Biology subjects

Chan, C. K.

Publications and source records attributed to Chan, C. K..

11 recordsLinked to original sources

bioRxiv2025

Intratumoral cDC1-T Cell Clusters Serve as Sites of Local Costimulation to Enhance CTL-Mediated Tumor Rejection

T cells are essential for anti-tumor immunity, but their ability to eliminate tumors depends on coordinated interactions with type 1 conventional dendritic cells (cDC1s). While cDC1s are known for cross-presenting tumor-derived antigens in lymph nodes to prime CD8+ T cells, their role within the tumor itself remains less well understood. Here, we use the Skin Tumor Array by Micro-Poration (STAMP) model to investigate how cDC1-T cell interactions shape immune responses and influence tumor fate. Our data reveal that it is the spatial distribution of both cDC1s and T cells that determines whether a tumor can be rejected. We defined three primary immunotypes based on the spatial distribution of T cells and cDC1s: T cell-inflamed/dendritic cell-inflamed (TC-In/DC-In) tumors, where T cells and cDC1s co-infiltrate the tumor; T cell-inflamed/dendritic cell-excluded (TC-In/DC-Ex) tumors, where T cells infiltrate but cDC1s remain at the periphery; and T cell-excluded/dendritic cell-excluded (TC-Ex/DC-Ex) tumors, which lack both cDC1 and T cell infiltration. Notably, TC-In/DC-In tumors are more likely to undergo rejection, whereas TC-In/DC-Ex tumors persist despite T cell infiltration. Within TC-In/DC-In tumors, cDC1s engage in direct interactions with T cells, upregulate co-stimulatory molecules, and sustain effector T cell responses, while cDC1s in TC-In/DC-Ex tumors express higher migration-associated genes, suggesting a propensity to exit the tumor. We further show that chemokine modulation, particularly through CXCL9, CCL5, and XCL1, can reshape immune infiltration patterns to promote intra-tumoral cDC1-T cell clustering and improve tumor rejection. These findings underscore the unexpectedly important role of cDC1 positioning and function in sustaining effective anti-tumor immunity and highlight spatially organized cDC1-T cell clusters as critical hubs for local T cell activation.

immunology↗

bioRxiv2025

Basigin Links Altered Skeletal Stem Cell Lineage Dynamics with Glucocorticoid-induced Bone Loss and Impaired Angiogenesis

Glucocorticoid (GC) induced osteoporosis (GIOP) and osteonecrosis remain a significant health issue with few approved therapies that can treat the bone loss and dysfunction of skeletal vasculature. Therefore, we aimed to investigate the cellular and molecular processes by which GCs affect osteogenesis and angiogenesis, as well as how treatment with parathyroid hormone (hPTH 1-34) modifies these effects in a mouse model of GIOP. GC treatment reduced bone mass through decreased bone formation by skeletal stem cells (SSCs) while also increasing osteoclast mediated resorption. Concomitantly, endothelial cells were increased in numbers but displayed distorted phenotypical features. However, hPTH treatment reversed GC induced changes in osteogenesis and angiogenesis to control levels. Transplantation studies of SSCs combined with molecular analysis by single cell RNA-sequencing and functional testing of primary human cells tied GC-induced skeletal changes to altered stem and progenitor cell differentiation dynamics. This in turn perpetuated reduced osteogenesis and vascular malformation through direct SSC-endothelial crosstalk mediated at least in part by Basigin. Intriguingly, antibody-mediated blockade of Basigin during GC treatment prevented detrimental bone loss. In addition, when administered to aged mice, anti-Basigin therapy reinstated bone remodeling to significantly improve bone mass independent of sex. These findings, while helping to explain the cellular and molecular basis of how hPTH treatment can mitigate GC induced bone loss, provide new therapeutic vantage points for GIOP and other conditions associated with bone loss.

cell biology↗

bioRxiv2024

Transient protein structure guides surface diffusion pathways for electron transport in membrane supercomplexes

The biological significance of protein supercomplexes have remained contentious, particularly how they tune the shuttling of charge-carrier redox proteins across cell membranes during biological energy conversion. We employ multiscale modeling and single particle cryo-electron microscopy (cryo-EM) to determine the mechanisms of diffusive electron transfer in mitochondrial supercomplexes, composed of respiratory complexes III and IV (CIII and CIV). Using a combination of bioinformatic and entropy maximization tools, we model an ensemble of structures representing the conformational space of CIIIs disordered QCR6 hinge within the yeast CIII2CIV2 supercomplex. Molecular and Brownian Dynamics simulations of the entire supercomplex reveal a mechanism for electrostatic coupling between these negatively charged hinge conformations, and binding and directional diffusion of the redox proteins on the mitochondrial membrane, which is simulated over the millisecond timescale. Anionic lipids reinforce this conformationally-coupled recognition of the supercomplex by retaining a pool of the redox proteins in the vicinity of the membrane when the hinges are of a critical length. Cryo-EM models reveal a large-scale rearrangement of the {Delta}QCR6 supercomplex, which retains a surprisingly robust electrostatic environment for recognition of the redox protein, despite compromise in the supercomplexs negative charge, still enabling a surface-mediated electron transfer in this CIII2CIV2 variant. Altogether, the evolutionary need of confining electron carriers on the surface of bioenergetic membranes is found to give rise to a refolding-guided diffusion model of the redox proteins, which improves the energy conversion efficiency within the supercomplex by nearly 30%.

biophysics↗

bioRxiv2024

Regulation of Versican Expression in Macrophages is Mediated byCanonical Type I Interferon Signaling via ISGF3

Growing evidence supports a role for versican as an important component of the inflammatory response, with both pro- and anti-inflammatory roles depending on the specific context of the system or disease under investigation. Our goal is to understand the regulation of macrophage-derived versican and the role it plays in innate immunity. In previous work, we showed that LPS triggers a signaling cascade involving TLR4, the Trif adaptor, type I interferons, and the type I interferon receptor, leading to increased versican expression by macrophages. In the present study, we used a combination of chromatin immunoprecipitation, siRNA, chemical inhibitors, and mouse model approaches to investigate the regulatory events downstream of the type I interferon receptor to better define the mechanism controlling versican expression. Results indicate that transcriptional regulation by canonical type I interferon signaling via the heterotrimeric transcription factor, ISGF3, controls versican expression in macrophages exposed to LPS. This pathway is not dependent on MAPK signaling, which has been shown to regulate versican expression in other cell types. The stability of versican mRNA may also contribute to prolonged versican expression in macrophages. These findings strongly support a role for macrophage-derived versican as a type I interferon-stimulated gene and further our understanding of versicans role in regulating inflammation.

cell biology↗

bioRxiv2024

Upregulation of the Proto-Oncogene Src Kinase in Alzheimer's Disease: From Molecular Interactions to Therapeutic Potential

Alzheimers disease (AD) is a progressive neurodegenerative disease, resulting in an irreversible deterioration of multiple brain regions associated with cognitive dysfunction. Phosphorylation of the microtubule-associated protein, Tau, is known to occur decades before symptomatic AD. The Src family of tyrosine kinases are known to phosphorylate select tyrosine sites on Tau and promote microtubule disassembly and subsequent neurofibrillary tangle (NFT) formation. Our data show that the proto-oncogene, non-receptor tyrosine kinase Src colocalizes with a range of late (PHF1) to early (MC1) AD-associated phosphorylated Tau epitopes. The strongest co-occurrence is seen with MC1 (probability of MC1 given Src =100%), an early AD-specific conformational dependent epitope. Single-cell RNA sequencing data of 101 subjects show that Src is upregulated in both AD inhibitory and excitatory neurons. The most significantly affected, by orders of magnitude, were excitatory neurons which are the most prone to pathological Tau accumulation. We measured Src phosphorylation by mass spectrometry across a cohort of 48 patient neocortical tissues and found that Src has increased phosphorylation on Ser75, Tyr187, and Tyr440 in AD, showing that Src kinase undergoes distinct phosphorylation alterations in AD. Through Brownian dynamics simulations of Src and Tau, we show that as Tau undergoes the transition into disease-associated paired helical filaments, there is a notable seven-fold increase in Src contact with Tau. These results collectively emphasize Src kinases central role in Tau phosphorylation and its close association with Tau epitopes, presenting a promising target for potential therapeutic intervention.

neuroscience↗

bioRxiv2022

Mapping variation in the morphological landscape of human cells with optical pooled CRISPRi screening

The contributions of individual genes to cell-scale morphology and cytoskeletal organization are challenging to define due to the wide intercellular variation of these complex phenotypes. We leveraged the controlled nature of image-based pooled screening to assess the impact of CRISPRi knockdown of 366 genes on cell and nuclear morphology in human U2OS osteosarcoma cells. Screen scale-up was facilitated by a new, efficient barcode readout method that successfully genotyped 85% of cells. Phenotype analysis using a deep learning algorithm, the {beta}-variational autoencoder, produced a feature embedding space distinct from one derived from conventional morphological profiling, but detected similar gene hits while requiring minimal design decisions. We found 45 gene hits and visualized their effect by rationally constrained sampling of cells along the direction of phenotypic shift. By relating these phenotypic shifts to each other, we construct a quantitative and interpretable space of morphological variation in human cells.

cell biology↗

bioRxiv2021

The Structure of ChAdOx1/AZD-1222 Reveals Interactions with CAR and PF4 with Implications for Vaccine-induced Immune Thrombotic Thrombocytopenia

Adenovirus derived vectors, based on chimpanzee adenovirus Y25 (ChAdOx1) and human adenovirus type 26 are proving critical in combatting the 2019 SARS-CoV-2 pandemic. Following emergency use authorisation, scale up in vaccine administration has inevitably revealed vaccine related adverse effects; too rare to observe even in large Phase-III clinical trials. These include vaccine-induced thrombotic thrombocytopenia (VITT), an ultra-rare adverse event in which patients develop life-threatening blood clots 5-24 days following vaccination. To investigate vector-host interactions of ChAdOx1 underpinning VITT we solved the structure of the ChAdOx1 capsid by CryoEM, and the structure of the primary receptor tropism determining fiber-knob protein by crystallography. These structural insights have enabled us to unravel key protein interactions involved in ChAdOx1 cell entry and a possible means by which it may generate misplaced immunity to platelet factor 4 (PF4), a protein involved in coagulation. We use in vitro cell binding assays to show that the fiber-knob protein uses coxsackie and adenovirus receptor (CAR) as a high affinity binding partner, while it does not form a stable interface with CD46. Computational simulations identified a putative mechanism by which the ChAdOx1 capsid interacts with PF4 by binding in the spaces between hexon proteins, with downstream implications for the causes of VITT. SummaryWe present the structure of the ChAdOx1 viral vector, derived from chimpanzee adenovirus Y25 at 4.2[A] resolution1. ChAdOx1 is in global use in the AstraZeneca vaccine, ChAdOx1 nCoV-19/AZD-1222, to combat the SARS-CoV-2 coronavirus pandemic. Recently observed, rare, adverse events make detailed mechanistic understanding of this vector key to informing proper treatment of affected patients and the development of safer viral vectors. Here, we determine a primary mechanism ChAdOx1 uses to attach to cells is coxsackie and adenovirus receptor (CAR), a protein which is identical in humans and chimpanzees. We demonstrate the vector does not form a stable CD46 interaction, a common species B adenovirus receptor, via its primary attachment protein. Further, we reveal the surface of the ChAdOx1 viral capsid has a strong electronegative potential. Molecular simulations suggest this charge, together with shape complementarity, are a mechanism by which an oppositely charged protein, platelet factor 4 (PF4) may bind the vector surface. PF4 is a key protein involved in the formation of blood clots2, and the target of auto-antibodies in heparin-induced immune thrombotic thrombocytopenia (HITT)3, an adverse reaction to heparin therapy which presents similarly to vaccine-induced immune thrombotic thrombocytopenia (VITT), a rare complication of ChAdOx1 nCoV-19 vaccination4-6. We propose a mechanism in which the ChAdOx1-PF4 complex may stimulate the production of antibodies against PF4, leading to delayed blood clot formation, as observed in VITT.

molecular biology↗

bioRxiv2020

Quantitative comparison of principal component analysis and unsupervised deep learning using variational autoencoders for shape analysis of motile cells

Cell motility is a crucial biological function for many cell types, including the immune cells in our body that act as first responders to foreign agents. In this work we consider the amoeboid motility of human neutrophils, which show complex and continuous morphological changes during locomotion. We imaged live neutrophils migrating on a 2D plane and extracted unbiased shape representations using cell contours and binary masks. We were able to decompose these complex shapes into low-dimensional encodings with both principal component analysis (PCA) and an unsupervised deep learning technique using variational autoencoders (VAE), enhanced with generative adversarial networks (GANs). We found that the neural network architecture, the VAE-GAN, was able to encode complex cell shapes into a low-dimensional latent space that encodes the same shape variation information as PCA, but much more efficiently. Contrary to the conventional viewpoint that the latent space is a "black box", we demonstrated that the information learned and encoded within the latent space is consistent with PCA and is reproducible across independent training runs. Furthermore, by including cell speed into the training of the VAE-GAN, we were able to incorporate cell shape and speed into the same latent space. Our work provides a quantitative framework that connects biological form, through cell shape, to a biological function, cell movement. We believe that our quantitative approach to calculating a compact representation of cell shape using the VAE-GAN provides an important avenue that will support further mechanistic dissection of cell motility. AUTHOR SUMMARYDeep convolutional neural networks have recently enjoyed a surge in popularity, and have found useful applications in many fields, including biology. Supervised deep learning, which involves the training of neural networks using existing labeled data, has been especially popular in solving image classification problems. However, biological data is often highly complex and continuous in nature, where prior labeling is impractical, if not impossible. Unsupervised deep learning promises to discover trends in the data by reducing its complexity while retaining the most relevant information. At present, challenges in the extraction of meaningful human-interpretable information from the neural networks nonlinear discovery process have earned it a reputation of being a "black box" that can perform impressively well at prediction but cannot be used to shed any meaningful insight on underlying mechanisms of variation in biological data sets. Our goal in this paper is to establish unsupervised deep learning as a practical tool to gain scientific insight into biological data by first establishing the interpretability of our particular data set (images of the shapes of motile neutrophils) using more traditional techniques. Using the insight gained from this as a guide allows us to shine light into the "black box" of unsupervised deep learning.

cell biology↗

bioRxiv2020

Directed effective connectivity and synaptic weights of invitro neuronal cultures revealed from high-density multielectrode array recordings

Studying connectivity of neuronal cultures can provide insights for understanding brain networks but it is challenging to reveal neuronal connectivity from measurements. We apply a novel method that uses a theoretical relation between the time-lagged cross-covariance and the equal-time cross-covariance to reveal directed effective connectivity and synaptic weights of cortical neuron cultures at different days in vitro from multielectrode array recordings. Using a stochastic leaky-integrate-and-fire model, we show that the simulated spiking activity of the reconstructed networks can well capture the measured network bursts. The neuronal networks are found to be highly nonrandom with an over-representation of bidirectionally connections as compared to a random network of the same connection probability, with the fraction of inhibitory nodes comparable to the measured fractions of inhibitory neurons in various cortical regions in monkey, and have small-world topology with basic network measures comparable to those of the nematode C. elegans chemical synaptic network. Our analyses further reveal that (i) the excitatory and inhibitory incoming degrees have bimodal distributions the excitatory and inhibitory incoming degrees have bimodal distributions, which are that distributions that have been indicated to be optimal against both random failures and attacks in undirected networks; (ii) the distribution of the physical length of excitatory incoming links has two peaks indicating that excitatory signal is transmitted at two spatial scales, one localized to nearest nodes and the other spatially extended to nodes millimeters away, and the shortest links are mostly excitatory towards excitatory nodes and have larger synaptic weights on average; (iii) the average incoming and outgoing synaptic strength is non-Gaussian with long tails and, in particular, the distribution of outgoing synaptic strength of excitatory nodes with excitatory incoming synaptic strength is lognormal, similar to the measured excitatory postsynaptic potential in rat cortex. Author summaryTo understand how the brain processes signal and carries out its function, it is useful to know the connectivity of the underlying neuronal circuits. For large-scale neuronal networks, it is difficult to measure connectivity directly using electron microscopy techniques and methods that can estimate connectivity from electrophysiological recordings are thus highly desirable. Existing methods focus mainly on estimating functional connectivity, which is defined by statistical dependencies between neuronal activities but the relevant direct casual interactions are captured by effective connectivity. Here we apply a novel covariance-relation based method to estimate the directed effective connectivity and synaptic weights of cortical neuron cultures from recordings of multielectrode array of over 4000 electrodes taken at different days in vitro. The neuronal networks are found to be nonrandom, small-world, excitation/inhibition balanced as measured in monkey cortex, and with feeder hubs. Our analyses further suggest some form of specialisation of nodes in receiving excitatory and inhibitory signals and the transmission of excitatory signals at two spatial scales, one localized to nearest nodes and the other spatially extended to nodes millimeters away, and reveal that the distributions of the average incoming and outgoing synaptic strength are skewed with long tails.

neuroscience↗

bioRxiv2018

Resolving the hypotheticome: annotating M. tuberculosis gene function through bibliomic reconciliation and structural modeling

Accurate and timely functional genome annotation is essential for translating basic pathogen research into clinically impactful advances. Here, through literature curation and structure-function inference, we systematically update the functional genome annotation of Mycobacterium tuberculosis virulent type strain H37Rv. First, we systematically curated annotations for 589 genes from 662 publications, including 282 gene products absent from leading databases. Second, we modeled 1,711 under-annotated proteins and developed a semi-automated pipeline that captured shared function between 400 protein models and structural matches of known function on protein data bank, including drug efflux proteins, metabolic enzymes, and virulence factors. In aggregate, these structure- and literature-derived annotations update 940/1,725 under-annotated H37Rv genes and generate hundreds of functional hypotheses. Retrospectively applying the annotation to a recent whole-genome transposon mutant screen provided missing function for 48% (13/27) of under-annotated genes altering antibiotic efficacy and 33% (23/69) required for persistence during mouse TB infection. Prospective application of the protein models enabled us to functionally interpret novel laboratory generated Pyrazinamide-resistant (PZA) mutants of unknown function, which implicated the emerging Coenzyme A depletion model of PZA action in the mutants PZA resistance. Our findings demonstrate the functional insight gained by integrating structural modeling and systematic literature curation, even for widely studied microorganisms. Functional annotations and protein structure models are available at https://tuberculosis.sdsu.edu/H37Rv in human- and machine-readable formats. IMPORTANCEMycobacterium tuberculosis, the primary causative agent of tuberculosis, kills more humans than any other infectious bacteria. Yet 40% of its genome is functionally uncharacterized, leaving much about the genetic basis of its resistance to antibiotics, capacity to withstand host immunity, and basic metabolism yet undiscovered. Irregular literature curation for functional annotation contributes to this gap. We systematically curated functions from literature and structural similarity for over half of poorly characterized genes, expanding the functionally annotated Mycobacterium tuberculosis proteome. Applying this updated annotation to recent in vivo functional screens added functional information to dozens of clinically pertinent proteins described as having unknown function. Integrating the annotations with a prospective functional screen identified new mutants resistant to a first-line TB drug supporting an emerging hypothesis for its mode of action. These improvements in functional interpretation of clinically informative studies underscores the translational value of this functional knowledge. Structure-derived annotations identify hundreds of high-confidence candidates for mechanisms of antibiotic resistance, virulence factors, and basic metabolism; other functions key in clinical and basic tuberculosis research. More broadly, it provides a systematic framework for improving prokaryotic reference annotations.

genomics↗

bioRxiv2017

Genetically modified human type II collagen for N- and C-terminal covalent tagging

Collagen is the predominant structural protein in vertebrates, where it contributes to connective tissues and the extracellular matrix; it is also widely used in biomaterials and tissue engineering. Dysfunction of this protein and its processing can lead to a wide variety of developmental disorders and connective tissue diseases. Recombinantly engineering the protein is challenging due to posttranslational modifications generally required for its stability and secretion from cells. Introducing end labels into the protein is problematic, because the N- and C-termini of the physiologically relevant tropocollagen lie internal to the initially flanking N- and C-propeptide sequences. Here, we introduce mutations into human type II procollagen in a manner that address these concerns, and purify the recombinant protein from a stably transfected HT1080 human fibrosarcoma cell line. Our approach introduces chemically addressable groups into the N- and Ctelopeptide termini of tropocollagen. Simultaneous overexpression of formylglycine generating enzyme (FGE) allows the endogenous production of an aldehyde tag in a defined, substituted sequence in the N-terminus of the mutated collagen, while the C-terminus of each chain presents a sulfhydryl group from an introduced cysteine. These modifications are designed to enable specific covalent end-labelling of collagen. We find that the doubly-mutated protein folds and is secreted from cells, while higher-order assembly into well-ordered collagen fibrils is demonstrated through transmission electron microscopy. Chemical tagging of thiols is successful, however background from endogenous aldehydes present in wildtype collagen has thus far obscured the desired specific N-terminal labelling. Strategies to overcome this challenge are proposed.

biochemistry↗