Biology subjects

Qu, X.

Publications and source records attributed to Qu, X..

At least 19 recordsLinked to original sources

bioRxiv2026

Design of synthetic activators for programmable gene expression

Transcription factors control gene expression by recruiting transcriptional coactivators through intrinsically disordered transcriptional activation domains (TADs), whose weak and promiscuous interactions make it difficult to establish quantitative relationships between coactivator recruitment and transcriptional output, and to precisely modulate gene expression. To overcome this limitation, we set out to design coactivator-recruiting proteins using AlphaFold3-predicted TAD-coactivator interactions as a guide. We developed a high-throughput functional screening platform in mammalian cells and identified over 300 well folded nanomolar affinity binders to the transcription coactivators Mediator (multiple subunits), BRD4, NCOA, and p300; targeted interface mutations enabled systematic tuning of their binding affinities. Using a panel of p300 binders spanning nanomolar to micromolar affinities, we found an ultrasensitive relationship between coactivator-binding affinity and transcriptional output, with a sharp transcriptional activation threshold at ~500 nM. Incorporating p300 binder designs into CRISPRa yielded compact synthetic effectors that drove stronger activation than VPR across multiple genomic loci with minimal detectable cytotoxicity. Combining p300- and Mediator-specific binders further enhanced transcription, revealing functional synergy between these coactivators. Together, our work establishes a quantitative framework linking coactivator-binding affinity to transcriptional output and provides compact synthetic effectors for programmable gene activation.

synthetic biology↗

bioRxiv2026

Functional Robustness of Food Webs: A Dynamic Biomass Framework with Vital Species Sets and Cluster Influence

1. Species loss can erode food-web functioning not only through secondary extinctions, but also through biomass redistribution, weakened energy pathways, and threshold-like functional collapse. Common topology-, connectivity-, and extinction-based robustness metrics provide valuable summaries of structural disassembly and cascade risk, but they are not designed to quantify continuous biomass retention, collapse-associated species sets, and non-additive group-level effects within a single dynamic framework. 2. We develop a dynamic biomass-based framework for assessing food-web robustness under progressive species removal. The framework introduces Dynamic Area-based Robustness (DAR), which quantifies the weighted area between slow- and fast-collapse reference trajectories of total ecosystem biomass retention. Building on these trajectories, we operationally define the Minimal Vital Species Set (MVSS) as the smallest fast-collapse-prefix species set whose removal first drives biomass below a predefined functional-collapse threshold. We further propose Cluster Influence (CI), which compares the biomass effect of simultaneous group removal with the mean effect of removing the same species individually. 3. We evaluated the framework using 120 niche-model virtual food webs spanning controlled gradients of species richness and connectance, and further demonstrated its applicability on 16 empirical stream food webs. We compared DAR with AUC- and secondary-extinction-based robustness metrics and assessed the sensitivity of DAR, MVSS, and CI to key bioenergetic parameters and parameter uncertainty. 4. DAR captured biomass-based robustness patterns that were only partly aligned with structural and extinction-based metrics, indicating that dynamic functional degradation provides complementary information. In virtual food webs, MVSS subsets were strongly enriched in basal species or basal resource nodes, and smaller MVSS proportions were associated with stronger positive CI under fast-collapse trajectories. Together, DAR, MVSS, and CI provide a reproducible framework for linking food-web structure, biomass dynamics, collapse thresholds, and non-additive species-set effects, offering a practical tool for dynamic robustness assessment in theoretical and empirical food webs.

ecology↗

bioRxiv2026

A Divergent Class of Arylamine N-Acetyltransferases Catalyzes Convergent Amidative Condensation of Polyketides in Manumycins Biosynthesis

Polyketides are prized for their structural complexity and therapeutic potential, yet the incorporation of amide bonds into their frameworks typically relies on linear, nonribosomal peptide synthetase (NRPS)-dependent assembly. The direct, convergent coupling of distinct polyketide chains via amide bond formation--an ideal strategy for combinatorial biosynthesis--has remained largely elusive. Here, we report the discovery of a novel family of arylamine N-acetyltransferases (NATs) from manumycin-type biosynthetic pathways that catalyze an unprecedented intermolecular amidative chain transfer/condensation between an acyl carrier protein (ACP)-tethered polyketide donor and a free polyketide acceptor. Biochemical, structural, and molecular dynamics studies reveal that the representative enzyme, ColC2, possesses a distinctive substrate-binding pocket that diverges from canonical arylamine NATs, conferring exceptional promiscuity toward diverse acyl donors and acceptors. We demonstrate the utility of this biocatalyst by coupling arylamines with either synthetic acyl-thioesters or polyketide synthase (PKS) machinery to generate a library of non-natural polyketide amides and manumycin derivatives. These findings establish a new paradigm for amide bond formation in polyketide biosynthesis and position arylamine NATs as powerful tools for the development of novel therapeutics through combinatorial synthesis.

synthetic biology↗

bioRxiv2026

Ubiquitination-mediated mitochondrial protein degradation ensures seedling emergence by regulating ER-mitochondrial interaction and mitophagy

Seedling emergence is a pivotal step of plant survival, requiring rapid hypocotyl elongation for soil penetration. This energy-demanding process necessitates active mitochondrial respiration, which inevitably induces oxidative damage. Therefore, plants evolved a quality control mechanism that selectively removes dysfunctional mitochondria through the mitophagy pathway. Here, we identified SPL2, a mitochondrial E3 ligase which is essential for hypocotyl elongation and seedling emergence through degrading mitochondrial outer membrane proteins, such as TRB1 and FIS1A. Intriguingly, these proteins also interact with an ER protein, VAP27-1, forming a complex at the ER-mitochondria contact sites, which is essential for mitophagy initiation. The spl2 mutant exhibits enhanced ER-mitochondrial tethering and mitophagy activation, whereas its overexpression has the opposite effects. The expression of SPL2 increases after light perception, in agreement with the reduced mitophagy. Collectively, our findings reveal novel mechanistic insights into seedling emergence, which are coordinated through protein ubiquitination, ER-mitochondrial interaction, and mitophagy.

plant biology↗

bioRxiv2025

Fast Loading and Transformation of Large-Volume Bioimaging Data Consistently Approaching the I/O Peak Speed

High-resolution large-volume biological imaging techniques are now widely used in biological research, but inefficiencies in data processing and visualization persist due to bottlenecks in loading/saving pipelines and limitations of conventional pyramid formats. To address these issues, we have decoupled data loading process into reading from disk and in-memory decompression, while data saving has been separated into in-memory compression and writing to disk. This approach allows us to dedicate more CPU cores to decompression and compression tasks, optimizing resource allocation and maximizing disk I/O throughput. As a result, both loading and saving processes operate near the disks peak data transfer capacity. Further, we integrated a video stream-inspired format into the pyramid structure, allowing direct access to regions of interest (ROIs) without loading extraneous data into RAM, reducing memory overhead. Compared to the latest methods, our approach improves pyramid data transformation efficiency by at least 6x and dramatically accelerates downstream tasks like visualization and deep learning inference. TeaserNovel bioimaging data processing pipeline achieves near-peak I/O throughput via decoupled architecture and video-formatting.

bioinformatics↗

bioRxiv2025

Therapeutic Potential of Neural Rosette Stem Cell-Derived Small Extracellular Vesicles in the Management of Brain Edema

Small extracellular vesicles (sEVs) derived from neural stem cells (NSCs) have gained significant interest for their potential roles in neuroprotection, brain edema management, and the preservation of blood-brain barrier (BBB) integrity. These nanoscale particles facilitate intercellular communication and carry a diverse array of bioactive molecules, including proteins and RNAs, which influence various neurobiological processes. The composition of their cargo depends on the parental cell type, affecting their interactions with recipient cells. In this study, we characterized the cargo of sEVs derived from neural rosette stem cells (NRSCs) at both transcriptomic and proteomic levels and compared it to sEVs derived from cardiomyocyte progenitor cells (CMPCs) and human pluripotent stem cells (hPSCs). Furthermore, we assessed the therapeutic effects of NRSC-derived sEVs in a rat model of transient middle cerebral artery occlusion (tMCAO) to investigate neuronal injury and the progression of brain edema. Our proteomic and miRNA analyses revealed that the sEVs from NRSCs, CMPCs, and hPSCs possess distinct cargo compositions. In an in vitro neuronal ischemia model, NRSC-derived sEVs significantly enhanced cell viability, while in an endothelial model, they expedited the restoration of cell barrier integrity. Furthermore, the in vivo studies utilizing the rat model of tMCAO showed that intravenous administration of NRSC-derived sEVs at 3, 24 and 48 hours after reperfusion resulted in a significantly faster resolution of brain edema. These findings underscore the influence of the parental cell type on sEV composition and highlight the therapeutic potential of NRSC-derived sEVs in treating brain edema, paving the way for novel therapeutic strategies.

neuroscience↗

bioRxiv2025

Catalytic pocket-informed augmentation of enzyme kinetic parameters prediction via hierarchical graph learning

Predicting enzyme kinetic parameters is crucial for enzyme engineering and mining, but development of accurate tools for this task is still challenging due to the complexity of influencing factors. Large language models (LLMs)-based deep learning methods show satisfactory performance in Kcat prediction by encoding substrate and enzyme sequence information, but this further enhancement of the performance can be largely limited by the ignorance of the substrate-binding information. Here, we introduce GraphKcat, a deep learning framework that integrates enzyme-substrate 3D binding conformations for precise kinetic parameter prediction. The method employs Chai-1 to generate enzyme-substrate complex conformations, followed by a developed multi-scale hierarchical graph neural network to systematically characterize active site features from all-atom (AA) level to coarse-grained (CG) level, which are subsequently fused with LLMs embedded substrate, sequence, and environmental related information for prediction. To enable efficient multimodal feature integration, we proposed a multi modal cross-attention fusion (MMCAF) module for feature alignment and update. Experimental evaluations demonstrate that GraphKcat can effectively identifying catalysis-critical residues, learning sequence similarity-independent features through its multi-scale graph architecture, and maintaining robust performance under low sequence similarity conditions. Critically, GraphKcat captures conserved structural and physicochemical patterns related to enzymatic activity, enabling accurate identification of high-activity enzyme variants and functional mutants even for low-homology targets. These capabilities highlight its potential as a transformative tool for enzymatic activity prediction, rational enzyme engineering, and enzyme mining in industrial applications.

bioinformatics↗

bioRxiv2025

De novo design of miniprotein agonists and antagonists targeting G protein-coupled receptors

G protein-coupled receptors (GPCRs) play key roles in physiology and are central targets for drug discovery and development, but the design of protein agonists and antagonists has been challenging as GPCRs are integral membrane proteins and conformationally dynamic. Here we describe computational de novo design methods and a high throughput "receptor diversion" microscopy-based screen for generating GPCR binding miniproteins with high affinity, potency and selectivity, and the use of these methods to generate agonists for MRGPRX1, NK1R and CCR5, as well as antagonists for CXCR4, CCR5, OXTR, GLP1R, GIPR, GCGR, PTH1R and CGRPR.. Cryo-electron microscopy data reveals atomic-level agreement between designed and experimentally determined structures for CGRPR- and CXCR4-bound antagonists and MRGPRX1-bound agonists. Our de novo design and screening approach opens new frontiers in GPCR drug discovery and development.

bioengineering↗

bioRxiv2024

ApoDock: Ligand-Conditioned Sidechain Packing for Flexible Molecular Docking

Molecular docking is a crucial technique for elucidating protein-ligand interactions. Machine learning-based docking methods offer promising advantages over traditional approaches, with significant potential for further development. However, many current machine learning-based methods face challenges in ensuring the physical plausibility of generated docking poses. Additionally, accommodating protein flexibility remains difficult for existing methods, limiting their effectiveness in real-world scenarios. Diffusion based models has already show a good solution of those problems, such as Alphafold3(AF3). Herein, we present ApoDock, a modular docking paradigm that combines machine learning-driven conditional sidechain packing based on protein backbone and ligand information with traditional sampling methods to ensure physically realistic poses. With accurate sidechain packing and physical based pose sampling, ApoDock demonstrates competitive performance across diverse applications, highlighting its potential as a valuable tool for protein-ligand binding studies and related applications.

bioinformatics↗

bioRxiv2024

PointTree: Automatic and accurate reconstruction of long-range axonal projections of single-neuron

Single-neuron axonal projections reveal the route map of neuron output and provide a key cue for understanding how information flows across the brain. Reconstruction of single-neuron axonal projections requires intensive manual operations in tens of terabytes of brain imaging data, and is highly time-consuming and labor-intensive. The main issue lies in the need for precise reconstruction algorithms to avoid reconstruction errors, yet current methods struggle with densely distributed axons, focusing mainly on skeleton extraction. To overcome this, we introduce a point assignment-based method that uses cylindrical point sets to accurately represent axons and a minimal information flow tree model to suppress the snowball effect of reconstruction errors. Our method successfully reconstructs single-neuron axonal projections across hundreds of GBs (Gigabytes) images with an average of 80% F1-score, while current methods only provide less than 40% F1-score reconstructions from a few hundred MBs (Megabytes) images. This huge improvement is helpful for high-throughput mapping of neuron projections.

neuroscience↗

bioRxiv2024

3D-Aligner: An advanced computational tool designed to correct image distortion in expansion microscopy for precise 3D reconstitution and quantitative analysis

Expansion Microscopy (ExM) is an innovative and cost-effective super-resolution microscopy technique that has become popular in cell biology research. It achieves super-resolution by physically expanding specimens. Since its introduction, ExM has undergone continuous methodological developments to enhance its resolution and labeling capabilities. However, ExM imaging often encounters sample drift during image acquisition due to the physical movement of the expanded hydrogel, posing a significant challenge for accurate image reconstruction. Despite many proposed experimental solutions to mitigate sample drift, a universal solution has yet to be established. In response to this challenge, we developed 3D-Aligner, an advanced and user-friendly image analysis tool designed to computationally correct drift in ExM images for precise three-dimensional image reconstruction and downstream quantification. We demonstrate that 3D-Aligner effectively determines and corrects drift in ExM images with different expansion rates and various fluorescently labeled biological targets, showcasing its capabilities and robustness in drift correction. Additionally, we validate the precision of 3D-Aligner by comparing drift values across different labeled targets and highlight the importance of drift correction in quantification of biological structures.

cell biology↗

bioRxiv2024

Modeling glioblastoma tumor progression via CRISPR-engineered brain organoids

Glioblastoma (GBM) is an aggressive form of brain cancer that is highly resistant to therapy due to significant intra-tumoral heterogeneity. The lack of robust in vitro models to study early tumor progression has hindered the development of effective therapies. Here, we develop engineered GBM organoids (eGBOs) harboring GBM subtype-specific oncogenic mutations to investigate the underlying transcriptional regulation of tumor progression. Single-cell and spatial transcriptomic analyses revealed that these mutations disrupt normal neurodevelopment gene regulatory networks resulting in changes in cellular composition and spatial organization. Upon xenotransplantation into immunodeficient mice, eGBOs form tumors that recapitulate the transcriptional and spatial landscape of human GBM samples. Integrative single-cell trajectory analysis of both eGBO-derived tumor cells and patient GBM samples revealed the dynamic gene expression changes in developmental cell states underlying tumor progression. This analysis of eGBOs provides an important validation of engineered cancer organoid models and demonstrates their utility as a model of GBM tumorigenesis for future preclinical development of therapeutics.

bioengineering↗

bioRxiv2024

Long-lived chondroprogenitors are generated by fetal-limb Gli1+ cells and are replenished upon mosaic cell-cycle arrest in the cartilage

The growth-plate cartilage of the developing long bones is a well-known system of spatially segregated stem/progenitor, transient amplifying and terminally differentiated cells. However, the regulation of the number and activity of long-lived cartilage progenitors (LLCPs) is poorly understood, despite its relevance for understanding human-height variation, the evolution of limb size and proportions and the aetiology of skeletal growth disorders. Moreover, whether their behaviour can adapt to developmental perturbations, generating robustness, has not been explored. Here, we show that Gli1+ cells are the fetal precursors of postnatal LLCPs. Moreover, while during normal growth they remain mostly dormant until postnatal stages, in response to cartilage-targeted cell-cycle arrest they increase their cartilage contribution, compensating for the perturbation. We further show that, during the response to challenge, some of the reparative Gli1+ cells originate from Pdgfra+ cells outside the cartilage anlage. Elucidating how stromal cells become Gli1+ LLCPs could shed light on developmental robustness and lead to growth-boosting therapies.

developmental biology↗

bioRxiv2024

Crk mediates Csk-Hippo signaling independently of Yap tyrosine phosphorylation to induce cell extrusion

Src family kinases (SFKs), including Src, Fyn and Yes, play important roles in development and cancer. Despite being first discovered as the Yes-associated protein, the regulation of Yap by SFKs remains poorly understood. Here, through single-cell analysis and genetic lineage tracing, we show that the pan-epithelial ablation of C-terminal Src kinase (Csk) in the lacrimal gland unleashes broad Src signaling but specifically causes extrusion and apoptosis of acinar progenitors at a time when they are shielded by myoepithelial cells from the basement membrane. Csk mutants can be phenocopied by constitutively active Yap and rescued by deleting Yap or Taz, indicating a significant functional overlap between Src and Yap signaling. Although Src-induced tyrosine phosphorylation has long been believed to regulate Yap activity, we find that mutating these tyrosine residues in both Yap and Taz fails to perturb mouse development or alleviate the Csk lacrimal gland phenotype. In contrast, Yap loses Hippo signaling-dependent serine phosphorylation and translocates into the nucleus in Csk mutants. Further chemical genetics studies demonstrate that acute inhibition of Csk enhances Crk/CrkL phosphorylation and Rac1 activity, whereas removing Crk/CrkL or Rac1/Rap1 ameliorates the Csk mutant phenotype. These results show that Src controls Hippo-Yap signaling through the Crk/CrkL-Rac/Rap axis to promote cell extrusion.

developmental biology↗

bioRxiv2024

Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT

Identifying cell types and states remains a time-consuming and error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data, using unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000-cells; 51-cell types) from three niches (brain, intestine, gland), TACIT outperformed existing unsupervised methods in accuracy and scalability. Integration of TACIT-identified cell with a novel Shiny app revealed new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discover under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications.

bioinformatics↗

bioRxiv2024

Polyphosphate mobilization influences the ability of Cryptococcus neoformans to cause disease in a murine model of cryptococcosis

Cryptococcus neoformans, an invasive basidiomycete fungal pathogen, causes one of the most prevalent, life-threatening diseases in immunocompromised individuals and accounts for [~]15% of AIDS-associated deaths. A dire need for the development of novel antifungal drugs, vaccines, and improved diagnostics has emerged with the increased frequency of fungal infections. Therefore, understanding the pathogenesis of C. neoformans and its interactions with the host immune system is critical for the development of therapeutics against cryptococcosis. Previous research demonstrated that C. neoformans cells lacking polyphosphate (polyP), an immunomodulatory polyanionic storage molecule, display altered cell surface architecture. However, the relevance of surface changes and the role of polyP in the virulence of C. neoformans remain unclear. Here we show that mutants lacking the polyphosphatases (Xpp1 and Epp1) are attenuated for virulence in a murine inhalational model of cryptococcosis, demonstrate reduced proliferation in host tissue, and provoke an altered immune response. An analysis of mutants lacking the polyphosphatases and the Vtc4 protein for polyP synthesis indicated that the Xpp1 and Epp1 contribute to the organization of the cell surface, virulence factor production, the response to stress, and mitochondrial function. Overall, we conclude that polyP mobilization plays a multifaceted role in the pathogenesis of C. neoformans. Author SummaryCryptococcus neoformans causes one of the most prevalent fungal diseases in people with compromised immune systems and accounts for 15-20% of AIDS-associated deaths worldwide. The continual increase in the incidence of fungal infections and limited treatment options necessitate the development of new antifungal drugs and improved diagnostics. Polyphosphate (polyP), an under-explored biopolymer, functions as a storage molecule, modulates the host immune response, and contributes to the ability of many fungal and bacterial pathogens to cause disease. However, the role of polyP in cryptococcal disease remains unclear. In this study, we report that the enzymes that regulate polyP synthesis and turnover contribute to the virulence of C. neoformans in a mouse model of cryptococcosis. The polyphosphatases, Xpp1 and Epp1, influenced the survival of C. neoformans in macrophages and altered the host immune response. The loss of Xpp1 and Epp1 led to changes in cell surface architecture, cell size, impaired growth, and defects in both mitochondrial function and the stress response of C. neoformans. Thus, our work establishes polyP as a key factor in the disease caused by C. neoformans, and identifies polyP mobilization as a novel target to support new therapeutic approaches.

microbiology↗

bioRxiv2024

HIGH THROUGHPUT QUANTITATION OF HUMAN NEUTROPHIL RECRUITMENT AND FUNCTIONAL RESPONSES IN AN AIR-BLOOD BARRIER ARRAY

Dysregulated neutrophil recruitment drives many pulmonary diseases, but most preclinical screening methods are unsuited to evaluate pulmonary neutrophilia, limiting progress towards therapeutics. Namely, high throughput therapeutic screening systems typically exclude critical neutrophilic pathophysiology, including blood-to-lung recruitment, dysfunctional activation, and resulting impacts on the air-blood barrier. To meet the conflicting demands of physiological complexity and high throughput, we developed an assay of 96-well Leukocyte recruitment in an Air-Blood Barrier Array (L-ABBA-96) that enables in vivo-like neutrophil recruitment compatible with downstream phenotyping by automated flow cytometry. We modeled acute respiratory distress syndrome (ARDS) with neutrophil recruitment to 20 ng/mL epithelial-side interleukin 8 (IL-8) and found a dose dependent reduction in recruitment with physiologic doses of baricitinib, a JAK1/2 inhibitor recently FDA-approved for severe COVID-19 ARDS. Additionally, neutrophil recruitment to patient-derived cystic fibrosis sputum supernatant induced disease-mimetic recruitment and activation of healthy donor neutrophils and upregulated endothelial e-selectin. Compared to 24-well assays, the L-ABBA-96 reduces required patient sample volumes by 25 times per well and quadruples throughput per plate. Compared to microfluidic assays, the L-ABBA-96 recruits two orders of magnitude more neutrophils per well, enabling downstream flow cytometry and other standard biochemical assays. This novel pairing of high-throughput in vitro modeling of organ-level lung function with parallel high-throughput leukocyte phenotyping substantially advances opportunities for pathophysiological studies, personalized medicine, and drug testing applications.

bioengineering↗

bioRxiv2024

Glutathione metabolism impacts fungal virulence by modulating the redox environment

Pathogens must overcome the hostile conditions of their hosts to survive, proliferate and cause disease. The fungal pathogen Cryptococcus neoformans is particularly adept at mitigating challenges in the host environment and has developed an arsenal of defense mechanisms to evade oxidative and nitrosative agents released by phagocytic cells during infection. Among these mechanisms, melanin production is crucially linked to both fungal virulence and defense against harmful free radicals that facilitate host innate immunity and clearance of invading pathogens. Here, we employed comparative global metabolomics to demonstrate that metabolism of the antioxidant glutathione (GSH) is inextricably linked to redox-active processes that facilitate melanin production, and that genetic perturbations in GSH biosynthesis affect fungal growth and virulence in a murine model of cryptococcosis. Furthermore, we show that disruption of GSH biosynthesis leads to overaccumulation of reducing and acidic compounds in the extracellular environment of mutant cells. These changes not only impacted melanin formation but also influenced titan cell and urease production as well as survival in macrophages. Overall, these findings highlight the importance of redox homeostasis and metabolic compensation in pathogen adaptation to the host environment and suggest new avenues for antifungal drug development.

microbiology↗