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Zhou, X.

Publications and source records attributed to Zhou, X..

At least 73 records · Page 4Linked to original sources

Epistasis of two classical color genes, B and L-2, synergistically controls carotenoid accumulation in squash

Carotenoid accumulation underlies fruit color and nutritional quality in squash (Cucurbita pepo). One pair of dominant genes, B and L-2, have been long known to interact epistatically, substantially boosting carotenoid accumulation and producing intensely orange-fleshed fruit. However, their molecular identities and regulatory mechanism are unknown. Here, we show that B encodes a truncated H subunit of magnesium chelatase (CpCHLHB) and L-2 encodes a homolog of Arabidopsis Pseudo-Response Regulator 2 (CpAPRR2-A). Significantly, expression of phytoene synthase (CpPSY-A), which encodes the major rate-limiting enzyme in carotenoid biosynthesis, was dramatically upregulated in fruit of B/B L-2/L-2 plants compared with b/b L-2/L-2 or B/B l-2/l-2, showing that the B and L-2 interaction affects CpPSY-A transcription. A similar upregulation was also observed in Arabidopsis gun5 L-2 transgenic plants, where gun5 is a genetic mimic of the C. pepo B gene. The wild-type CpCHLHb physically interacted with CpAPRR2-A, attenuating the CpAPRR2-A-mediated activation of CpPSY-A. In contrast, the truncated CpCHLHB lost its ability to interact with CpAPRR2-A, enabling CpAPRR2-A to activate CpPSY-A and produce intensely orange fruit. These findings uncover the mechanism underlying the epistatic interaction through which B and L-2 act synergistically to boost carotenoid production, offering novel mechanistic insights and key targets for improving crop quality. One-sentence summarySynergistic epistasis between B and L-2 arises from loss of interaction between their encoded proteins, resulting in dramatically upregulating the key rate-limiting enzyme in carotenoid biosynthesis pathway to produce intensely orange-fleshed fruit in squash.

plant biology↗

TipQUANT: A robust algorithm for quantitative analysis of spatiotemporally dynamic activities in tip-growing cells

Cell polarity, essential for cell development and function, relies on dynamic subcellular distribution of structural and signaling molecules. Tip growth, an extreme form of polar growth, involves unidirectional expansion at the apical region of cells and requires precise spatiotemporal coordination to achieve periodic and directional growth. Understanding their spatiotemporal dynamics is critical for elucidating mechanisms and functions of cell polarity. However, manual quantification of such dynamics is extremely time-consuming, hindering advancements in the field. Current algorithms have limited power and flexibility in analyzing the distribution and dynamics of molecules and structures, particularly for tip-growing cells with oscillatory and dynamic behavior. To address this challenge, we present TipQuant, an automated analysis tool that robustly detects tips and analyzes spatiotemporal dynamics of fluorescently labeled molecules/structures on plasma membranes and in cytoplasm at apices of tip-growing cells, enabling quantitative understanding of signaling and structural components in these systems.

cell biology↗

The Magnaporthe oryzae Effector AvrPii Attenuates Rice Immunity by Targeting the Calcium-Associated ANK Protein AVIN8

Rice blast, caused by the fungus Magnaporthe oryzae, poses a severe threat to global rice production. Although numerous M. oryzae effectors have been identified, the molecular mechanisms remain poorly understood. The effector AvrPii triggers ETI upon recognition by NLR receptor Pii and has been shown to target host exocytosis and oxidative metabolic pathways to suppress immunity. Here, we identify AVIN8 (AvrPii-interacting protein 8) as a new target of AvrPii. AVIN8 is an ankyrin-repeat (ANK) protein localized to the plasma membrane and nuclear envelope, with sequence similarity to known ANK calcium channels. AVIN8 expression is induced during early M. oryzae infection. Overexpression of AVIN8 enhances blast resistance, whereas its silencing increases susceptibility. AVIN8 promotes chitin-triggered ROS production in a Ca2+-dependent manner. In contrast, AvrPii impairs Ca2+ influx and chitin-triggered ROS burst. Our findings reveal a virulence strategy in which M. oryzae effector AvrPii hijacks the Ca2+ influx-associated ANK protein AVIN8 to suppress early calcium and PTI signaling in rice.

plant biology↗

Integrated Metagenomics and Metabolomics Studies Reveal Core Bacterial Guild Regulating Carbohydrate Metabolism in Pediatric MASLD

BackgroundMetabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent pediatric disorder with limited treatment options, primarily due to an incomplete understanding of its molecular drivers. Recent research underscores the role of microbial guilds in metabolic health, but the mechanisms by which dysbiosis driven by core species and co-abundant symbionts disrupt metabolic homeostasis in pediatric MASLD remain unclear. ResultsHere, we conducted integrated metagenomic and metabolomic analyses on 285 pediatric subjects including MASLD patients, obese and healthy controls. The gut dysbiosis in MASLD was characterized by a depletion of Phocaeicola vulgatus, Bacteroides uniformis, Parabacteroides distasonis, and Bacteroides thetaiotaomicron. Co-abundance network analysis, integrating our cohort with four public datasets, identified these species as core guild members associated with MASLD. Microbial enrichment analysis showed significant disruptions in carbohydrate metabolism, particularly the downregulation of the tricarboxylic acid (TCA) cycle, fructose and sucrose metabolism, and pentose and glucuronate interconversions. P. vulgatus and B. uniformis were identified as dominant species linked to the downregulation of KEGG orthologs (KOs) in these disrupted pathways that were inversely correlated with hepatic injury biomarkers. CAZyme database analysis further emphasized P. vulgatus as the primary contributor to glycoside hydrolases involved in monosaccharide utilization. Finally, both untargeted and targeted metabolomics analysis validated a disrupted metabolic network centered on the TCA cycle and monosaccharide metabolism in pediatric MASLD. ConclusionOur findings suggest the core guild species P. vulgatus and B. uniformis may serve as critical regulators of carbohydrate metabolism in pediatric MASLD, offering potential mechanistic targets for gut microbiome-based interventions.

microbiology↗

PromptBio-Bench: Benchmarking LLM-based Bioinformatics Agents for End-to-End Data Analysis

Large language model (LLM)-based agents hold transformative potential for automating bioinformatics workflows; however, systematic evaluations of their capabilities remain limited, hindering a clear assessment of their readiness for real-world application. We introduce PromptBio-Bench, a comprehensive evaluation suite of 244 expert-curated tasks spanning bioinformatics and data science at varied difficulty levels, and an evaluation framework for structured file comparison and scoring against expert reference answer files. Evaluation of three state-of-the-art bioinformatics agents revealed comparable performance between Biomni and ToolsGenie, with all agents showing a marked decline in accuracy as task difficulty increased. As foundation models and agent frameworks continue to evolve, PromptBio-Bench provides a valuable benchmark infrastructure for systematically tracking progress in agentic bioinformatics.

bioinformatics↗

DOMINO: Learning Domain Co-occurrence for Multidomain Protein Design

Multidomain proteins arise through the reuse and recombination of structural domains, yet natural architectures represent a sparse, structured sample of the possible domain-combination space. Here, we introduce DOMINO, a two-stage framework that learns domain co-occurrence from TED-annotated multidomain proteins and uses the learned patterns to generate new multidomain sequences. DOMIN, a contrastive retrieval model, embeds domains into a latent compatibility space and retrieves candidate partners for a query domain from a TED-derived domain pool, including pairings not observed in the TED-derived co-occurrence set. DOMO, a conditional autoregressive sequence model, converts each retrieved domain pair into a full-length protein sequence by jointly generating the specified domain regions and the non-domain sequence context between and around them. DOMIN recovers hierarchical patterns of natural domain co-occurrence and expands the observed CATH homologous-superfamily co-occurrence network with candidate novel pairings. DOMO realizes both held-out natural pairs and DOMIN-retrieved pairs as proteins with high domain recovery and high AlphaFold-predicted structural confidence. Applied at scale, DOMINO generated 5 million retrieval-derived multidomain proteins, with sampled designs showing recovery of the specified domains, diverse CATH annotations, and sequence novelty relative to UniRef100. Together, these results support domain co-occurrence as a predictive design prior and demonstrate a scalable strategy for exploring multidomain protein architectures through new combinations of existing structural modules.

bioinformatics↗

Phylogenetic Analysis and Structural Evaluation of Staphylococcus aureus Serine-Aspartate Repeat-Containing Protein D with a Focus on Periprosthetic Joint Infection

Serine-aspartate repeat-containing protein D (SdrD) is a Staphylococcus aureus cell wall-anchored, calcium-binding adhesin member of the MSCRAMM Sdr subfamily that may contribute to bacterial adhesion and virulence. S. aureus is the most common cause of periprosthetic joint infection (PJI). Population-level distribution and sequence diversity of SdrD among clinical PJI isolates have not been systematically characterized, and the SdrD binding mechanism is still not well understood. To address these gaps, sdrD alleles were queried across 156 newly sequenced PJI isolates and compared to publicly available S. aureus genomes, and nucleotide- and protein-level phylogenies of the sdrCDE locus constructed. The SdrD crystal structure from S. aureus JH1 was determined, with solution small-angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations, and assessment of conformational changes with calcium depletion. Three dominant sdrD subtypes were defined, associating with USA300, JH1, and TCH60; the JH1 sdrD subtype was predominant among PJI isolates. Structural studies showed that the conformation of individual domains and interdomain organization of the multidomain SdrD have limited flexibility in solution, and that the calcium-binding B domain retains its core fold under conditions of calcium depletion. Together, the findings presented support functional diversification among Sdr family members in mediating host attachment and inform a re-evaluation of the ligand-binding mechanism previously proposed for SdrD. AUTHOR SUMMARYStaphylococcus aureus is the leading cause of infections that develop around joint implants (periprosthetic joint infection, PJI). This bacterium has a large arsenal of surface proteins that allow it to stick to human tissues and implanted devices. This work focused on one such protein, SdrD, which has been linked to implant-associated infections but the structure and diversity of which among patients with PJI had not been well characterized. The genetic sequences of SdrD were analyzed across thousands of bacterial genomes, including those from patients with PJI. Distinct genetic variants of the protein were found, one of which was particularly common with PJI. The three-dimensional structure of SdrD was determined at atomic resolution and solution small-angle X-ray scattering (SAXS) and molecular dynamics used to study how it moves and responds to changes in its environment. Contrary to what was previously described, SdrD was shown to be relatively rigid. These findings change how SdrDs mechanism of action should be considered, potentially informing design strategies to block bacterial attachment before infection takes hold.

biophysics↗

Behavior-Driven Marine Larval Dispersal and Settlement with AI Agent-Based Modeling

Larval dispersal models are central to mapping and predicting ichthyoplankton dynamics in the ocean, yet despite decades of refinement they remain fundamentally limited by their ability to represent adaptive behaviors, relying instead on static trait parameterizations. This deficiency constrains our capacity to design effective restoration and mitigation strategies in an increasingly stressed ocean. SWARM (Simulating Waterborne Agent Routes for Marine connectivity) overcomes this barrier by integrating Large Language Model (LLM)-based behavioral agents with a standard biophysical model to simulate active decision-making during the pelagic larval stage. In both idealized and realistic conditions focusing on Red Snapper larvae in the Gulf of Mexico, agents develop adaptive behaviors that improve settlement and generate explainable vertical distribution patterns. SWARM demonstrates that LLMs can overcome long-standing limitations in dispersal modelling by explicitly representing behavioral drivers of movement, opening new pathways for predicting connectivity and designing effective marine-ecosystem restoration.

ecology↗

Advancing ab initio genome annotation with OrionGeno

The rapid expansion of eukaryotic genome sequencing has created an urgent demand for accurate and scalable genome annotation. Existing ab initio methods often struggle to reconstruct complex gene architectures and generalize across distant lineages, limiting their use for large-scale annotation. Here we present OrionGeno, a phylogeny-aware deep learning model for end-to-end eukaryotic genome annotation. OrionGeno integrates phylogenetic context, long-range sequence modeling and joint prediction of gene structures and repetitive elements to annotate exons, introns, untranslated regions and repeats directly from genomic sequences. Applied to chromosome-level eukaryotic genomes from NCBI that lack annotations, OrionGeno generates annotations for more than 5,300 genomes, substantially expanding public annotation resources. Across diverse eukaryotic lineages, OrionGeno outperforms state-of-the-art methods at the exon, gene, protein-sequence, and protein-structural levels. It also identifies candidate protein-coding loci absent from reference protein-coding annotations in well-curated genomes. Together with a web platform and integrated annotation database, OrionGeno provides a scalable and accessible framework for translating genome assemblies into functional biological resources and supporting large-scale biodiversity initiatives such as the Earth BioGenome Project.

bioinformatics↗

Pro-Cognitive Effects of IgM Isotype Anti-NMDAR1 Autoantibodies in Mice

Natural anti-NMDAR1 autoantibodies are present at varying levels in the general human population, but their effects on cognitive function remain unclear. Recent human studies reported significant associations between higher blood levels of natural anti-NMDAR1 autoantibodies and potential neuroprotective outcomes in Alzheimers disease, traumatic brain injury-associated depression and PTSD symptoms, and schizophrenia. However, whether these natural autoantibodies play a causal role in emotional and cognitive function has not been investigated. Since natural autoantibodies in human blood are predominantly of the IgM isotype, we immunized Aicda mutant mice to produce only IgM isotype anti-NMDAR1 autoantibodies without IgG and IgA isotypes. Mice were tested for sensorimotor gating and conditioned fear and extinction, cross species measures of information processing and emotional memory, respectively. Mice with higher levels of IgM anti-NMDAR1 autoantibodies exhibited significantly increased sensorimotor gating and improved fear extinction recall compared with mice with baseline levels of these autoantibodies. These findings indicate that IgM anti-NMDAR1 autoantibodies are pro-cognitive, unlike previous reports of poor cognition associated with IgG anti-NMDAR1 autoantibodies. Together, these studies suggest that IgM may hold therapeutic potential for a range of neurodegenerative, neurological, and psychiatric disorders.

neuroscience↗

Dysregulated tissue-resident lymphocytes drive age-associated emphysema by impairing alveolar regeneration

Aging is often associated with progressive tissue degeneration and chronic inflammation, yet the role of immune cells in mediating structural and functional decline in organs remains poorly defined. Here, we investigated immune-tissue interactions in the aged lung and identified emphysematous remodeling characterized by alveolar loss. Notably, aged lungs exhibited a marked expansion of tissue-resident lymphocytes (TRLs) with senescent features, accompanied by a significant reduction in alveolar stem/progenitor cell (AT2) abundance. In vivo adoptive T cell transfer and 3D immune-stem cell organoid assays revealed that these expanded TRLs suppressed AT2 growth via secretion of oncostatin M and interferon gamma. In vivo blockade of IL-7 receptor (IL-7R) reduced TRL accumulation in the lungs and ameliorated age-related emphysematous changes, including restoration of alveolar density. Our findings identify TRLs as key drivers of alveolar degeneration in aging and propose IL-7R inhibition as a therapeutic strategy to mitigate pulmonary decline. TeaserBlocking IL-7R clears harmful lymphocytes and helps rebuild the damaged air sacs of the aging lung.

cell biology↗

CellPulse: A Foundation Model of Coordinated Gene Dynamics Simulating Viral Infectious Diseases

Understanding how cells respond to perturbations like viral infections requires models capturing coordinated gene dynamics. However, current gene expression foundation models are predominantly reliant on single-cell data and static gene expression, limiting their applicability in real clinical scenarios. We present CellPulse, a direction-aware foundation model trained on the Virus Stimulated Atlas (VISTA), a newly curated atlas of over 23 million bulk RNA-sequencing differential expression profiles from viral infections. CellPulse models the direction and magnitude of gene expression changes via a structured representation of differential expression and a direction-aware attention mechanism, enabling the learning of coherent regulatory programs. It shows powerful diagnosing capability by accurately classifying 31 distinct virus types across diverse clinical and laboratory samples, solely from host transcriptional signatures. Crucially, without prior knowledge injection, CellPulses interpretability reveals virus-associated host factors that mediate infection. Using a selection of host factors for in silico drug screening yielded numerous compounds with confirmed efficacies in wet-lab assays, while cell-based and animal experiments further verified the causal relationship between host targets and viral infections. Overall, CellPulse represents a generalizable foundation model for deciphering coordinated gene dynamics from bulk transcriptomics, bridging host response modeling with clinical relevance and therapeutic discovery for infectious diseases and beyond.

bioinformatics↗

CHORD: a framework for cross-species single-cell integration across gene, cell and cell-type levels

Quantifying cross-species relationships among cell types from single-cell transcriptomic data can reveal both conserved and divergent patterns of cell-type hierarchies. However, existing cross-species integration methods can be limited in modeling genes beyond orthologs by leveraging cell-type-resolved transcriptional context, or in learning explicit type-level representations. Here we present CHORD, a cross-species integration framework that jointly learns representations of genes, cells and cell types. We demonstrate that CHORD can integrate cross-species single-cell atlases and support cell-type annotation with unknown cell-type detection. In the frog-zebrafish embryogenesis and mammalian motor cortex atlases, CHORD infers cell-type trees that place conserved cell types from different species in relative proximity and summarize hierarchical relationships among cell types. CHORD also supports cross-species comparison of continuous phenotypic variation by placing embryonic cells along an aligned developmental timeline. CHORD further yields gene embeddings that capture orthologous and functional relationships, and gene importance scores linking genes to cell types.

bioinformatics↗

A Nonsteroidal Reversal Agent Inhibits Allopregnanolone Modulation of α1β3δ GABAA Receptors

The neurosteroid allopregnanolone is a positive allosteric modulator of GABA(A) receptors, which has proved beneficial in the treatment of major depressive disorder and epilepsies. It also has a role in treating the mood swings that are associated with fluctuations in its level during the menstrual cycle. Nonetheless, a subset of women do not tolerate high levels of allopregnanolone. Iso-allopregnanolone, a negative allosteric modulator, as well as synthetic steroid antagonists are used to treat such conditions. However, steroid-based medications are difficult to deliver and their specificity of action can be unclear. Recently introduced novel nonsteroidal agents that, like iso-allopregnanolone, can reverse the action of positive allosteric modulators without changing the positive action of GABA, might provide an alternative. We surveyed a number of them on human 1{beta}3{delta} GABAARs using a [3H]muscimol binding assay. A 6-membered ring spiro-hydantoin, DKD99, allosterically reversed the positive allosteric action of allopregnanolone over a wide concentration range (6 to 1,000 nM). DKD99 shifted allopregnanolones modulation curve 10-fold to the right. Furthermore, it has a much lower affinity when exerting similar actions on 1{beta}3{gamma}2 receptors. Agents such as this have utility for elucidating underlying mechanisms and may offer an alternative pathway for the development of nonsteroidal therapies against the positive allosteric modulatory actions of neurosteroids.

pharmacology and toxicology↗

LLM-autonomous development of deep learning models for quantitative microscopy

Deep learning can extract quantitative measurements from microscopy images that are inaccessible to classical analysis, but developing these models requires machine learning expertise that most imaging scientists do not have. Here we present a framework in which a researcher describes their microscopy problem to a large language model (LLM) agent in under ten minutes of conversation--specifying what they image, what they want to measure, and what success looks like--and the agent autonomously handles the rest: designing physics-based training data, implementing a neural network, training, diagnosing failures, and iterating without human intervention. A researcher can start the agent before leaving the lab; overnight, it tests tens to a hundred model variations, each one an experiment that would otherwise demand active attention. We validate the framework across six microscopy modalities and four problem types. On the BBBC039 nuclear segmentation benchmark, the agent autonomously trains a U-Net with 3-class semantic segmentation and morphological post-processing, achieving pixel-level Dice of 0.97 and object-level F1 of 0.84--within 7% of the published baseline--while diagnosing a data pipeline bug that no amount of hyperparameter tuning could resolve. On single-protein holographic microscopy, the agent reads a published paper, designs a simulator, and develops an optimized model in a single session. On PatchCamelyon histopathology classification, the agent autonomously evolves through four optimization phases--from scratch training through transfer learning and regularization to inference-time ensembling--completing 97 iterations on 262,144 images to reach 89.3% test accuracy and 96.3% AUC, nearly matching the published rotation-equivariant baseline. This framework enables microscopy researchers to use deep learning-based image analysis without machine learning domain knowledge.

bioengineering↗

Z-TAC enables custom and combinatorial degradation of cell surface proteins

Cell-surface degrader platforms typically require target-specific engineering and have therefore been applied to a relatively small set of protein targets. Here we report Z-TAC, a strategy that enables plug-and-play conversion of existing IgG antibodies into cell-surface protein degraders. Across multiple targets from distinct protein families, Z-TAC induced efficient and sustained degradation of both individual receptors and receptor combinations. For a multi-pass membrane receptor lacking selective antagonists, Z-TAC mediated complete receptor degradation and functional inhibition, demonstrating the ability of this platform to overcome the limitations of conventional pharmacological approaches. This study delineates a generalizable and scalable strategy for functional perturbation of the cell-surface proteome.

bioengineering↗

Cryo-EM elucidation of stoichiometric plasticity, asymmetric ligand recognition and allosteric coupling in human P2X2/3 heterotrimeric channels

P2X receptors are trimeric ATP-gated ion channels that assemble as homo- or heterotrimers, with heteromeric forms exhibiting intrinsic asymmetry that influences function. Here, we report four high-resolution cryo-EM structures of human P2X2/3 heterotrimers representing distinct functional states, including ATP-bound assemblies (P2X332 and P2X223), the apo form, and a ligand/ATP-bound closed conformation. The three ATP-binding sites show asymmetric recognition of MgATP{superscript 2}- and ATP-, and channel activation requires occupancy of only two MgATP{superscript 2}- molecules. Gefapixant binds a single allosteric site and selectively inhibits MgATP{superscript 2}-, but not ATP-, binding, indicating orthosteric-allosteric coupling within the heterotrimer. Structural features of the transmembrane domain define ion permeation, particularly for Ca{superscript 2}. Despite asymmetric ligand interactions, gating remains largely symmetric, with minor differences in desensitization. These findings provide a structural framework linking asymmetry to coordinated channel function and open avenues for subtype-selective therapeutic intervention.

biophysics↗

GPLD1 Regulates the Shedding of IZUMO1R to Block Polyspermy in Porcine Oocyte

Mammalian fertilization commences with the essential interaction between sperm IZUMO1 and its oocyte-surface receptor, JUNO. Following gamete fusion, JUNO is rapidly shed from the oocyte to establish a definitive membrane block to polyspermy, a pathological condition that remains a major hurdle in porcine in vitro fertilization (IVF). Despite its biological importance, the molecular networks driving JUNO cleavage has remained elusive. Here, by integrating proteomics, dual-color live-cell imaging, and functional perturbations, we identify the extended existence of JUNO and the GPI-specific phospholipase D1 (GPLD1) as the requisite enzyme mediating JUNO shedding in porcine oocytes. Targeted knockdown or pharmacological inhibition of GPLD1 stabilizes oocyte JUNO, prolongs the window of oocyte receptivity, and significantly exacerbates polyspermy, ultimately compromising embryonic developmental competence. Conversely, GPLD1 overexpression restricts redundant sperm adherence and enhances the efficiency of monospermic zygote formation and blastocyst development. Live-cell imaging reveals that fertilization triggers a transient, pulsed recruitment of GPLD1 in the oocyte, which precisely coincides with the biphasic kinetics of JUNO depletion. Our findings establish that the enzymatic cleavage of the GPI-anchor by GPLD1 is critical for JUNO release, defining a fundamental mechanism for the membrane-level block to polyspermy. This work provides a molecular framework for ensuring sperm-oocyte recognition and improving in vitro fertilization outcomes in mammals.

developmental biology↗