Biology subjects

Zhan, X.

Publications and source records attributed to Zhan, X..

At least 19 recordsLinked to original sources

bioRxiv2026

High efficiency gene editing and phenotype generation in avian embryos using lipid nanoparticles as a vector delivery platform

Birds are a longstanding developmental biology model because easy access to their fertilized eggs allows for direct observation and experimental manipulation during any stage of embryonic growth prior to hatching. However, functional genetics studies in avian embryos continue to rely primarily on in ovo electroporation and viral transduction, each of which is limited by variable efficiency, procedure-induced embryonic defects, substantial embryo mortality, and ineffective delivery of gene editing reagents such as CRISPR/Cas9. Here we establish lipid nanoparticle (LNP)-mediated delivery as a reproducible alternative for manipulating gene expression in avian embryos with high viability. Using the chicken embryo (Gallus gallus), we targeted migrating cranial NCCs, which contribute extensively to the craniofacial skeleton. We optimized LNP formulation and injection timing and frequency to build a robust and adaptable pipeline that offers efficient delivery of plasmid DNA and RNA cargos. We show that LNP delivery can support the piggyBac transposon system, enabling persistent reporter expression and long-term labeling of cranial NCC derivatives. To test if LNP-mediated delivery of RNA-based CRISPR/Cas9 components can achieve efficient somatic genome editing in ovo, we targeted Runt-related transcription factor 2 (RUNX2), which is required for osteogenesis. Embryos with RUNX2 edits in their cranial NCCs show a severe loss and disruption of craniofacial ossification on the targeted side of their craniofacial skeletons, demonstrating the potential of this approach for rapid functional interrogation of gene function in vivo. We also show that LNPs can mediate transfection in domestic pigeon (Columba livia) and Pekin duck (Anas platyrhynchos) embryos, indicating this method can be deployed across avians as a vector delivery platform for studying development, disease, and evolution.

developmental biology↗

bioRxiv2026

SpatialOmicsLab: an integrated research environment for AI co-scientists in spatial transcriptomics

Spatial profiling is moving from method development into a central platform for biological discovery and clinical translation, but analyzing such complex data has become the bottleneck of scientific discovery. Translating spatial transcriptomic measurements into biological insight requires reproducible workflows that integrate diverse analytical methods. Although language-model agents can plan and execute multistep analyses, fragmented data and software ecosystems hinder their use as reliable research assistants. In this study, we introduce SpatialOmicsLab, an integrated research environment for spatial transcriptomics comprising 113 curated computational tools, 63 reference datasets and 4,614 task specifications. We also introduce ST-Coscientist, an AI co-scientist that uses this environment to assemble, execute and refine spatial transcriptomic analyses. Within SpatialOmicsLab, ST-Coscientist outperformed expert-led workflows and other scientific agents across six benchmark settings spanning spatial clustering, cell-type deconvolution and spatially variable gene detection. It constructed alignment-aware workflows for three-dimensional MERFISH data and addressed biological questions by selecting and analysing relevant reference datasets. Procedural memory increased the success rate of integrating and validating additional analytical tools from 48% to 95%. Together, SpatialOmicsLab and ST-Coscientist establish an extensible ecosystem for reproducible spatial transcriptomic research, enabling AI co-scientists to address evolving biological questions with an expanding analytical repertoire.

bioinformatics↗

bioRxiv2026

T50 O-GlcNAc PKM2 promotes aerobic glycolysis and PDAC progression via ARNT-CDC27-AKT pathway

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and more evidence suggests that glucose metabolism plays a significant role in the development and progression with glycosylation at multiple sites potentially being a key characteristic. However, the underlying mechanisms remain insufficiently studied. Here, we first confirmed the presence of O-GlcNAc glycosylation modifications at Thr50 on PKM, which is highly expressed in PDAC and strongly correlated with poor prognosis. Further, we found that PKM expression was significantly positively correlated with CDC27 levels, and mutation of the Thr50 O-GlcNAc site in PKM abolished the upregulation of CDC27. We confirmed that O-GlcNAc-modified PKM enhances nuclear translocation of ARNT, which binds to the CDC27 promoter to upregulate its expression. Finally, we demonstrated that reduced expression of CDC27, as a key component of the APC/C complex, leads to downregulation of ubiquitination at the K11 site of PPP2CA, resulting in upregulation of PPP2CA expression, in turn, reduces AKT phosphorylation, ultimately driving PDAC regression by inhibiting aerobic glycolysis. Thus, we delineate a novel O-GlcNAcylation-dependent pathway where PKM drives PDAC progression through ARNT-mediated CDC27 transcriptional activation and AKT-mediated glycolysis.

cancer biology↗

bioRxiv2026

GPSNorm: Gaussian Process Spatial Normalization for Spatial Transcriptomics

Spatial transcriptomics technologies enable measurement of gene expression while preserving spatial tissue organization, but they remain highly sensitive to technical variability such as library size differences, slide-level effects, and spatial artifacts. Most existing normalization approaches treat normalization as a preprocessing step and perform downstream analyses on normalized values as fixed inputs, ignoring the uncertainty introduced during normalization. We introduce GPSNorm (Gaussian Process Spatial Normalization), a Bayesian spatial normalization framework that jointly models technical variation, spatial structure, and biological signal within a unified hierarchical model. GPSNorm represents gene expression counts using a negative binomial latent Gaussian model whose spatial component is a Gaussian Markov random field approximating a Gaussian process and performs efficient approximate Bayesian inference using the Integrated Nested Laplace Approximation (INLA), producing posterior estimates that propagate normalization uncertainty into downstream differential expression analysis. In simulations anchored to empirical spatial transcriptomics data, GPSNorm accurately recovers spatial technical structure and improves log-fold change estimation compared with existing normalization methods. Applications to three spatial transcriptomics datasets--including human dorsolateral prefrontal cortex Visium data, a GeoMx COVID-19 lung damage study, and the Spatial Organ Atlas kidney dataset--demonstrate improved preservation of biologically expected spatial patterns and marker gene contrasts. These results show that jointly modeling normalization and downstream inference can improve the robustness and interpretability of spatial transcriptomics analyses.An open-source R implementation of GPSNorm is available at https://github.com/Tiny-Quant/GPSNorm.

bioinformatics↗

bioRxiv2026

MAERM: Predicting Enzyme-Reaction Matching Relationships with a Mixed-Attention Model

Harnessing enzyme specificity requires a thorough understanding of enzyme promiscuity, which determines enzymes catalytic scope; however, measuring this scope still relies heavily on labor-intensive analytical approaches. While data-driven approaches have emerged to predict the catalytic scope of enzymes, these methods continue to face challenges such as restricted datasets and insufficient integration of enzyme structural information and reaction transformations. Here, we introduce MAERM, an innovative mixed-attention model designed to predict enzyme-reaction matching relationships. Built on our MAERM-DB, a dataset with broad coverage of validated and chemoenzymatic catalysis data, MAERM utilizes a local-global attention module to integrate multimodal enzyme information with fine-grained reaction representations, thereby predicting enzyme-reaction matching probabilities. Results show that MAERM consistently outperforms all baselines, with an average F1-score of 0.984. Notably, on challenging test samples with less than 40% sequence identity to the training set, MAERM outperforms the second-ranked model by 5.9% in F1-score. In addition, MAERM achieves the highest top-10 success rate of 51.7% on Enzyme-405 and the highest balanced accuracy of 0.697 on BioCat-547, further supporting its generalizability in enzyme screening and chemoenzymatic catalysis. Finally, MAERM can serve as an efficient scoring module. When integrated with ProteinMPNN, MAERM has successfully guided novel enzyme design for two carbonyl reduction reactions, resulting in enhanced catalytic potential for the native substrate and demonstrating broad compatibility. Overall, MAERM has the potential to reduce the experimental cost of measuring enzymes catalytic scope, facilitate enzyme design, and ultimately accelerate the design-build-test-learn cycle in enzyme engineering.

bioinformatics↗

bioRxiv2026

Water as a thermal contrast agent for artificial-intelligence-enhanced in vivo mid-infrared thermography

In vivo infrared thermography is limited by the inherently poor spatial resolution at long wavelengths, low contrast, and the lack of biocompatible contrast agents. Here, we present 3-5 m mid-wave infrared (MWIR) thermography enhanced by an artificial intelligence (AI) network and cold phosphate-buffered saline (PBS) as a thermal contrast agent for noninvasive in vivo imaging with high contrast and resolution. MWIR imaging enabled high thermal sensitivity with microscale spatial resolution, strong relative thermal contrast, and facilitated visualization of the subcutaneous vasculature in the human arm, hand, ankle, the femoral artery and vein in rats, and the femoral vessels in mice, with image contrast further enhanced by AI networks. In a 4T1 tumor-bearing mouse model, AI-enhanced MWIR resolved early-stage tumors of [~]2.3 mm and metastases as small as [~]1.7 mm. Using cold PBS as a MWIR thermal contrast agent, we achieved precise tumor boundary visualization and real-time imaging-guided tumor resection. AI-enhanced MWIR offers a promising solution for early diagnosis and improved surgical precision.

bioengineering↗

bioRxiv2026

From definition to discovery: metabolite markers of high temperature in green grape berries

Understanding how plants respond to high temperature is critical under global warming. Metabolite markers can provide insights into stress-responsive mechanisms and help guide strategies to maintain crop quality. However, heat-associated metabolite markers in grape berries remain poorly defined, particularly at the green stage, a critical phase of berry development during which early metabolic perturbations can influence subsequent ripening and ultimately determine berry composition and quality. Here, we applied berry-scale heat treatments of eight durations of two major wine cultivars, Cabernet Sauvignon and Merlot. Untargeted LC-MS profiling revealed both conserved and cultivar-dependent responses to heat. Based on these patterns, three time points were selected for targeted GC-MS analysis, and subsequent statistical analyses identified robust "cultivar-common heat markers": glycine decreased, whereas galactinol increased consistently across time points and cultivars. "Cultivar-dependent heat markers" were identified: xylose, lyxose, citrulline, quinic acid, and glutamine, that consistently distinguished CS and Merlot fruits under heat stress. Notably, xylose, lyxose, citrulline, and quinic acid also differentiated the two cultivars under ambient conditions, underscoring their potential as stable cultivar-discriminating metabolites. Together, these results reveal dynamic metabolic remodeling in grape berries under heat stress, particularly in amino acid, nitrogen, central carbon metabolism, raffinose family oligosaccharides pathway and the glutathione-ascorbate cycle.

plant biology↗

bioRxiv2026

Oligonucleotide genetics for Pseudomonas aeruginosa enables high throughput hypomorph screening

The continual advancement of genetic tools has been critical to our modern understanding of bacteria, with transposons, plasmids, and homologous recombination becoming workhorses of molecular microbiology. However, precisely specified reverse genetic approaches remain painstakingly slow and inaccessible, particularly in non-model strains. This reality is exemplified by the opportunistic pathogen, Pseudomonas aeruginosa (Pa), where conventional allelic exchange remains the dominant reverse genetic method. Here, we adapt a rapid genetic toolkit for use in Pa, relying directly on commercially available oligonucleotides (120 bases) to create precise genomic mutations through homologous recombination (i.e. oligo recombineering). Oligo Recombineering followed by Bxb-1 Integrase Targeting (ORBIT) uses a short attachment site for an integrating plasmid, which provides traditional antibiotic selection and can also carry flexible cargo. We establish Pa ORBIT works effectively for gene deletion without off target mutations, optimize protocol parameters (e.g. oligo length, electroporation), and demonstrate markerless and clean deletions. Importantly, our toolkit works well in clinical Pa strains as demonstrated by constructing efflux pump deletions in three different isolates. To test the high throughput capabilities of Pa ORBIT, we created over 160 degron-based hypomorphs (i.e. knockdowns) across 43 essential proteins in a pooled mutant library. Upon screening this library with and without antibiotics, we identify highly vulnerable essential proteins and hypomorphs that display synergy with clinical drugs. Therefore, ORBIT can be used for cutting edge low and high throughput investigations in this priority pathogen, setting the stage for answering critical basic and clinical science questions. SignificanceTo understand bacterial genomes, researchers need access to rapid, flexible, precise and high throughput genetic perturbation tools. Here we present an oligonucleotide-based method that satisfies these requirements for use in the opportunistic pathogen, Pseudomonas aeruginosa. By relying on oligos to encode genomic homology arms, no molecular cloning is required - making these tools rapid, robust, and scalable. We benchmark gene deletions in both lab and clinical strains, opening the possibility of rapid genetic studies across the P. aeruginosa pangenomic space. At high throughput, we use an oligo pool to create a mutant library of degron tagged essential proteins. These knockdowns (i.e. hypomorphs) show certain essential genes are highly vulnerable and others are synergistic with clinical drugs, providing insight into future antibiotic and co-therapy development.

microbiology↗

bioRxiv2026

A microbiota-derived bile acid overcomes antibiotic-induced hyporesponsiveness to immune checkpoint therapy by enhancing CD8+ T cell antitumor immunity

Gut microbiota are critical determinants of effective immune checkpoint therapy (ICT), yet the microbial mediators and host mechanisms that enhance antitumor immunity remain poorly understood. Here, we identify the microbiota-derived bile acid taurodeoxycholic acid (TDCA) as a metabolite associated with immune checkpoint therapy (ICT) response. TDCA administration alone is sufficient to overcome antibiotic-induced ICT hyporesponsiveness across multiple murine tumor models. Mechanistically, TDCA directly enhances CD8 T cell-mediated antitumor immunity, increasing cytotoxicity. These effects required signaling through the bile acid receptor TGR5. Together, these findings reveal TDCA as a gut microbial metabolite that restores ICT efficacy after antibiotic disruption by directly augmenting CD8 T cell anti-tumor activity. This work supports metabolite replacement as a therapeutic strategy to mitigate antibiotic-associated loss of cancer immunotherapy response. SignificanceTDCA is a microbiota-derived metabolite that restores immune checkpoint therapy efficacy after antibiotic disruption by directly enhancing CD8 T-cell-mediated anti-tumor immunity through bile acid receptor TGR5 signaling. Our findings suggest that supplementation with defined microbial metabolites can mitigate antibiotic-associated loss of immunotherapy response without requiring broader microbiome reconstitution.

cancer biology↗

bioRxiv2026

DNA Adenine Methylation Clock in Brain Aging and Alzheimer's Disease Progression

N6-methyldeoxyadenosine (N6medA) is a recently identified endogenous DNA modification widely found in bacteria, plants, and eukaryotes. In mammals, N6medA has been implicated in brain function, immunity, and response to environmental stress, but its relevance to gene regulation and mammalian aging remains controversial due to its extremely low abundance (< 1 per 10 million adenines) and an uncertainty regarding its genomic origin. We have developed and validated an ultrasensitive isotope dilution nano liquid chromatography-nanospray ionization Orbitrap mass spectrometry methodology to quantify N6medA in genomic DNA. Applying this approach to human prefrontal cortex tissues, we found that genomic N6medA levels increase linearly with chronological age (Pearson correlation coefficient, 0.95). Individuals with mild cognitive impairment (MCI) and Alzheimers disease (AD) exhibited a trend toward elevated cortical N6medA levels relative to age-matched controls. Genome-wide profiling of N6medA in human prefrontal cortex was conducted using two independent methods: NAME-Seq and MeDIP-Seq, which revealed age associated adenine methylation changes reminiscent of established epigenetic aging signatures such as the 5-methylcytosine clocks. N6medA mapping experiments identified a subset of genomic loci that were altered in MCI and AD. Pathway analysis of cross-validated adenine methylation sites revealed an enrichment of genes involved in neuronal function and age-related neurological processes, including glutamatergic synapse, axon guidance, and long-term depression. Finally, mass-spectrometry-based photoaffinity proteomics with synthetic DNA representing a region of the APP gene identified N6medA reader proteins with known roles in DNA repair, replication and transcription. Together, these findings identify N6medA as an age-associated DNA modification in the human brain and suggest that its accumulation and recognition by specific protein readers may contribute to molecular processes underlying brain aging and age-related neurodegeneration.

biochemistry↗

bioRxiv2026

Protein-peptide Interaction Representation Learning with Pretrained Language Models

Protein-peptide Interactions (PpIs) paly essential roles in diverse cellular processes, yet their systematic identification remains challenging due to the limited availability of experimentally annotated protein-peptide interaction data. To address this challenge, we present PepInter, a sequence-based Deep Learning (DL) framework that leverages large-scale pretraining on structurally derived pseudo protein-peptide pairs to learn interaction-relevant representations. Specifically, energy-dominant peptide fragments are extracted from protein complexes curated from non-redundant Protein Data Bank (PDB) structures, enabling the construction of pseudo protein-peptide interaction pairs that capture interface interaction patterns shared with canonical protein-protein interactions. This strategy allows the model to acquire interaction-aware priors in the absence of large-scale annotated protein-peptide complex datasets. Built upon the ESM-Cambrian (ESMC) architecture, PepInter adopts a two-stage pretraining strategy. In the first stage, masked language modeling is used to learn general protein sequence representations. In the second stage, the model is further trained to predict Rosetta-derived energetic scores, explicitly incorporating structural interaction signals into the learned embeddings. Following pretraining, PepInter is fine-tuned for both protein-peptide interaction classification and peptide bioactivity regression tasks. Across multiple benchmark datasets, including protein-peptide binding affinity prediction, PepInter consistently outperforms existing baseline methods and demonstrates strong generalization in identifying biologically meaningful PpIs. Case studies further highlight its ability to recover known interaction patterns and predict novel protein-peptide interactions. Together, these results establish PepInter as a scalable and effective framework for protein-peptide interaction prediction, with strong potential to accelerate peptide-based drug discovery.

bioinformatics↗

bioRxiv2025

Metabolic dysfunction related to specific genetic variant sites of methylmalonic acidemia

Methylmalonic acidemia (MMA) is a genetic metabolic disease, with mut (mut-MMA, caused by MMUT gene variation) and cblC (cblC-MMA, caused by MMACHC gene variation) being the predominant subtypes. MMA patients carrying c.729_730insTT and c.609G>A exhibit severe clinical manifestations and have poorer outcomes. Little information is known on the potential mechanism. The metabotypes of MMA patients carrying c.729_730insTT and c.609G>A were profiled. The potential effect of genetic variation sites on the metabolism were explored. Leveraging the gene variation spectrum and the targeted metabolomics, propionic acid, indolelactic acid, myristoleic acid, tetradecenoic acid, tridecanoic acid, and oxoadipic acid showed strong enrichment in c.729_730insTT carrying mut-MMA patients. Whereas mutation c.609G>A caused adipic acid, pimelic acid, fructose, AMP, hexanylcarnitine, and methionine significnatly altered in cblC-MMA patients. Furthermore, the metabolomic profiles were obviously different in c.729_730insTT and non-c.729_730insTT carrying mut-MMA patients after treatment. Different metabolic alterations in cblC-MMA patients carrying c.609G > A and non-c.609G > A under treatment were also observed. Our findings provide unique insights into the metabolic basis of MMA specific subtypes and its implications across different variants.

molecular biology↗

bioRxiv2025

Ultrastructure of Inputs to the Granule Cell Domain of the Dorsal Cochlear Nucleus

The granule cell domain of the cochlear nucleus (GCD) receives input from multimodal brain regions including the second cervical spinal ganglion, the spinal trigeminal nucleus, the cuneate nucleus, and the lateral reticular nucleus. Most of the input are in the form of large mossy fibers and small boutons containing round synaptic vesicles and forming asymmetric synapses. A smaller number of inputs arise from the lateral reticular nucleus and the spinal trigeminal nucleus that contain pleomorphic synaptic vesicles implying inhibitory action. This circumstance positions the GCD for integrating polysensory excitatory and inhibitory inputs at the earliest stages of auditory processing, which suggests a role for segregating sound streams. The structural substrate for such a function is naturally elaborate. Mossy fibers from these different origins are large, multilobed endings that form synaptic glomeruli with postsynaptic targets that include granule cells, unipolar brush cells, cartwheel cells, and chestnut cells, which in turn project to principal cells in the dorsal cochlear nucleus (DCN). Numerous synapses contribute to the integration of the different modalities, but the organization of these synapses is still not well understood. We investigated this issue by labelling the presynaptic endings of different somatosensory inputs using biotinylated dextran amine or Phaseolus vulgaris leucoagglutinin, and studying their post-synaptic relationships in the GCD. The anterogradely-labeled endings and associated targets were visualized by light and electron microscopy. Following computer-aided, three-dimensional reconstructions, we found that unipolar brush cells are the main target of the C2 DRG and cuneate nucleus. Mossy fibers from the spinal trigeminal nucleus project to Golgi cells, an inhibitory neuron. In contrast, inhibitory boutons originated from the lateral reticular nucleus. These results reveal that somatosensory integration in the granule cell domain is achieved by distinctive synaptic wiring and specialized chemical signaling.

neuroscience↗

bioRxiv2025

Monitor individual health and improve breeding success in Crested ibis (Nipponia nippon)

Biodiversity loss has become a pressing issue, requiring effective conservation measures. Drawing lessons from successful examples is essential. The Crested ibis (Nipponia nippon), once critically endangered but now recovering through intensive conservation programs, provides an informative model for evaluating and improving conservation practices. Using 17 years of monitoring data from a captive population spanning eight generations, we applied quantitative genetic tools (Animal Model) to characterize individual growth and improve breeding success of this endangered species. We found that body weight from Day 0 to Day 42 exhibited significant heritability (h2 = 0.195, 95% HPD 0.139 [~] 0.250). As for growth curve parameters, growth time-scale parameter a (h2 = 0.33, 95% HPD: 0.14 [~] 0.55) and growth-rate coefficient b (h2 = 0.22, 95% HPD: 0.10 [~] 0.34) were also significantly heritable, while the heritability of potential maximum weight M was relatively low (h2 = 0.023, 95% HPD: 0.00 [~] 0.16). We further estimated the breeding values of these phenotypical traits to inform future breeding and selections. Intergenerational analyses showed the estimated breeding values for body weight exhibited a tendency to increase, accompanied by a slower growth rate, though trends did not differ significantly from expectations under genetic drift. Comparison of parental and offspring growth trajectories revealed that healthy offspring closely follow parental growth trajectories, whereas unhealthy individuals display reduced growth. Given the multiple breeding times of this long-lived species, this approach enables effective monitoring of individual growth and health, allowing timely veterinary interventions so to enhance conservation efficiency.

zoology↗

bioRxiv2025

Integrating physiology and transcriptomics reveals the mechanisms underlying the differences in cotton (Gossypium hirsutum L.) growth and development under different photoperiods

Photoperiod is a crucial environmental factor that regulates plant growth and flowering. While the photoperiodic flowering pathway has been extensively studied, the mechanisms underlying photoperiodic regulation of cotton (Gossypium hirsutum L.) growth and development remain unclear. In this study, we selected different genotypes of cotton varieties, XJ12 and XJ21-11, to investigate the effects of long-day (LD) and short-day (SD) conditions on cotton photosynthesis, dry matter accumulation, flowering time, and related gene expression, utilizing physiological index measurements, transcriptomics, and WGCNA analysis. The results indicated that LD treatment significantly enhanced photosynthetic parameters and dry matter weight in both cotton varieties, with the net photosynthetic rate (Pn) being 154.79% and 111.86% higher under LD compared to SD treatment. Additionally, the flowering time of the two varieties was advanced by 12 and 13 days, respectively, under LD treatment relative to SD treatment. LD conditions promoted gene expression in sucrose and starch metabolism, facilitating cotton growth. Furthermore, LD inhibited the expression of genes associated with flavonoid biosynthesis, thereby altering the secondary metabolism of cotton, and activated transcription factors through signaling pathways (including phytohormone signaling and MAPK signaling pathways) and biological clock rhythms, which induced earlier flowering in cotton. This study provides new insights into the molecular mechanism of the effect of photoperiod on cotton growth and development.

physiology↗

bioRxiv2025

Candling analysis of egg development in an endangered bird species Crested ibis (Nipponia nippon)

Identifying key factors influencing the survival of animals, particularly rare and endangered species is crucial to biodiversity conservation. In birds, hatching failure is pronounced in endangered species. Accurate assessment of egg development and the ability to distinguish non-viable eggs are essential prerequisites for identifying the causes of hatching failure and applying appropriate conservation practices. The Crested ibis (Nipponia nippon), a flagship endangered species, has a long history of captive breeding, which has contributed to population recovery. However, little is known about the specific process and characteristics of its egg development. In this study, we provided the first comprehensive description of normal egg development in the Crested ibis, including both the changes observed in unfertilized eggs during incubation and embryonic development via candling (n = 106 eggs, with 1,423 candling images), a commonly used non-invasive method for assessing egg fertility and embryonic status, and offered a practical reference for assessing fertilization status and embryo viability. In addition, we estimated the timing of embryo mortality and found that most deaths occurred during mid-incubation (Day 7[~]15) (n = 12, 60.0%) and shortly before hatching (Day 23[~]29) (n = 7, 35.0%), highlighting these critical periods that require particular attention. This species-specific documentation of egg development provides a valuable reference for accurately assessing embryonic progress, evaluating environmental effects on survival, and guiding adaptive management, benefiting both captive breeding programs and field conservation efforts.

zoology↗

bioRxiv2025

Timing of immune checkpoint blockade shapes anti-tumor immunity via a clock-dependent chemokine axis

Circadian clocks regulate immunity, yet how they shape the tumor immune microenvironment and influence cancer immunotherapy remains unclear. Here, we show that tumor immune infiltration and immune checkpoint inhibitor efficacy vary by time of day in mice, driven by intrinsic clocks in dendritic cells and CD8+ T cells. Time-of-day modulates the abundance, spatial organization, and cytokine-chemokine production of tumor-infiltrating immune cells. Mechanistically, dendritic cell clocks control expression of Cx3cl1, driving recruitment of CX3CR1+CD8+ T cells and thereby reshaping the tumor immune microenvironment to enhance immunotherapy efficacy. Disruption of this axis abolishes time-of-day-dependent differences in treatment response. These findings identify a circadian mechanism of immune cell recruitment to tumors and provide mechanistic insight into clinical observations linking treatment timing to immunotherapy outcomes. One sentence summaryTime of day determines cancer immunotherapy efficacy through a circadian clock-dependent chemokine axis.

immunology↗

bioRxiv2025

Aβ Antibodies Target Not Only Amyloid Plaques But Also Distinct Brain Cells And Vessels

BackgroundAntibodies against amyloid-{beta} (A{beta}) are the only therapies capable of slowing cognitive decline and reducing A{beta} plaque burden in Alzheimers disease (AD). Yet the precise sites where antibodies engage A{beta} in the brain, and the mechanisms that drive A{beta} clearance, are not fully defined. Defining the A{beta} antibody engagement with A{beta} in brain is essential to understand how immunotherapy can be beneficial for AD. MethodsWe administered the well-establsihed N-terminal A{beta} antibody 6E10 via intrahippocampal (IH), cisterna magna (CM), or intraperitoneal (IP) injection in AD mouse models. N-terminal A{beta} antibodies were seen as most effective in AD mouse models. Antibody 6E10 was not only assessed in A{beta} plaques, but also examined in association with diverse brain cells and vascular compartments. Glymphatic dynamics were evaluated following A{beta} antibody treatment. ResultsAs expected, A{beta} antibody 6E10 bound to A{beta} plaques but remarkably also localized to vulnerable neurons, such as hippocampal CA1 pyramidal cells, as well as microglia, astrocytes, oligodendrocytes, perivascular macrophages (PVMs), and blood vessels. Glymphatic function showed no significant alterations after antibody administration. ConclusionsA{beta} antibodies distribute not only to amyloid plaques but also to neurons, glial cells, and blood vessels. This study provides detailed localizations of antibody in the AD brain, offering valuable insights into cellular targets and spatial dynamics of A{beta} antibody-based immunotherapy.

neuroscience↗