Biology subjects

Cao, Z.

Publications and source records attributed to Cao, Z..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Astrocyte-neuron mitochondrial transfer via mitoEVs supports neuronal energy metabolism and is impaired in early Alzheimer's disease

BackgroundMitochondrial dysfunction is an early and central feature of Alzheimers disease (AD). In particular, intercellular mitochondrial transfer has emerged as a mechanism of neuronal support in brain injury and neurodegeneration. However, pathways governing astrocyte-to-neuron transfer and its role in AD pathogenesis remain unknown. MethodsUsing the AppNL-G-F knock-in AD model, we combined high-resolution 4D live-cell imaging with quantitative fluorescence-based reporters to assess synaptic function and mitochondrial network dynamics in neurons and astrocytes. Direct and extracellular vesicle (EV)-restricted neuron-astrocyte co-culture systems were used to investigate bidirectional mitochondrial transfer. We performed the first in-depth structural, proteomic, and functional characterization of astrocyte-derived mitochondrial extracellular vesicles (mitoEVs) using cryo-electron microscopy, quantitative mass spectrometry, and bioenergetic analyses to define their cargo composition and metabolic effects. ResultsWe identified cell-type-specific mitochondrial remodeling in early AD, with compartmentalized synaptic energy deficits in neurons and hyperdynamic, less interconnected, yet metabolically preserved networks in astrocytes, preceding global bioenergetic decline. Bidirectional mitochondrial transfer between astrocytes and neurons, also at axonal terminals, was mediated by specialized mitoEVs but significantly reduced in the AppNL-G-Fmodel. Comprehensive proteomic and functional profiling revealed that WT astrocyte-derived mitoEVs are enriched in inner membrane and matrix proteins, supporting oxidative phosphorylation, lipid and amino acid metabolism, and redox homeostasis. In contrast, AppNL-G-F mitoEVs are selectively depleted of respiratory and fatty acid oxidation components and exhibit impaired respiration with reduced Complex IV activity. Functionally, WT mitoEVs promote mobilization of abnormal accumulation of lipid droplets in AppNL-G-Fneurons, restore fatty acid oxidation, and increase neuronal bioenergetics, including at the synapses. In contrast, disease-derived mitoEVs fail to engage these pathways. ConclusionsTogether, these findings identify mitoEV-mediated mitochondrial transfer as a glia-to-neuron metabolic pathway compromised in early AD and reveal a coordinated role for oxidative phosphorylation and fatty acid oxidation in supporting synaptic energy homeostasis.

neuroscience↗

bioRxiv2026

Identification of a FANCD2-interacting protein motif (DIP-box) linking DNA Damage Response processes

Repair of DNA damage is critical to genome integrity and the DNA clamp, FANCD2, is a major player in the response to interstrand crosslinks tying the two DNA strands together. It has long been suggested FANCD2 recruits other factors to coordinate repair these crosslinks, however it remains unclear if FANCD2 directly mediates this and how this is achieved. Here we show that FANCD2 has a conserved acidic region that directly recognises established DNA repair proteins containing a short linear motif. We discover new candidate interactors engaging with FANCD2 in a similar mode that are involved in histone modification, and RNA processing. Our data demonstrate FANCD2 is a hub for protein-protein interactions providing a structural explanation for FANCD2s central role in repair of DNA interstrand crosslinks. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/707417v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@10d0f59org.highwire.dtl.DTLVardef@6acac2org.highwire.dtl.DTLVardef@125a277org.highwire.dtl.DTLVardef@1cd4194_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

ER-stress signaling and Alzheimer's proteins adjust the quality of human protein synthesis

Proteostasis is the balance of protein synthesis, protein maintenance and protein degradation. Proteostasis is disturbed in neurodegenerative disorders like Alzheimers disease (AD) of the aging human body. Protein synthesis by the ribosome is the most error-prone process in gene expression. If and how the error-rate of protein synthesis is regulated during human aging and contributes to AD is unknown. Here we show that ribosomal error-rate is adapted in cellular models of human aging, but not in mouse aging. This adaptation involves ER-stress signaling and the Alzheimers disease-related proteins amyloid-beta precursor protein and presenilin 1. Our results suggest that ribosomal error-rate is a relevant parameter in human aging and disease.

cell biology↗

bioRxiv2026

Lung-Targeting Interleukin-10 mRNA Lipid Nanoparticles Ameliorate Acute Lung Injury

Acute respiratory distress syndrome (ARDS) is the most severe manifestation of acute lung injury (ALI), characterized by diffuse pulmonary inflammation, impaired gas exchange, and high morbidity and mortality. Despite its clinical significance, no specific or effective pharmacological therapies are currently available for its treatment. In this study, we developed a lung -targeted mRNA-sulfonium lipid nanoparticle (mRNA/sLNP) delivery system for the treatment of ALI in a mouse model. We first optimized sulfonium lipid structures, and the optimized sLNP was comprehensively characterized and subsequently loaded with interleukin-10 (IL-10) mRNA. In a lipopolysaccharide (LPS)-induced ALI mousemodel, IL-10/sLNPdemonstrated both prophylactic and therapeutic efficacy, significantly attenuating pulmonary and systemic inflammation, restoring barrier integrity, and reducing tissue injury.

bioengineering↗

bioRxiv2026

Efficiency, accuracy and robustness of probability generating function based parameter inference method for stochastic biochemical reactions

Biochemical reactions are inherently stochastic, with their kinetics commonly described by chemical master equations (CMEs). However, the discrete nature of molecular states renders likelihood-based parameter inference from CMEs computationally intensive. Here, we introduce an inference method that leverages analytical solutions in the probability generating function (PGF) space and systematically evaluate its efficiency, accuracy, and robustness. Across both steady-state and time-resolved count data, our numerical experiments demonstrate that the PGF-based method consistently outperforms existing approaches in terms of both computational efficiency and inference accuracy, even under data contamination. These favorable properties further enable the extension of the PGF-based framework to model selection--a task typically considered computationally prohibitive. Using timeresolved data, we show that the method can correctly identify complex gene expression models with more than three gene states, a task that cannot be reliably achieved using steady-state data alone.

systems biology↗

bioRxiv2025

FlashAffinity: Bridging the Accuracy-Speed Gap in Protein-Ligand Binding Affinity Prediction

Accurate prediction of protein-ligand interactions is central to computational drug discovery. Recent foundation models such as Boltz-2 have achieved remarkable accuracy in binding affinity prediction, yet their prohibitive computational cost remains a major barrier to large-scale virtual screening. Here we introduce FlashBind, a lightweight structure-based model that achieves a 50x speedup over Boltz-2 at inference time by replacing expensive structure prediction with a fast docking model and substituting costly PairFormer modules with a streamlined EGNN architecture. FlashBind attains early enrichment competitive with Boltz-2 on standard virtual screening benchmarks and demonstrates strong generalization to enzyme-substrate specificity prediction. To evaluate real-world applicability, we apply FlashBind to target-based antibiotic screening against the essential bacterial proteins in E. coli and show that FlashBind substantially outperforms Boltz-2 and other virtual screening baselines. Notably, several top-ranked candidates exhibit potent inhibition of DnaG and effective bacterial growth inhibition against E. coli in wet-lab validation. Together, these results demonstrate that FlashBind bridges the gap between accuracy and efficiency, enabling ultra-fast and accurate screening of massive chemical libraries for drug discovery.

molecular biology↗

bioRxiv2025

The Notch/snRNA negative feedback regulation and its implications in Alzheimer's disease

Alzheimers disease (AD) is a multifactorial disorder whose hallmark lesions have been recognized for more than a century, yet its molecular pathobiology remains fragmentary. During our AD research, we discovered a direct mechanistic axis that links the Notch signalling pathway -- a crucial regulator of cell differentiation and proliferation--with the small nuclear RNAs (snRNAs) that drive pre-mRNA splicing. For the first time, our discovery unifies two previously siloed paradigms -- cell signal transduction and RNA splicing, expanding the fundamental knowledge of gene expression and its regulation. The first contribution of the present study is the discovery and validation of the Notch/snRNA negative feedback regulation. More implications include: (1) it reframes splicing-related diseases as dynamic consequences of pathway dysregulation rather than static snRNA-gene mutations; (2) it explains why global splicing abnormalities appear in AD, aging, and cancer even when snRNA genes themselves are not mutated; and (3) it provides an immediate, druggable "splicing rheostat" for any Notch-hyperactive disorder, such as AD.

cell biology↗

bioRxiv2025

Mechanical Disturbance of Trichomes Enhances Cd2+ Uptake via Malate-Mediated Stomatal Opening in Arabidopsis

Phytoremediation of cadmium-contaminated soils relies on efficient metal uptake and translocation in plants. We previously found that gentle mechanical stimulation of non-glandular trichomes by brushing significantly enhances shoot Cd{superscript 2} accumulation in Arabidopsis thaliana, although the underlying physiological mechanism remained unclear. Here, we demonstrate that trichome brushing specifically induces stomatal opening, elevates transpiration, and promotes Cd{superscript 2} uptake, as evidenced by measurements of stomatal aperture, stomatal conductance, transpiration rate, and Cd{superscript 2} concentration in wild-type and mutant leaves. These responses are absent in the glabrous gl1-1 mutant and are not elicited by brushing epidermal pavement cells, indicating a trichome-specific mechanosensory pathway. Mechanistically, this pathway requires MPK3/MPK6 activity and employs malic acid as a key regulator of stomatal aperture, functioning independently of canonical ABA signaling. Our findings uncover a previously unrecognized trichome-to-stomatal signaling cascade that links trichome mechanostimulation to enhanced heavy metal accumulation.

plant biology↗

bioRxiv2025

Transcriptomic insights into developmental toxicity of per- and poly-fluoroalkyl substances (PFAS) in Caenorhabditis elegans: The potential key role of xenobiotic detoxification pathway

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants known to induce developmental toxicity across multiple species, yet the molecular mechanisms are still not fully understood. This study aims to evaluate the developmental toxicity of four long-chain legacy PFAS (PFOA, PFOS, PFNA, PFDA) and one short-chain alternative (PFBA) at environmentally relevant concentrations (1-5 M) using the model organism Caenorhabditis elegans, with a focus on elucidating the underlying molecular mechanisms. Phenotypic analysis indicated that PFDA and PFOS significantly delayed development of worms, and reduced the number of fertilized eggs in the uterus. RNA-seq and subsequent bioinformatic analysis revealed strong impacts of PFDA and PFOS on physiological age. A core set of xenobiotic detoxification genes (e.g., cyp-13A4, cyp-13A6, and cyp-13A7), which were found to be primarily regulated by nuclear hormone receptors (NHR-102, NHR-85, NHR-28), showed consistent up-regulation upon PFAS exposure. Gene co-expression network analysis (WGCNA) further linked this detoxification gene signature to developmental impairment. Cross-species comparison using public databases identified several evolutionarily conserved detoxification genes that are associated with PFAS-induced developmental toxicity, among which CYP3A4 and its orthologs appear to be emerging biomarkers of PFAS exposure. Our findings demonstrate that activation of conserved xenobiotic detoxification pathways is a central transcriptomic signature of PFAS exposure, providing mechanistic insights into the structure-dependent developmental toxicity of this kind of pervasive pollutant. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/689658v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@c50b2borg.highwire.dtl.DTLVardef@605b6dorg.highwire.dtl.DTLVardef@125fa8org.highwire.dtl.DTLVardef@464d34_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

pharmacology and toxicology↗

bioRxiv2025

Analytical Solutions for the Time-Dependent Dynamics of Stochastic Gene Expression with mRNA-sRNA Interactions

The antagonistic interaction between small RNAs (sRNAs) and messenger RNAs (mRNAs) constitutes a fundamental regulatory mechanism of gene expression in both prokaryotic and eukaryotic cells. However, the stochastic nature of transcription renders mean-field approximations inadequate for quantitative analysis of such systems. In the regime of strong sRNA-mRNA antagonism, we generalize the conventional probability-generating-function (PGF) framework and derive a novel approximate solution in the form of a generalized PGF, which can be analytically transformed into the time-dependent joint distribution of sRNA and mRNA via Laurent series expansion. The proposed approximation accurately captures the full stochastic dynamics across diverse systems exhibiting strong antagonism, while incorporating key biological features such as transcriptional burstiness, translation and sRNA recycling over the entire temporal range. Building on this analytical foundation, we further develop a generalized-PGF-based parameter-inference method that enables efficient and precise estimation of kinetic parameters, achieving inference speeds up to three orders of magnitude faster than traditional maximum-likelihood estimation approaches.

systems biology↗

bioRxiv2025

FOXA1 mutations co-opt nascent transcription factor networks in partnership with androgen receptor to enhance prostate tumorigenicity

Mutations in the pioneer transcription factor FOXA1, found in 10-40% of human prostate cancers, alter global chromatin accessibility and promote growth in prostate cells. Through analysis of a novel cohort of 874 primary and metastatic prostate tumors with somatic FOXA1 mutations, we confirm the high frequency of missense mutations (n=339) and indels (n=335) in the Wing2 region of the Forkhead domain, as well as frameshift mutations that truncate the C-terminus (n=287). To investigate the transcriptomic consequences of each mutation subgroup as well as elevated levels of wild-type FOXA1 (WT) (a fourth well documented subgroup), we performed single nucleus multiome sequencing in primary mouse organoids following inducible expression of representative alleles. Whereas each mutant induced distinct transcriptomic and DNA accessibility features, a prominent feature of all mutants was perturbed epithelial lineage specification, ranging from basal-like fates in cells expressing indel mutants to secretory (L1-like) luminal fates in cells expressing C-terminal truncation, missense mutations or excess WT. Integrated RNA-seq, ATAC-seq and ChIP-seq analysis of L1-like fate specification revealed enrichment of a composite androgen receptor-FOXA1 hybrid motif and cooperativity with the Oct family transcription factor POU2F1. Importantly, L1-like fate specification is seen in vivo tumorigenicity assays where, in combination with Trp53/Pten loss, expression of these mutants results in a histologic switch from basal-like to secretory luminal histology.

genomics↗

bioRxiv2025

Dysregulation of transcription networks regulating oligodendrogenesis in age-related decline in CNS remyelination

In demyelinating diseases like multiple sclerosis (MS), efficient remyelination is critical for functional recovery. Remyelination efficiency declines with age, and is linked to progressive disability. The gene regulatory network underlying remyelination, and how it is altered with aging, remains unclear. Here we present a comparative single-nucleus RNA and ATAC sequencing analysis of remyelination in young and aged mice. We identified gene modules dynamically expressed throughout oligodendrocyte differentiation, revealing age-dependent changes in key processes related to myelination. Multi-omic analysis allowed us to map the regulatory network driving efficient remyelination within oligodendrocyte lineage cells in young mice. We highlight key transcription factors in the network dysregulated with age, and we describe similar dysregulations in MS lesions. Modifying the expression of these transcription factors in primary oligodendrocyte progenitor cells impacts differentiation. These findings provide a foundational understanding of this regenerative process in the context of aging and in chronic demyelinating diseases.

neuroscience↗

bioRxiv2025

OT-RMSD: A Generalization of the Root-Mean-Square Deviation for Aligning Unequal-Length Protein Structures

The Root-Mean-Square Deviation (RMSD) metric, coupled with the Kabsch algorithm for optimal superposition, represents a cornerstone of quantitative structural biology. However, its application is fundamentally limited to the comparison of structures with a pre-defined, one-to-one correspondence of equal length, precluding its direct use for the vast majority of biologically relevant comparisons involving proteins of different lengths or unknown equivalences. To overcome this limitation, we introduce OT-RMSD, a novel, parameter-light method that generalizes the RMSD for comparing unequal-length protein structures without a pre-defined alignment. Our approach recasts the correspondence problem as an Optimal Transport (OT) task, utilizing a fundamental, distance-based cost function to identify the most economical mapping between two protein structures. This transport plan is then used as a soft-weighting scheme within an iterative Sinkhorn-Kabsch framework to simultaneously solve for correspondence and optimal superposition. We test our method on a benchmark dataset of 40 challenging protein pairs and demonstrate its superiority over both the widely-used PyMOL align command and a more complex, heuristic-driven variant of our own framework. On average, OT-RMSD identifies larger and more accurate alignment cores, achieving a significantly lower RMSD. We conclude that OT-RMSD provides a mathematically principled, robust, and effective extension of the classic RMSD concept, offering a powerful tool for modern structural analysis.

bioinformatics↗

bioRxiv2025

LieOTMap: A Differentiable Approach to Cryo-EM Fitting via Lie-Theoretic Optimal Transport

MotivationThe integration of high-resolution atomic models with lower-resolution cryo-electron microscopy (cryo-EM) maps is a fundamental task in structural biology. However, this rigid-body fitting problem is challenged by complex scoring landscapes and the non-differentiable nature of standard structural similarity metrics like TM-score, precluding their direct use in modern gradient-based optimization pipelines. MethodWe present LieOTMap, a novel, fully differentiable framework for cryo-EM fitting. Our approach introduces two key innovations. First, we parameterize rigid-body transformations on the Lie algebra se(3), which provides a minimal, singularity-free representation of the motion. Second, we formulate a loss function based on the Sinkhorn divergence, a differentiable proxy for the Optimal Transport (Wasserstein) distance. This loss function serves as a robust, geometrically meaningful surrogate for non-differentiable scores, allowing us to leverage powerful gradient descent optimizers to navigate the search space. ResultsWe demonstrate the effectiveness of LieOTMap by fitting the apo-state structure of E. coli GroEL (PDB: 1aon) into the ATP-bound state cryo-EM map (EMD-1046). Our method successfully navigates a large-scale conformational change, achieving a highly accurate final RMSD of 3.08 [A] with respect to the ground-truth structure (PDB: 1GRU). This result showcases the power of combining Lie-theoretic representations with differentiable geometric loss functions for complex structural alignment tasks. AvailabilityThe source code is available at https://github.com/YueHuLab/LieOTMap.

bioinformatics↗

bioRxiv2025

LieMap: A Fast and Transparent Gradient-Based Method for Cryo-EM Structure Fitting

Fitting high-resolution atomic models into lower-resolution cryo-electron microscopy (cryo-EM) maps is a critical yet challenging step in structural biology. We present LieMap, a straightforward, fast, and transparent gradient-based fitting protocol designed for practical applications. Our method parameterizes rigid-body motion on the Lie algebra se(3) to provide a minimal and singularity-free representation. The optimization objective is to directly maximize a real-space cross-correlation score, calculated as the mean density value sampled from the map at the atomic coordinates. This score is computed differentiably using trilinear interpolation and optimized with the Adam optimizer in a multi-stage fashion. We demonstrate the effectiveness of LieMap by fitting the apo-state structure of E. coli GroEL (PDB: 1aon) into its ATP-bound state cryo-EM map (EMD-1046). The protocol successfully navigates the large conformational change, achieving a final RMSD of 3.23 [A]. LieMaps simplicity, computational efficiency, and transparent optimization process make it a valuable and practical tool for structural biologists.

bioinformatics↗

bioRxiv2025

LieOTDock: A Protein Docking Framework via Lie Group Optimization and Optimal Transport

Protein-protein interactions (PPIs) are fundamental to cellular function, and structurally characterizing them is a key goal in molecular biology. Computational docking, a crucial tool for predicting the structure of protein complexes, faces significant challenges in search efficiency and scoring accuracy. Here, we introduce LieOTDock, a novel framework that leverages the mathematics of Lie groups and Optimal Transport (OT) for highly efficient protein docking pose generation. The core of our method is a data-driven surface sampling technique that identifies salient geometric features for initial pairing. These initial poses are then refined via a gradient-based optimization directly on the Lie group SE(3), the natural space of rigid-body motions. This optimization is guided by a differentiable score based on Sinkhorn-regularized Optimal Transport, which measures the geometric complementarity of surface patches. We tested LieOTDock on the 1AVW benchmark, where it successfully generated a near-native pose of 3.43 [A] RMSD from a 5000-candidate run in under three minutes on a consumer-grade Apple M4 Mini. Our results demonstrate that LieOTDock, by its principled use of Lie theory and OT, provides a powerful and efficient framework for generating high-quality candidate structures. This work decouples the challenge of geometric sampling from final scoring, offering a robust and modular approach to the protein docking problem, though future work is needed to integrate more physically realistic scoring functions for improved candidate ranking.

bioinformatics↗

bioRxiv2025

An Objective Detection Method for Tinnitus using Memristors

Approximately 10-20% of the world population experiences tinnitus, which is increased with age and comorbid with frustration, annoyance, anxiety, depression, cognitive dysfunction, insomnia and stress. The underlying mechanisms of tinnitus remain largely unknown and there are no quantifiable biomarkers, making this prevalent disorder a global burden. There are no FDA-approved therapeutics and the current assessment mainly relies on self-reports (e.g., questionnaires), which underscore the need for diagnostic tools for tinnitus. Memristors, nanoscale electronic components mimicking synaptic plasticity, can monitor and control neuronal activity, hold transformative potential for detecting and characterizing tinnitus objectively. Here, we harvested the blood from human and mice with tinnitus, and engineered memristors via magnetron sputtering and made the functional layer with serum. Its core mechanism lies in regulating the reversible formation and rupture of conductive filaments which is formed by utilizing abnormally elevated glutamate concentrations in serum assisted Ag+ ions. This serum-based memristor achieves unprecedented discrimination between tinnitus and non-tinnitus individuals. Furthermore, this platform provides mechanistic insights into tinnitus pathogenesis, with potential implications in other neurological disorders.

pathology↗

bioRxiv2025

LieOTAlign: A Differentiable Protein Structure Alignment Framework Combining Optimal Transport and Lie Algebra

The comparison of protein structures is fundamental to understanding biological function and evolutionary relationships. Existing methods, while powerful, often rely on heuristic search algorithms and non-differentiable scoring functions, which limits their direct integration into end-to-end deep learning pipelines. This paper introduces LieOTAlign1, a novel and fully differentiable protein structure alignment framework built on the mathematical principles of Lie algebra and Optimal Transport (OT). LieOTAlign represents rigid body transformations within the Lie algebra of SE(3), which intrinsically preserves the geometric validity of rotations and translations during optimization. We formulate the alignment task as an optimal transport problem, seeking the most efficient mapping between two protein structures. This approach leads to a differentiable version of the TM-score, the Sinkhorn score, which is derived from the entropically regularized OT solution computed via the Sinkhorn algorithm. The entire LieOTAlign pipeline is differentiable, enabling the use of gradient-based optimizers like AdamW to maximize structural similarity. Benchmarking against the official TM-align on the RPIC dataset shows that LieOTAlign can identify longer, topologically significant alignments, achieving higher TM-scores. While the current RMSD is higher, LieOTAlign provides a powerful and flexible framework for protein structure alignment, paving the way for its integration into next-generation deep learning models for diverse bioinformatics challenges.

bioinformatics↗