Biology subjects

Hou, W.

Publications and source records attributed to Hou, W..

At least 19 recordsLinked to original sources

bioRxiv2026

Plasma titration provides a physical ruler for cross-platform proteomics

Plasma proteomics is expanding across platforms and cohorts, and integrating these data for AI demands comparability at the protein level, not merely concordant associations1,2. Affinity and MS platforms use distinct probes (antibodies, aptamers, or peptides) and signal readouts, yielding contradictory cross-platform results3,4. Without a known quantitative truth, we cannot distinguish biology from measurement distortion, leaving no gold standard for integration. Here we introduce Plasmix--a plasma reference suite with predefined male:female ratios (M, 1:0; Y, 3:1; P, 1:1; X, 1:3; F, 0:1)--and show that preserving this quantitative titration gradient, not just technical repeatability, predicts cross-platform concordance and identifies protein measurements suitable for integration. Profiling Plasmix across five platforms (Olink, SomaScan, NULISA, AAgAtlas, and MS-DIA) and 12 protocols across 17 batches, we found discordance is dominated by signal generation, not sample identity, and platforms distort signals in a protein-specific manner. Crucially, proteins retaining the titration response showed stronger agreement in an independent cohort; anchoring to the Plasmix midpoint (P) via sample-to-reference ratios reduced distortions, extending harmonizable coverage by 10-20%. Plasmix thus provides a physical ruler to benchmark accuracy, identifying genuinely integrable measurements before pooling datasets or training AI models--a critical bottleneck for plasma proteomics.

molecular biology↗

bioRxiv2026

Beyond Respiration: Heme A Synthase (CtaA) Orchestrates Hydrogen Sulfide Production and Virulence via Metabolic Reprogramming in Staphylococcus aureus

Endogenous hydrogen sulfide (H2S) serves as a pivotal gasotransmitter conferring antimicrobial resistance and shielding bacteria from oxidative stress. While cystathionine {gamma}-lyase (CSE)-dependent H2S production is critical for Staphylococcus aureus survival, the upstream regulatory nodes linking metabolic status to H2S-mediated redox defense remain elusive. Here, we identify heme A synthase (CtaA) as a master regulator that couples respiratory metabolism with H2S biosynthesis, specifically under glucose-depleted conditions mimicking host niches such as abscesses and phagosomes. Using a Himar1 transposon screen followed by a high efficiency detection method, we demonstrate that CtaA deficiency precipitates a catastrophic collapse in H2S levels (<10% of wild-type), leading to severe virulence and antimicrobial resistance changes. Specifically, the {Delta}ctaA mutant exhibits attenuated hemolytic activity and significantly reduced virulence in a Galleria mellonella model, despite displaying paradoxical resistance to specific antimicrobials. Mechanistically, CtaA deletion triggers a maladaptive metabolic reprogramming characterized by the downregulation of the L-cysteine transporter TcyP and dysregulation of arginine metabolism, which collectively impair the bacteriums capacity to produce endogenous H2S to defense oxidative stress. Notably, this redox vulnerability is linked to altered endogenous nitric oxide (NO) dynamics, suggesting a disrupted H2S-NO crosstalk essential for stress adaptation. Our findings elucidate a novel metabolic-redox axis where CtaA governs H2S homeostasis to counteract host-imposed oxidative stress. Targeting the CtaA-H2S axis represents a promising therapeutic strategy to sensitize S. aureus to host immune clearance by dismantling its critical redox shield.

microbiology↗

bioRxiv2026

Integrin-engaged Cellular Patches Mechanically Impose a Mitochondrial Respiratory Bottleneck to Suppress Cancer Cell Motility

The nanoscale organization of cell-adhesive ligands is increasingly recognized as a determinant of cell behavior, yet whether it directly regulates cellular metabolism remains unclear. Here we show that supramolecular clustering of integrin-binding ligands regulates mitochondrial respiratory capacity through integrin-mediated mechanotransduction. Supramolecular ligand clustering induces integrin redistribution and cytoskeletal remodeling, leading to mitochondrial reorganization and a selective constraint on oxidative phosphorylation. This respiratory limitation functionally constrains tumor cell migration and invasion and cannot be overcome by restoring cytoskeletal contractility, whereas replenishing mitochondrial metabolic substrates effectively rescues motility. In a HeLa xenograft model, the integrin-binding supramolecular system suppresses tumor growth and reduces extracellular matrix deposition. These findings identify mitochondrial respiratory capacity as a critical downstream effector of integrin mechanosignaling and establish extracellular ligand organization as a previously unrecognized driver of mechanically encoded metabolic regulation.

biophysics↗

bioRxiv2026

Lipid monounsaturation confers cold tolerance and improves tissue viability during hypothermic storage

The rapid loss of tissue viability during hypothermic storage frequently results in organ failure and high discard rates in transplantation. In contrast, ectothermic animals can tolerate prolonged exposure to low temperatures in their natural environments. Elucidating the mechanisms underlying this cold tolerance may therefore inform improved strategies for tissue preservation. Here, we investigate proteomic and metabolomic responses to cold exposure in the livers of two frog species from distinct habitats: the cold-sensitive African clawed frog (Xenopus laevis) and the cold-tolerant Northeastern Asian brown frog (Rana dybowskii). Cold exposure induced lipid mobilization in X. laevis, whereas it promoted phospholipid mono-unsaturation in R. dybowskii. Notably, treatment with monounsaturated fatty acids (MUFAs) or overexpression of stearoyl-CoA desaturase (SCD) reduced cell death during cold storage. Moreover, MUFA infusion significantly improved cell viability in mouse liver tissue under hypothermic conditions. Together, these findings suggest that lipid mono-unsaturation, an adaptive feature of cold-tolerant frogs, can be leveraged to enhance tissue preservation during cold storage.

biochemistry↗

bioRxiv2026

Multi-Omics Reveals Activated Fibroblasts with Dual Functional Roles in Repair and Negative Barrier in the CD8+ T Cell Cytotoxic Niche for Lung Cancer Neoadjuvant Therapy

A major challenge in elucidating immune activation and tolerance is that single-omics technologies are inherently limited. Since single-cell transcriptomics lacks spatial information and spatial transcriptomics lacks resolution or depth, neither can adequately model the multidimensional tumor immune microenvironment (TiME) features. We present NICHE (Niche Integrated Cellular Heterogeneity Elucidator), an integrative multi-omics framework. By aligning physical cell-cell interactions with single-cell ligand-receptor (L-R) expression, NICHE leverages single-cell transcriptomics, spatial proteomics, and AI modeling to systematically decode the composition, spatial interactions, and communications within immune functional niches. To validate NICHEs capabilities, we applied it to human tonsil tissue. The framework successfully identified key structural and functional units and deeply elucidated the cell-cell interactions and molecular crosstalk within them. Furthermore, these results were validated through orthogonal methods, including single-cell spatial transcriptomics and multiplexed immunofluorescence, which confirmed the accuracy and reproducibility of our analyses. In non-small cell lung cancer (NSCLC) patients undergoing neoadjuvant immunotherapy, CD8+ T cell cytotoxic niches mediate tumor clearance with variable outcomes, to define the cellular and molecular drivers behind this functional spectrum, we utilized NICHE to analyze the TiME from patients with complete versus partial pathological response, both pre- and post-treatment. We found that neoadjuvant immunotherapy may induce the activation of CXCL12+, NECTIN2+, POSTN+, and COL6A1+ fibroblasts through the activation of cytotoxic T cells. Paradoxically, these fibroblasts support tissue repair but also establish an immunosuppressive niche that protects residual tumor cells, ultimately attenuating treatment efficacy. Our work establishes a multidimensional framework for dissecting dynamic immune activity, with direct implications for understanding tumor immunology and identifying novel therapeutic targets.

immunology↗

bioRxiv2025

GPTAnno: Ontology-tree-guided hierarchical cell type annotation based on GPT models for single-cell data

Cell type annotation is critical for interpreting single-cell transcriptomic data but remains challenging due to uncertain cellular clustering granularity and inconsistent labeling across studies. Here we present GPTAnno, an automated, ontology-tree-guided, uncertainty-aware, hierarchical cell type annotation method based on GPT models. GPTAnno directly handles gene expression matrices, integrates multi-resolution clustering with large language model reasoning constrained by the cell ontology to produce standardized, ontology-aware, and reproducible annotations with automatic resolution selection. GPTAnno selects optimal clustering resolutions based on the annotation distance on the ontology tree and quantifies annotation uncertainty to flag ambiguous clusters for expert review. Benchmarking across twelve large-scale datasets demonstrates GPTAnnos superior accuracy on annotating cell types across various species, tissues, and disease contexts against existing methods. Implemented in R and Python with Seurat and Scanpy compatibility, GPTAnno allows simple inputs to streamline the reproducible annotation, considerably reducing human efforts in repeated reclustering, assigning and examining the labels.

genomics↗

bioRxiv2025

Engineering Apical Integrin-binding Cellular Patches to Directed Cell Reprogramming via Mechanical Remodeling

Engineering extracellular microenvironments to control stem cell fate remains a central challenge in regenerative medicine. Here, we develop ECM-mimetic cellular patches formed by the supramolecular assembly of laminin-derived, integrin-binding ligands. The resulting fibrillar networks exhibit well-defined molecular packing and nanoscale ligand distribution, enabling specific engagement of apical integrin {beta}1 on mesenchymal stem cells. This controlled interface converts molecular assembly into hierarchical mechanotransduction, coordinating cytoskeletal remodeling, nuclear deformation, and chromatin reorganization to drive neuronal reprogramming without genetic or chemical induction. Mechanistic studies reveal that the interplay between ligand assembly, spatial orientation, and network stability governs integrin activation and downstream transcriptional regulation These findings demonstrate how molecularly programmed assemblies can transform passive matrices into active, cell-instructive materials. This work establishes a framework for designing supramolecular systems that couple structural hierarchy with mechanotransductive control to direct stem cell fate and advance regenerative material strategies.

bioengineering↗

bioRxiv2025

A ratio-based framework using Quartet reference materials for integrating long- and short-read RNA-seq

Long-read RNA sequencing (lrRNA-seq) enables full-length transcript profiling but is confounded by technical batch effects that compromise quantification and prevent data integration across platforms, protocols, and laboratories. The lack of a transcriptome-wide biological ground truth has hindered objective benchmarking. To address these dual challenges, we leveraged certified Quartet reference materials to generate one of the largest multi-center lrRNA-seq resources to date: over one billion long reads from 144 libraries across four PacBio and Nanopore protocols in four independent laboratories. We first establish that ratio-based quantification against built-in reference samples effectively removes technical noise, revealing underlying biological signals. We then constructed the first ratio-based reference datasets for full-length transcripts-- comprising 10,218 isoforms and 6,032 alternative splicing (AS) events--and orthogonally validated them with RT-qPCR. Finally, a comprehensive benchmark using these ground truths reveals that a hybrid strategy integrating long- and short-read data (hybrid-seq) achieves the highest quantification accuracy for both isoforms and AS events. Our work provides a foundational framework and resource for evaluating lrRNA-seq technologies and accelerating the standardization of full-length transcriptomics for research and clinical applications.

genomics↗

bioRxiv2025

Quantitative Analysis of Meloidogyne incognita Population Density Using Real-Time PCR and Its Correlation with Root-Knot Disease Index in Tabacco (Nicotiana tabacum)

Tobacco root-knot disease represents a significant threat to tobacco production, particularly in the western Henan region, where Meloidogyne incognita is the predominant species. This study collected samples of M. incognita and soil from Luoyang, Henan, and designed specific primers MiF and MiR based on the amplified 735 bp sequence of the ITS1-5.8S-ITS2 region. These primers exhibit mismatches with related species, including M. javanica, M. minor, M. hapla, and M. arenaria, showing 1, 6, 10, and 10 base differences in the forward direction, respectively, 3, 10, and 9 base mismatches in the reverse direction. Although the primers were used to detect corn-wheat soil samples, no amplification was observed. Additionally a real-time quantitative PCR curve for M. incognita in soil was constructed, revealing a negative correlation between the Ct value (Y) and the log-transformed number of nematodes (x) per 20 g of dry soil, represented by the equation y = -0.9757x + 35.565; (R2 = 0.9999, P < 0.01). According to the decomposition efficiency of nematode DNA in soil, the results showed that nematode DNA degrades rapidly in soil, with a degradation rate of approximately 87.3% at 3 days and 99.97% at 14 days. Furthermore, significant differences were observed in the real-time PCR detection efficiency among various nematode forms: the Ct value of J1 was significantly higher than that of J2, while abnormal eggs (empty eggs or internal bubble eggs) exhibited the highest Ct value. The proportion of abnormal eggs in the soil before planting was significantly higher at 64.08% compared to only 15.3% at harvest, indicating that the activity of nematodes in the soil is significantly reduced after harvesting in October and planting in March of the following year. A survey of 126 tobacco plants indicated a significant positive correlation between the root-knot index (RKI) and root-knot nematode density (r = 0.80, p < 0.01). The study identified a minimum disease threshold of 234 individuals / 20 g soil at harvest and revealed a nonlinear relationship between disease severity and nematode density. Specifically, a weak correlation was observed at low density (Low RKI: 0-1; nematode density<2000 individuals/20 g soil, r = 0.49, P<0.05), while a significant correlation was noted at moderate density (High RKI: nematode density>2000 individuals/20 g soil, r = 0.69, P<0.05). This study showed that as the RKI increases, the rate of increase in nematode density in the soil diminishes. These findings provide valuable insights for the development of effective scientific strategies for nematode control.

plant biology↗

bioRxiv2025

Intratumoral Resident Microbes Directly Participate in Resistance to EGFR-TKI Targeted Drug Therapy through Metabolism in NSCLC

EGFR-TKIs targeted therapy represents a cornerstone in the treatment of non-small cell lung cancer (NSCLC). Nonetheless, a subset of patients inevitably develops acquired resistance to EGFR-TKIs, posing a significant clinical challenge. An increasing number of evidence has documented the presence of microbial communities within tumor microenvironments, but the potential interplay between tumor-resident microbiota and resistance to EGFR-TKIs remains poorly understood. Here, we report that prolonged EGFR-TKIs treatment markedly alters the diversity of tumor-resident microbiota, characterized by a notable enrichment of Bacillus mycoides and Pseudomonas veronii. In vitro experiments revealed that co-culture of gefitinib with either B. mycoides or P. veronii significantly attenuated its tumor-suppressive efficacy, both in vitro and in vivo. Mechanistically, gefitinib underwent extensive biotransformation in the presence of these bacteria, yielding redox derivatives and conjugation products, which collectively diminished its antitumor activity. Specifically, B. mycoides metabolized gefitinib into B.mPM9-C33 H45 Cl F N3 O4, while P. veronii generated P.vPM2-C24 H29 Cl N4 O3, both of which were consistently detected in in vivo and in vitro models. Furthermore, heterologous expression of maltose acetyltransferase from B. mycoides in E. coli BL21 conferred the capacity to metabolize gefitinib, underscoring the enzymatic basis of this metabolic conversion. Collectively, these findings demonstrate that B. mycoides and P. veronii drive tumor resistance to EGFR-TKIs through metabolic inactivation of the drug, unveiling a previously unrecognized role of tumor-resident microbiota in modulating therapeutic responses and providing novel insights into the mechanisms underlying EGFR-TKIs resistance in NSCLC.

microbiology↗

bioRxiv2025

EpiFoundation: A Foundation Model for Single-Cell ATAC-seq via Peak-to-Gene Alignment

Foundation models exhibit strong capabilities for downstream tasks by learning generalized representations through self-supervised pre-training on large datasets. While several foundation models have been developed for single-cell RNA-seq (scRNA-seq) data, there is still a lack of models specifically tailored for single-cell ATAC-seq (scATAC-seq), which measures epigenetic information in individual cells. The principal challenge in developing such a model lies in the vast number of scATAC peaks and the significant sparsity of the data, which complicates the formulation of peak-to-peak correlations. To address this challenge, we introduce EpiFoundation, a foundation model for learning cell representations from the high-dimensional and sparse space of peaks. Epi-Foundation relies on an innovative cross-modality pre-training procedure with two key technical innovations. First, EpiFoundation exclusively processes the non-zero peak set, thereby enhancing the density of cell-specific information within the input data. Second, EpiFoundation utilizes dense gene expression information to supervise the pretraining process, aligning peak-to-gene correlations. EpiFoundation can handle various types of downstream tasks, including cell-type annotation, batch correction, and gene expression prediction. To train and validate EpiFoundation, we curated MiniAtlas, a dataset of 100,000+ single cells with paired scRNA-seq and scATAC-seq data, along with diverse test sets spanning various tissues and cell types for robust evaluation. EpiFoundation demonstrates state-of-the-art performance across multiple tissues and diverse downstream tasks.

genomics↗

bioRxiv2025

MethylProphet: A Generalized Gene-Contextual Model for Inferring Whole-Genome DNA Methylation Landscape

AO_SCPLOWBSTRACTC_SCPLOWDNA methylation (DNAm) is a key epigenetic modification that regulates gene expression and is pivotal in development and disease. However, profiling DNAm at genome scale is challenging: of [~]28 million CpG sites in the human genome, only about 1-3% are typically assayed in common datasets due to technological limitations and cost. Recent deep learning approaches, including masking-based generative Transformer models, have shown promise in capturing DNAm-gene expression relationships, but they rely on partially observed DNAm values for unmeasured CpGs and cannot be applied to completely unmeasured samples. To overcome this barrier, we introduce MethylProphet, a gene-guided, context-aware Transformer model for whole-genome DNAm inference without any measured DNAm input. MethylProphet compresses comprehensive gene expression profiles ([~]25K genes) through an efficient bottleneck multilayer perceptron, and encodes local CpG sequence context with a specialized DNA tokenizer. These representations are integrated by a Transformer encoder to predict site-specific methylation levels. Trained on large-scale pan-tissue whole-genome bisulfite sequencing data from ENCODE (1.6 billion CpG-sample pairs, [~]322 billion tokens), MethylProphet demonstrates strong performance in hold-out evaluations, accurately inferring DNAm at unmeasured CpGs and generalizing to unseen samples. Furthermore, application to TCGA pan-cancer data (chromosome 1, 9,194 samples; [~]450 million training pairs, 91 billion tokens) highlights its potential for pan-cancer whole-genome methylome imputation. MethylProphet offers a powerful and scalable foundation model for epigenetics, providing high-resolution methylation landscape reconstruction and advancing both biological research and precision medicine.

bioinformatics↗

bioRxiv2024

Differential destinations, dynamics, and functions of high- and low-order features in the feedback signal during object processing

Brain is a hierarchical information processing system, in which the feedback signals from high-level to low-level regions are critical. The feedback signals may convey complex high-order features (e.g., category, identity) and simple low-order features (e.g., orientation, spatial frequency) to sensory cortex to interact with the feedforward information, but how these types of feedback information are represented and how they differ in facilitating visual processing is unclear. The current study used the peripheral object discrimination task, 7T fMRI, and MEG to isolate feedback from feedforward signals in human early visual cortex. The results showed that feedback signals conveyed both low-order features natively encoded in early visual cortex and high-order features generated in high-level regions, but with different spatial and temporal properties. The high-order feedback information targeted both superficial and deep layers, whereas the low-order feedback information reached only deep layers in V1. In addition, MEG results revealed that the feedback information from occipito-temporal to early visual cortex emerged around 200 ms after stimulus onset, and only the representational strength of high-order feedback information was significantly correlated with behavioral performance. These results indicate that the complex and simple components of feedback information play different roles in predictive processing mechanisms to facilitate sensory processing.

neuroscience↗

bioRxiv2024

DNA methylation memory of pancreatic acinar-ductal metaplasia transition state altering Kras-downstream PI3K and Rho GTPase signaling in the absence of Kras mutation

A critical area of recent cancer research is the emergence of transition states between normal and cancer that exhibit increased cell plasticity which underlies tumor cell heterogeneity. Pancreatic ductal adenocarcinoma (PDAC) can arise from the combination of a transition state termed acinar-to-ductal metaplasia (ADM) and a gain-of-function mutation in the proto-oncogene KRAS. During ADM, digestive enzyme-producing acinar cells acquire a transient ductal epithelium-like phenotype while maintaining their geographical acinar organization. One route of ADM initiation is the overexpression of the Kruppel-like factor 4 gene (KLF4) in the absence of oncogenic driver mutations. Here, we asked to what extent cells acquire and retain an epigenetic memory of the ADM transition state in the absence of oncogene mutation. We identified differential DNA methylation at Kras-downstream PI3K and Rho/Rac/Cdc42 GTPase pathway genes during ADM, as well as a corresponding gene expression increase in these pathways. Importantly, differential methylation persisted after gene expression returned to normal. Caerulein exposure, which induces widespread digestive system changes in addition to ADM, showed similar changes in DNA methylation in ADM cells. Regions of differential methylation were enriched for motifs of KLF and AP-1 family transcription factors, as were those of human pancreatic intraepithelial neoplasia (PanIN) samples, demonstrating the relevance of this epigenetic transition state memory in human carcinogenesis. Finally, single-cell spatial transcriptomics revealed that these ADM transition cells were enriched for PI3K pathway and AP1 family members, linking epigenetic memory to cancer cell plasticity even in the absence of oncogene mutation.

cancer biology↗

bioRxiv2024

Surface display of proximity labeling enzymes on extracellular vesicles for surfaceome and target cell mapping

Extracellular vesicles (EV) surface proteins have important extracellular functions and determine cellular tropism; however, characterizing the EV surfaceome remains challenging with available methods. EV-mediated intercellular communication takes place primarily through interactions at the recipient cell membrane, underscoring the importance of methodological advances to map this interplay. Here, we leverage the proximity labeling enzyme APEX2 (Apurinic/Apyrimidinic Endodeoxyribonuclease 2) for high-fidelity analysis of the EV surfaceome and cellular tropism. Surface display of APEX2 on EVs is achieved through its genetic fusion with EV-sorting domains, such as CD63 and TSPAN2. Upon adding the substrates biotin-phenol and hydrogen peroxide, vesicle surface APEX2 enables biotinylation of EV integral and corona proteins as well as target cells in vitro. Further data mining of the EV surfaceome reveals potential scaffolds for the bioengineering of EVs. Altogether, we introduce a robust tool for EV surfaceome and target cell mapping and uncover novel EV-sorting domains for bioengineering.

synthetic biology↗

bioRxiv2024

Trajectory-guided dimensionality reduction for multi-sample single-cell RNA-seq data reveals biologically relevant sample-level heterogeneity

The analysis of single-cell RNA-sequencing (scRNA-seq) data with multiple biological samples remains a pressing challenge. We present MUSTARD, a trajectory-guided dimension reduction method for multi-sample multi-condition scRNA-seq data. This all-in-one decomposition reveals major gene expression variation patterns along the trajectory and across multiple samples simultaneously, providing opportunities to discover sample endotypes along with associated genes and gene modules. In data-driven simulation, MUSTARD achieves high accuracy in distinguishing sample-level group differences that existing methods fail to capture. MUSTARD also demonstrates a robust ability to capture gene markers and pathways associated with phenotypes of interest across multiple real-world case studies.

bioinformatics↗

bioRxiv2024

Pipeline for Assessing Tumor Immune Status Using Superplex Immunostaining and Spatial Immune Interaction Analysis

The characteristics of the tumor microenvironment (TME) are closely linked to tumor progression and treatment response. The TME comprises various cell types, their spatial distribution, cell-cell interactions, and their organization into cellular niches or neighborhoods. To capture this complexity, several spatial profiling technologies have been developed. However, challenges such as low throughput, high costs, and complicated data analysis have limited their widespread use in immune research. In this study, we introduce the Cyclic-multiplex TSA (CmTSA) staining platform, a high-throughput superplex staining technology based on tyramide signal amplification (TSA) immunostaining combined with an efficient fluorophore recycling method. The CmTSA platform allows for the labeling of 30-60 antigens across multiple parallel formalin-fixed paraffin-embedded (FFPE) slides. Furthermore, the automated CmTSA workflow requires only standard histological equipment and conventional immunohistochemistry (IHC) primary antibodies (Abs), significantly reducing costs. While the superplex images produced contain extensive multidimensional information, extracting the spatial features of the TME from raw pixel data can be challenging. To address this, we present a computer vision-based analysis pipeline, which begins with deep learning-based algorithms to segment individual cells and identify cell types based on defined annotation rules. It then evaluates the spatial distribution tendencies of each cell type, the interaction intensity between paired cells, and the multicellular functional niches. This comprehensive approach enables researchers to visualize and quantify the types, states, and levels of immune activities within the TME effectively, advancing tumor immunology research and precision immune medicine.

cancer biology↗

bioRxiv2024

Intra-subgenome regulation induces unbalanced expression and function among bread wheat homoeologs

The differential expression of homoeologous genes confers expression plasticity and facilitates the adaptation and domestication of major polyploid crops. However, how this homoeolog expression bias (HEB) is regulated remains elusive. Here, transcriptome analysis of 406 wheat (Triticum aestivum; AABBDD) accessions revealed great variation in HEB among accessions. We identified 14,727 QTLs regulating HEB (hebQTLs), indicating that HEB is genetically regulated and can be predicted using genotyping data. The hebQTLs only regulate the expression of homoeologs in the same subgenome and downregulate their expression to result in HEB, suggesting that intra-subgenomic rather than inter-subgenomic interactions induce HEB. Furthermore, non-hebQTL-regulated homoeologs have stronger biological functions, are under higher selection pressure and exhibit lower genetic diversity than hebQTL-regulated homoeologs and compensate for the downregulated expressions of hebQTL-regulated homoeologs. Our findings reveal how homoeolog expression is coordinated at the genetic level and provide an avenue for leveraging HEB to improve polyploid crops.

genomics↗