Biology subjects

Kong, S.

Publications and source records attributed to Kong, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Base Editing as a Therapeutic Strategy for Autosomal Dominant Tubulointerstitial Kidney Disease caused by Pathogenic UMOD Variants

Autosomal dominant tubulointerstitial kidney disease caused by pathogenic UMOD variants (ADTKD-UMOD) is an untreatable monogenic kidney disease in which misfolded uromodulin is retained in the endoplasmic reticulum and exerts a toxic gain-of-function effect. Here we systematically evaluate the full spectrum of base editing strategies targeting the UMOD locus. Base editors corrected a representative missense patient variant, p.Cys170Tyr, with A-to-G conversion efficiencies of up to 83.4%, restoring uromodulin trafficking to the plasmamembrane in cell lines and in human 3D kidney tubuloids. Mutation-agnostic inactivation wasachieved by four routes: premature stop codon introduction, splice-site disruption, allele-specific stop codon introduction and promoter attenuation. Maximum on-target efficienciesranged from 44.1% to 71.9%, providing a strategy set applicable across the UMOD allelicspectrum. Endogenous UMOD expression is rarely recapitulated in human cell models,leaving strategies that act on the endogenous gene difficult to assess in patient cells. Wetherefore established CRISPR activation (CRISPRa) of the endogenous UMOD promoterand confirmed its ability to upregulate UMOD expression in human urine-derived cells. Thisapproach further enabled quantification of endogenous UMOD transcript reduction followingbase editing in HEK293 cells. These results establish base editing as a versatile approachfor ADTKD-UMOD and provide a primary human system for testing interventions targetingendogenous UMOD expression, from nucleic acid-based silencing to small molecules.

genetics↗

bioRxiv2026

High glucose confers senescence resistance via GLUT1 epigenetic rewiring to blunt immunotherapy responses in esophageal squamous cell carcinoma

Therapeutic resistance and undefined predictive biomarkers severely hinder the clinical popularization of immunotherapy in esophageal squamous cell carcinoma (ESCC). Herein, we identify the glucose transporter 1 (GLUT1) as a critical determinant of immunotherapy resistance. Elevated expression of GLUT1 correlates with poor immunotherapy response and unfavorable prognosis in ESCC patients. GLUT1 deletion or inhibition enhances CD8 T cell infiltration and cytotoxicity, and sensitizes ESCC tumors to anti-PD-1 (-PD1) therapy. Importantly, dietary glucose restriction exhibits equivalent antitumor efficacy to GLUT1 inhibition when combined with -PD1. Mechanistically, GLUT1 establishes a positive feedback loop with HAT1 and FOXM1, which epigenetically remodels chromatin accessibility to suppress tumor cell senescence, thereby impeding CD8 T cell-mediated antitumor immunity. Our findings highlight GLUT1 as a predictive biomarker of immunotherapy resistance and suggest dietary glucose restriction as a viable strategy to potentiate immunotherapy efficacy in ESCC. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=188 HEIGHT=200 SRC="FIGDIR/small/738372v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@c2c5d5org.highwire.dtl.DTLVardef@14a34c7org.highwire.dtl.DTLVardef@cf05e4org.highwire.dtl.DTLVardef@18bc354_HPS_FORMAT_FIGEXP M_FIG C_FIG SHORT SUMMARYDong et al. identify glucose transporter 1 (GLUT1) as a predictor of poor response to immunotherapy in esophageal squamous cell carcinoma. GLUT1 promotes immune evasion by forming a positive feedback loop with the HAT1/FOXM1 anti-senescence axis, thereby epigenetically remodeling chromatin accessibility. GLUT1 inhibition or dietary glucose restriction restores CD8 T-cell-mediated antitumor immunity and improves the efficacy of anti-PD-1 therapy in preclinical models.

cancer biology↗

bioRxiv2026

Cellular consequences, citrullination substrates, and antigenicity resulting from wild-type and targeted PAD4 on cell surfaces

Protein arginine deiminase-4 (PAD4) catalyzes hydrolysis of arginine to citrulline in proteins that promotes widespread changes in cellular phenotypes through transcriptional regulation that can induce innate immunity and promote cancer. Overexpression and hyperactivity of PAD4 leads to a form of cell death called NETosis that releases PAD4 to the extracellular space. In excess, release of PAD4 is believed to be a major cause of various autoimmune diseases through the generation of anti-citrulline protein antibodies (ACPAs). Little is known about the specific protein substrates that become citrullinated and lead to autoimmunity, but there is growing evidence that PAD4 can be localized to the cell surface in response to inflammation. Here, we further characterize the cellular consequences for exogenous treatment with PAD4 showing that it induces morphological changes that increase cell migration, a hallmark of cancer. We then devised a more simplified and robust proteomics approach to identify PAD4 substrates. We identified some 1000 endogenously citrullinated peptides from 500 proteins, and 3000 citrullinated peptides from 1300 proteins upon exogenous addition of PAD4 both inside and outside of cells. This extracellular set can be further augmented by targeting PAD4 to a cancer target, HER2, using a binding protein conjugate. Finally, we studied how citrullinated cells can induce a robust humoral response in a syngeneic vaccine model to produce ACPAs. We believe these studies further our understanding of cell phenotypic consequences of extracellular PAD4 and new PAD4 substrates both inside and outside of cells that are potential neoepitopes for generation of ACPAs.

cancer biology↗

bioRxiv2025

SNaQ.jl: Improved Scalability for Phylogenetic Network Inference

Phylogenetic networks represent complex biological scenarios that are overlooked in trees, such as hybridization and horizontal gene transfer. Although numerous methods have been developed for phylogenetic network inference, their scalability is severely limited by the computational demands of likelihood optimization and the vastness of network space. Composite (or pseudo-) likelihood approaches like SNaQ have improved computational tractability for network inference, but they remain inadequate for datasets of sizes routinely handled by tree inference methods. Here, we introduce SNaQ.jl, a new standalone Julia package with the composite likelihood inference originally implemented within PhyloNetworks.jl as well as new scalability features that enhance computational efficiency through (1) parallelization of quartet likelihood calculations during composite likelihood computation, (2) weighted random selection of quartets, and (3) probabilistic decision-making during network search. Through a simulation study and empirical data analysis, we show that this new version of SNaQ.jl (version 1.1) improves average runtimes by up to 499% on average with no change in function parameters or method accuracy.

evolutionary biology↗

bioRxiv2025

High-salt diet disturbs the physiological uterine endothelial junction remodeling during pregnancy

A high-salt diet (HSD) induced excessive sodium intake is a major risk factor for various diseases. However, its specific effects on uterine endothelial cell and pregnancy outcomes remain poorly understood. In this study, we demonstrated that HSD induces gestation specific hypertension and adverse pregnancy outcomes in murine models. The proper spiral artery remodeling, which involved the decidual vascular endothelia, is thought to be essential for normal regulation of gestational blood pressure. We explored the molecular basis for the adaptation of vascular endothelial cell based on the transcriptome data, and uncovered the significant changes for the cell junction in the endothelia during this process. Furthermore, it was revealed that HSD induced more salt accumulation in the uteri and triggered the disturbed vascular endothelium cytoskeletal remodeling, which was associated impaired trophoblast cell invasion and incorporation into the vascular wall. Overall, our study identifies unique roles of decidual endothelium for pregnancy vascular remodeling, and demonstrates that HSD disrupts physiological spiral artery remodeling through endothelial maladaptation, which may concurrently induce systemic endothelial damage in maternal systemic vascular.

physiology↗

bioRxiv2025

Oncogenic KRAS regulates secretion of extracellular vesicles and surface membrane charge via regulation of phosphatidylserine in pancreatic cancer

Extracellular vesicles (EVs) have the potential to be used as a liquid biopsy for cancer detection and treatment response assessment. Although the potential of EVs as disease-specific biomarkers has been promising, the rapid and specific enrichment of EVs from body fluids in the clinical setting remains challenging. To address this limitation, we developed a Microfluidic Electrophoresis (MEP) device, that allows label-free enrichment of EVs based on charge from the serum of patients with pancreatic cancer (PaCa). The ability of MEP to enrich anionic EVs was validated using cell line-derived EVs and PaCa serum-derived EVs. We observed a positive correlation between the KRAS status, secretion of EVs, and net negative zeta potential ({zeta}-potential) of EVs. Further analyses identified phosphatidylserine (PS) and extraluminal DNA as molecular determinants of the enhanced anionic nature of PaCa-derived EVs. Overall, this work introduces a new microfluidic device to enrich cancer EVs in circulation with potential for further rapid detection of PaCa.

bioengineering↗

bioRxiv2025

Intravital Two-Photon Imaging of Touch Sensory Axon Morphology in Mouse Skin

Low-threshold mechanoreceptors (LTMRs) are somatosensory neurons that detect innocuous light touch stimuli such as vibration, hair deflection, and pressure. They form subtype-specific terminals in the periphery and project axons centrally to the spinal cord to transmit tactile information. Current understanding about LTMR development and organization comes from fixed-tissue studies that cannot reveal the dynamic and temporal processes of neuronal wiring and remodeling. Here, we demonstrate a two-photon imaging method for visualizing LTMR axon morphology in the mouse right forepaw during development and in young adults. Two-photon microscopy can achieve high-resolution imaging within intact skin, allowing repeated imaging of the same axon terminals over postnatal timepoints. These approaches provide an in vivo system for the study of the cellular mechanisms that regulate LTMR patterning and plasticity. Its application to longitudinal analyses will make it possible to observe the assembly of touch circuits and their repair following injury. This technique may provide essential information about somatosensory axon structure and function in the skin. SummaryThis study presents an in vivo two-photon imaging approach to visualize the structures of low-threshold mechanoreceptor (LTMR) axon terminals in the forepaw skin in mice. By enabling repeated, high-resolution imaging of individual axons, the method provides a new platform for studying the sensory circuit structure and function during development and in adults.

neuroscience↗

bioRxiv2025

TDP-43 pathology induces CD8+ T cell activation through cryptic epitope recognition

Aggregation and nuclear depletion of the RNA binding protein TDP-43 are the crucial pathological features of amyotrophic lateral sclerosis (ALS) and inclusion body myositis (IBM), two degenerative diseases of the CNS and muscle. The loss of TDP-43 nuclear function results in the aberrant inclusion of cryptic exons in mRNA transcripts, leading to the expression of de novo proteins. Clonally expanded and highly differentiated CD8+ T cells have been observed in individuals with TDP-43 proteinopathies and therapeutics modulating the T cell response have recently been found to extend survival. However, the target antigens mediating T cell activation have remained elusive. Here, we investigate whether the de novo proteins induced by aberrant cryptic splicing due to TDP-43 nuclear loss can act as neo-antigens. We detect the HDGFL2 cryptic peptide and multiple other TDP-43 cryptic exons in IBM skeletal muscle, where their presence correlates with enrichment of T cells and class I antigen presentation pathways. Furthermore, we identify epitopes deriving from HDGFL2 and IGLON5 cryptic peptides which are recognized by clonally expanded and functionally differentiated populations of CD8+ T cells in ALS and IBM Patients. Finally, we demonstrate that T cells engineered to express the identified TCRs can bind and activate in response to the cryptic peptide derived epitopes (cryptic epitopes) and are able to kill TDP-43 deficient astrocytes. This work identifies for the first time specific T cell antigens in ALS and IBM, directly linking adaptive immune response to TDP-43 pathology.

immunology↗

bioRxiv2025

A method for massively scalable phylogenetic network inference

Recent advances in sequencing technologies have enabled large-scale phylogenomic analyses. While these analyses often rely on phylogenetic trees, increasing evidence suggests that non-treelike evolutionary events, such as hybridization and horizontal gene transfer, are prevalent in the evolutionary histories of many species, in which case tree-based models are insufficient. Phylogenetic networks can capture such complex evolutionary histories, but current methods for accurately inferring them lack scalability. Implicit network inference methods are fast but lack biological interpretability. Here, we introduce a novel method called InPhyNet that merges a set of non-overlapping, independently inferred level-1 networks into a unified topology, achieving linear scalability while maintaining high accuracy under the multispecies network coalescent model. We prove that a statistically consistent and scalable pipeline for inferring phylogenetic networks can be constructed with InPhyNet. Using simulation, we infer networks with up to 200 taxa and show that divide-and-conquer pipelines utilizing InPhyNet allow for accurate network inference at scales and speeds previously unseen. Re-analyzing a phylogeny of 1,158 land plants with InPhyNet, we recover well-accepted reticulate events and illustrate how InPhyNet enables large-scale analyses of biologically meaningful reticulate phylogenies at previously unprecedented scales.

evolutionary biology↗

bioRxiv2025

Metagenomes and Metagenome-Assembled Genomes from Tidal Lagoons at a New York City Waterfront Park

We report the sequencing and analysis of 15 shotgun metagenomes, including the reconstruction of 129 high-quality metagenome-assembled genomes, from tidal lagoons and bay water at Bush Terminal Piers Park in Brooklyn, NY sampled from July to September 2024.. Our metagenomic database for this site provides an important baseline for ongoing studies of the microbial communities of public parks and waterfront areas. In particular, we provide rich functional and taxonomic annotations that enable the use of these metagenomes and metagenome-assembled genomes for a wide variety of downstream applications.

microbiology↗

bioRxiv2025

In vivo base editing reduces liver cysts in autosomal dominant polycystic kidney disease

Autosomal dominant polycystic kidney disease (ADPKD) is the most prevalent genetic kidney disorder, affecting over 10 million individuals worldwide. Cystic expansion typically progresses to kidney failure and also involves the liver with limited treatment options. Pathogenic variants in PKD1 or PKD2 account for 85-90% of cases. Genetic re-expression of Pkd1 or Pkd2 has been shown to partially reverse key characteristics of the disease phenotype in mice. Despite advancements in the understanding of the genetic basis, it remains unclear whether the correction of underlying pathogenic variants can effectively prevent, modify, or reverse the disease. Additionally, the feasibility of extrinsically delivered genome editing as a treatment option for ADPKD remains largely unexplored. In this study, we employed CRISPR base editing to correct a spectrum of representative pathogenic PKD1 variants selected from a patient cohort achieving precise and efficient editing in vitro. Correction of a representative murine missense variant (c.6646C>T (R2216W)) in primary renal epithelial cells successfully increased polycystin-1 expression and reduced levels of the endoplasmic reticulum stress marker sXBP1. In vivo, base editor delivery to the c.6646C>T (R2216W) knock-in mouse enabled correction of the pathogenic variant, resulting in a significant reduction in liver cysts. These findings provide the first evidence of ADPKD reversibility through genome editing, opening promising novel therapeutic perspectives for affected patients and their families.

genetics↗

bioRxiv2025

Pollen morphology, deep learning, phylogenetics, and the evolution of environmental adaptations in Podocarpus

O_LIPodocarpus pollen morphology is shaped by both phylogenetic history and the environment. We analyzed the relationship between pollen traits quantified using deep learning and environmental factors within a comparative phylogenetic framework. C_LIO_LIWe investigated the influence of mean annual temperature, annual precipitation, altitude, and solar radiation in driving morphological change. We used trait-environment regression models to infer the temperature tolerances of 31 Neotropical Podocarpidites fossils. Ancestral state reconstructions were applied to the Podocarpus phylogeny with and without the inclusion of fossils. C_LIO_LIOur results show that temperature and solar radiation influence pollen morphology, with thermal stress driving an increase in pollen size and higher UV-B radiation selecting for thicker corpus walls. Fossil temperature tolerances inferred from trait-environment models aligned with paleotemperature estimates from global paleoclimate models. Incorporating fossils into ancestral state reconstructions revealed that early ancestral Podocarpus lineages were likely adapted to warm climates, with cool-temperature tolerance evolving independently in high-latitude and high-altitude species. C_LIO_LIOur results highlight the importance of deep learning-derived features in advancing our understanding of plant environmental adaptations over evolutionary timescales. Deep learning allows us to quantify subtle interspecific differences in pollen morphology and link these traits to environmental preferences through statistical and phylogenetic analyses. C_LI

evolutionary biology↗

bioRxiv2025

Addressing the open world: detecting and segmenting pollen on palynological slides with deep learning

In the "open world", categorical classes are imbalanced, test classes are not known a priori, and test data are captured across different domains. Paleontological data can be described as open-world, as specimens may include new, unknown taxa, and the data collected, such as measurements or images, may not be standardized across different studies. Fossil pollen analysis is one example of an open-world problem in paleontology. Pollen samples capture large numbers of specimens, including not only common types but also rare and even novel taxa. Pollen is diverse morphologically and features can be altered during fossilization. Additionally, there is little standardization in the methods used to capture and catalog pollen images and most collections are mounted on microscope slides. Therefore, generalized workflows for automated pollen analysis require techniques that are robust to these differences and can work with microscope images. We focus on a critical first step, the detection of pollen specimens on a palynological slide and review how existing methods can be employed to build robust and generalizable analysis pipelines. First, we demonstrate how a mixture-of-experts approach - the fusion of a general pollen detector with an expert model trained on minority classes - can be used to address taxonomic biases in detections, particularly the missed detections of rarer pollen types. Second, we demonstrate the efficiency of domain fine-tuning in addressing domain gaps - differences in image magnification and resolution across microscopes, and of taxa across different sample sources. Third, we demonstrate the importance of continual learning workflows, which integrate expert feedback, in training detection models from incomplete data. Finally, we demonstrate how cutting-edge segmentation models can be used to refine and clean detections for downstream deep learning classification models. Non-technical AbstractFossil pollen analysis presents challenges that result from the complexity of the "open world". It is not possible to anticipate all the taxa that will be encountered in geologic samples. Each sample has the potential to produce new species and introduce specimens with unique shapes and sizes. Pollen embodies diverse morphologies, its appearance can be altered depending on the conditions under which it was fossilized, and there is little standardization in the microscopy equipment used to image and catalog pollen images. AI applications in palynology need to address this complexity. We applied four machine learning methods (mixture-of-expert models, domain fine-tuning, continual learning, and foundation models for segmentation) to fossil pollen data to demonstrate their effectiveness in the detection and isolation of pollen specimens on a pollen sample slide.

paleontology↗

bioRxiv2024

KAT8 facilitates the proliferation of cancer cells through enhancing E7 function in HPV-associated cervical cancer

Persistent human papillomavirus (HPV) infection serves as the principal etiological factor in cervical cancer, with the oncoprotein E7, encoded by the virus, playing a key role in tumorigenesis. Despite this, targeted therapeutic strategies against E7 remain underexplored. KAT8, a lysine acetyltransferase, significantly contributes to oncogenesis through the regulation of transcription. However, its involvement in cervical cancer remains inadequately characterized. This study employs HPV18-positive HeLa and HPV16-positive SiHa cell lines to investigate how KAT8 modulates E7 expression and function in cervical cancer cells. Upon KAT8 knockdown, a marked reduction in cell viability is observed, alongside a downregulation of E7 expression. This is associated with elevated levels of retinoblastoma protein (pRb) and decreased E2F1 expression, indicating that KAT8 depletion inhibits E7 expression, resulting in E2F1 inactivation and cell cycle arrest. Furthermore, KAT8 is found to directly bind to the promoter regions of the HPV18 LCR, enhancing transcription of the HPV18 E7 gene. This study also demonstrates that KAT8 is essential for the acetylation of E7, and plays a critical role in facilitating the interaction between pRb/E2F1 and E7 in cervical cancer cells. In conclusion, these results highlight KAT8 as a key driver of cervical cancer progression, promoting HPV E7 expression and its associated oncogenic signaling pathways.

cancer biology↗

bioRxiv2024

Microsaccades strongly modulate but do not cause the N2pc EEG marker of spatial attention

The N2pc is a popular human-neuroscience marker of covert and internal spatial attention that occurs 200-300 ms after being prompted to shift attention - a time window also characterised by the spatial biasing of microsaccades. Here we show how co-occurring microsaccades profoundly modulate N2pc amplitude during the top-down deployment of spatial attention in both perception and working memory. At the same time, we show that a significant - even if severely weakened - N2pc can still be established in the absence of co-occurring microsaccades. Thus, while microsaccade presence and direction strongly modulate N2pc amplitude, microsaccades are not strictly a prerequisite for the N2pc to be observed.

neuroscience↗

bioRxiv2024

Stochastic Gene Expression in Auxin Signaling in the Floral Meristem of Arabidopsis thaliana

Cells display striking stochasticity in gene expression, which plays an important role in development, disease, and regeneration. Previous studies have found stochastic gene expression in bacteria, yeast, and from constitutive promoters in Arabidopsis. However, most promoters are non-constitutive. Stochastic gene expression from non-constitutive promoters in a multicellular organism, especially those with key developmental roles, remains largely uncharacterized. Here, we report stochastic expression of auxin responsive genes in the Arabidopsis floral meristem, using promoter reporters of DR5, ARABIDOPSIS HISTIDINE PHOSPHOTRANSFER PROTEIN6 (AHP6), and DNA BINDING WITH ONE FINGER5.8 (DOF5.8). We found highly variable DR5 expression patterns among younger meristems. Such variability is not attributed to heterogeneity in auxin perception, but to downstream stochastic expression of DR5, mainly influenced by cell-intrinsic molecular noise. Expression of AHP6 and DOF5.8 is also noisy, although their noise is lower and has distinct spatiotemporal patterns unlike DR5. Finally, we propose spatial averaging as a mechanism that buffers cellular gene expression noise, allowing the formation of robust global expression patterns. Our study reveals stochastic gene expression downstream of auxin signaling, a key developmental player. Thus, stochastic gene expression from non-constitutive promoters, including those involved in hormone signaling, is an ordinary part of multicellular life.

developmental biology↗

bioRxiv2024

Reconstructing the diversity dynamics of paleo-grasslands using deep learning on superresolution images of fossil Poaceae pollen

Grass pollen is largely overlooked in investigating grassland evolution because the pollen of most species cannot be differentiated using traditional optical microscopy. However, deep learning can quantify small variations in pollen morphology visible under superresolution microscopy. We use the abstract features output by deep learning to estimate the taxonomic diversity and physiology of fossil grass pollen assemblages. Using a semi-supervised learning strategy, we trained convolutional neural networks (CNNs) on superresolution pollen images of modern grasses and unlabeled fossil Poaceae. Our models captured features that reflected both the taxonomic diversity of grass communities along an elevational gradient and morphological differences between C3 and C4 species. We applied our trained models to fossil grass pollen assemblages from a 25,000-year lake-sediment record from eastern equatorial Africa (Mt. Kenya) and correlated past shifts in grass diversity with atmospheric CO2 concentration and proxy records of local temperature, precipitation, and fire occurrence. We quantified changes in grass diversity using morphological variability of fossil pollen assemblages, approximated by the Shannon entropy of CNN features. Our data show that grassland species diversity was strongly reduced between 21,500 and 16,000 years ago, coincident with most severe regional cooling during the last ice age. C3:C4 ratios reconstructed using a gradient-boosted decision tree classifier infer a gradual decrease in C4 grasses since the late-glacial to Holocene transition, associated with decreasing fire activity and elevated temperatures. Our results demonstrate that CNN features of pollen morphology can advance palynological analysis, enabling robust estimation of grass diversity and C3:C4 ratio in ancient grassland ecosystems. SignificanceAlthough the pollen of most grass species are morphologically indistinguishable using traditional optical microscopy, we show that they can be differentiated through deep learning analyses of superresolution images. Abstracted morphological features derived from convolutional neural networks can be used to quantify the biological and physiological diversity of grass pollen assemblages, without a priori knowledge of the species present, and used to reconstruct past changes in the taxonomic diversity and relative abundance of C4 grasses in ancient grasslands. This approach unlocks ecological information previously unattainable from the fossil pollen record and demonstrates that deep learning can solve some of the most intractable identification problems in the reconstruction of past vegetation dynamics.

paleontology↗

bioRxiv2024

Chemoproteomics reveals immunogenic and tumor-associated cell surface substrates of ectokinase CK2α

New epitopes for immune recognition provide the basis of anticancer immunity. Due to the high concentration of extracellular adenosine triphosphate in the tumor microenvironment, we hypothesized that extracellular kinases (ectokinases) could have dysregulated activity and introduce aberrant phosphorylation sites on cell surface proteins. We engineered a cell-tethered version of the extracellular kinase CK2, demonstrated it was active on cells under tumor-relevant conditions, and profiled its substrate scope using a chemoproteomic workflow. We then demonstrated that mice developed polyreactive antisera in response to syngeneic tumor cells that had been subjected to surface hyperphosphorylation with CK2. Interestingly, these mice developed B cell and CD4+ T cell responses in response to these antigens but failed to develop a CD8+ T cell response. This work provides a workflow for probing the extracellular phosphoproteome and demonstrates that extracellular phosphoproteins are immunogenic even in a syngeneic system.

biochemistry↗