Biology subjects

Sun, C.

Publications and source records attributed to Sun, C..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

Redirecting cytotoxic lymphocytes to breast cancer tumors via metabolite-sensing receptors

Insufficient infiltration of cytotoxic lymphocytes to solid tumors limits the efficacy of immunotherapies and cell therapies. Here, we report a programmable mechanism to mobilize Natural Killer (NK) and T cells to breast cancer tumors by engineering these cells to express orphan and metabolite-sensing G protein-coupled receptors (GPCRs). First, in vivo and in vitro CRISPR activation screens in NK-92 cells identified GPR183, GPR84, GPR34, GPR18, FPR3, and LPAR2 as top enhancers of both tumor infiltration and chemotaxis to breast cancer. These genes equip NK and T cells with the ability to sense and migrate to chemoattracting metabolites such as 7,25-dihydroxycholesterol and other factors released from breast cancer. Based on Perturb-seq and functional investigations, GPR183 also enhances effector functions, such that engineering NK and CAR NK cells to express GPR183 enhances their ability to migrate to, infiltrate, and control breast cancer tumors. Our study uncovered metabolite-based tumor immune recruitment mechanisms, opening avenues for spatially targeted cell therapies.

immunology↗

bioRxiv2025

Characterization of deep-sea viruses reveals their unexpected diversity and roles in facilitating host metabolism of complex organic matter

Viruses exert a pervasive influence on biogeochemical cycles in deep-sea ecosystems. By integrating multi-omics and viral isolation data from four distinct deep-sea sediment sites, we uncover the diversity of DNA and RNA viral communities, which vary significantly across geographically separated cold seep and seamount environments. Host range predictions reveal both prokaryotic and eukaryotic communities are susceptible to viral infection. Metatranscriptomic analyses suggest viral functional genes are actively expressed, potentially enhancing host metabolism of complex organic matter (COM). Indeed, purified viruses from deep-sea sediments were shown to facilitate COM metabolism in certain deep-sea microorganisms, enabling the isolation of previously uncultured bacterial taxa. Notably, in these newly isolated bacteria, polysaccharides induced chronic virus release without cell lysis, a process that may assist host metabolism of polysaccharides. Our findings illuminate the diversity and roles of viral communities in distinct deep-sea sediments, underscoring their significant contribution to COM degradation and the deep-sea carbon cycle.

microbiology↗

bioRxiv2025

Metabolic reinvigoration of NK cells by IL-21 enhances immunotherapy against MHC-I deficient solid tumors

Natural killer (NK) cells, a type of potent cytotoxic lymphocytes, are particularly promising for the treatment of cancers that lose or downregulate major histocompatibility complex class I (MHC-I) expression to evade T cell-mediated immunotherapy. However, the hostile and immune suppressive tumor microenvironment (TME) greatly hinders the function of tumor-infiltrating NK cells limiting the therapeutic efficacy against solid tumors. Here, we show that a fusion protein of interleukin-21 (IL-21-Fc), as a direct in vivo intervention, can safely and effectively reprogram NK cell metabolism and enhance their effector function in the TME. Our research demonstrates that combining IL-21-Fc with IL-15 superagonist (IL-15SA) or NK cell transfer leads to the eradication of MHC-I-deficient tumors and confers durable protection in syngeneic and xenograft tumor models. Mechanistically, we uncover that IL-21-Fc enhances NK cell effector function by upregulating glycolysis in a lactate dehydrogenase A (LDHA)-dependent manner. These findings not only underscore the considerable potential of IL-21-Fc as an in vivo therapeutic intervention to bolster NK cell-based immunotherapy, but also unveil an innovative strategy of metabolic reprogramming for NK cell rejuvenation within tumors. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=183 SRC="FIGDIR/small/643961v2_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@daf24forg.highwire.dtl.DTLVardef@5a5029org.highwire.dtl.DTLVardef@144c978org.highwire.dtl.DTLVardef@106ed1c_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LINK cells display functional exhaustion in MHC-I deficient solid tumors C_LIO_LIIL-21-Fc as an in vivo-applicable and safe immunotherapy reinvigorates intratumoral NK cells for enhanced effector function C_LIO_LIIL-21-Fc enhances NK cell function by elevating glycolysis in a LDHA-dependent manner C_LIO_LICombining IL-21-Fc with low-dose IL-15SA or NK cell transfer eradicates syngeneic and xenografted solid tumors C_LI

cancer biology↗

bioRxiv2025

Nitrate-driven dark oxygen production by diverse deep-sea microorganisms

To date, only three biotic pathways for light-independent oxygen production (i.e., dark oxygen) have been reported: chlorate dismutation, nitric oxide dismutation, and methanobactin-dependent water lysis. Oxygen has been shown to be produced and consumed in dark and anoxic environments, as evidenced by its prevalence in deep-sea surface sediments. However, the microbial communities and pathways driving deep-sea dark oxygen production (DOP) remain poorly understood. Here we identify a novel DOP pathway driven by dissimilatory nitrate reduction to ammonium (DNRA) in two deep-sea Deferribacterota strains. The DOP activity of these Deferribacterota strains potentially promotes the formation of manganese nodules and influences the growth and metabolism of both aerobic and anaerobic microbes, underscoring the significant role of microbial DOP in shaping deep-sea geological and ecological systems. We also cultured several deep-sea DOP-capable microorganisms from the Campylobacterota, Deinococcota, and Pseudomonadota phyla, which generate dark oxygen through unidentified nitrate-driven pathways. Combined with previous geological evidence, our results suggest that nitrate-driven dark oxygen, alongside photosynthetic oxygen, jointly contributed to early Earths oxygenation, driving the Great Oxidation Event. Overall, our findings provide novel insights into deep-sea oxygen sources, shed light on the origins of early Earths oxygen, and expand perspectives on the potential for terraforming other planets.

microbiology↗

bioRxiv2025

Conditional Diffusion with Locality-Aware Modal Alignment for Generating Diverse Protein Conformational Ensembles

Recent advances in AI have enabled the accurate prediction of a single stable protein structure solely based on its amino acid sequence. However, capturing the complete conformational landscape of a protein and its dynamic flexibility remains challenging. In this work, we developed Modal-aligned conditional Diffusion (Mac-Diff), a score based diffusion model for generating the conformational ensembles for unseen proteins. Central to Mac-Diff is an innovative attention module that enforces a delicate, locality-aware alignment between the conditional view (protein sequence) and the target view (residue pair geometry) to compute highly contextualized features for effective structural denoising. Furthermore, Mac-Diff leverages semantically rich sequence embedding from Protein Language Models like ESM-2 in enforcing the protein sequence condition that captures evolutionary, structural and functional information. This compensates for protein structural heterogeneity more effectively than embeddings from structure prediction models that are possibly biased to the dominant conformation. Mac-Diff showed promising results in generating realistic and diverse protein structures. It successfully recovered conformational distributions of fast folding proteins, captured multiple meta-stable conformations that were only observed in long MD simulation trajectories and efficiently predicted alternative conformations for allosteric proteins. We believe that Mac-Diff offers a useful tool to improve understanding of protein dynamics and structural variability, with broad implications for structural biology, drug discovery, and protein engineering.

bioinformatics↗

bioRxiv2025

Infectome Analysis of Small Mammals in Southern China Reveals Ecological Associations and Emerging Threats from Diverse Pathogens

Small mammals harbor a diverse array of zoonotic pathogens. To date, however, metagenomic surveys of these species have primarily focused on viral diversity, with limited attention paid to bacteria and eukaryotic pathogens. Additionally, the ecological determinants of pathogen diversity within these mammals have not been systematically examined. Herein, we employed a metatranscriptomics approach to survey the pathogen infectome of 2,408 individual samples, representing lung, liver, and gut tissues from 858 animals collected throughout Guangdong province, China, with a study design that accounted for host species, tissue, season, and geographic location. We identified 76 pathogen species, including 29 RNA viruses, 12 DNA viruses, 5 bacteria, and 30 eukaryotic pathogens, 33 of which were newly discovered. Tissue distribution analysis revealed distinct organotropisms, suggesting varied transmission routes, while host distribution analysis showed that each animal carried an average of one pathogen, with 10 pathogens widely distributed among mammalian orders. Our characterization of the geographic and seasonal patterns revealed that pathogen richness was primarily influenced by region, host species, and season, while pathogen composition was largely shaped by host genetic distance. Collectively, these data provide the first comprehensive insight into the dynamics of the pathogen infectome in these key mammalian disease reservoirs, highlighting the major factors driving pathogen diversity and transmission.

microbiology↗

bioRxiv2025

IQI: Individualized Quantitative trait loci (QTL) Index

There is currently no standard efficient method to precisely detecting and characterizing the difference of quantitative trait loci (QTL) effect among groups and/or populations at individual level. The main reason is that the traditional global regression based QTL analyses can only derive an overall effect of genetic variants on the quantitative trait in a specific group. It is a big challenge to obtain the individual-level QTL effect, which is necessary for accurate testing and precise characterization. Here, we first proposed an individualized QTL index (IQI) that represents the degree of regulation between genetic variants and the trait for each individual. Based on IQI, Differential QTL (DQ) analysis framework was presented to accurately detect the case-control differences in more detail. In addition, IQI can be a new feature/biomarker applied to various accurate bioinformatics analysis, such as differential analysis, disease prediction, and survival analysis. Overall, IQI affords a new perspective on pathogenic mechanisms and will facilitate the study of genetic regulation more detailedly and precisely.

bioinformatics↗

bioRxiv2025

Evolution of CYP71D as a driving force of the diversification of monoterpene indole alkaloid biosynthesis

Monoterpene indole alkaloids (MIAs) constitute a vast group of plant natural products synthesized within the Gentianales order. MIAs possess outstanding pharmacological properties that explain their wide use in the treatment of human diseases. These biological activities result from the complex structure of MIAs that originate from intricated biosynthetic pathways involving several enzyme families and notably cytochrome P450s. The early steps of MIA synthesis involve several P450s from the 71 clade, which catalyse the conversion of various reduced strictosidine aglycones into MIAs from the mavacurane, strychnane, akuammilane, sarpagane, and heteroyohimane groups. An extensive study of these P450 distribution in genomic resources reveals that they all belong to a single evolutionary lineage named GAS clade, restricted to the MIA producing Gentianales (Gelsemiaceae and Rauvolfioideae). A related but distinct P450 lineage (named sister GAS clade) was found in non-MIA producing Gentianales and species producing structurally distinct MIAs (Rubiaceae). Functional characterization of 36 members of the GAS clade revealed that these enzymes felt into four main groups of activity depending on substrate acceptance (ex: tetrahydroalstonine, geissoschizine, ajmalicine...) and the nature of the catalyzed reaction (aromatization, cyclisation). These characterizations also lead to the identification of a yohimbane aromatization activity for several of these P450s, increasing the number of MIA scaffolds associated with GAS activity to six. Lastly, the chronology of emergence of these GAS activities was assessed by using ancestral sequence reconstitution, establishing that the initial ancestor of the whole GAS clade exhibits a main alstonine synthase activity. Subsequent gene duplication events, combined with neofunctionalization, facilitated the progressive emergence of eight additional activities, variably distributed among the four GAS subgroups. This comprehensive enzyme characterization establishes the evolutionary trajectory of the GAS clade, demonstrating how its diversification has progressively shaped the chemical diversity of MIAs in Gentianales.

biochemistry↗

bioRxiv2025

Dual Ribosome Profiling reveals metabolic limitations of cancer and stromal cells in thetumor microenvironment

Cancer cells, immune cells, and stromal cells within the tumor microenvironment (TME) collaboratively influence disease progression and therapeutic responses. The nutrient-limited conditions of the TME, particularly the scarcity of glucose, amino acids, and lipids, challenge cancer cell survival1-4. However, the metabolic constraints faced by immune and stromal cells in comparison to cancer cells, and how these limitations affect therapeutic outcomes, remain poorly understood. Here, we introduce Dual Ribosome Profiling (DualRP), a method that allows for simultaneous analysis of translation and identification of ribosome stalling, revealing amino acid shortages in different cell types within tumors. Using DualRP, we uncover that interactions between cancer cells and fibroblasts trigger an inflammatory response, mitigating amino acid limitations during glucose starvation. In immunocompetent mouse models, we observe that immune checkpoint blockade therapy induces serine and glycine restrictions specifically in T cells, but not in cancer cells. We further demonstrate that these amino acids are essential for optimal T cell function both in vitro and in vivo, highlighting their critical role in effective immunotherapy. Our findings show that therapeutic interventions create distinct metabolic demands across different tumor cell types, with nutrient availability significantly influencing the success of immunotherapy. DualRPs ability to explore cell type-specific metabolic vulnerabilities offers a promising tool for advancing our understanding of tumor biology and improving therapeutic strategies.

cancer biology↗

bioRxiv2025

A comprehensive benchmark and guide for sequence-function interpretable deep learning models in genomics

The development of sequence-based deep learning methods has greatly increased our understanding of how sequence determines function. In parallel, numerous interpretable algorithms have been developed to address complex tasks, such as elucidating sequence regulatory syntax and analyzing non-coding variants from trained models. However, few studies have systematically compared and evaluated the performance and interpretability of these algorithms. Here, we introduce a comprehensive benchmark framework for evaluating sequence-to-function models. We systematically evaluated multiple models and DNA language foundation models using 369 ATAC-seq datasets, employing diverse training strategies and evaluation metrics to uncover their critical strengths and limitations. Our benchmark study highlights that different model architectures and interpretability methods are better suited to specific scenarios. Negative samples derived from naturally inactive regions outperform synthetic sequences, whereas single-cell tasks require specialized models. Additionally, we demonstrate that interpretable sequence-function models can complement traditional sequence alignment methods in studying cross-species enhancer regulatory logic. We also provide a pipeline to help researchers select the optimal sequence-function prediction and interpretability algorithms.

bioinformatics↗

bioRxiv2024

Soft, 3D printed muscle ultrasound phantom with structurally tunable B-mode echo intensity

OBJECTIVESImaging phantoms for training and validation are vital to improving the performance and adoption of ultrasound imaging modalities in clinical and pre-clinical applications, and the goal of this study was to assess the viability of 3D printed muscle ultrasound phantoms to meet this need. METHODSWe used a soft stereolithography resin to 3D print phantoms that mimicked the fascicle- and perimysium-scale structure of skeletal muscle and compared the long axis B-mode imaging quality and pattern of the phantom to that of healthy, adult Biceps brachii. We used a pulse-echo, time-of-flight method to measure the acoustic impedance of the resin for comparison to skeletal muscle and common soft tissue mimicking materials. We analyzed the echo intensity (EI) of muscle images to establish a physiological range and compared the EI of different phantom designs to assess the ability to control imaging brightness through structural modification. RESULTSA linear, striated hyper-/hypo-echoic B-mode imaging pattern mimicking long axis Biceps brachii muscle images was achieved with two 3D structure paradigms, rod and honeycomb. Acoustic impedance of Elastic 50A resin is higher than skeletal muscle in bulk, but appears suitable for use in a 3D structured phantom. EI measured in the Biceps images were found to vary both within and across images with an overall mean {+/-} SD of 87 {+/-}13 AU. EI measured in honeycomb phantoms (55 {+/-}15 AU) was higher than in rod phantoms (42 {+/-}13 AU), and a latticed honeycomb further increased EI (90 {+/-}11 AU). CONCLUSIONSThis study serves as proof-of-concept for soft, 3D printed phantoms that replicate the characteristic muscle ultrasound imaging pattern with the ability to tune clinically relevant EI values via structural design.

bioengineering↗

bioRxiv2024

Targeting SLC7A11-mediated cysteine metabolism for the treatment of trastuzumab resistant HER2 positive breast cancer

Trastuzumab resistance remains a challenge for HER2 positive breast cancer treatment. Targeting metabolic reprogramming would provide novel insights for therapeutic strategies. Here, we integrated metabolomics, transcriptomics and epigenomics data of trastuzumab sensitive and primary resistant HER2 positive breast cancer to identify metabolic alterations. Aberrant cysteine metabolism was discovered in trastuzumab primary resistant breast cancer at both circulating and intracellular levels. The inhibition of SLC7A11 and cysteine starvation could synergize with trastuzumab to induce ferroptosis. Mechanistically, increased H3K4me3 and decreased DNA methylation enhanced SLC7A11 transcription and cystine uptake in trastuzumab resistant breast cancer. The regulation of epigenetic modifications modulated cysteine metabolism and ferroptosis sensitivity. These results revealed an innovative approach for overcoming trastuzumab resistance by targeting specific amino acid metabolism.

cancer biology↗

bioRxiv2024

Ribosome abundance, not paralogue composition, is essential for germline development

Ribosomes catalyse all protein synthesis, and mutations altering their levels and function underlie many developmental diseases and cancer. Historically considered to be invariant machines, ribosomes differ in composition between tissues and developmental stages, incorporating a diversity of ribosomal proteins (RP) encoded by duplicated paralogous genes. Here, we use Drosophila to systematically investigate the origins and functions of non-canonical RP paralogues. We show that new paralogues mainly originated through retroposition, and that only a few new copies retain coding capacity over time. Although transcriptionally active non-canonical RP paralogues often present tissue-specific expression, we show that the majority of those are not required for either viability or fertility in Drosophila melanogaster. The only exception, RpS5b, which is required for oogenesis, is functionally interchangeable with its canonical paralogue, indicating that the RpS5b-/- phenotype results from insufficient ribosomes rather than the absence of an RpS5b-specific, functionally-specialised ribosome. Altogether our results provide evidence that, instead of new functions, RP gene duplications provide a means to regulate ribosome levels during development.

developmental biology↗

bioRxiv2024

A Full Window Data Independent Acquisition Method for DeeperTop-down Proteomics

Top-down proteomics (TDP) is emerging as a vital tool for the comprehensive characterization of proteoforms. However, as its core technology, top-down mass spectrometry (TDMS) still faces significant analytical challenges. While data-independent acquisition (DIA) has revolutionized bottom-up proteomics and metabolomics, they are rarely employed in TDP. The unique feature of protein ions in an electrospray mass spectrum, as well as the data complexity require the development of new DIA strategies. This study introduces a machine learning assisted Full Window DIA (FW-DIA) method that eliminates precursor ion isolation, making it compatible with a wide range of commercial mass spectrometers. Moreover, FW-DIA leverages all precursor protein ions to generate high-quality tandem mass spectra, enhancing signal intensities by [~]50-fold and protein sequence coverage by threefold in a modular protein analysis. The method was successfully applied to the analysis of a five-protein mixture under native conditions and Escherichia coli ribosomal proteoform characterization.

bioinformatics↗

bioRxiv2024

AI Assisted Native Proteomics: Delineating Ribosomal Protein Conformations Pre- and Post-Assembly

The simultaneous identification of proteins as well as their conformations in a biological system would greatly enhance our understanding of cellular mechanisms and disease. As an emerging technique, native proteomics analyzes proteins in their native states, facilitating the acquisition of protein stoichiometry, post-translational modifications (PTMs), and interactions with ligands. However, revealing protein conformations at the proteome scale remains a significant challenge. In this study, we propose an AI-assisted native proteomics method that integrates a protein structure prediction (PSP) module with top-down proteomics (TDP) and native mass spectrometry (nMS) to acquire both proteome identity and conformations. First, protein sequences and PTMs are obtained using the TDP method, while protein solvent-accessible surface area (SASA) is measured in parallel by nMS. These data are then input into the PSP module to acquire the most probable conformation of the protein under nMS experimental conditions. We validate the feasibility and accuracy of this method through the analysis of both a globular protein and an intrinsically disordered protein. Additionally, we apply this approach to delineate the conformations of ribosomal proteins pre- and post-assembly. Interactions of ribosomal proteins with drug molecules are also explored. By enabling proteome identification and conformation characterization from relatively small amounts of endogenous proteins, this method would bridge the gap between structural biology and conventional proteomics technologies.

molecular biology↗

bioRxiv2024

Three positively charged binding sites on the eastern equine encephalitis virus E2 glycoprotein coordinate heparan sulfate- and protein receptor-dependent infection

Naturally circulating strains of eastern equine encephalitis virus (EEEV) bind heparan sulfate (HS) receptors and this interaction has been linked to its neurovirulence. Previous studies associated EEEV-HS interactions with three positively charged amino acid clusters on the E2 glycoprotein. One of these sites has recently been reported to be critical for binding EEEV to very-low-density lipoprotein receptor (VLDLR), an EEEV receptor protein. The proteins apolipoprotein E receptor 2 (ApoER2) isoforms 1 and 2, and LDLR have also been shown to function as EEEV receptors. Herein, we investigate the individual contribution of each HS interaction site to EEEV HS- and protein receptor-dependent infection in vitro and EEEV replication in animals. We show that each site contributes to both EEEV-HS and EEEV-protein receptor interactions, providing evidence that altering these interactions can affect disease in mice and eliminate mosquito infectivity. Thus, multiple HS-binding sites exist in EEEV E2, and these sites overlap functionally with protein receptor interaction sites, with each type of interaction contributing to tissue infectivity and disease phenotypes.

microbiology↗

bioRxiv2024

A 3D Image Segmentation Study of U-Net on CT Images of the Human Aorta with Morphologically Diverse Anatomy

For the machine learning -assisted diagnosis of cardiac diseases, such as thoracic aortic aneurysm, the geometries of the heart and blood vessels need to be reconstructed from medical images, which is usually done by image segmentation followed by meshing. In this study, we applied U-Net (2D and 3D versions), a deep neural network with a U-shaped architecture, to segment human aorta from CT images. From our experiments, we have the following observations: (1) 2D U-Net, which segments each of the 2D slices of a 3D CT image independently, produced erroneous fragments (e.g., missing part of the aorta) and boundaries (i.e., aortic walls) in 3D; (2) 3D U-Net, which does segmentation in 3D regions of a 3D CT image, performed much better than 2D U-Net. We also observe the major weakness of the 3D U-Net: the reconstructed geometries of the aortic wall had large errors (measured by HD95) for some cases. The 3D U-Net in this study serves as a baseline for developing more advanced architectures of deep neural networks for more accurate geometry reconstruction of human aorta.

bioengineering↗

bioRxiv2024

The multimodal transcriptional response of denervated skeletal muscle involves alterations in cholesterol homeostasis impacting muscle size

The development and maintenance of the neuromuscular junction (NMJ) requires reciprocal signals between the nerve terminals and the multinucleated skeletal muscle fiber (myofiber). This interaction leads to highly specialized transcription in the sub-synaptic or NMJ myonuclei within mature myofibers leading to clustering of acetylcholine receptors (AChRs). Here, we utilized single-nucleus RNA sequencing (snRNA-seq) to delineate the transcriptional response of myonuclei to denervation. Through snRNA-seq on skeletal muscle from two independent mouse models of denervation, sciatic nerve transection and amyotrophic lateral sclerosis, we identify a multimodal transcriptional response of NMJ-enriched genes and an alteration in cholesterol homeostasis in both slow and fast myofibers. Gramd1, a family of genes involved in non-vesicular cholesterol transport, are enriched at the NMJ in innervated muscle and upregulated in both models of denervation by the NMJ and extrasynaptic myonuclei. In vivo gain and loss of function studies indicate that NMJ-enriched Gramd1 genes regulate myofiber sizes independent of an obvious impact on AChR clustering. We uncovered a dynamic transcriptional response of myonuclei to denervation and highlight a critical role for cholesterol transport to maintain myofiber sizes.

cell biology↗