biopsy sections were subjected to repeated IHC/imaging cycles with antibody removal using an optimized sodium dodecyl sulfate-glycerol buffer stripping protocol. Images were then co-registered, and analysis was performed using a variety of methodologies, including color deconvolution, cell segmentation, and spatial clustering. ResultsThis optimized sIHC method successfully detected up to 20 antigens on a single slide. Combining image analysis and artificial intelligence software, for example with HALO (Indica Labs), the assay assembles high-dimensional images and enables quantitative histology and single-cell spatial analysis. Using this advanced method, we were able to identify rare cell populations, such as double-negative T cells, that are challenging to detect conventionally. ConclusionWe have developed a validated, high-capacity sIHC protocol that uses standard IHC procedures with commercially available, clinically validated off-the-shelf antibodies. This method is a valuable, cost-effective tool for obtaining extensive, high-dimensional single-cell-resolved spatial data from limited pathology samples, such as a human kidney biopsy.

pathology↗

bioRxiv2026

RNA m6A demethylase ALKBH4 governs whitefly development

N6-methyladenosine (m6A) modification is the most predominant and ubiquitous internal modification of RNA in eukaryotes, serving as a key post-transcriptional regulator of gene expression that is dynamically modulated by methyltransferases (writers) and demethylases (erasers). However, while the functions of m6A methylases have been partially elucidated in insects, the identity of m6A erasers in arthropods and their chemical catalytic mechanisms, as well as biological functions, remains largely enigmatic. Here, we uncovered 2499 putative methylase genes and 1148 putative demethylase genes in 266 insect genomes, and demonstrated that ALKBH4 functions as an m6A demethylase in the whitefly, Bemisia tabaci, catalyzing the oxidative reversal of mRNA m6A modifications both in vitro and in vivo. Furthermore, we established that ALKBH4, in coordination with other core components of the m6A pathway, fulfills an essential function in regulating the transcript stability of Imaginal Disk Growth Factor 1 (IDGF1) during whitefly development. Collectively, our findings expand the evolutionary scope of the eukaryotic m6A modification system, and reveal a conserved yet insect-specific epitranscriptomic regulatory mechanism governing fundamental physiological processes and adaptive phenotypes. Significance statementThe addition of a methyl group to the N6-position of adenosine (m6A) is a highly abundant chemical modification of RNA. However, the functional role of m6A in insects and the key enzymes that regulate its levels remains poorly understood. In this study, we explored putative methylase genes and demethylase genes in hundreds insect genomes, and identified an m6A RNA demethylase, ALKBH4, in the whitefly, Bemisia tabaci. We demonstrate that ALKBH4 oxidatively reverses mRNA methylation in vivo and in vitro, in combination with other components of the m6A pathway, plays an important role in whitefly development. These findings provide new insight into m6A methylation system of insect.

zoology↗

bioRxiv2026

The cellular and genetic basis of inflorescence divergence between maize and teosinte

The domestication of maize from teosinte involved dramatic remodeling of the ear, yet the cellular and genetic bases of this transformation remain unclear. Here, we generate a single-nucleus and spatial transcriptome atlas of developing maize and teosinte ears. Comparative analysis reveals divergence in cob-associated cell types, with enhanced cytokinin signaling and reduced growth-inhibitory signals collectively driving cob thickening and enlargement in maize. We further demonstrate that domestication expanded the spatial expression domain of key transcription factors in maize meristem cells, enhancing the potential for increasing kernel number. Additionally, we verified a major domestication gene, ZmSPD1, in which two nonsynonymous SNPs differentiate maize from teosinte and alter jasmonic acid (JA) levels in the ear, thereby suppressing spikelet abortion to effectively double kernel production. These findings provide a cell-resolved mechanistic framework for how cob architecture and kernel number were shaped during maize domestication, offering new insights into the formation of key agronomic traits.

plant biology↗