Biology subjects

Gao, J.

Publications and source records attributed to Gao, J..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Integrated histopathologic modeling of detailed tumor subtypes and actionable biomarkers

Accurate cancer subtyping with accompanying molecular characterization is critical for precision oncology. While machine learning approaches have been applied to both digital pathology and cancer genomics, previous work has been limited in sample size and has typically aggregated granular cancer subtypes into coarse groupings, likely obfuscating informative molecular and prognostic associations and phenotypic variation of more detailed tumor subtypes. Accordingly, we collated 378,123 hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) with matched targeted DNA clinical sequencing results and OncoTree detailed cancer subtypes from a real-world cohort of 71,142 patients. Using this scaled, granular dataset and a cancer subtype knowledge graph, we developed Mosaic: a family of calibrated machine learning models using H&E WSI embeddings to classify tumors and identify molecular phenotypes across 163 detailed subtypes. The cancer subtyping module (Aeon) achieved an area under the receiver operating characteristic curve (AUROC) of 0.992 overall, with 161/163 subtypes reaching an AUROC [≥] 0.90 and improved performance over a state-of-the-art genomics-based classifier. The genomic inference module (Paladin) achieved an AUROC [≥] 0.80 for 167 pairs of detailed subtypes and genomic targets. We further used the learned histopathologic representations to i) identify key associations of the histopathologic embeddings with clinical biomarkers; ii) identify unsupervised sub-clusters of tumors with genomic determinants of tumor phenotype; iii) specify granular diagnoses for cancers of unknown primary, evaluated by genomic associations and expected clinical outcome distributions; iv) annotate functional significance for variants of uncertain significance (VUS); and v) identify cases that mimic the phenotypic effect of known DNA variants on H&E in the absence of detectable DNA alterations. Taken together, this work advances our understanding of phenotypic variation of granular tumor subtypes, their relevance to enhanced diagnostics, and their potential utility in risk stratification with multimodal machine learning in cancer.

cancer biology↗

bioRxiv2025

Evaluating scientific theories as predictive models in language neuroscience

Modern data-driven encoding models are highly effective at predicting brain responses to language stimuli. However, these models struggle to explain the underlying phenomena, i.e. what features of the stimulus drive the response? We present Question Answering encoding models, a method for converting qualitative theories of language selectivity into highly accurate, interpretable models of brain responses. QA encoding models annotate a language stimulus by using a large language model to answer yes-no questions corresponding to qualitative theories. A compact QA encoding model that uses only 35 questions outperforms existing baselines at predicting brain responses in both fMRI and ECoG data. The model weights also provide easily interpretable maps of language selectivity across cortex; these maps show quantitative agreement with meta-analyses of the existing literature and selectivity maps identified in a follow-up fMRI experiment. These results demonstrate that LLMs can bridge the widening gap between qualitative scientific theories and data-driven models.

neuroscience↗

bioRxiv2025

The Dynamic Monitoring of Tourism Environment of Emeishan Mountain Based on High Score Remote Sensing Iimage and Image Fusion

Sustainable tourism is widely advocated as a low-impact development strategy that promotes economic growth while preserving ecological integrity. However, empirical evidence increasingly reveals complex environmental trade-offs associated with tourism expansion, particularly in ecologically sensitive heritage sites. Mount Emeishan, a UNESCO World Cultural and Natural Heritage Site in China, exemplifies a destination where tourism has driven regional socio-economic development through culturally meaningful product innovation. Yet, this growth has also imposed significant ecological pressures, particularly in waste management, where hazardous waste accumulation correlates strongly with increasing visitor numbers--posing a growing threat to local ecosystem stability. To address these challenges, this study proposes a dynamic environmental monitoring framework integrating high-resolution remote sensing imagery (HR-RSI) and advanced image fusion techniques (IFT). The primary objective is to develop a systematic, data-driven methodology for assessing and mitigating tourism-induced environmental degradation, thereby supporting sustainable management of the Emeishan National Park. Empirical evaluation of the framework demonstrated a system stability rate of 88%, representing a 12.7% statistically significant improvement (p<0.05) over traditional monitoring systems under a sample size of 100 test cases. Through the synthesis of multi-source remote sensing data, the study quantitatively assessed the impacts of tourism activities on key environmental indicators, including vegetation cover, land use dynamics, and water quality. Results revealed a noticeable decline in vegetation coverage and significant land use transitions, particularly in high-traffic zones. Furthermore, elevated water pollution risks were observed during peak tourist seasons, underscoring the seasonal nature of tourism-related environmental stressors. These findings provide robust scientific support for policy-makers and tourism authorities in formulating evidence-based environmental protection strategies and adaptive resource management policies. The integration of HR-RSI and IFT offers a scalable and replicable approach for monitoring environmental impacts in heritage-rich, tourism-intensive regions, contributing to the global discourse on sustainable tourism and ecological resilience.

ecology↗

bioRxiv2025

Protein Folding accompanied by disulfide bond formation drives glutenin polymerization into multidimensional gluten networks

Wheat flour dough, which is routinely used to make bread, pasta and noodles, can be stretched several thousand-fold without rupture. The molecular basis of this extraordinary property arises due to the ability to crosslink glutenin subunits, the major constituent of dough, into linear and branched elastic networks. The mechanism by which the glutenin subunits self-assemble and polymerize is unknown. Here, we show, using mass spectrometry, confocal imaging and molecular dynamics simulations that structuring of two hydrophobic residues, phenylalanine and tyrosine, in the core region of the monomeric glutenin 1Dx5 N-terminal domain (1Dx5-NTD) initiates inter molecular interactions and network formation. Upon folding of monomeric 1Dx5-NTD, two cysteine residues (Cys10 and Cys40) form an intramolecular disulfide bond and poises the third cysteine (Cys25) to engage in inter molecular crosslink with other glutenin subunits. Propagation of such a disulfide linkage pattern results in the formation of a cohesive linear gluten network. In alternate pathways, crosslinking of Cys10 with Cys25 with a third glutenin subunit results in the creation of a junction, which drives the formation of a three-dimensional network. The disulfide patterns in the networks accord well with the measured chemical reactivity of each cysteine residue and the solvent accessibility of the associated side chains during 1Dx5-NTD interactions. Our study shows that the diversity in the folding of a single glutenin subunit that exposes the third cysteine to the solvent is the key event that initiates polymerization and directs the formation of networks with differing architecture. Significance statementGlutenin proteins polymerize to form intricate molecular networks that support the expansion of dough by several thousand-fold. Although network formation occurs spontaneously, the polymerization mechanism is unknown. There are multiple cysteine residues in glutenin that can form intra and intermolecular disulfide bonds. Multiple experimental techniques and molecular dynamics simulations are used to show that folding of glutenin, initiated by two hydrophobic residues, exposes cysteines that are poised to form intermolecular disulfide bonds. The disulfide crosslinking pattern, determined by the initial folding process, dictates the creation of a linear or branched network. The study demonstrates that the propensity to form linear or branched networks is encoded by the diversity of the disulfide patterns created at the level of monomeric folding.

biophysics↗

bioRxiv2025

Mitochondrial calcium influx-driven bioenergetics in the dopaminergic system selectively enable drug reward

Drugs of abuse hijack the brains reward system, driving pathological dopamine surges that underlie compulsive behavior and addiction. However, directly targeting dopamine signaling for treatment risks disrupting natural reward processes. Here, we identify a bioenergetic mechanism that selectively promotes addiction-related dopamine release and behaviors. Opioids and methamphetamine, but not natural rewards, induce mitochondrial calcium (Ca2+) influx via the mitochondrial calcium uniporter (MCU) in dopaminergic terminals of the nucleus accumbens. Optogenetic stimulation reveals that this mitochondrial Ca2+ influx occurs exclusively during high-intensity dopaminergic neuronal activation. This Ca2+ influx drives rapid ATP production, compensating for energy deficits caused by neuronal hyperactivity and enabling sustained dopamine release. Genetic deletion or pharmacological inhibition of MCU in dopaminergic neurons selectively reduces drug-induced dopamine release and prevents addictive behaviors while sparing natural reward processing in mice. These findings uncover a distinct mitochondrial bioenergetic mechanism underlying drug reward and propose MCU as a promising therapeutic target for addiction treatment.

neuroscience↗

bioRxiv2025

Biological causes and impacts of rugged tree landscapes in phylodynamic inference

Phylodynamic analysis has been instrumental in elucidating epidemiological and evolutionary dynamics of pathogens. Bayesian phylodynamics integrates out phylogenetic uncertainty, which is typically substantial in phylodynamic datasets due to limited genetic diversity. Phylodynamic inference does not, however, scale with modern datasets, partly due to difficulties in traversing tree space. Here, we characterize tree space and landscape in phylodynamic inference and assess its impacts on analysis difficulty and key biological estimates. By running extensive Bayesian analyses of 15 classic large phylodynamic datasets and carefully analyzing the posterior samples, we find that the posterior tree landscape is diffuse yet rugged, leading to widespread tree sampling problems that usually stem from sequences in a small part of the tree. We develop clade-specific diagnostics to show that a few sequences--including putative recombinants and recurrent mutants--frequently drive the ruggedness and sampling problems, although existing data-quality tests show limited power to detect them. The sampling problems can significantly impact phylodynamic inferences or distort major biological conclusions; the impact is usually stronger on "local" estimates (e.g., introduction history) associated with particular clades than on "global" parameters (e.g., demographic trajectory) governed by general tree shape. We evaluate existing and newly-developed MCMC diagnostics, and offer strategies for optimizing phylodynamic analysis settings and mitigating sampling problem impacts. Our findings highlight the need and directions to develop efficient traversal over rugged tree landscapes, ultimately advancing scalable and reliable phylodynamics. Significance StatementBayesian phylodynamics is central to epidemiological studies, but exploring the vast and complex tree space is computationally challenging. Phylodynamic datasets comprise many highly similar sequences, sampled through time, creating a uniquely structured landscape of optimal trees. Here, we show that phylodynamic tree landscapes are often highly rugged, with multiple peaks separated by difficult-to-cross valleys. These features lead to widespread sampling problems which are often driven by a few sequences. These problems can significantly impact phylodynamic estimates, especially those associated with particular clades, distorting biological conclusions. We develop diagnostics to identify problematic sequences and provide solutions to mitigate their impacts. We offer strategies to optimize phylodynamic analysis workflows and to develop algorithms for navigating rugged landscapes, thereby advancing infectious disease investigation.

evolutionary biology↗

bioRxiv2025

Genome-wide CRISPR/Cas9 knockout screen identifies host factors essential for Bovine Parainfluenza Virus Type 3 replication

Bovine Parainfluenza Virus Type 3 (BPIV3) is a leading cause of respiratory illness in cattle and a primary component of Bovine Respiratory Disease Complex (BRDC), resulting in significant economic losses. Understanding the mechanisms of BPIV3 infection, particularly the entry process, is essential for developing effective control measures. Identifying specific host factors that viruses exploit during their life cycle can reveal critical vulnerabilities for potential antiviral targets. We established a genome-wide CRISPR/Cas9 knockout screen in bovine cells to identify host factors involved in viral infections. Our screen identified several key host factors required for BPIV3 infection, including the sialic acid transporter SLC35A1 and the protein LSM12. Further mechanistic analysis revealed that these factors play critical roles at distinct stages of the BPIV3 entry process. These findings not only advance our understanding of how BPIV3 infects host cells, but also identify potential host targets for inhibiting infection and developing novel antiviral strategies.

microbiology↗

bioRxiv2025

Roles of histone chaperone Nap1 and histone acetylation in regulating phase-separation of chromatin arrays

Chromatin condensation is dynamically regulated throughout the cell cycle and plays key roles in modulating gene accessibility at the highest physical level in a cell. The DNA-histone dynamics in short- and long-range inter-nucleosomal interactions are central to the regulation mechanisms of chromatin condensation which remain under active investigation. We employed 12-mer nucleosome arrays to investigate the roles of histone chaperone Nap1, and histone H3 and H4 N-terminal tails and their acetylation in regulating the formation and the properties of phase-separated chromatin condensates. These arrays form liquid-like condensate droplets under a physiological salt condition of 150 mM NaCl. According to our results from bright-field microscopy, fluorescence recovery after photobleaching, optical super-resolution imaging, and microrheology with optical tweezers, histone H4 tail lysine residues are the main drivers of liquid-liquid phase separation of chromatin arrays. We also found that the condensed liquid-like droplets contain both a mobile fraction and a relatively immobile structural scaffold and that histone chaperone Nap1 and histone H3 tail acetylation facilitate DNA-histone dynamics within the structural scaffold to lower the overall viscosity of the droplets. These results suggest that histone chaperone and histone H3/H4 tails play critical roles in regulating chromatin condensation and gene accessibility in condensed chromatin.

biophysics↗

bioRxiv2025

A Compact Base Editor Rescues AATD-associated Liver and Lung Disease in Mouse Models

Alpha-1 antitrypsin deficiency (AATD) is commonly caused by a G-to-A mutation in the SERPINA1 gene (the PiZ mutation). The mutant PiZ AAT protein is sequestered in hepatocytes, causing lung emphysema due to insufficient AAT protein to inhibit neutrophil elastase in the lung. Here we show that a compact adenine base editor (ABE) with an evolved Cas9 nickase derived from Neisseria meningitidis (eNme2.C) can be packaged in a single AAV and correct the PiZ mutation in mouse models of AATD. An all-in-one eNme2.C-TadA8e/guide 2 plasmid achieved approximately 20% on-target editing in PiZ reporter cells. TadA9e, which has a narrower editing window than TadA8e, reduced bystander editing without significantly affecting the on-target edit. In PiZ transgenic mice, eNme2.C-TadA9e AAV showed approximately 23% editing efficiency after 8 weeks and reduced liver disease burden in treated mice. In a new AAT-null;PiZ transgenic mouse model, ABE restored serum levels of AAT to beyond the 570 g/mL therapeutic level. Moreover, ABE treatment was able to significantly correct lung functions in AAT-null;PiZ animals with emphysema. This study demonstrates the feasibility of an eNme2.C-based ABE in a single AAV to treat both AATD-associated liver and lung disease.

bioengineering↗

bioRxiv2025

Structures of human organellar SPFH protein complexes

Stomatin, Prohibitin, Flotillin, and HflK/C (SPFH) family proteins are found in all kingdoms of life and in multiple eukaryotic organelles. SPFH proteins assemble into homo- or hetero-oligomeric rings that form domed structures. Most SPFH assemblies also abut a cellular membrane, where they are implicated in diverse functions ranging from membrane organization to protein quality control. However, the precise architectures of different SPFH complexes remain unclear. Here, we report single-particle cryo-EM structures of the endoplasmic reticulum (ER)-resident Erlin1/2 complex and the mitochondrial prohibitin (PHB1/2) complex, revealing assemblies of 13 heterodimers of Erlin1 and Erlin2 and 11 heterodimers of PHB1 and PHB2, respectively. We also describe key interactions underlying the architecture of each complex and conformational heterogeneity of the PHB1/2 complex. Our findings elucidate the distinct stoichiometries and properties of human organellar SPFH complexes and highlight common principles of SPFH complex organization.

molecular biology↗

bioRxiv2025

Glycosylated IgG antibodies accelerated the recovery of haemorrhagic fever with renal syndrome patients

AbstractHaemorrhagic fever with renal syndrome (HFRS) is a fatal disease caused by Hantaan virus (HTNV) infection. Humoral immunity is essential for effective viral clearance, however, the glycosylation characteristics of immunoglobulin G (IgG) in HFRS patients is not well known. Peripheral blood mononuclear cells from HFRS patients were obtained for B subset analysis using scRNA-seq and flow cytometry. HTNV specific IgG antibody titers were detected by enzyme-linked immunosorbent assay, and IgG glycosylation was analyzed by ultra-performance liquid chromatography. The proportions of the antibody-secreting memory (ASM) B cells and plasmablasts (PB) were significantly expanded among acute HFRS patients. We discovered significantly increased fucosylated IgG and decreased bisecting N-acetylglucosamine during the convalescent phase of HTNV infection. Meanwhile, positive correlations were observed between ASM subsets and galactosylation/sialylation in the IgG Fc region, and between PB subsets and sialylation. Notably, the glycosylation-related genes, such as RPN1 and RPN2, were primarily expressed differentially in the ASM and PB subclusters which were enriched in the N-glycosylation modifications of proteins through asparagine. Our findings indicated that IgG N-glycosylation may play a crucial role in combating HTNV infection and contributing to clinical recovery, which provided new insights for optimizing glycoengineered therapeutic antibodies.

immunology↗

bioRxiv2025

A class of COPI adaptors regulates processing of transmembrane receptors by reinforcing their Golgi retention

Many transmembrane (TM) receptors undergo essential post-translational modification in the Golgi prior to their delivery to the plasma membrane. Whether and how the passage and accompanied modification of these receptors across the Golgi is controlled remains unclear. Here, we show that leptin receptor overlapping transcript (LEPROT) and LEPROT-like 1 (LEPROTL1) regulate receptor activation by securing their sufficient Golgi retention. LEPROTs localize to cis and medial Golgi in a COPI-dependent manner. Deletion of LEPROTs in cells causes expedited release of receptors transiting through the Golgi, and leakage of some Golgi-enriched proteins into the endomembrane compartments. LEPROTs interact directly with COPI coats and simultaneously engage a variety of integral membrane proteins with relatively long TM domains at acidic pH. Loss of LEPROTs dysregulates receptor activity, including that of EGFR and TFRC, due to defective modification. Collectively, LEPROTs serve as a class of COPI adaptors for TM receptors, ensuring adequate preparation, which is vital for subsequent action on the plasma membrane.

cell biology↗

bioRxiv2025

Self-contrastive learning enables interference-resilient and generalizable fluorescence microscopy signal detection without interference modeling

Every weak signal in fluorescence microscopy may contain critical biological information. However, the inter-ference resilience required to detect such signals has traditionally relied on task-specific interference modeling, which limits generalizability. Here, we present a self-contrastive learning-based signal detection solution that achieves interference resilience without the need for interference modeling, thereby offering high generalizability. The method, DEep PAttern Fitting (DEPAF), is a module that contrasts asynchronously generated data views from asymmetric model paths to extract signals from interference, while incorporating highly parallel signal recognition and localization in the process. Benchmarking shows that DEPAF substantially improves signal detection performance across diverse imaging modalities and dimensions, especially under challenging conditions such as low SNRs and ultra-high signal densities. It is also compatible with, and consistently enhances the performance of various imaging techniques, such as super-resolution imaging, spatial transcriptomic imaging, and two-photon calcium imaging. Notably, DEPAF relies only on image patches with signal patterns and one tunable hyperparameter to adapt to new tasks, making it accessible to users without domain-specific expertise and lowering the barrier to broader adoption. DEPAF is expected to advance the signal-centric fluorescence microscopy techniques and inspire further advancements, especially in the era of image-based multi-omics.

bioinformatics↗

bioRxiv2025

Repurposing without Reinventing the Wheel - Ensemble Models for Differential Analysis

Inspired by ensemble models in machine learning, we propose a general framework for aggregating multiple distinct base models to enhance the power of published differential association analysis (DAA) methods. We demonstrate this approach by augmenting popular DAA models with one or more biologically motivated alternatives. This creates an ensemble that bypasses the challenge of selecting an optimal model and instead combines the strengths of complementary statistical models to achieve superior performance. Our proposed ensemble learning approach is platform-agnostic and can augment any existing DAA method, providing a general and flexible framework for various downstream modeling tasks across domains and data types. We performed extensive benchmarking across both simulated and experimental datasets spanning single-cell gene expression, bulk transcriptomics, and microbiome metagenomics, where the ensemble strategy vastly outperformed non-ensemble methods, identified more differential patterns than the competing methods, and displayed good control of false positive and false discovery rates across diversified scenarios. In addition to highlighting a substantial performance boost for state-of-the-art DAA methods, this work has practical implications for mitigating the so-called reproducibility crisis in omics data science. An open-source R package implementing the ensemble strategy is publicly available at https://github.com/himelmallick/DAssemble.

bioinformatics↗

bioRxiv2025

Transmembrane Serine Protease TMPRSS11B promotes an acidified tumor microenvironment and immune suppression in lung squamous cell carcinoma

Lung cancer is the leading cause of cancer-related deaths worldwide. Existing therapeutic options have limited efficacy, particularly for lung squamous cell carcinoma (LUSC), underscoring the critical need for the identification of new therapeutic targets. We previously demonstrated that the Transmembrane Serine Protease TMPRSS11B promotes transformation of human bronchial epithelial cells and enhances lactate export from LUSC cells. To determine the impact of TMPRSS11B activity on the host immune system and the tumor microenvironment (TME), we evaluated the effect of Tmprss11b depletion in a syngeneic mouse model. Tmprss11b depletion significantly reduced tumor burden in immunocompetent mice and triggered an infiltration of immune cells. RNA FISH analysis and spatial transcriptomics in the autochthonous Rosa26-Sox2-Ires-GfpLSL/LSL; Nkx2-1fl/fl; Lkb1fl/fl (SNL) model revealed an enrichment of Tmprss11b expression in LUSC tumors, specifically in Krt13+ hillock-like cells. Ultra-pH sensitive nanoparticle imaging and metabolite analysis identified regions of acidification, elevated lactate, and enrichment of M2-like macrophages in LUSC tumors. These results demonstrate that TMPRSS11B promotes an acidified and immunosuppressive TME and nominate this enzyme as a therapeutic target in LUSC.

cancer biology↗

bioRxiv2025

Macroscopic cerebral energy efficiency corresponds to neuron reorganization in awake and anesthetized mice

Non-invasive imaging of brain function and energy supply is crucial for diagnosing and treating brain disorders. Conventional imaging struggles to capture altered relationships between energy supply and utilization caused by brain diseases. A novel method, which can be translated to human patients, is to calculate relative power (rPWR) and relative cost (rCST) to assess cerebral energy efficiency. However, whether rPWR/rCST can track individual changes and neural activity remains unproven. Our study compared these non-invasive measures with invasive two-photon microscopy in awake and anesthetized mice. We found that rPWR/rCST distributions were similar between awake mice and humans, but changed in anesthetized mice, indicating a shift in the brains economic balance. Furthermore, changes in rPWR/rCST were linked to the reorganization of microscopic neural networks, observed with two-photon microscopy. Our work highlights the potential of rPWR/rCST for medical applications, and that neural network reorganization is linked to the brains economic balance.

neuroscience↗

bioRxiv2025

FX-Cell: a method for single-cell RNA sequencing on difficult-to-digest and cryopreserved plant samples

Single-cell RNA sequencing (scRNA-seq) in plants requires the isolation of high-quality protoplasts-- cells devoid of cell walls. However, many plant tissues and organs are resistant to enzymatic digestion, posing a significant barrier to advancing single-cell multi-omics research in plants. Furthermore, for many field-grown crops, the lack of immediate laboratory access presents another major challenge for protoplast preparation. To address these limitations, we developed the FX-Cell method and its derivatives, FXcryo-Cell and cryoFX-Cell, to enable scRNA-seq with both difficult-to-digest and cryopreserved plant samples. By optimizing the fixation buffer and minimizing RNA degradation, our approach ensures efficient cell wall digestion at high temperatures while maintaining high-quality single cells, even after long-term storage at -80{degrees}C, and circumvents use of nuclei, which are not representative of the pool of translatable mRNAs. Using these methods, we successfully constructed high-quality cell atlases for rice tiller nodes, rhizomes of wild rice, and maize crown roots grown in field conditions. Moreover, these methods enable the accurate reconstruction of plant acute wounding responses at single-cell resolution. Collectively, these advancements expand the applicability of plant single-cell genomics across a wider range of species and tissues, paving the way for comprehensive Plant Cell Atlases for plant species.

plant biology↗

bioRxiv2025

NtLLG4-mediated unconventional polar exocytosis of NtPPME1 coordinates cell wall rigidity and membrane dynamics to control pollen tube integrity

Plant fertilization relies on controlled pollen tube growth that integrates membrane dynamics and cell wall expansion. We previously identified an unconventional exocytic pathway wherein Golgi-derived secretory vesicles (GDSVs) bypass the trans-Golgi network to deliver Nicotiana tabacum pectin methylesterase 1 (NtPPME1), thereby modulating cell wall rigidity. However, the mechanisms linking this patwhay with membrane dynamics and signaling remain elusive. Here, we used cryo-FIB-SEM and 3D tomography to identified GDSVs as a distinct vesicle population at the pollen tube tip. We further demonstrated that tobacco LORELEI-like-GPI-anchored protein 4 (NtLLG4), a key signaling molecule controlling membrane dynamics and integrity, functions as a receptor for NtPPME1, regulating its polar exocytosis via GDSVs to control cell wall stiffness. Furthermore, we identified trafficking signals which direct the unconventional exocytosis of NtPPME1 across intracellular organelles. Our findings reveal a crucial mechanism coupling cell wall rigidity with membrane signaling to control pollen tube growth and integrity during fertilization. TeaserWe reveal a mechanism coupling cell wall rigidity with member signaling to control pollen tube growth and integrity during plant fertilization.

plant biology↗