Biology subjects

Guo, G.

Publications and source records attributed to Guo, G..

At least 19 recordsLinked to original sources

bioRxiv2026

CAR T cell targeting of inflammatory myeloid progenitors in the bone marrow remodels border-associated macrophages and reverses cognitive aging

Aging is associated with progressive neuroinflammation and cognitive decline, yet the cellular sources that sustain this process and whether they can be targeted peripherally remain unclear. Here, we identify inflammatory border-associated macrophages (BAMs) as key drivers of neuroinflammation in aging and show that their therapeutic and prophylactic elimination through intrathecal CAR T cell therapy restores cognitive performance in mouse models of aging and Alzheimer's disease. Furthermore, targeting of aged inflammatory bone marrow myeloid progenitors through either intravenous CAR T cells, which do not infiltrate the brain, or through transplantation of CAR T-treated progenitors is sufficient to reduce neuroinflammation and cognitive impairment. These findings reveal that bone marrow myeloid progenitors harbor a heritable inflammatory transcriptional state that is transmitted to their BAM progeny, driving neuroinflammation and cognitive deterioration, and conserved in human aging. Critically, this proinflammatory state is marked by the upregulation of surface proteins, enabling precise peripheral CAR T cell targeting of these progenitors for long-lasting therapeutic effects in cognitive aging.

cell biology↗

bioRxiv2026

Exposure to a mixture of long-chain PFAS disrupts ovarian follicle development, ovulation, and luteinization in female mice

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants associated with adverse female reproductive outcomes. However, most prior studies examined individual PFAS, despite that humans are exposed to complex PFAS mixtures. Here, we investigated the ovarian effects of a mixture of five commonly detected long-chain PFAS, including PFOS, PFOA, PFNA, PFDA, and PFHxS, using complementary in vivo mouse and in vitro 3D ovarian follicle culture models. Young adult female mice were exposed to a range of concentrations of the PFAS mixture (PFASmix) through daily drinking water for 8 weeks. PFASmix at high concentrations (PFASmix-high) disrupted mouse estrous cyclicity, decreased circulating estradiol, increased testosterone, and promoted follicular atresia. Antral follicles from exposed mice showed decreased Lhcgr and increased Amh expression. Although the number of ovulated oocytes was unchanged following superovulation, PFASmix-high reduced oocyte size and polar body extrusion and altered oocyte cytoskeletal characteristics. In the 3D follicle culture model, direct PFASmix exposure impaired follicle growth, ovulation, and oocyte maturation in a concentration-dependent manner. PFASmix-high suppressed the periovulatory expression of Areg, Ereg, Tnfaip6, and Adamts1 and impaired corpus luteal (CL) development and function, as evidenced by reduced CL spheroid growth and cell survival, decreased expression of Star, Cyp11a1, Hsd3b1, and Lhcgr, and reduced progesterone secretion. PFASmix-high exposure additionally altered the expression of multiple PPAR{gamma}-associated genes in cultured ovarian follicles. Benchmark dose modeling identified estrous cyclicity and follicle rupture as sensitive in vivo and in vitro endpoints, respectively. Together, these findings demonstrated that exposure to the mixture of long-chain PFAS disrupts multiple stages of ovarian function, from follicular development, oocyte maturation to ovulation and luteal development and function and provided mechanistic and quantitative evidence for the reproductive toxicity of PFAS mixtures.

pharmacology and toxicology↗

bioRxiv2026

Constructing a full-length single-nucleus transcriptomic atlas of the obese mouse by UURNA-seq

Single-nucleus RNA-seq (snRNA-seq) offers advantages in sample preparation for atlas construction and data mining of gene regulation. However, current snRNA-seq protocols struggle to balance sensitivity and throughput. Here we present ultra-throughput and ultra-sensitivity single-nucleus total RNA sequencing (UURNA-seq), a platform that enables large-scale atlas construction in a single day. UURNA-seq outperforms existing single-cell and single-nucleus protocols, with reduced coverage bias, higher gene detection efficiency, and greater throughput at lower per-cell cost. This performance extends to primary tissues and is consistent in fresh and frozen samples, supporting flexible workflows for clinical and archival specimens. Notably, UURNA-seq also detects substantially more non-coding RNAs, including lncRNAs and sncRNAs, alongside mRNAs at single-nucleus resolution, uncovering previously inaccessible layers of cellular regulation in complex tissues. Leveraging these capabilities, we constructed the first comprehensive multi-tissue atlas of obesity and characterized RNA dynamics at atlas scale, revealing both conserved and tissue-specific transcriptional alterations and identifying critical dysregulation of hormone signaling and metabolic pathways. Together, these results establish UURNA-seq as a sensitive, scalable, and cost-effective platform that provides a route to dissect the molecular basis of complex diseases such as obesity.

genomics↗

bioRxiv2026

ParSeek: Accurate cryo-EM particle picking with a deep learning model trained on synthetic data

Accurate particle picking from noisy cryo-EM micrographs is essential for high-resolution reconstruction. Current deep learning methods rely on manually annotated data, which is labor-intensive, subjective, and limits particle recall under low signal-to-noise ratio (SNR). Here we introduce ParSeek, an automated picker trained entirely on synthetic data without human annotation. Synthetic micrographs are generated by projecting known 3D structures into realistic background patches that reproduce experimental noise. On seven public cryo-EM datasets, ParSeek outperformed Topaz and CryoSegNet on four datasets, achieving the highest F1-score (up to 0.82) and reaching 0.63 on a challenging membrane protein dataset. Density maps from ParSeek-picked particles showed cross-correlation coefficients up to 0.995 with the reference and a minimal resolution difference of 0.1 [A]. ParSeek also overcame severe orientation bias on an influenza dataset, yielding a reasonable reconstruction. Applied to three experimental datasets (an antibody-antigen complex and two GPCRs), ParSeek enabled reconstructions at 5.0 [A], 4.0 [A], and 2.8 [A], respectively. The 2.8 [A] map resolved side-chain densities and ligand flexibility. This study establishes a fully synthetic-data-driven strategy that eliminates manual annotation for training cryo-EM deep-learning models, paving the way for automated, unbiased particle picking.

molecular biology↗

bioRxiv2026

IntelliFold-2: Surpassing AlphaFold 3 via Architectural Refinement and Structural Consistency

IntelliFold-2 is an open-source biomolecular structure prediction model that improves accuracy and robustness through architectural refinement and multiscale structural consistency. We introduce latent space scaling in Pairformer blocks, a principled atom-attention formulation with stochastic atomization, policy-guided optimization for diffusion sampling and difficulty-aware loss reweighting. On Foldbench, IntelliFold-2 improves performance in therapeutically relevant settings, with particularly strong gains for antibody-antigen interactions and protein-ligand co-folding relative to AlphaFold 3. We release three variants (Flash, v2, and Pro) to cover efficient fine-tuning through high-precision server-side inference.

bioinformatics↗

bioRxiv2025

RNA viruses in water shape the viromes of shrimp and fish in aquaculture systems

Aquatic environments host immense RNA viral diversity, yet the extent to which waterborne viruses influence the virome of aquatic animals remains unclear. Here, we conducted a comprehensive longitudinal metatranscriptomic analysis of water, shrimp, and fish across multiple culture stages in aquaculture systems. We identified 3,211 RNA viruses spanning 21 viral supergroups, with 97.4% representing novel species. Water exhibited the greatest viral diversity (3,093 species), followed by shrimp (1,280) and fish (398), with most viruses detected in shrimp and fish also detected in water. Diversity analyses revealed increasing richness and significant compositional shifts in 92 high-abundance viruses (>1,000 RPM) across culture stages. Viral abundance in aquatic animals was strongly correlated with that in water, and the temporal trajectories of the 16 most abundant viruses were closely synchronized between water and shrimp. Furthermore, 337 virus species were shared among shrimp and two fish species, of which 309 were present in water. These findings demonstrated that RNA viruses in water shape the composition and dynamics of aquatic animal viromes, underscoring the importance of water virome surveillance and management for disease prevention in shrimp aquaculture.

microbiology↗

bioRxiv2025

GSDME unlocks astrocyte-driven neurotoxicity in Alzheimer's Disease

Astrocytic calcium dysregulation and reactivity precede A{beta} deposition in amyloid-{beta} deposition in Alzheimers disease (AD) but the neurotoxic mechanisms remain unclear. We show that GSDME acts as a switch, linking MAM-mediated calcium release to astrocyte-driven neurotoxicity. Specifically, A{beta}-activated microglial signals activate astrocytic GSDME, releasing its N-terminal fragment, which targets MAMs and triggers ER calcium efflux. This induces biphasic CaMKII phosphorylation, initially boosting NRF2 defenses, then activating NF-{kappa}B-driven inflammation, shifting astrocytes from protective to toxic states. GSDME activation also drives astrocyte-derived exosomes (ADEs) to carry neurotoxic tau, proinflammatory miRNAs, and toxic lipids, propagating toxicity. GSDME deletion in AD mice reduces A{beta} burden, restores NF-{kappa}B/NRF2 balance, reprograms astrocytes and ADEs to protective states, and rescues cognition. Multi-omics profiling of serum ADEs from AD patients reveals a disease-specific signature with central neurotoxicity and peripheral immune regulation. These findings position GSDME as a promising dual diagnostic and therapeutic target for early AD invention.

neuroscience↗

bioRxiv2025

An activated wheat CCG10-NLR immune receptor forms an octameric resistosome

Nucleotide-binding and leucine-rich repeat (LRR) receptors (NLRs) are widespread intracellular immune sensors across kingdoms. In plants, the G10-type coiled-coil (CCG10)-NLRs form a distinct phylogenetic clade that remains poorly characterized. Here, we identified a gain-of-function (GOF) mutant of Wheat Autoimmunity 3 (WAI3), designated WAI3GOF, which encodes a constitutively activated CCG10-NLR protein due to an amino acid substitution. Cryo-EM structural analysis revealed that activated WAI3 assembles into a distinctive octameric resistosome. Arabidopsis RPS2, another CCG10-NLR, also forms an octamer, indicating a conserved structural property across monocot and dicot plants. The WAI3 resistosome mediates a prolonged and sustained increase in cytosolic calcium influx, facilitated by a unique channel architecture arising from its divergent CC domain configuration. Notably, this domain arrangement may be shared by many plant NLRs that lack the conserved EDVID motif in their CC domains. Our findings uncover a previously uncharacterized resistosome structure and provide insights into plant immune receptor plasticity.

plant biology↗

bioRxiv2025

Study on physiological and ecological responses of alfalfa during the germination stage under drought, bicarbonate, and combined stress conditions

Alfalfa (Medicago sativa L.) is frequently constrained by drought and salinity during cultivation and production. This study aimed to investigate the differential response characteristics and underlying physiological and ecological mechanisms of alfalfa during the germination stage under drought stress, bicarbonate stress, and their combined effects. Twelve alfalfa varieties were subjected to germination tests using PEG-6000 to simulate drought stress (0-20%) and NaHCO{square} to simulate bicarbonate stress (0-30 mM). Based on the semi-inhibitory concentrations and phenotypic indices observed under single-stress conditions, a combined stress treatment was designed. The subordinate function method was employed to comprehensively evaluate the variations in stress responses among the twelve alfalfa varieties during the germination stage. Additionally, this study explored the physiological and ecological response mechanisms of drought-tolerant, salt-tolerant, and drought-salt-sensitive varieties. Among the 12 alfalfa varieties, WL363HQ has the strongest drought and salt tolerance, while WL319HQ is the most sensitive to drought and salt stress. Based on this, the varieties can be classified into drought-tolerant, salt-tolerant, and sensitive categories. At low concentrations, the drought-salt combination shows antagonism (salt alleviates drought inhibition); at high concentrations, it turns into synergy, and the inhibitory effect is amplified.WL363HQ adapts to the combined stress by maintaining stable SOD activity and gradually accumulating soluble sugar; WL319HQ shows sensitivity due to fluctuating SOD, membrane lipid peroxidation, and uncontrolled soluble sugar.

plant biology↗

bioRxiv2025

SEAL: Spatially-resolved Embedding Analysis with Linked Imaging Data

Dimensionality reduction techniques help analysts make sense of complex, high-dimensional spatial datasets, such as multiplexed tissue imaging, satellite imagery, and astronomical observations, by projecting data attributes into a two-dimensional space. However, these techniques typically abstract away crucial spatial, positional, and morphological contexts, complicating interpretation and limiting insights. To address these limitations, we present SEAL, an interactive visual analytics system designed to bridge the gap between abstract 2D embeddings and their rich spatial imaging context. SEAL introduces a novel hybrid-embedding visualization that preserves image and morphological information while integrating critical high-dimensional feature data. By adapting set visualization methods, SEAL allows analysts to identify, visualize, and compare selections--defined manually or algorithmically--in both the embedding and original spatial views, facilitating a deeper understanding of the spatial arrangement and morphological characteristics of entities of interest. To elucidate differences between selected sets of items, SEAL employs a scalable surrogate model to calculate feature importance scores, identifying the most influential features governing the position of objects within embeddings. These importance scores are visually summarized across selections, with mathematical set operations enabling detailed comparative analyses. We demonstrate SEALs effectiveness and versatility through three case studies: colorectal cancer tissue analysis with a pharmacologist, melanoma investigation with a cell biologist, and exploration of sky survey data with an astronomer. These studies underscore the importance of integrating image context into embedding spaces when interpreting complex imaging datasets. Implemented as a standalone tool while also integrating seamlessly with computational notebooks, SEAL provides an interactive platform for spatially informed exploration of high-dimensional datasets, significantly enhancing interpretability and insight generation.

bioinformatics↗

bioRxiv2025

High precision fluorescence tomography-guided system for pre-clinical radiation research: system design and validation

PurposeCone-beam computed tomography (CBCT), commonly used for image guidance in pre-clinical studies, is limited in soft tissue localization and lacks the functional information needed for image-guided radiation studies and treatment assessment. To address these limitations, we developed 3D fluorescence tomography (FT) for high precision functional image-guided research and integrated it with small animal irradiators. MethodsThe FT system, integrated with a commercial bioluminescence-guided platform in a standalone configuration, enabling compact multi-projection and multi-spectral imaging. A dual-axis galvo mirror scanner was used for laser spots scanning to excite internal fluorophore. A transportable mouse bed allows animals imaged in the optical system and transferred to irradiators for CBCT imaging and FT-guided irradiation. Spatial geometry-based methods were developed to map the laser spots and fluorescence images onto the animal numerical mesh surface, generated from the CBCT image, to serve as input data for FT reconstruction. A self-calibration method using Born-ratio data, fluorescence normalized to the excitation image, was adopted for reconstruction. Mouse phantom embedded with QD800 reporter and orthotopic glioblastoma (GBM) model injected with IRDye 800CW labelled U87-EGFR cells were used to verify the FT-guided system in target localization. ResultsThe laser spot mapping accuracy are within 0.7 mm maximum deviation. Our system can reconstruct the target QD800 as deep as 17 mm in the mouse phantom with single projection fluorescence imaging at localization accuracy < 1 mm deviation. Using the Born ratio approach, we effectively eliminated excitation light leakage and autofluorescence, enabling our FT system to localize IRDye 800CW-labeled EGFR-overexpressing GBM cells in the mouse brain with approximately 1 mm accuracy. ConclusionWe developed a compact FT system integrated with a small animal irradiator, achieving sub-millimeter localization accuracy in phantom and in vivo models. Its precision, flexibility, and compatibility position it as a powerful tool for advancing functional imaging-guided pre-clinical radiation research.

bioengineering↗

bioRxiv2025

Computational nanobody design using graph neural networks and Metropolis Monte Carlo sampling

Nanobodies have emerged as promising protein therapeutics due to their high-stability, low immunogenicity, and ease of production. However, experimental screening of high-affinity nanobodies for specific antigens and their post optimization remain costly and time-consuming, mainly due to the large number of possible variants. Here, we developed a computational approach that integrates graph neural networks (GNNs) with Monte Carlo Metropolis algorithm for nanobody design. We constructed a GNN model, AiPPA, to predict the protein-protein binding free energy (BFE) without requiring the complex structure, achieving a Pearson correlation of 0.62 on benchmark. We then combined AiPPA with Metropolis importance sampling to design low-BFE nanobodies from a non-affinity template. We applied this method to the antigen TL1A, and generated two affinity nanobodies. This work establishes a physics-informed deep learning method for computational nanobody design, providing a novel development strategy for protein therapeutics.

bioinformatics↗

bioRxiv2025

A new resource of clonal pluripotent human stem cell lines exhibiting inter- and intra-embryo consistency and variability

Human naive pluripotent stem cells can generate all somatic tissues and extra-embryonic components of the blastocyst. We derived multiple clonal naive pluripotent stem cell lines from individual embryos by physical separation of inner cell mass cells and subsequent individual expansion of each resulting dome-shaped colony, providing the foundation for a resource to investigate intra- and inter-embryo variation. Twenty lines were derived from ten embryos donated from nine couples. While differences between lines are observed, the overarching pluripotency circuitry is preserved in each. They can differentiate into extra-embryonic lineages and readily acquire post-implantation pluripotent identity when exposed to culture conditions driving in vitro capacitation, subsequently to generate derivatives of the three germ layers: ectoderm, mesoderm and endoderm. Some lines exhibit intra-chromosomal amplification and deletions and are therefore anticipated to provide a valuable, accessible system for modelling chromosomal mosaicism and its potential consequences using chimeric organoids, such as blastoids and gastruloids.

developmental biology↗

bioRxiv2025

De novo design of protein-binding aptamers through deep reinforcement learning assembly of nucleic acid fragments

Nucleic acid aptamers targeting proteins are becoming increasingly important in biopharmaceuticals and molecular diagnostics. Traditionally, aptamers are discovered through labor-intensive screening of nucleic acid libraries using the SELEX method. However, de novo design approaches without experimental screening remain a significant challenge. Here, we employed deep reinforcement learning to develop an artificial intelligence (AI) agent capable of de novo aptamer design, termed AiDTA (AI-driven Docking-Then-Assembling). First, nucleic acid fragments were docked to the target protein to identify target-binding fragments. Then, AiDTA automatically assembled these fragments into aptamers using the Monte Carlo tree search algorithm and a policy-value neural network to guide the agent in generating aptamers with secondary structures similar to the original constituent fragments. Experimental validation demonstrated that the AiDTA-designed DNA aptamers targeting disease-related proteins exhibited high binding affinities in the nanomolar range, achieving the de novo design of protein-binding aptamers for the first time. Our study establishes a new approach to obtaining protein-binding aptamers for potential applications in biopharmaceuticals and diagnostics.

bioinformatics↗

bioRxiv2025

Reverse remodelling of the mitochondria and cytoskeleton after respiratory heart rate variability pacing of the failing sheep heart

We have previously demonstrated that pacing the failing sheep heart with respiratory heart rate variability (RespHRV), a natural variability in the heart rate that is linked to respiration on a breath-by-breath basis, improves cardiac output dramatically. In this study, we used proteomics and super-resolution microscopy to explore the role of energetics and T-tubule cellular remodelling in response to ResPHRV pacing in an ischaemic ovine model of heart failure (HF). After 2 weeks of RespHRV pacing, cardiac output improved by 1.1 {+/-} 0.2 L/min (**p=0.003). Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH-MS) was used to probe differences between three groups: HF without any intervention, HF with RespHRV pacing and a healthy control group. Orthogonal Partial Least Squares (OPLS) discriminant analysis demonstrated a clear separation of all three groups by T score (***p<0.001) with the HF+RespHRV pacing group placed intermediate between the HF and control groups. The top 50 proteins negatively correlated with T score (down in HF, restored after RespHRV) were dominated by mitochondrial proteins, as confirmed by Pathway Enrichment Analysis (***p<0.001). Multiple Reaction Monitoring Mass Spectrometry (MRM-MS) analysis confirmed this finding in selected targets (ACAA2, ACADS, CRAT, NDUFA8, and SUCLG1, *p<0.05). STimulated Emission Depletion (STED) microscopy identified a disruption of mitochondria structure in HF (*p<0.05) that was restored in the HF+R group (p=0.051). The area of mitochondria labelling was increased in the HF+RespHRV group compared to HF (**p=0.005). Many cytoskeletal proteins linked to mitochondria regulation and T-tubule remodelling were upregulated in HF and were reduced by RespHRV. MRM-MS was able to confirm these findings for selected targets (ANAXA2, CAVIN2, SPTBN1, TUBA4A). STED microscopy of collagen VI and the ryanodine receptor revealed cellular hypertrophy and remodelling of the T-tubules and cardiac junctions in HF sheep (*p<0.05), RespHRV showed a trend for reversing these structural changes. These data support the hypothesis that within the first two weeks of RespHRV pacing, there is an increase in mitochondrial repair and function coupled with re-organisation of the cellular cytoskeleton, which is consistent with the improvement in cardiac pump function.

cell biology↗

bioRxiv2025

Recognition-dependent activation of the RRS1-R/RPS4 immune receptor complex

The Arabidopsis TIR-NLR immune receptors RPS4 and RRS1 function together to enable recognition of multiple effector proteins including AvrRps4 and PopP2. We show here that both in the presence and absence of effector, RPS4 and RRS1 form an oligomer that does not change in size upon effector provision. Oligomer formation involves interactions between the RPS4 and RRS1 TIR domains and requires nucleotide binding capacity in RPS4. RPS4 mutants that lose TIR domain NADase activity abrogate immune activation but retain oligomerization. A cysteine residue in the RPS4 LRR domain contributes to oligomer stabilization. We propose that upon effector recognition, conformational changes in the complex relieve inhibition of RPS4 TIR domains by RRS1 TIR domains, enabling proximity between RPS4 TIR domains to create NADase activity.

plant biology↗

bioRxiv2025

Integrating transcriptomic data with metabolic model unravels the electron transfer mechanisms of Methanosarcina barkeri

Methanogenic archaea, particularly Methanosarcina, are pivotal to the global carbon cycle and renewable energy production due to their versatile metabolic capabilities. Although transcriptomic analysis is widely employed to identify key genes and pathways in Methanosarcina under various methanogenic conditions--including the emerging direct interspecies electron transfer (DIET)-based methanogenesis--the weak correlation between gene expression levels and protein abundances poses challenges for interpreting transcriptomic data. To address this, we integrated transcriptomic data into a metabolic model of Methanosarcina barkeri for the first time, enabling more refined predictions and enhancing the interpretability of transcriptomic insights. This novel integrated model was subsequently utilized to simulate aceticlastic, hydrogenotrophic, and DIET-based methanogenesis. The results revealed that previous assumptions failed to account for the role of the CO2 reduction pathway in aceticlastic methanogenesis. The model also successfully captured key transcriptomic features of DIET-based methanogenesis, clarifying the functional roles of crucial enzymes like the F420H2 dehydrogenase Fpo and the transmembrane hydrogenase Vht in electron transfer. This integrative approach provided a deeper understanding of electron transfer mechanisms in M. barkeri and offered valuable insights for advancing methanogen-based biotechnologies. Moreover, the study critically evaluated the relationship between gene expression and metabolic flux, establishing a practical framework for deriving meaningful insights from the growing volume of transcriptomic data.

bioengineering↗

bioRxiv2024

A wheat tandem kinase and NLR pair confers resistance to multiple fungal pathogens

Recently discovered tandem kinase proteins (TKPs) are pivotal to the innate immune systems of cereal plants, yet how they initiate plant immune responses remains unclear. This report identifies the wheat protein WTN1, a non-canonical NLR receptor featuring tandem NB-ARC domains, as crucial for WTK3-mediated disease resistance. Both WTK3 and its allelic variant Rwt4, known for conferring resistance to wheat powdery mildew and blast respectively, are capable of recognizing the blast effector PWT4, and activate WTN1 to form calcium-permeable channels, akin to ZAR1 and Sr35. This study unveils a unique plant defense mechanism wherein TKPs and associated NLRs operate as "sensor-executor" pairs against fungal pathogens. Additionally, evolutionary analyses reveal a co-evolutionary trajectory of the TKP-NLR module, highlighting their synergistic role in triggering plant immunity. One Sentence SummaryAn ancient synergistic TKP-NLR pair triggers innate immunity for multiple disease resistance in wheat.

plant biology↗