Sci Rep. 2025 Apr 25;15(1):14484. doi: 10.1038/s41598-025-98463-5.

ABSTRACT

The tumour microenvironment is composed of a complex cellular network involving cancer, stromal and immune cells in dynamic interactions. A large proportion of this network relies on direct physical interactions between cells, which may impact patient responses to clinical therapy. Doublets in scRNA-seq are usually excluded from analysis. However, they may represent directly interacting cells. To decipher the physical interaction landscape in relation to clinical prognosis, we inferred a physical cell-cell interaction (PCI) network from 'biological' doublets in a scRNA-seq dataset of approximately 18,000 cells, obtained from 7 treatment-naive ovarian cancer patients. Focusing on cancer-stromal PCIs, we uncovered molecular interaction networks and transcriptional landscapes that stratified patients in respect to their clinical responses to standard therapy. Good responders featured PCIs involving immune cells interacting with other cell types including cancer cells. Poor responders lacked immune cell interactions, but showed a high enrichment of cancer-stromal PCIs. To explore the molecular differences between cancer-stromal PCIs between responders and non-responders, we identified correlating gene signatures. We constructed ligand-receptor interaction networks and identified associated downstream pathways. The reconstruction of gene regulatory networks and trajectory analysis revealed distinct transcription factor (TF) clusters and gene modules that separated doublet cells by clinical outcomes. Our results indicate (i) that transcriptional changes resulting from PCIs predict the response of ovarian cancer patients to standard therapy, (ii) that immune reactivity of the host against the tumour enhances the efficacy of therapy, and (iii) that cancer-stromal cell interaction can have a dual effect either supporting or inhibiting therapy responses.

PMID:40280979 | DOI:10.1038/s41598-025-98463-5

Nat Commun. 2025 Apr 25;16(1):3873. doi: 10.1038/s41467-025-58907-y.

ABSTRACT

Familial adenomatous polyposis (FAP) is an inherited gastrointestinal syndrome associated with duodenal adenoma formation. Even among carriers of the same genetic variant, duodenal phenotypes vary, indicating that additional factors, such as the local immune system, play a role. We observe an increase in duodenal IL-17A(+)NKp44(-) innate lymphoid type 3 cell (ILC3) in FAP, localized near the epithelium and enriched in adenomas and carcinomas. Elevated IL1B, IL23A, and DLL4 transcript levels correlate with IL-17A(+)NKp44(-)ILC3 accumulation, and in vitro studies with duodenal organoids confirmed this relationship. Bulk RNA sequencing reveals upregulated Reactive oxygen species (ROS)-inducing enzymes DUOX2 and DUOXA2 in FAP adenomas. IL-17A-stimulated FAP organoids show increased DUOX2/DUOXA2 expression, Duox2 protein, and ROS production, leading to DNA damage, suggesting a mechanism by which these immune cells promote tumorigenesis. These findings suggest IL-17A(+)NKp44(-)ILC3s may contribute to a local environment that makes the epithelium more submissive for oncogenic transformation in FAP.

PMID:40280932 | DOI:10.1038/s41467-025-58907-y

BMC Plant Biol. 2025 Apr 25;25(1):535. doi: 10.1186/s12870-025-06493-y.

ABSTRACT

BACKGROUND: Plants must continuously adapt to environmental fluctuations, which significantly influence their photosynthetic performance and overall metabolism. The sucrose cycling system within plant cells plays a critical regulatory role during stress conditions. This study employed a systems biology approach to analyze system stabilities mathematically under various regulatory conditions impacting sucrose cycling dynamics. We investigated the effects of mutations within this cycle, specifically HEXOKINASE1 (Arabidopsis thaliana gin2-1), alongside high-light exposure. Finally, we confirmed the modeling output in vitro by enzyme assays.

RESULTS: The implementation of experimental subcellular metabolite data into a Structural Kinetic Model (SKM) enabled exploration of regulatory responses and system stabilities within a three-compartment model. Within system instabilities, gin2-1 was more instable than its wild type. The gin2-1 mutation particularly was destabilized when fructokinase function was impaired by phosphorylated sugars. Additionally, we confirmed that phosphorylated sugars serve as stronger activators of sucrose-phosphate synthase (SPS) than glucose. Interestingly, models with fructose SPS activation exhibited a similar stability pattern. Consequently, we proposed and confirmed in silico a triple activation of SPS by highly activating phosphorylated sugars and lower activating non-phosphorylated hexoses. Additionally, we biochemically confirmed the previously unknown, but now predicted, activation of SPS by fructose in vitro.

CONCLUSION: In summary, our study highlights the essential role of sucrose cycling in plant cells under stress conditions. The in silico findings reveal that phosphorylated sugars are stronger activators of SPS than glucose and introduce a previously unknown activation mechanism by fructose. These potential activation capacities were confirmed in vitro through SPS enzyme activity assays, underscoring the efficiency of our systems biology approach. Overall, this research provides valuable insights into carbohydrate metabolism regulation and paves the way for future investigations to deepen our understanding of the complexities involved in sucrose cycling and biosynthesis in plants.

PMID:40281434 | DOI:10.1186/s12870-025-06493-y

NPJ Precis Oncol. 2025 Apr 25;9(1):120. doi: 10.1038/s41698-025-00818-8.

ABSTRACT

Patients with high-grade serous ovarian carcinoma (HGSC) are usually diagnosed with advanced-stage disease, and the tumors often have immunosuppressive characteristics. Together, these factors are important for disease progression, drug resistance, and mortality. In this study, we used a combination of single-cell sequencing and spatial transcriptomics to identify the molecular mechanisms that lead to immunosuppression in HGSC. Primary tumors consistently showed a more active immune microenvironment than did omental tumors. In addition, we found that untreated primary tumors were mostly populated by dysfunctional CD4 and CD8 T cells in later stages of differentiation; this, in turn, was correlated with expression changes in the interferon α and γ pathways in epithelial cells, showing that cross-communication between the epithelial and immune compartments is important for immune suppression in HGSC. These findings could have implications for the design of clinical trials with immune-modulating drugs.

PMID:40281242 | DOI:10.1038/s41698-025-00818-8

Trends Ecol Evol. 2025 Apr 24:S0169-5347(25)00073-4. doi: 10.1016/j.tree.2025.03.010. Online ahead of print.

ABSTRACT

Biological invasions alter ecosystems by disrupting ecological processes that can degrade biodiversity, harm human health, and cause massive economic burdens. Existing frameworks to classify the ecological impacts either miss many types of impact or conflate mechanisms (causes) with the impacts themselves (consequences). We propose a comprehensive typology of 19 types of ecological impact across six levels of ecological organisation. This allows more accurate diagnosis of the cause of impact and can help triage management options to tackle each impact-mechanism combination. We integrated the typology with broad ecological concepts such as energy, mass, and information flow and storage. By highlighting cascading effects across multiple levels, this typology provides a clearer framework for documenting, and communicating invasion impacts, thereby improving management and research.

PMID:40280812 | DOI:10.1016/j.tree.2025.03.010

J Hazard Mater. 2025 Apr 21;493:138345. doi: 10.1016/j.jhazmat.2025.138345. Online ahead of print.

ABSTRACT

Butyl xanthate (BuX) is an emerging pollutant due to wide use as flotation collector, posing a serious threat to ecosystem health in mining areas. Here we develop a combinational plant-microbe remediation strategy for restoration of BuX-contaminated mining areas. A novel bacterial strain that completely degraded up to 1000 mg/L of BuX within 12 h was isolated and identified as Pseudomonas monteilii W50. It was found to harbor good tolerance to extreme environmental conditions and multiple plant growth-promoting traits such as phosphate and potassium solubilization, indole-3-acetic acid and gibberellin production, and cellulose degradation. This strain can colonize in the rhizosphere of an emergent macrophyte Cyperus alternifolius, improving removal of BuX and chemical oxygen demand (COD) from simulated wastewater. Compared to the phytoremediation alone, the removal of BuX and COD increased from 70 % to 98 % and from 21 % to 46 % respectively in the combined remediation The strain W50 protected the macrophyte from the phytotoxicity of BuX and the macrophyte provided it with a suitable habitat for return, benefiting each other. Compared to the individual treatment using C. alternifolius or strain W50, the combinational treatment significantly improved the plant growth and the residence of inoculated bacteria. Overall, C. alternifolius and strain W50 are the perfect combination for efficient and sustainable remediation of BuX-contaminated mine wastewater, overcoming the constraints of individual phytoremediation or bioaugmentation methods.

PMID:40280067 | DOI:10.1016/j.jhazmat.2025.138345

J Hazard Mater. 2025 Apr 21;493:138350. doi: 10.1016/j.jhazmat.2025.138350. Online ahead of print.

ABSTRACT

Microbial metal resistance, a trait that enables microorganisms to withstand high levels of toxic metals, has been studied for over a century. The significance of uncovering these mechanisms goes beyond basic science as they have implications for human health through their connection to microbial pathogenesis, metal bioremediation, and biomining. Recent advances in analytical chemistry and molecular biology have accelerated the discovery and understanding of genetic mechanisms underlying microbial metal resistance, identifying specific metal resistance genes and their operons. The emergence of omics tools has further propelled research towards a comprehensive understanding of how cells respond to metal stress at the systemic level, revealing the complex regulatory networks and evolutionary dynamics that drive microbial adaptation to metal-rich environments. In this article, we present a historical overview of the evolving understanding of the genetic determinants of metal resistance in microbes. Through multiple narrative threads, we illustrate how our knowledge of microbial metal resistance and genetics has interacted with genetic tools and concept development. This review also discusses how our understanding of microbial metal resistance has progressed from the Mendelian perspective to the current systems biology viewpoint, particularly as omics approaches have considerably enhanced our understanding. This system-level understanding has opened new possibilities for genetically engineered microorganisms to regulate metal homeostasis.

PMID:40280066 | DOI:10.1016/j.jhazmat.2025.138350

Comput Biol Med. 2025 Apr 24;192(Pt A):110157. doi: 10.1016/j.compbiomed.2025.110157. Online ahead of print.

ABSTRACT

It is known that microbiome and health are related, in addition, recent research has found that microbiome has potential clinical uses. These facts highlight the importance of the microbiome in actual science. However, microbiome data has some characteristics that makes its statistical study challenging. In recent years, longitudinal and non-longitudinal methods have been designed to analyze the microbiota and knowing more about the bacterial behavior. In this article in the form of a review we summarize the characteristics of microbiome data and the statistical methods most widespread to analyze it. We have taken into account if the strategies are longitudinal or not. We also classify the methods based on their specific analytical objectives and based on their mathematical characteristics. The methods are structured according to their biological goals and mathematical features, ensuring that the insights provided are both relevant and accessible to professionals in biology and statistics. We present this review as a reference for the most widely used methods in microbiome data analysis and as a foundation for identifying potential areas for future research. We want to point out that this review can be particularly useful to remark the importance of the methodology designed in order to study microbiome longitudinal datasets.

PMID:40279974 | DOI:10.1016/j.compbiomed.2025.110157

Adv Sci (Weinh). 2025 Apr 25:e2408173. doi: 10.1002/advs.202408173. Online ahead of print.

ABSTRACT

Discovering dominant patterns and exploring dynamic behaviors especially critical state transitions and tipping points in high-dimensional time-series data are challenging tasks in study of real-world complex systems, which demand interpretable data representations to facilitate comprehension of both spatial and temporal information within the original data space. This study proposes a general and analytical ultralow-dimensionality reduction method for dynamical systems named spatial-temporal principal component analysis (stPCA) to fully represent the dynamics of a high-dimensional time-series by only a single latent variable without distortion, which transforms high-dimensional spatial information into one-dimensional temporal information based on nonlinear delay-embedding theory. The dynamics of this single variable is analytically solved and theoretically preserves the temporal property of original high-dimensional time-series, thereby accurately and reliably identifying the tipping point before an upcoming critical transition. Its applications to real-world datasets such as individual-specific heterogeneous ICU records demonstrate the effectiveness of stPCA, which quantitatively and robustly provides the early-warning signals of the critical/tipping state on each patient.

PMID:40279642 | DOI:10.1002/advs.202408173

Phys Rev Lett. 2025 Apr 11;134(14):148401. doi: 10.1103/PhysRevLett.134.148401.

ABSTRACT

Biomolecules stochastically occupy different configurations that correspond to distinct functional states. Changing biochemical inputs such as rate constants alters the output probability distribution of configurations, and thus constitutes a form of computation. In the cell, such computations are often coupled to thermodynamic forces such as ATP hydrolysis that drive systems far from equilibrium, resulting in energy expenditure even during times when computations are not being performed. The information-theoretic advantage of this costly computational paradigm is unclear. Here we introduce a theoretical framework showing how much the thermodynamic force enables changes in probability distributions, quantified by the information gain, beyond what is possible at equilibrium. Using this framework, we derive a general expression relating the force to the maximum information gain in an arbitrary computation, revealing how small input changes can exponentially alter outputs. We numerically show that biomolecular systems can closely approach this universal bound, illustrating how energy expenditure is needed to achieve the information processing capabilities observed in nature.

PMID:40279610 | DOI:10.1103/PhysRevLett.134.148401

Sci Adv. 2025 Apr 25;11(17):eadt9712. doi: 10.1126/sciadv.adt9712. Epub 2025 Apr 25.

ABSTRACT

Coordinated cell cycle regulation is essential for homeostasis, with most cells in the body residing in quiescence (G0). Many pathologies arise due to disruptions in tissue-specific G0, yet little is known about the temporal-spatial mechanisms that establish G0 and its signaling hub, primary cilia. Mechanistic insight is limited by asynchronous model systems and failure to connect context-specific, transient mechanisms to function. To address this gap, we developed STAMP (synchronized temporal-spatial analysis via microscopy and phosphoproteomics) to track changes in cellular landscape occurring throughout G0 transition and ciliogenesis. We synchronized ciliogenesis and G0 transition in two cell models and combined microscopy with phosphoproteomics to order signals for further targeted analyses. We propose that STAMP is broadly applicable for studying temporal-spatial signaling in many biological contexts. The findings revealed through STAMP provide critical insight into healthy cellular functions often disrupted in pathologies, paving the way for targeted therapeutics.

PMID:40279433 | DOI:10.1126/sciadv.adt9712

Annu Rev Immunol. 2025 Apr;43(1):693-722. doi: 10.1146/annurev-immunol-090122-042631.

ABSTRACT

The immune system, critical for human health and implicated in many diseases, defends against pathogens, monitors physiological stress, and maintains tissue and organismal homeostasis. It exhibits substantial variability both within and across individuals and populations. Recent technological and conceptual progress in systems human immunology has provided predictive insights that link personal immune states to intervention responses and disease susceptibilities. Artificial intelligence (AI), particularly machine learning (ML), has emerged as a powerful tool for analyzing complex immune data sets, revealing hidden patterns across biological scales, and enabling predictive models for individualistic immune responses and potentially personalized interventions. This review highlights recent advances in deciphering human immune variation and predicting outcomes, particularly through the concepts of immune setpoint, immune health, and use of the immune system as a window for measuring health. We also provide a brief history of AI; review ML modeling approaches, including their applications in systems human immunology; and explore the potential of AI to develop predictive models and personal immune state embeddings to detect early signs of disease, forecast responses to interventions, and guide personalized health strategies.

PMID:40279304 | DOI:10.1146/annurev-immunol-090122-042631

J Agric Food Chem. 2025 Apr 25. doi: 10.1021/acs.jafc.5c02528. Online ahead of print.

ABSTRACT

Nutmeg (Myristica fragrans) has been traditionally valued for its culinary and medicinal properties. In our ongoing efforts to discover pharmacologically active compounds from this spice, five new furan acids (2-6, jusahos B-F), one new neolignan (7, jusaho G), and six known compounds (1 and 8-12) were isolated from its nutmegs. The chemical structures of the compounds were elucidated using NMR spectroscopy and HRESIMS. Among them, compound 3 (jusaho C) demonstrated promising antineuroinflammatory and neuroprotective effects in BV2 and HT22 cells by modulating the MAPK/NF-κB signaling pathway, which was explored through network pharmacology, molecular docking, and experimental verification. Compound 3 also showed the improvement of locomotor activity in Caenorhabditis elegans model infected with Pseudomonas aeruginosa. These findings expand the phytochemical profile of M. fragrans, where only one furan acid was previously reported, and highlight nutmeg-derived compounds, particularly jusaho C, as potential functional food ingredients or nutraceuticals for managing neuroinflammatory conditions.

PMID:40278862 | DOI:10.1021/acs.jafc.5c02528

Metabolites. 2025 Apr 15;15(4):274. doi: 10.3390/metabo15040274.

ABSTRACT

BACKGROUND: The presence and concentration of lipids in serum of dairy cows have significant implications for both animal health and productivity and are potential biomarkers for several common diseases. However, information on serum lipid composition is rather fragmented, and lipid remodelling during the transition period is only partially understood.

METHODS: Using a combination of reversed-phase liquid chromatography-mass spectrometry (RP-LC-MS), hydrophilic interaction-mass spectrometry (HILIC-MS), and lipid annotation software, we performed a comprehensive identification and quantification of serum of dairy cows in pasture-based Holstein-Friesian cows. The lipid remodelling induced by negative energy balance was investigated by comparing the levels of all identified lipids between the fresh lactation (5-14 days in milk, DIM) and full lactation (65-80 DIM) stages.

RESULTS: We identified 535 lipid molecular species belonging to 19 classes. The most abundant lipid class was cholesteryl ester (CE), followed by phosphatidylcholine (PC), sphingomyelin (SM), and free fatty acid (FFA), whereas the least abundant lipids included phosphatidylserine (PS), phosphatidic acid (PA), phosphatidylglycerol (PG), acylcarnitine (AcylCar), ceramide (Cer), glucosylceramide (GluCer), and lactosylceramide (LacCer).

CONCLUSIONS: A remarkable increase in most lipids and a dramatic decrease in FFAs, AcylCar, and DHA-containing species were observed at the full lactation compared to fresh lactation stage. Several serum lipid biomarkers for detecting negative energy balance in cows were also identified.

PMID:40278403 | DOI:10.3390/metabo15040274

Metabolites. 2025 Apr 9;15(4):255. doi: 10.3390/metabo15040255.

ABSTRACT

BACKGROUND/OBJECTIVES: Drug development for complex diseases such as NAFLD is often lengthy and expensive. Drug repurposing, the process of finding new therapeutic uses for existing drugs, presents a promising alternative to traditional approaches. This study aims to identify potential repurposed drugs for NAFLD by leveraging disease-disease relationships and drug-target data from the BioSNAP database.

METHODS: A bipartite network was constructed between drugs and their target genes, followed by the application of the BiClusO bi-clustering algorithm to identify high-density clusters. Clusters with significant associations with NAFLD risk genes were considered to predict potential drug candidates. Another set of candidates was determined based on disease similarity.

RESULTS: A novel ranking methodology was developed to evaluate and prioritize these candidates, supported by a comprehensive literature review of their effectiveness in NAFLD treatment.

CONCLUSIONS: This research demonstrates the potential of drug repurposing to accelerate the development of therapies for NAFLD, offering valuable insights into novel treatment strategies for complex diseases.

PMID:40278384 | DOI:10.3390/metabo15040255

Elife. 2025 Apr 25;12:RP88794. doi: 10.7554/eLife.88794.

ABSTRACT

Plasmid construction is central to life science research, and sequence verification is arguably its costliest step. Long-read sequencing has emerged as a competitor to Sanger sequencing, with the principal benefit that whole plasmids can be sequenced in a single run. Nevertheless, the current cost of nanopore sequencing is still prohibitive for routine sequencing during plasmid construction. We develop a computational approach termed Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You (SAVEMONEY) that guides researchers to mix multiple plasmids and subsequently computationally de-mixes the resultant sequences. SAVEMONEY defines optimal mixtures in a pre-survey step, and following sequencing, executes a post-analysis workflow involving sequence classification, alignment, and consensus determination. By using Bayesian analysis with prior probability of expected plasmid construction error rate, high-confidence sequences can be obtained for each plasmid in the mixture. Plasmids differing by as little as two bases can be mixed as a single sample for nanopore sequencing, and routine multiplexing of even six plasmids per 180 reads can still maintain high accuracy of consensus sequencing. SAVEMONEY should further democratize whole-plasmid sequencing by nanopore and related technologies, driving down the effective cost of whole-plasmid sequencing to lower than that of a single Sanger sequencing run.

PMID:40277466 | DOI:10.7554/eLife.88794

Anal Chem. 2025 Apr 25. doi: 10.1021/acs.analchem.5c01648. Online ahead of print.

ABSTRACT

As a visualization technology for the in situ characterization of surface material molecules, mass spectrometry imaging (MSI) analysis is being increasingly used in various fields, especially in the biomedical field. However, the preparation of biological tissue section samples for MSI analysis remains time-consuming and labor-intensive, and sample loss or damage occurs frequently. The inability to stably and consecutively obtain suitable section samples and perform concise and efficient imaging analysis limits the analysis throughput. Herein, a preparation method is proposed. It enables consecutive sectioning, batch preservation, and dry processing through the use of ordinary adhesive tape, enhancing the adhesion between section and tape and rapid freeze-drying. Furthermore, based on the air flow assisted desorption electrospray ionization (AFADESI) MSI system, a vacuum adsorption platform is introduced, which simplifies the process of MSI analysis. Moreover, compared with general tape-based MSI methods, the signal intensity of 73%-85% of the annotated ions is improved for positive ion mode. The signal-to-noise (S/N) ratios of characteristic ions in the corresponding regions in the images of the tissue section samples increase by an average of more than two times, and a clearer organ outline can be seen in the images. By integrating the sample preparation method with the adsorption platform, high-throughput imaging of serial whole-body or scattered organ tissue sections can be conducted more easily and concise and efficient MSI analysis can be performed, which will provide a new strategy to meet rapidly growing MSI research demands.

PMID:40277201 | DOI:10.1021/acs.analchem.5c01648

iScience. 2025 Apr 2;28(5):112333. doi: 10.1016/j.isci.2025.112333. eCollection 2025 May 16.

ABSTRACT

Overexpression of the antiapoptotic oncogene BCL-2 predicts poor prognosis in diffuse large B cell lymphoma (DLBCL) treated with anthracycline-based chemoimmunotherapy. Anthracyclines exert antitumor effects by multiple mechanisms including inhibition of ribosome biogenesis (RiBi) through rRNA synthesis blockade. RiBi inhibitors induce p53 stabilization through the ribosomal proteins-MDM2-p53 pathway, with stabilized p53 levels depending on baseline rRNA synthesis rate. We found that the BH3-mimetic venetoclax could not fully reverse BCL-2-mediated resistance to RiBi inhibitors in DLBCL cells. BCL-2 overexpression was associated with decreased baseline rRNA synthesis rate, attenuating p53 stabilization by RiBi inhibitors. Drugs stabilizing p53 irrespective of RiBi inhibition reversed BCL-2-induced resistance in vitro and in vivo, restoring p53 activation and apoptosis. A small nucleolar size, indicative of low baseline rRNA synthesis, correlated with high BCL-2 levels and poor outcomes in DLBCL patients. These findings uncover alternative BCL-2-dependent chemoresistance mechanisms, providing a rationale for specific combination strategies in BCL-2 positive lymphomas.

PMID:40276769 | PMC:PMC12020883 | DOI:10.1016/j.isci.2025.112333

iScience. 2025 Mar 30;28(5):112321. doi: 10.1016/j.isci.2025.112321. eCollection 2025 May 16.

ABSTRACT

Sleep deprivation is one of concomitant symptoms of cancer patients, particularly those with non-small cell lung cancer (NSCLC). The potential effect of sleep deprivation on tumor progression and underlying mechanisms remain to be fully investigated. Using a sleep-deprived tumor-bearing mouse model, we found that sleep deprivation altered immune cell composition and regulated pro-tumoral M2 macrophage polarization by the sympathetic nervous system. Furthermore, we identified a role of catecholaminergic neurons in the rostral ventrolateral medulla (RVLM) in influencing NSCLC metastasis. Clinical analyses revealed a correlation between sympathetic-related indicators and poor prognosis. Mechanistically, our findings indicate that sleep deprivation facilitates the polarization of pro-tumoral macrophages by upregulating β2-adrenergic receptor (ADRB2), which subsequently enhances the expression of Kruppel-like transcription factor 4 (KLF4) through the JAK1/STAT6 phosphorylation pathway. These findings highlight a neuro-immune mechanism linking sleep deprivation to NSCLC metastasis, suggesting that targeting the ADRB2/KLF4 axis could improve outcomes for sleep-deprived NSCLC patients.

PMID:40276761 | PMC:PMC12018092 | DOI:10.1016/j.isci.2025.112321

iScience. 2025 Apr 2;28(5):112343. doi: 10.1016/j.isci.2025.112343. eCollection 2025 May 16.

ABSTRACT

Mesenchymal stem cells (MSCs) hold potential in cancer therapy; however, insufficient tumor homing ability and heterogeneity limit their therapeutic benefits. Obviously, the homogeneous induced pluripotent stem cell (iPSC)-derived mesenchymal stem cells (iMSCs) with enhanced ability of tumor targeting could be the solution. In this study, a CAR containing the NKG2D extracellular domain was targeted at the B2M locus of iPSCs to generate NKG2D-CAR-iPSCs, which were subsequently differentiated into NKG2D-CAR-iMSCs. In vitro, NKG2D-CAR significantly enhanced migration and adhesion of iMSCs to a variety of solid tumor cells expressing NKG2D ligands. RNA sequencing (RNA-seq) revealed significant upregulation of genes related to cell adhesion, migration, and binding in NKG2D-CAR-iMSCs. In A549 xenograft model, NKG2D-CAR-iMSCs demonstrated a 57% improvement in tumor-homing ability compared with iMSCs. In conclusion, our findings demonstrate enhanced targeting specificity of NKG2D-CAR-iMSCs to tumor cells expressing NKG2D ligands in vitro and in vivo, facilitating future investigation of iMSCs as an off-the-shelf living carrier for targeted delivery of anti-tumor agents.

PMID:40276759 | PMC:PMC12020857 | DOI:10.1016/j.isci.2025.112343