Biology subjects

Chen, K.

Publications and source records attributed to Chen, K..

At least 145 records · Page 8Linked to original sources

bioRxiv2025

Agent-based modeling of cellular dynamics in adoptive cell therapy

Adoptive cell therapies (ACT) leverage tumor-immune interactions to cure cancer. Despite promising phase I/II clinical trials of chimeric-antigen-receptor natural killer (CAR-NK) cell therapies, molecular mechanisms and cellular properties required to achieve clinical benefits in broad cancer spectra remain underexplored. While in vitro and in vivo experiments are required in this endeavor, they are typically expensive, laborious, and limited to targeted investigations. Here, we present ABMACT (Agent-Based Model for Adoptive Cell Therapy), an in silico approach employing agent-based models (ABM) to simulate the continuous course and dynamics of an evolving tumor-immune ecosystem, consisting of heterogeneous "virtual cells" created based on knowledge and omics data observed in experiments and patients. Applying ABMACT in multiple therapeutic context indicates that to achieve optimal ACT efficacy, it is key to enhance immune cellular proliferation, cytotoxicity, and serial killing capacity. With ABMACT, in silico trials can be performed systematically to inform ACT product development and predict optimal treatment strategies.

bioinformatics↗

bioRxiv2025

High-throughput, unbiased single-molecule displacement mapping with deep learning reveals spatiotemporal heterogeneities in intracellular diffusivity

Single-molecule displacement/diffusivity mapping (SMdM) has revolutionized the study of molecular motion in live cells by providing high spatial resolution insights. However, its utility is restricted by measurement biases and low throughput, making it challenging to capture temporal dynamics of intracellular diffusivity. Here we report a high-throughput single-molecule diffusivity microscopy approach (Hi-SMdM) that rapidly and unbiasedly maps nanoscale heterogeneities in molecular motion inside mammalian cells, enabling time-resolved imaging of local diffusivity dynamics. Hi-SMdM employs a self-supervised deep-learning denoising framework with a general noise model to effectively restore the scarce signals of fast-moving single molecules, while eliminating artifact motions without relying on spatial redundancies or temporal correlations. It then provides unbiased estimations of nanoscale diffusion coefficient and improve both throughput and temporal resolution by up to an order of magnitude. We demonstrate the versatility of Hi-SMdM through time-resolved mapping of the spatially heterogeneous diffusivity of free proteins in the cytoplasm. Hi-SMdM also unveils the temporal dynamics of intracellular diffusion under hypotonic conditions in live cells. Additionally, Hi-SMdM tracks the spatiotemporal dynamics of intraorganellar diffusivity during rapid rearrangements of the ER network and functional activities of mitochondria. Overall, Hi-SMdM provides exceptional opportunities for high-throughput and unbiased single-molecule diffusivity mapping with excellent spatiotemporal resolution.

biophysics↗

bioRxiv2025

Spatial distribution of the proteome in human body and cancers

A comprehensive spatial distribution of the proteome in human body and cancers is fundamental for understanding human biology and diseases including cancers. Here, we present an anatomically resolved human proteome derived from 1781 benign and malignant samples from 58 major tissue types encompassing 251 specific tissues and 25 carcinomas. Based on a spectral library covering over 75% of the human protein-coding genes with 208 understudied and 82 missing proteins characterized, we quantified over 13,000 proteins in these samples using data-independent acquisition proteomics. This data resource presents the so far most comprehensive quantitative proteomic landscape of human tissues and common carcinomas. It allows systematic evaluation of tissue-specific drug responses, identification of drug candidates that may be repurposed as antineoplastics, and discovery of novel targets for anticancer therapy. This resource, available as an online knowledgebase, refines our knowledge of spatial distribution of the human proteome and tumor-specific protein modulation.

systems biology↗

bioRxiv2025

Sequence similarity estimation by random subsequence sketching

Sequence similarity estimation is essential for many bioinformatics tasks, including functional annotation, phylogenetic analysis, and overlap graph construction. Alignment-free methods aim to solve large-scale sequence similarity estimation by mapping sequences to more easily comparable features that can approximate edit distances efficiently. Substrings or k-mers, as the dominant choice of features, face an unavoidable compromise between sensitivity and specificity when selecting the proper k-value. Recently, subsequence-based features have shown improved performance, but they are computationally demanding, and determining the ideal subsequence length remains an intricate art. In this work, we introduce SubseqSketch, a novel alignment-free scheme that maps a sequence to an integer vector, where the entries correspond to dynamic, rather than fixed, lengths of random subsequences. The cosine similarity between these vectors exhibits a strong correlation with the edit similarity between the original sequences. Through experiments on benchmark datasets, we demonstrate that Sub-seqSketch is both efficient and effective across various alignment-free tasks, including nearest neighbor search and phylogenetic clustering. A C++ implementation of SubseqSketch is openly available at https://github.com/Shao-Group/SubseqSketch.

bioinformatics↗

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↗

bioRxiv2025

Tissue-specific modulation of NADH consumption as an anti-aging intervention in Drosophila

Aging is characterized by extensive metabolic dysregulation. Redox coenzyme nicotinamide adenine dinucleotide (NAD) can exist in oxidized (NAD+) or reduced (NADH) states, which together form a key NADH/NAD+ redox pair. Total levels of NAD decline with age in a tissue-specific manner, thereby playing a significant role in the aging process. Supplementation with NAD precursors boosts total cellular NAD levels and provides some therapeutic benefits in human clinical trials. However, supplementation studies cannot determine tissue-specific effects of an altered NADH/NAD+ ratio. Here, we created transgenic Drosophila expressing a genetically encoded xenotopic tool LbNOX to directly manipulate the cellular NADH/NAD+ ratio. We found that LbNOX expression in Drosophila impacts both NAD(H) and NADP(H) metabolites in a sex-specific manner. LbNOX rescues neuronal cell death induced by the expression of mutated alpha-B crystallin in the Drosophila eye, a widely used system to study reductive stress. Utilizing LbNOX, we demonstrate that targeting redox NAD metabolism in different tissues may have drastically different outcomes, as the expression of LbNOX solely in the muscle is much more effective for rescuing paraquat-induced oxidative stress compared to whole-body expression. Excitingly, we demonstrate that perturbing NAD(P) metabolism in non-neuronal tissues is sufficient to rejuvenate sleep profiles in aged flies to a youthful state. In summary, we used xenotopic tool LbNOX to identify tissues and metabolic processes which benefited the most from the modulation of the NAD metabolism thereby highlighting important aspects of rebalancing the NAD and NADP pools, all of which can be translated into novel designs of NAD-related human clinical trials. Significance statementTotal levels of NAD decline with age in a tissue-specific manner, thereby playing a significant role in the aging process. Supplementation with NAD precursors boosts organismal NAD levels but cannot determine tissue-specific effects of altered NAD metabolism. Here, we created transgenic Drosophila expressing a genetically encoded xenotopic tool, LbNOX, to directly manipulate NAD metabolism. We demonstrate that targeting NAD metabolism in just one tissue may be more effective than altering whole-body metabolism and can reverse some aging-related manifestations in a sex-specific manner. We anticipate that our work will define the tissues that benefit the most from targeting NAD metabolism and aid in designing better human clinical trials.

synthetic biology↗

bioRxiv2024

Chronic Behavioral and Seizure Outcomes following Experimental Traumatic Brain Injury and Comorbid Klebsiella pneumoniae Lung Infection in Mice

Traumatic brain injury (TBI) is a leading cause of long-term disability, and infections such as pneumonia represent a common and serious complication for TBI patients in the acute and subacute post-injury period. While the acute effects of infections have been documented, their long-term consequences on neurological and behavioral recovery as well as the potential precipitation of seizures after TBI remain unclear. This study aimed to investigate the chronic effects of Klebsiella pneumoniae infection following TBI, focusing on post-traumatic seizure development and neurobehavioral changes. Using a mouse model, we assessed the long-term effects of TBI and K. pneumoniae infection both in isolation and in combination. We found that, while infection with K. pneumoniae resulted in loss of body weight and increased mortality compared to vehicle-inoculated mice, there was no additional mortality in TBI animals. Further, although TBI alone induced chronic hyperactivity and reduced anxiety-like behaviors, K. pneumoniae lung infection had no lasting effect on these long-term outcomes. Thirdly, while TBI resulted in both spontaneous and evoked seizures long-term post-injury, early post-injury K. pneumoniae infection did not affect late onset seizure susceptibility. Together with recent findings on acute outcomes in this combined insult model of TBI and K. pneumoniae infection, this study suggests that K. pneumoniae does not significantly alter long-term neurobehavioral outcomes or the development of post-traumatic epilepsy. This research highlights the need to further explore the interplay between additional immune insults such as infection that may influence long-term recovery.

neuroscience↗

bioRxiv2024

Comparison of imaging-based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed, paraffin-embedded tumor samples

Imaging-based spatial transcriptomics (ST) is evolving rapidly as a pivotal technology in studying the biology of tumors and their associated microenvironments. However, the strengths of the commercially available ST platforms in studying spatial biology have not been systematically evaluated using rigorously controlled experiments. In this study, we used serial 5-m sections of formalin-fixed, paraffin-embedded surgically resected lung adenocarcinoma and pleural mesothelioma tumor samples in tissue microarrays to compare the performance of the single cell ST platforms CosMx, MERFISH, and Xenium (uni/multi-modal) platforms in reference to bulk RNA sequencing, multiplex immunofluorescence, GeoMx Digital Spatial Profiler, and hematoxylin and eosin staining data for the same samples. In addition to objective assessment of automatic cell segmentation and phenotyping, we performed pixel-resolution manual evaluation of phenotyping to carry out pathologically meaningful comparison between ST platforms. Our study detailed the intricate differences between the ST platforms, revealed the importance of parameters such as tissue age and probe design in determining the data quality, and suggested reliable workflows for accurate spatial profiling and molecular discovery.

bioinformatics↗

bioRxiv2024

cancerSimCraft: A Multi-resolution Cancer Genome Simulator with Comprehensive Ground Truth Tracking

Cancer evolution follows complex trajectories involving diverse genomic alterations and clonal dynamics, making it challenging to validate computational methods for single-cell DNA sequencing analysis. Here we present cancerSimCraft, a comprehensive framework for simulating cancer genome data at both clonal and single-cell resolution. cancerSimCraft combines deterministic rules with stochastic processes to model various genomic events including CNVs, SNVs, and WGDs. The framework enables integration of real cancer genome patterns with user-defined parameters, supporting customizable simulation designs that reflect both empirical data and theoretical models. Through systematic benchmarking, we demonstrate cancerSimCrafts utility in evaluating computational methods under various conditions, particularly focusing on the impact of dataset size and parameter sensitivity. We further demonstrated its application in exploring clonal evolution and mutation patterns through controlled in silico experiments. These comprehensive simulation capabilities make cancerSimCraft a valuable resource for both computational method development and theoretical studies in cancer genomics.

bioinformatics↗

bioRxiv2024

An Exact and Fast SAT Formulation for the DCJ Distance

Reducing into a satisfiability (SAT) formulation has been proven effective in solving certain NP-hard problems. In this work, we extend this research by presenting a novel SAT formulation for computing the double-cut-and-join (DCJ) distance between two genomes with duplicate genes. The DCJ distance serves as a crucial metric in studying genome rearrangement. Previous work achieved an exact solution by transforming it into a maximum cycle decomposition (MCD) problem on the corresponding adjacency graph of two genomes, followed by reducing this problem into an integer linear programming (ILP) formulation. Using both simulated datasets and real genomic datasets, we firmly conclude that the SAT-based method scales much better and runs faster than using ILP, being able to solve a whole new range of instances which the ILP-based method cannot solve in a reasonable amount of time. This underscores the SAT-based approach as a versatile and more powerful alternative to ILP, with promising implications for broader applications in computational biology.

bioinformatics↗

bioRxiv2024

Augmenting Transcriptome Annotations through the Lens of Splicing Evolution

Alternative splicing (AS) is a ubiquitous mechanism in eukaryotes. It is estimated that 90% of human genes are alternatively spliced. Despite enormous efforts, transcriptome annotations remain, nevertheless, incomplete. Conventional means of annotation were largely driven by experimental data such as RNA-seq and protein sequences, while little insight was shed on understanding transcriptomes and alternative splicings from the perspective of evolution. This study addresses this critical gap by presenting TENNIS (Transcript EvolutioN for New Isoform Splicing), an evolution-based model to predict unannotated isoforms and refine existing annotations without requiring additional data. The model of TENNIS is based on two minimal premises-AS isoforms evolve sequentially from existing isoforms, and each evolutionary step involves a single AS event. We formulate the identification of missing transcripts as an optimization problem and parsimoniously find the minimal number of novel transcripts. Our analysis showed approximately 80% of multi-transcript groups from six transcriptome annotations satisfy our evolutionary model. At a high confidence level, 40% of isoforms predicted by TENNIS were validated by deep long-read RNA-seq. In a simulated incomplete annotation scenario, TENNIS dramatically outperforms two randomized baseline approaches by a 2.25-3 fold-change in precision or a 3.5-3.9 fold-change in recall, after controlling the same level of recall or precision of the baseline methods. These results demonstrate that TENNIS effectively identifies missing transcripts by complying with minimal propositions, offering a powerful approach for transcriptome augmentations through the lens of alternative splicing evolutions. TENNIS is freely available at https://github.com/Shao-Group/tennis.

bioinformatics↗

bioRxiv2024

Aging drives a program of DNA methylation decay in plant organs

How organisms age is a question with broad implications for human health. In mammals, DNA methylation is a biomarker for biological age, which may predict age more accurately than date of birth. However, limitations in mammalian models make it difficult to identify mechanisms underpinning age-related DNA methylation changes. Here, we show that the short-lived model plant Arabidopsis thaliana exhibits a loss of epigenetic integrity during aging, causing heterochromatin DNA methylation decay and the expression of transposable elements. We show that the rate of epigenetic aging can be manipulated by extending or curtailing lifespan, and that shoot apical meristems are protected from this aging process. We demonstrate that a program of transcriptional repression suppresses DNA methylation maintenance pathways during aging, and that mutants of this mechanism display a complete absence of epigenetic decay. This presents a new paradigm in which a gene regulatory program sets the rate of epigenomic information loss during aging.

plant biology↗

bioRxiv2024

Chromatin mimicry by human JC virus

Chronically persistent viruses are integral components of the organismal ecosystem in humans and animals 1 2. Many of these viruses replicate and accumulate within the cell nucleus 3. The nuclear location allows viruses to evade cytoplasmic host viral sensors and promotes viral replication 4. One of the unexplored and puzzling aspects of the viral nuclear lifecycle involves the viruss ability to deal with the physical constraints of nuclear architecture. To replicate and accumulate within the nucleus in large numbers sufficient for infection spreading, DNA viruses need to overcome the spatial limitations imposed by chromatin and the nuclear matrix. We found that one of the most widespread and potentially lethal human viruses, the JC polyomavirus 5, interferes with nuclear heterochromatin to create virus-occupied space. The JC viruss impact on heterochromatin is mediated by the viral nonstructural protein, Agnoprotein (Agno). Agnos interference with heterochromatin is governed by structurally diverse mimics of host epigenetic regulators that facilitate virus-induced chromatin reorganization and a dramatic decline in nuclear stiffness in the infected cells. The JCV epigenetic mimicry is critical for the virus infection, as evident from reduced replication of mimic-mutant viruses. Our data suggest that modulation of nuclear mechanical properties is a novel strategy enabling chronicity of the JC and possibly other nuclear virus infections.

microbiology↗

bioRxiv2024

Divergent CD8 memory T cell subsets in an encephalitis model of Toxoplasma gondii infection

CD8 T cell exhaustion has been reported in mice susceptible to Toxoplasma gondii infection. While the differentiation of CD8 exhausted subsets has been extensively reported, most of these studies have been conducted in chronic viral and cancer models. During chronic T. gondii infection, phenotypic and transcriptomic analyses of the polyclonal antigen-specific CD8 T cell response characterize four populations based on KLRG1 and CD62L expression. Pop1 (KLRG1+CD62Llo) bears the attributes of a terminal effector subset, and pop2 (KLRG1-CD62Llo) is similar to effector memory CD8 T cells. Akin to chronic viral infection and cancer systems, pop3 (KLRG1-CD62Lhi) exhibits the characteristics of stem-like progenitor CD8 T cells (high Tcf7, Slamf6, and Cxcr5 expression), whereas pop4 (KLRG1+CD62Lhi) closely resembles a transitory subset (elevated Tbx21, low Tcf1, and Tox expression). During chronic viral infection, the stem-like progenitor CD8 T cells transition into a terminally differentiated exhausted subset via an intermediate population. However, in our system, pop3 generates pop4, which does not convert into a conventional terminally differentiated exhausted subset but instead transitions into effector pop1. Notably, during the chronic phase of the infection, pop1 cannot retain its functionality, irrespective of its origin, which may hamper its ability to control reactivation. Our observations emphasize that the differentiation of exhausted CD8 T cells in non-viral infections, like chronic toxoplasmosis, follows a different pattern than established models and highlights the need to develop new immune strategies better tailored for a broad range of pathogens.

immunology↗

bioRxiv2024

Deep generalizable prediction of RNA secondary structure via base pair motif energy

Deep learning methods have demonstrated great performance for RNA secondary structure prediction. However, generalizability is a common unsolved issue on unseen out-of-distribution RNA families, which hinders further improvement of the accuracy and robustness of deep learning methods. Here we construct a base pair motif library that enumerates the complete space of the locally adjacent three-neighbor base pair and records the thermodynamic energy of corresponding base pair motifs through de novo modeling of tertiary structures, and we further develop a deep learning approach for RNA secondary structure prediction, named BPfold, which learns relationship between RNA sequence and the energy map of base pair motif. Experiments on sequence-wise and family-wise datasets have demonstrated the great superiority of BPfold compared to other state-of-the-art approaches in accuracy and generalizability. We hope this work contributes to integrating physical priors and deep learning methods for the further discovery of RNA structures and functionalities.

bioinformatics↗

bioRxiv2024

Sequence-dependent scale for translocon-mediated insertion of interfacial helices in membranes

Biological membranes consist of a lipid bilayer studded with integral and peripheral membrane proteins. Most -helical membrane proteins require protein-conducting insertases known as translocons to assist in their membrane insertion and folding. While the sequence-dependent propensities for a helix to either translocate through the translocon or insert into the membrane have been codified into numerical hydrophobicity scales, the corresponding propensity to partition into the membrane interface remains unraveled. By engineering diagnostic glycosylation sites around test peptide sequences inserted into a host protein, we devised a system that can differentiate between water-soluble, surface-bound, and transmembrane (TM) states of the sequence based on its glycosylation pattern. Using this system, we determined the sequence-dependent propensities for transfer from the translocon to a TM, interfacial or extramembrane space. UMAP analysis of a large collection of TM and water-soluble helices provide useful embeddings for analysis of these propensities and aid in understanding the physical properties and functions of antimicrobial, lytic, and fusogenic peptides.

molecular biology↗

bioRxiv2024

Targetable Treatment Resistance in Thyroid Cancer with Clonal Hematopoiesis

Anaplastic thyroid cancer (ATC) is a clinically aggressive malignancy with a dismal prognosis. Combined BRAF/MEK inhibition offers significant therapeutic benefit in patients with BRAFV600E-mutant ATCs. However, relapses are common and overall survival remains poor. Compared with differentiated thyroid cancer, a hallmark of ATCs is significant infiltration with myeloid cells, particularly macrophages. ATCs are most common in the aging population, which also has an increased incidence of TET2-mutant clonal hematopoiesis (CH). CH-mutant macrophages have been shown to accelerate CH-associated pathophysiology including atherosclerosis. However, the clinical and mechanistic contribution of CH-mutant clones to solid tumour biology, prognosis and therapeutic response has not been elucidated. Here we show that TET2-mutant CH is enriched in the tumour microenvironment of patients with solid tumours and associated with adverse prognosis in ATC patients. We find that Tet2-mutant macrophages selectively infiltrate mouse BrafV600E-mutant ATC and that their overexpression of Tgf{beta}-family ligands mediates resistance to BRAF/MEK inhibition. Importantly, inhibition of Tgf{beta} signaling restores sensitivity to MAPK pathway inhibition, opening a path for synergistic strategies to improve outcomes of patients with ATCs and concurrent CH.

cancer biology↗

bioRxiv2024

Gas1-Mediated Suppression of Hepatoblastoma Tumorigenesis

Background and AimsHepatoblastoma (HB), the most common pediatric liver cancer, often dysregulates the Wnt/{beta}-catenin, Hippo and NFE2L2/NRF2 pathways. Pairwise combinations of oncogenically active forms of the terminal transcription factor effectors of these pathways, namely {beta}-catenin (B), YAP (Y) and NRF2 (N) generate HBs in mice, with the triple combination (B+Y+N) being particularly potent. Each tumor group alters the expression of thousands of B-,Y- and N-driven unique and common target genes. Identifying those most responsible for transformation is thus an important question as it might reveal new mechanistic insights and therapeutic opportunities. Approach and ResultsTranscriptional profiling of >60 murine HBs driven by the above oncogenic combinations and different B mutants and in genetic backgrounds that impair tumor growth rates but not initiation has revealed a common set of 22 "BYN genes" that are similarly deregulated in all cases. Many are associated with multiple "Cancer Hallmarks" and their expression levels often correlate with survival in human HBs, hepatocellular carcinomas and other cancers. Among the most down-regulated of these is Gas1, which encodes a Glycosylphosphatidylinositol (GPI)-linked outer membrane protein. We show here that restoring Gas1 expression impairs B+Y+N-driven HB tumor growth in vivo and in HB-derived immortalized cell lines in vitro in a manner than requires membrane anchoring of the protein via its GPI moiety. ConclusionsOur findings implicate Gas1 as a proximal mediator of HB pathogenesis and validate the BYN gene set as one deserving of closer additional scrutiny in future studies.

cancer biology↗