Z Orthop Unfall. 2025 Jun 24. doi: 10.1055/a-2616-0819. Online ahead of print.

ABSTRACT

Osteoarthritis is a common degenerative disease of joint cartilage that affects millions of people in the world, especially the elderly. Progression of osteoarthritis is associated with a plethora of genetic and non-genetic factors. The CRISPR/Cas9 system is emerging as a powerful tool for genome engineering and has remarkable potential for guiding further research into osteoarthritis and may be a viable means for treating the disease. This review discusses existing and potential applications of the CRISPR/Cas9 system in osteoarthritis studies and treatments. Firstly, we briefly summarize the current status and mechanism of this technology. Next, we focus on the latest advances in the application of CRISPR/Cas9 system in elucidating the contributions of various factors to the pathogenesis of osteoarthritis as demonstrated through in vitro studies and animal models. Finally, we provide our perspective on the direction and challenges of studying and treating osteoarthritis with CRISPR/Cas9.

PMID:40555270 | DOI:10.1055/a-2616-0819

Genetics. 2025 Jun 24:iyaf115. doi: 10.1093/genetics/iyaf115. Online ahead of print.

ABSTRACT

Homologous recombination is a key process in bacterial genome evolution. By analyzing sequencing collections of 12 bacterial species encompassing >100,000 genomes, we determine how recombination and clonal evolution shape bacterial lineages and genome structures. Previous analyses proposed that for some bacterial species recombination is so dominant that almost no clonal genomic fraction remains. Further, it has been suggested that bacterial phylogenies are entirely structured by scale-free distributions of recombination rates, based on measurement of private SNP distributions that exhibit power-law tails. Using a coalescent model of populations that recombine with different gene pools, we find a substantial clonal signal in all global bacterial populations analyzed, and infer recombination rates that typically vary by less than an order of magnitude within species. Additionally, for a local population of Escherichia coli isolates that exhibit power-law private SNP distributions, we infer narrowly-distributed recombination rates and a substantial clonal signal, and show that their clonal genealogy exhibits a distribution of coalescence times spanning several orders of magnitude. Using simulations and theory, we demonstrate that power-law SNP distributions are not indicative of widely-varying recombination rates, and can be generated by a clonal genealogy recombining with an external pool at a constant rate. We use regression analysis to quantify the relative impact of recombination and clonal evolution on the diversity and lineage structure of local and global populations. Our findings have implications for how of bacterial phylogeny is interpreted, and lays key groundwork for understanding which evolutionary forces determine species diversity.

PMID:40554592 | DOI:10.1093/genetics/iyaf115

PLoS Biol. 2025 Jun 24;23(6):e3003193. doi: 10.1371/journal.pbio.3003193. eCollection 2025 Jun.

ABSTRACT

Ovulation is a spatiotemporally coordinated process that involves several tightly controlled events, including oocyte meiotic maturation, cumulus expansion, follicle wall rupture and repair, and ovarian stroma remodeling. To date, no studies have detailed the precise window of ovulation at single-cell resolution. Here, we performed parallel single-cell RNA-seq and spatial transcriptomics on paired mouse ovaries across an ovulation time course to map the spatiotemporal profile of ovarian cell types. We show that major ovarian cell types exhibit time-dependent transcriptional states enriched for distinct functions and have specific localization profiles within the ovary. We also identified gene markers for ovulation-dependent cell states and validated these using orthogonal methods. Finally, we performed cell-cell interaction analyses to identify ligand-receptor pairs that may drive ovulation, revealing previously unappreciated interactions. Taken together, our data provides a rich and comprehensive resource of murine ovulation that can be mined for discovery by the scientific community.

PMID:40554460 | DOI:10.1371/journal.pbio.3003193

Mod Pathol. 2025 Jun 17:100821. doi: 10.1016/j.modpat.2025.100821. Online ahead of print.

ABSTRACT

Clear cell adenocarcinoma of the urinary tract (utCCA) is a rare, Müllerian-type tumor typically arising in the urethra of female patients with poorly understood pathogenesis. Here, we report the clinical, pathologic, and molecular characterization of a cohort of utCCA treated at a tertiary referral center. Cases were centrally reviewed and immunohistochemistry was performed as needed. Whole exome and targeted sequencing were performed. The landscape of somatic alterations was compared to ovarian CCA, uterine CCA, and urothelial carcinoma (UC). Among 35 utCCA, most patients were female (86%), and most common primary tumor site was the urethra (83%) in association with urethral diverticula (51%). Median disease-free and overall survivals were 42 and 65 months, respectively. The most common mutations were in ARID1A and TP53. Mutations in TERT promoter and other chromatin modifying genes were rare. Phylogenic analysis suggested that utCCA arises from a dysplastic clear cell precursor developing within diverticular lining. While the largest study of utCCA to date, the study is limited by small sample size, retrospective design, and clinical heterogeneity of the cohort. Molecular analysis of utCCA, including multi-region sequencing of tumor and adjacent urethral and diverticular lining, supports a potential mechanism of disease pathogenesis in which most utCCA arise from regions of clear cell dysplasia, possibly resulting from chronic inflammation in the setting of urinary stasis, and not through progression from intestinal metaplasia or divergent differentiation of a precursor UC.

PMID:40553939 | DOI:10.1016/j.modpat.2025.100821

Methods Mol Biol. 2025;2952:87-105. doi: 10.1007/978-1-0716-4690-8_6.

ABSTRACT

The field of computational biology and bioinformatics has seen remarkable progress in recent years, driven largely by advancements in artificial intelligence (AI) technologies. This review synthesizes the latest developments in AI methodologies and their applications in addressing key challenges within the field of computational biology and bioinformatics. This review begins by outlining fundamental concepts in AI relevant to computational biology, including machine learning algorithms such as neural networks, support vector machines, and decision trees. It then explores how these algorithms have been adapted and optimized for specific tasks in bioinformatics, such as sequence analysis, protein structure prediction, and drug discovery. AI techniques can be integrated with big data analytics, cloud computing, and high-performance computing to handle the vast amounts of biological data generated by modern experimental techniques. The chapter discusses the role of AI in processing and interpreting various types of biological data, including genomic sequences, protein-protein interactions, and gene expression profiles. This chapter highlights recent breakthroughs in AI-driven precision medicine, personalized genomics, and systems biology, showcasing how AI algorithms are revolutionizing our understanding of complex biological systems and driving innovations in healthcare and biotechnology. Additionally, it addresses emerging challenges and future directions in the field, such as the ethical implications of AI in healthcare, the need for robust validation and reproducibility of AI models, and the importance of interdisciplinary collaboration between computer scientists, biologists, and clinicians. In conclusion, this comprehensive review provides insights into the transformative potential of AI in computational biology and bioinformatics, offering a roadmap for future research and development in this rapidly evolving field.

PMID:40553329 | DOI:10.1007/978-1-0716-4690-8_6

FASEB J. 2025 Jun 30;39(12):e70754. doi: 10.1096/fj.202500600R.

ABSTRACT

Cellular retinaldehyde binding protein (CRALBP) plays a crucial role in the visual cycle by chaperoning 11-cis-retinoids. Mutations in its encoding gene RLBP1 lead to inherited retinal diseases with the common feature of poor night vision. Zebrafish possess two RLBP1 paralogs, rlbp1a and rlbp1b, with distinct retinal expression profiles, providing a bespoke opportunity to dissect cell-specific functions of CRALBP. Here, we first resolved conflicting reports on paralog expression by interrogating zebrafish single-cell RNA-sequencing datasets, which revealed predominant rlbp1a expression in Müller glia and rlbp1b expression in the RPE. Using CRISPR-generated zebrafish knockouts, we demonstrated that loss of RPE-expressed rlbp1b selectively impaired optokinetic responses (OKR) with a ~50% reduction in saccade frequency relative to wildtype. This impaired OKR response is only seen when under dim light conditions with no defect seen in standard or bright light rearing conditions. This recapitulates the night blindness presentation in patients with RLBP1 mutations. Retinoid profiling of rlbp1b knockout larvae showed significant decreases in 11-cis-retinal (62% reduced) and all-trans-retinal (69% reduced) levels. To explore mechanistic changes following rlbp1b loss, unbiased proteomic profiling was carried out on rlbp1b knockout adult zebrafish eyes. This confirmed the knockout of Cralbpb and revealed significant disruption of proteins involved in vitamin A metabolism, lipid metabolism/storage and ferroptosis. To explore the utility of zebrafish for in vivo pathogenicity assessment of RLBP1, we established a complementation assay using transgenic zebrafish. Although expression of wildtype EGFP-tagged human RLBP1 did not rescue the visual deficit, expression of zebrafish Cralbp to the RPE restored dim light vision, whereas zebrafish Cralbp harboring the human pathogenic p.R151Q mutation failed to do so. Together, these findings underscore the predominant role of RPE-expressed CRALBP in sustaining visual function under low-light conditions and establish a zebrafish platform for functional evaluation of RLBP1 variants.

PMID:40552921 | DOI:10.1096/fj.202500600R

Eur J Pain. 2025 Jul;29(6):e70069. doi: 10.1002/ejp.70069.

ABSTRACT

BACKGROUND: Glutamate, the primary neurotransmitter released by nociceptors, is predominantly synthesised by the enzyme Glutaminase 1 (GLS1). The involvement of GLS1 in pain pathways is well supported, as Gls1 heterozygous mice exhibit altered nociception and GLS1 levels increase in the dorsal root ganglia (DRG) under chronic peripheral inflammation. However, the specific contribution of GLS1 in sensory neurons to the development and maintenance of chronic neuropathic pain remains unclear. To explore this, we specifically targeted GLS1 expression in nociceptors.

METHODS: We used the Cre-LoxP system to generate a transgenic mouse with a specific deletion of Gls1 gene in neurons expressing the Nav1.8 sodium channel. Gene deletion was assessed by genomic PCR and immunofluorescence. GLS1 conditional knockout (cKO) mice and control littermates, under naïve conditions or following spared nerve injury (SNI), were analysed for mechanical allodynia and for expression of GLS1 and other components of the glutamatergic system using real-time PCR and Western blotting.

RESULTS: GLS1 cKO mice exhibited a significant reduction in GLS1 levels in the DRG, particularly in medium- to small-sized neurons. GLS1 deficiency prevented the development of mechanical allodynia following peripheral nerve injury. SNI induced GLS1 upregulation in the DRG of control mice, but not in cKO mice. In the spinal cord, NMDA receptor expression decreased after SNI only in naïve animals, while GLS1 and other glutamate receptors remained unchanged under all conditions.

CONCLUSIONS: Upregulation of GLS1 in sensory neurons after peripheral nerve injury contributes to mechanical allodynia. Targeting peripheral GLS1 could offer a potential analgesic strategy for neuropathic pain.

SIGNIFICANCE STATEMENT: We generated a transgenic mouse with a specific deletion of the Gls1 gene in Nav1.8-expressing neurons to assess the role of peripheral GLS1 in pain transmission. GLS1 is not required for physiological pain but is essential for the development of mechanical allodynia after nerve injury. GLS1 is upregulated in nociceptors following nerve injury, suggesting enhanced glutamate signalling. Taken together, results suggest that targeting GLS1 expression in neuropathic conditions could be a potential therapeutic strategy.

PMID:40552835 | DOI:10.1002/ejp.70069

Front Plant Sci. 2025 Jun 9;16:1601397. doi: 10.3389/fpls.2025.1601397. eCollection 2025.

ABSTRACT

Time lapse microscopy is a transformative technique for plant cell and developmental biology. Light sheet microscopy, which manipulates the amount of light a sample is exposed to in order to minimize phototoxicity and maximize signal intensity, is an increasingly popular tool for time lapse imaging. However, many light sheet imaging systems are not designed with the unique properties of plant samples in mind. Recent advances have decreased the cost and increased the technical accessibility of light sheet microscopy, but plant samples still require special preparation to be compatible with these new systems. Here, we apply a novel light sheet microscopy system to regenerating Arabidopsis roots damaged via laser ablation. To adapt this system for Arabidopsis roots we establish a new protocol for sample mounting, as well as an automated root tip tracking system that requires no additional proprietary software. The methods presented here can be used to increase researcher access to long-term time-lapse imaging in Arabidopsis biology.

PMID:40551774 | PMC:PMC12183276 | DOI:10.3389/fpls.2025.1601397

Front Plant Sci. 2025 Jun 9;16:1579376. doi: 10.3389/fpls.2025.1579376. eCollection 2025.

ABSTRACT

Genomic Prediction (GP) considering Genotype by Environment (G×E) interactions was, for the first time, used to assess the environment-specific seasonal performance and genetic potential of perennial ryegrass (Lolium perenne L.) in a regional evaluation system across southeastern Australia. The study analysed the Dry Matter Yield (DMY) of 72 base cultivars and endophyte symbiotic effects using multi-harvest, multi-site trial data, and genomic data in a best linear unbiased prediction framework. Spatial analysis corrected for field heterogeneities, while Leave-One-Out Cross Validation assessed predictive ability. Results identified two distinct mega-environments: mainland Australia (AUM) and Tasmania (TAS), with cultivars showing environment-specific adaptation (Base and Bealey in AUM; Platinum and Avalon in TAS) or broad adaptability (Shogun). The G×E-enhanced GP model demonstrated an overall 24.9% improved predictive accuracy (Lin's Concordance Correlation Coefficient, CCC: 0.542) over the Australian industry-standard best linear unbiased estimation model (CCC: 0.434), with genomic information contributing a 12.7% improvement (CCC: from 0.434 to 0.489) and G×E modelling providing an additional 10.8% increase (CCC: from 0.489 to 0.542). Narrow-sense heritability increased from 0.31 to 0.39 with G×E inclusion, while broad-sense heritability remained high in both mega-environments (AUM: 0.73, TAS: 0.74). These findings support informed cultivar selection for the Australian dairy industry and enable genomics-based parental selection in future breeding programs.

PMID:40551765 | PMC:PMC12183291 | DOI:10.3389/fpls.2025.1579376

Curr Stem Cell Res Ther. 2025 Jun 19. doi: 10.2174/011574888X365326250610113501. Online ahead of print.

ABSTRACT

BACKGROUND: Stem cells have recently gained prominence in regenerative medicine, particularly in the treatment of neurological disorders. As a result, Human-induced Pluripotent Stem Cells (hiPSCs) have become a significant focus.

OBJECTIVE: This study aimed to differentiate hiPSCs into neural lineages under in vitro conditions using forskolin and retinoic acid in an induction medium combined with chondroitin 4-sulfate and chondroitinase.

METHODS: Optimal component concentrations were determined using the MTT assay and acridine orange/ethidium bromide (AO/EB) staining. Subsequently, neural-specific genes (NSE, MAP-2, β-tubulin III, Oligo-2, and GFAP) and proteins (gamma enolase, MAP-2, and β-tubulin III) were assessed using Real-time PCR analysis and immunofluorescence staining to provide a comprehensive evaluation of differentiated cells.

RESULTS: Our study demonstrated a significant enhancement in neural-specific gene and protein markers during the 7th and 14th days of differentiation in the presence of combined chondroitin 4-sulfate and chondroitinase, demonstrating a higher efficacy compared with the application of isolated enzymes or substrates.

CONCLUSION: These findings emphasize the potential importance of chondroitin 4-sulfate and chondroitinase as important factors in promoting the neural differentiation of hiPSCs. It seems that chondroitin 4-sulfate may activate cellular signaling pathways that are effective in inducing neural differentiation. Our findings in this research provide new opportunities to advance regenerative therapies for neurological disorders.

PMID:40551682 | DOI:10.2174/011574888X365326250610113501

Genome Biol. 2025 Jun 23;26(1):178. doi: 10.1186/s13059-025-03638-y.

ABSTRACT

Machine learning methods, especially Transformer architectures, have been widely employed in single-cell omics studies. However, interpretability and accurate representation of out-of-distribution (OOD) cells remains challenging. Inspired by the global workspace theory in cognitive neuroscience, we introduce CellMemory, a bottlenecked Transformer with improved generalizability designed for the hierarchical interpretation of OOD cells. Without pre-training, CellMemory outperforms existing single-cell foundation models and accurately deciphers spatial transcriptomics at high resolution. Leveraging its robust representations, we further elucidate malignant cells and their founder cells across patients, providing reliable characterizations of the cellular changes caused by the disease.

PMID:40551223 | DOI:10.1186/s13059-025-03638-y

Anal Bioanal Chem. 2025 Jun 23. doi: 10.1007/s00216-025-05969-y. Online ahead of print.

ABSTRACT

Diabetic cardiomyopathy (DCM), a cardiac complication of diabetes, is characterized by diastolic dysfunction, myocardial fibrosis, and structural remodeling. Carbonyl-containing metabolites (CCMs) play a critical role in driving DCM pathogenesis through metabolic dysfunction, oxidative stress, and lipid peroxidation. In this study, we employed an on-tissue chemical derivatization (OTCD)-based air-flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) approach to investigate spatial metabolic alterations of CCMs in the diabetic rat heart. This method enabled the spatial profiling of 369 CCMs-including 137 fatty aldehydes (FALs), 214 oxo fatty acids (OFAs), and 18 sterol-type lipids (STs)-across cardiac tissue sections. This expanded metabolite coverage revealed marked spatial heterogeneity in cardiac metabolism. Comparative analysis between diabetic and control rats identified 162 significantly altered CCMs, highlighting localized metabolic dysregulation associated with DCM. To explore potential therapeutic interventions, we further evaluated the metabolic impact of ferulic acid, a candidate agent for myocardial protection. High-dose ferulic acid treatment significantly modulated 43 differential CCMs, attenuated pyruvate accumulation, restored fatty aldehyde levels, and improved the profile of oxidized fatty acids. These findings suggest that ferulic acid ameliorates metabolic dysfunction by exerting antioxidant and anti-inflammatory effects, while enhancing mitochondrial function and lipid metabolism. Overall, this study demonstrates the utility of OTCD-AFADESI-MSI for spatially resolved metabolomic analysis of carbonyl stress in DCM and supports the therapeutic potential of ferulic acid in managing diabetic heart injury.

PMID:40551014 | DOI:10.1007/s00216-025-05969-y

Nat Nanotechnol. 2025 Jun 23. doi: 10.1038/s41565-025-01958-5. Online ahead of print.

ABSTRACT

Lipid nanoparticles (LNPs) represent the leading delivery platform for mRNA vaccines with advantageous biocompatibility, scalability, adjuvant activity and often an acceptable safety profile. Here we investigate the physicochemical characteristics and adjuvanticity of four-component LNPs. Previous vaccine studies have demonstrated that altering the ionizable lipid influences the adjuvanticity of an LNP; however, the impact of the polyethylene glycol lipid and phospholipid has received less attention. Our mRNA-LNP vaccine formulations utilized different phospholipids and varying ratios of polyethylene glycol lipid, whereas the ionizable lipid and cholesterol remained approximately constant. We demonstrate that such modifications impact the magnitude and quality of the vaccine-elicited immune responses. We also dissect the underlying mechanisms and show that the biodistribution and cellular uptake of LNPs correlate with the magnitude and quality of the immune responses. These findings support the rational design of novel LNPs to tailor immune responses (cellular or humoral focused) based on the vaccine application.

PMID:40550975 | DOI:10.1038/s41565-025-01958-5

Nat Commun. 2025 Jun 23;16(1):5361. doi: 10.1038/s41467-025-61253-8.

NO ABSTRACT

PMID:40550789 | DOI:10.1038/s41467-025-61253-8

Comput Biol Med. 2025 Jun 22;195:110518. doi: 10.1016/j.compbiomed.2025.110518. Online ahead of print.

ABSTRACT

Epileptic seizures can occur unpredictably, making real-time monitoring and early warning systems critical, especially in neonatal patients, where timely intervention can significantly improve outcomes. Neonatal seizures are often subtle and difficult to detect, increasing the need for automated, early prediction methods to aid clinical decision-making. While machine learning models have been widely used for seizure detection, their application in preemptive seizure warning remains underexplored. In this study, we propose a self-attention-based neural network that processes raw EEG data to detect pre-ictal signals, enabling early seizure prediction. A key challenge in using attention mechanisms for EEG analysis is the computational burden of handling high-frequency, long-duration signals. To address this, we introduce a second-wise summary statistics-based embedding that significantly reduces the input sequence length while retaining essential features. We validate our model using a publicly available dataset of 79 neonatal patients with physician-annotated EEG recordings. Our classifier achieves a maximum accuracy of 91 percent in distinguishing pre-ictal and ictal events from non-ictal signals. Notably, we evaluate our model on completely unseen patients, demonstrating its potential for real-world applicability in neonatal seizure prediction. This study provides a proof-of-concept for a preemptive seizure warning system, paving the way for AI-driven neonatal epilepsy management and broader clinical applications in seizure detection.

PMID:40550202 | DOI:10.1016/j.compbiomed.2025.110518

J Vis Exp. 2025 Jun 6;(220). doi: 10.3791/67768.

ABSTRACT

For understanding drug efficacy, a critical need is to characterize the extent of drug-induced cell death. Efforts to quantify the level of drug-induced cell death are challenged by the existence of more than a dozen molecularly distinct forms of regulated death, each with its own activation timing and biochemical hallmark features. Furthermore, for some necrotic death subtypes, hallmark features are only observed transiently and are rapidly lost due to cell rupture. Thus, even when using a combination of death pathway-specific assays, it is challenging to accurately quantify the total amount of cell death or the relative contributions of each death subtype. Another issue is that many death-specific assays ignore how drugs affect cell proliferation, making it challenging to interpret if a drug-treated population is expanding or shrinking. The FLICK assay allows for quantification of the total level of cell death following stimulation in a manner that is specific to death but also largely agnostic to the type(s) of death activated. Additionally, the FLICK assay retains information about the total population size and cell proliferation rate. In this manuscript, we describe the basic use of the FLICK assay, how to troubleshoot this assay when using different types of biological material, and how to use the FLICK assay to quantify the contributions of each type of cell death to an observed drug response.

PMID:40549770 | DOI:10.3791/67768

Inflammopharmacology. 2025 Jun 23. doi: 10.1007/s10787-025-01783-1. Online ahead of print.

ABSTRACT

The phytochemical constituents of various plant extracts have shown potential in mitigating oxidative stress and inflammation. Hibiscus sabdariffa L. (HS), known as the roselle flower, has demonstrated antioxidant and anti-inflammatory effects in several models. Our study aimed to evaluate the effects of aqueous HS extract on modulating the TLR4/MyD88/NF-κB and Caspase-11/NLRP3 inflammasome signaling pathways during lipopolysaccharide (LPS)-induced mitochondrial stress and inflammation in macrophages by several in vitro assays. The results demonstrated that compared with LPS alone, HS extract significantly reduced the production of ROS, nitric oxide and proinflammatory cytokines (IL-6, TNF-α, PG-E2, IL-1β, and IFN-γ) by lowering the gene expression of p22phox and iNOS and suppressed IκBα-dependent NF-κB signaling. Furthermore, HS treatment induced the polarization of macrophages from the M1 phenotype to the M2 phenotype, as demonstrated by qPCR and flow cytometry. The proteomic data revealed that HS extract can downregulate the expression of several mitochondria-associated proteins and reduce mitochondrial DNA leakage during LPS activation, suppressing Caspase-11/Gasdermin-D /NLRP3 inflammasome activation. In summary, the protective effects of HS extract against LPS-induced inflammation, which is mediated through the TLR4/NF-κB/NLRP3 signaling pathway, may significantly promote anti-inflammatory responses and alleviate related diseases.

PMID:40549318 | DOI:10.1007/s10787-025-01783-1

Discov Oncol. 2025 Jun 23;16(1):1179. doi: 10.1007/s12672-025-02939-9.

ABSTRACT

BACKGROUND: Cervical cancer (CC), the most prevalent malignant tumor in the female reproductive system, is characterized by its high incidence and mortality rates. Chloroquine(CQ), a 4-aminoquinoline drug originally used to prevent and treat malaria, has gained recognition as an adjuvant anti-cancer agent. However, whether chloroquine exhibits anti-CC activity and its underlying mechanisms remain unclear. This study aimed to elucidate chloroquine's anti-cancer effects, focusing on the PI3K/AKT/MDM2 pathway, by integrating network pharmacology and in vitro experiments.

METHODS: Firstly, CQ and its associated targets related to cervical cancer were identified using the PharmMapper, SwissTargetPrediction, DrugBank, GeneCards, and DisGeNET databases. A protein-protein interaction (PPI) network was constructed to screen for key targets. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to predict the underlying biological functions and mechanisms involved in the anti-colorectal cancer effects of CQ. Subsequently, CCK-8, colony formation, and wound-healing assays were performed to assess the impact of CQ treatment on the proliferation ability of HeLa cells. Cell apoptosis was analyzed using flow cytometry, and the expression of apoptosis-related proteins was determined through Western blotting. Finally, the effects of CQ on the PI3K/AKT/MDM2 signaling pathway were evaluated via Western blotting.

RESULTS: A total of 7,846 potential treatment targets for CQ were identified, among which 126 were related to CC. Through PPI network analysis of 100 common targets, eight core targets were identified: TNF, MDM2, SLC6A4, GRIN1, CHRM1, EZH2, ACHE, and ADRB2. KEGG enrichment analysis revealed the PI3K/AKT signaling pathway as a central pathway of interest. Various concentrations of CQ (25, 50, and 75 µM) inhibited the viability of HeLa cells while showing no significant effect on H8 cells. Following treatment with CQ (25 µM and 75 µM), a reduction in proliferation and colony formation was observed in HeLa cells. Furthermore, CQ treatment led to an increase in the expression of apoptosis-related proteins (Bax, Bcl-2, cleaved-PARP) and a decrease in the expression of proteins associated with the PI3K/AKT/MDM2 signaling pathway (p-PI3K, p-AKT, and p-MDM2).

CONCLUSIONS: This study suggests that chloroquine may induce apoptosis by inhibiting the PI3K/AKT/MDM2 signalling pathway.

PMID:40549065 | DOI:10.1007/s12672-025-02939-9

Nucleic Acids Res. 2025 Jun 20;53(12):gkaf479. doi: 10.1093/nar/gkaf479.

ABSTRACT

Chemically modified small interfering RNAs (siRNAs) are a promising drug class that silences disease-causing genes via mRNA degradation. Both siRNA-specific features (e.g. sequence, modification pattern, and structure) and target mRNA-specific factors contribute to observed efficacy. Systematically defining the relative contributions of siRNA sequence, structure, and modification pattern versus the native context of the target mRNA is necessary to inform design considerations and facilitate the widespread application of this therapeutic platform. To address this, we synthesized a panel of ∼1260 differentially modified siRNAs and evaluated their silencing efficiency against therapeutically relevant mRNAs (APP, BACE1, MAPT, and SNCA) using both reporter-based and native expression assays. Our results demonstrate that the siRNA modification pattern (e.g. level of 2'-O-methyl content) significantly impacts efficacy, while structural features (e.g. symmetric versus asymmetric configurations) do not. Furthermore, we observed substantial differences in the number of effective siRNAs identified per target. These target-specific differences in hit rates are largely mitigated when efficacy is tested in the context of a reporter assay, confirming that native mRNA-specific features influence siRNA performance. Key target-specific factors, including exon usage, polyadenylation site selection, and ribosomal occupancy, partially explained efficacy variability. These insights led to a proposed framework of parameters for optimizing therapeutic siRNA design.

PMID:40548938 | DOI:10.1093/nar/gkaf479

Phys Rev Lett. 2025 Jun 6;134(22):228402. doi: 10.1103/PhysRevLett.134.228402.

ABSTRACT

Indentation tests are classical tools to determine material properties. For biological samples such as cysts of cells, however, the observed force-displacement relation cannot be expected to follow predictions for simple materials. Here, by solving the Pogorelov problem of a point force indenting an elastic shell for a purely nonlinear material, we discover that complex material behavior can even give rise to new scaling exponents in this force-displacement relation. In finite-element simulations, we show that these exponents are surprisingly robust, persisting even for thick shells indented with a sphere. By scaling arguments, we generalize our results to pressurized and prestressed shells, uncovering additional new scaling exponents. We find these predicted scaling exponents in the force-displacement relation observed in cyst indentation experiments. Our results thus form the basis for inferring the mechanisms that set the mechanical properties of these biological materials.

PMID:40548804 | DOI:10.1103/PhysRevLett.134.228402