Medicine (Baltimore). 2025 Aug 8;104(32):e43555. doi: 10.1097/MD.0000000000043555.

ABSTRACT

There have been multiple observational studies that have established a link between metabolite levels in the body and idiopathic pulmonary fibrosis (IPF), specifically focusing on metabolites derived from fatty acids. However, a complete understanding of the precise molecular and biological factors, as well as the causality between them, remains elusive. The main objective of our study was to evaluate the potential causal relationship between blood metabolites and IPF by using Mendelian randomization (MR). To achieve this goal, we utilized the most comprehensive genome-wide association study to date, which identified genetic variants associated with blood metabolites (1091 blood metabolites and 309 metabolite ratios). Summary statistics of IPF were collected from Finngen R8 (1812 IPF patients and 338,784 controls), inverse variance weighted method (IVW) is used as the main method in determining causality. Isovalerylcarnitine (C5) levels (OR = 1.2435, 95% CI: 1.0494-1.4736, Pval = .0119) was found significantly related to higher risk of IPF. There was no significant heterogeneity in our study (IVW method: Pval = .132; MR-Egger method: Pval = .105) and horizontal pleiotropy (β = -0.027; SE = 0.0337; Pval = .4310). The sensitivity analysis did not reveal any potential abnormal drivers (0.1 < All < 0.3). Two-sample MR method demonstrated the causal relationship between blood metabolites and IPF, and further studies found that Isovalerylcarnitine (C5) levels, as a potential biological risk factor for IPF, may provide a new target for the treatment of IPF.

PMID:40797413 | DOI:10.1097/MD.0000000000043555

IEEE Control Syst Lett. 2024;8:2205-2210. doi: 10.1109/lcsys.2024.3411041. Epub 2024 Jun 7.

ABSTRACT

This contribution explores mechanisms that regulate the dynamics of single-cell size, maintaining equilibrium around a target set point. Using the formalism of Stochastic Hybrid Systems (SHS), we consider continuous exponential growth in cell size (as determined by volume/mass/surface area). This continuous-time evolution is interspersed by cell division events that occur randomly as per a given size-dependent rate, and upon division, only one of the two daughter cells is tracked. We show that a size-independent division rate does not provide cell size homeostasis, in the sense that the variance in cell size increases unboundedly over time. Next, we consider a division rate proportional to cell size that yields the adder size control observed in several bacteria - a constant size is added on average between birth and division regardless of the newborn size. For this scenario, we obtain exact formulas for the steady-state moments (mean, variance, and skewness) of cell size. Expanding the SHS model, we explore a biologically relevant scenario where the time between successive division events is further divided into multiple discrete stages with size-dependent stage transitions. Exact moment computations demonstrate that increasing the number of stages reduces cell size variability (noise). We also find formulas considering uneven size partitioning between daughters during division, and where the division rate follows a power law of the cell size leading to deviations from adder size control. This letter provides a method for estimating model parameters from observed cell size distributions and enhances our understanding of mechanisms underlying cell size regulation.

PMID:40799839 | PMC:PMC12342050 | DOI:10.1109/lcsys.2024.3411041

Synth Biol (Oxf). 2025 Jun 24;10(1):ysaf012. doi: 10.1093/synbio/ysaf012. eCollection 2025.

ABSTRACT

Synthetic microbial consortia can leverage their expanded enzymatic reach to tackle biotechnological challenges too complex for single strains, such as biosynthesis of complex secondary metabolites or waste plant biomass degradation and valorisation. The benefit of metabolic cooperation comes with a catch-installing stable interactions between consortium members. Here, we established a mutualistic relationship in the synthetic consortium of Pseudomonas putida strains through reciprocal processing of two disaccharides-cellobiose and xylobiose-obtainable from lignocellulosic residues. Two strains were engineered to hydrolyse and metabolize these sugars: one grows on xylose and hydrolyses cellobiose to produce glucose, while the other grows on glucose and cleaves xylobiose to produce xylose. This specialization allows each strain to provide essential growth substrate to its partner, establishing a mutualistic interaction, which can be termed reciprocal substrate processing. Key enzymes from Escherichia coli (xylose isomerase pathway) and Thermobifida fusca (glycoside hydrolases) were introduced into P. putida to broaden its carbohydrate utilization capabilities and arranged in a way to instal the strain cross-dependency. A mathematical model of the consortium assisted in predicting the effects of substrate composition, strain ratios, and protein expression levels on population dynamics. Our results demonstrate that modulating extrinsic factors such as substrate concentration can help in balancing fitness disparities between the strains, but achieving this by altering intrinsic factors such as glycoside hydrolase expression levels is much more challenging. This study presents reciprocal substrate processing as a strategy for establishing an obligate dependency between strains in the engineered consortium and offers valuable insights into overcoming the challenges of fostering synthetic microbial cooperation.

PMID:40799623 | PMC:PMC12341930 | DOI:10.1093/synbio/ysaf012

Bioinform Adv. 2025 Jul 21;5(1):vbaf174. doi: 10.1093/bioadv/vbaf174. eCollection 2025.

ABSTRACT

MOTIVATION: Being able to broadly predict the function of novel metabolites based on their structures has applications in systems biology, environmental monitoring, and drug discovery. To date, machine learning models aiming to predict functional characteristics of metabolites have largely been limited in scope to predicting single functions, or only a small number of functions simultaneously.

RESULTS: Using the Human Metabolome Database as a source for a wider range of functional annotations, we assess the feasibility of predicting metabolite functions more broadly, as defined by four elements, namely location, role, the process it is involved in, and its physiological effect. We evaluated three graph neural network architectures to predict available functional ontology terms. We compared the graph models with two multilayer perceptron architectures using circular fingerprints and Chemical BiDirectional Encoder Representations from Transformers (ChemBERTa) embeddings. Among the models tested, the graph attention network, incorporating embeddings from the pretrained ChemBERTa model to predict the process metabolites are involved in, achieved the highest performance with a macro F1-score of 0.903 and an area under the precision-recall curve of 0.926.

AVAILABILITY AND IMPLEMENTATION: The model identified function-associated structural patterns within metabolite families, demonstrating the potential for interpretable prediction of metabolite functions from structural information.

PMID:40799496 | PMC:PMC12343106 | DOI:10.1093/bioadv/vbaf174

Transl Lung Cancer Res. 2025 Jul 31;14(7):2688-2699. doi: 10.21037/tlcr-2025-299. Epub 2025 Jul 28.

ABSTRACT

BACKGROUND: Prognostic factor assessment in metastatic or recurrent non-small cell lung cancer (NSCLC) is based primarily on older studies from the chemotherapy era or modern trials evaluating the safety and efficacy of specific treatment regimens. However, studies that compare prognostic factors across immunotherapy, molecularly guided therapy, and chemotherapy within the same real-world context remain scarce. This gap is addressed by the present study which aims to retrospectively evaluate prognostic factors for overall survival in patients treated with diverse systemic therapies.

METHODS: The analysis included 343 patients with metastatic or recurrent NSCLC treated between 2006 and 2022. Treatment consisted of immune checkpoint inhibitors (ICI) in 176 patients, epidermal growth factor receptor (EGFR) inhibitors in 72, ALK/ROS inhibitors in 25 and chemotherapy in 70. Adenocarcinoma was diagnosed in 210 patients, squamous-cell cancer in 110 and other types of NSCLC in 23. Several host and tumor-related variables evaluated before therapy were categorized (mainly according to their median values) and used to construct a multivariate Cox survival model. Therapies were classified (ranked) according to the effectiveness as assessed in a univariate analysis.

RESULTS: Hemoglobin concentration [hazard ratio (HR) 0.50, P<0.001], sex (HR 0.63, P=0.0009), T stage (HR 1.38, P=0.001), pathology (HR 1.43, P=0.15), performance status (HR 1.60, P=0.002), platelet count (HR 1.46, P=0.005), lymphocyte/neutrophil ratio (HR 0.69, P=0.008), and tumor volume (HR 1.45, P=0.008) significantly influenced OS in a univariable analysis. Treatment also influenced overall survival, with a median survival times of 1.57, 1.90, 0.60 and 0.80 years for ICI, anti EGFR, ALK/ROS and chemotherapy, respectively. Multivariable analysis revealed a significant and independent influence of T stage, hemoglobin concentration, performance status, lymphocyte/neutrophil ratio, and treatment on survival. Treatment, while significant, appeared as a relatively weak independent prognosticator of survival, compared to the other variables. For sensitivity assessment, several options of the basic analysis were performed without altering, however, the qualitative outcomes of the basic analysis.

CONCLUSIONS: The outcome of this study strongly encourages the routine use of readily available independent prognostic factors for survival such as T stage, hemoglobin concentration, patient performance status, and lymphocyte/neutrophil ratio, regardless of systemic treatment selected for therapy of patients with metastatic or recurrent NSCLC.

PMID:40799425 | PMC:PMC12337069 | DOI:10.21037/tlcr-2025-299

iScience. 2025 Jul 17;28(8):113131. doi: 10.1016/j.isci.2025.113131. eCollection 2025 Aug 15.

ABSTRACT

The cell surface abundance of many proteins is controlled by clathrin-mediated endocytosis (CME). CME is driven by the assembly of clathrin and other proteins on the inner leaflet of the plasma membrane into clathrin-coated pits (CCPs). Regulation of CCP dynamics allows for control of the function of specific cell surface proteins, impacting a range of cellular outcomes. AMP-activated protein kinase (AMPK) becomes activated upon metabolic insufficiency and facilitates cellular adaptation to nutrient stress. Here, we examined how AMPK regulates CME and the cell surface membrane traffic of β1-integrin. We find that AMPK controls CCP dynamics and regulates the abundance of the endocytic adaptor protein Dab2 within CCPs in a manner that requires the GTPase Arf6, thus selectively promoting the CCP recruitment and internalization of β1-integrin. This study reveals a signaling pathway for cargo-selective metabolic regulation of CME by AMPK that impacts the function of cell surface proteins such as integrins.

PMID:40799387 | PMC:PMC12341638 | DOI:10.1016/j.isci.2025.113131

Cureus. 2025 Jul 13;17(7):e87841. doi: 10.7759/cureus.87841. eCollection 2025 Jul.

ABSTRACT

This report describes the case of a 46-year-old woman with breast cancer and a history of multiple drug hypersensitivity reactions, who developed atypical allergic-like symptoms with three different adjuvant endocrine therapy regimens. As the use of an aromatase inhibitor was considered essential for her treatment, a three-day desensitization protocol to letrozole was implemented. During the protocol, she developed pruritic erythema following placebo administration. Despite this, the protocol was completed, with only mild side effects (oropharyngeal pruritus and headache), which were managed symptomatically. A diagnosis of nocebo reaction was disclosed to the patient after the desensitization process. She continued daily treatment with 2.5 mg of letrozole without further hypersensitivity complications.

PMID:40799898 | PMC:PMC12342109 | DOI:10.7759/cureus.87841

Curr Drug Saf. 2025 Aug 8. doi: 10.2174/0115748863312388240829190436. Online ahead of print.

ABSTRACT

INTRODUCTION: Cisplatin, a platinum-based antineoplastic agent belonging to the alkylating class, is one of the most widely used chemotherapeutic agents in the treatment of solid tumors and hematologic malignancies. Cisplatin works by forming covalent bonds in DNA, resulting in cell cycle arrest, inadequate repair, and ultimately, apoptotic or non-apoptotic cell death. Despite its efficacy, cisplatin is known to be highly toxic, showing nephrological, Gastrointestinal (GI), and hepatotoxicity, but there is limited data on its association with adverse vascular events. Hence, we aimed to investigate the potential risk of drug-related adverse vascular events associated with four cisplatin-based combination therapies using the FDA Adverse Events Reporting System (FAERS).

METHODS: We used the FDA Adverse Events Reporting System (FAERS) database to look for reported Adverse Events (AEs) for cisplatin-based combinations. In the current study, a case/non-case disproportionality analysis has been performed using the Reporting Odds Ratio (ROR) to investigate whether there is a signal for a potentially increased risk of drug-related vascular AE using the 2016-2020 FAERS datasets. To look for all vascular AEs, we included peripheral vascular events, cerebrovascular events, coronary artery-related events, venothromboembolic events, and other arterial events. "Cases" were defined as patients treated with cisplatin and any one of etoposide, gemcitabine, paclitaxel or docetaxel, and 5-fluorouracil or capecitabine, and have reported a composite event. Hence, cases were divided into 4 groups. Reporting Odds Ratio (ROR) and Information Component (IC) were derived to look for signals for these AEs being significant when compared to non-cases. All data processing and statistical analyses were performed using R 4.2.1.

RESULTS: Between 2016 and 2020, 23,513 AEs were reported for patients who used cisplatinbased combinations, and 6,952,691 AEs in patients who did not. Baseline characteristics, including age, sex, and geographic distribution, were also reported. Looking at ROR and IC, all 4 groups showed statistically significant vasculopathies reported for cisplatin-based combinations, except for cisplatin and paclitaxel/docetaxel where there was a trend in ROR, but it did not reach statistical significance. It also gave the least signal for associated vasculopathy, while cisplatin and gemcitabine gave the highest signal with both ROR and IC for associated vasculopathy.

CONCLUSION: Overall, these increased vasculopathies related to the use of cisplatin-based combinations can be related to the increased pro-thrombotic state in these patients. The results of this study highlight the need for caution when using cisplatin-based chemotherapy and the importance of monitoring patients for thrombotic events and other vasculopathies. Patient-specific factors, such as the type and stage of cancer, should be considered when determining the best treatment option and managing the risk of vascular complications.

PMID:40798964 | DOI:10.2174/0115748863312388240829190436

Medicine (Baltimore). 2025 Aug 8;104(32):e43778. doi: 10.1097/MD.0000000000043778.

ABSTRACT

The purpose of this study was to evaluate the adverse events (AEs) associated with tucatinib by mining data from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) and explore potential drug-related AEs, thereby guiding safe clinical use. We extracted AE reports involving tucatinib from the FAERS database spanning from the 1st quarter of 2020 to the 4th quarter of 2024. The reports were categorized based on preferred terms and system organ classes, and risk signals were subsequently grouped for further analysis. Among 12,225 AEs listing tucatinib as the primary suspected drug, a total of 103 preferred terms for AEs were identified across 22 different system organ classes. In these reports, the proportion of females was higher than males (97.02% vs 1.26%), and the highest number of AEs was reported in the 45 to 59 years (15.29%). The median (interquartile range) time to AE onset was 30.00 days (8.00-104.00). And the most of AEs occurred mainly within the 1st month (n = 152, 26.16%) or >60 days after drug administration (n = 106, 18.24%). Among the numerous positive risk signals, such as diarrhea, nausea, vomiting, stomatitis, rash, palmar-plantar erythrodysesthesia syndrome, hepatotoxicity, anemia, and peripheral neuropathy exhibited high signal strength, which largely aligned with the current prescribing information. In addition, some AEs not explicitly mentioned in the package insert were also observed. These included platelet count abnormal, ejection fraction decreased, aortic valve incompetence, dehydration, hypokalemia, and various nail or skin-related problems (e.g., fingerprint loss, ingrowing nail, onychalgia, onychomadesis, skin discoloration/hypertrophy/hyperpigmentation/exfoliation, pigmentation disorder, blister, etc), as well as nervous system disorders (e.g., memory impairment, brain edema, central nervous system lesion, hyperesthesia, taste disorder, emotional disorder) and paronychia. There is also a risk of various AEs in the treatment of tucatinib. In clinical application, it is so essential to monitor closely the dermatologic/nail conditions, gastrointestinal issues, cardiovascular abnormalities, and neuropsychiatric manifestations. Should any adverse events occur or disease progression be observed, timely intervention is necessary to prevent severe organ damage and further disease deterioration.

PMID:40797386 | DOI:10.1097/MD.0000000000043778

BMC Gastroenterol. 2025 Aug 12;25(1):579. doi: 10.1186/s12876-025-04174-6.

ABSTRACT

Disulfidptosis—a newly characterised mode of regulated cell death implicated in tumorigenesis—exhibits undefined prognostic utility in hepatocellular carcinoma (HCC), particularly concerning disulfidptosis-related long non-coding RNAs (DRLs). Integrating transcriptomic and clinical data from The Cancer Genome Atlas, we identified 807 DRLs and constructed a prognostic signature via univariate Cox regression, LASSO-Cox penalisation, and multivariate Cox analysis. The resulting four-DRL signature (AL031985.3, TMCC1-AS1, AL590705.3, AC026412.3) stratified patients into distinct risk cohorts, with high-risk groups demonstrating significantly reduced overall survival (OS; log-rank P < 0.001) and hazard ratios independent of conventional clinicopathological variables. Model discrimination was robust across multiple metrics: time-dependent receiver operating characteristic curves yielded AUCs of 0.750 (95% CI: 0.676–0.817) at 1 year, 0.709 (0.637–0.781) at 3 years, and 0.720 (0.641–0.799) at 5 years, outperforming established staging systems. Concordance indices (C-index: 0.681) and principal component analysis further validated stratification efficacy. Functional annotation linked the signature to extracellular matrix dysregulation, epithelial-mesenchymal transition, and immunosuppressive microenvironments. High-risk patients exhibited elevated tumour mutational burden (P = 0.04), increased M0 macrophage infiltration, and heightened tumour immune dysfunction and exclusion (TIDE) scores (P < 0.001)—indicating impaired immunotherapy response. Pharmacogenomic profiling revealed enhanced sensitivity to five agents in high-risk subgroups (BDP-00009066, GDC0810, Osimertinib, Paclitaxel, YK-4-279; all P < 0.01). Critical experimental validation confirmed AC026412.3 as an oncogenic driver: significantly overexpressed in HCC tissues (P < 0.001) and cell lines, its knockdown suppressed proliferation, invasion, and migration in vitro. In vivo models demonstrated its necessity for angiogenesis (chorioallantoic membrane assay), primary tumour growth (orthotopic implantation), pulmonary metastasis, and epithelial-mesenchymal transition activation. This molecularly annotated signature enables precise prognostic stratification and guides personalised therapeutic strategies in HCC.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12876-025-04174-6.

PMID:40790550 | PMC:PMC12341353 | DOI:10.1186/s12876-025-04174-6

Sci Rep. 2025 Aug 12;15(1):29565. doi: 10.1038/s41598-025-15286-0.

ABSTRACT

AI-based methods have been widely adopted in tourism demand forecasting. However, current AI-based methods are weak in capturing long-term dependency, and most of them lack interpretability. This study proposes a time series Transformer (Tsformer) with Encoder-Decoder architecture for tourism demand forecasting. The Tsformer encodes long-term dependencies with the encoder, merges the calendar of data points in the forecast horizon, and captures short-term dependencies with the decoder. Experiments on two datasets demonstrate that Tsformer outperforms nine baseline methods in short-term and long-term forecasting before and after the COVID-19 outbreak. Further ablation studies confirm that the adoption of the calendar of data points in the forecast horizon benefits the forecasting performance. Our study provides an alternative method for more accurate and interpretable tourism demand forecasting.

PMID:40796630 | DOI:10.1038/s41598-025-15286-0

Sci Rep. 2025 Aug 12;15(1):29528. doi: 10.1038/s41598-025-14047-3.

ABSTRACT

Biometric identification systems offer strong security by relying on unique personal traits. At the same time, they raise significant privacy concerns because compromised biometric data cannot be revoked. This paper explores the use of homomorphic encryption (HE) as a means to protect biometric data during classification and reduce the risk of exposing sensitive information. Our system comprises a feature extractor which operates locally and a classifier which processes encrypted data. We demonstrate the feasibility of our approach on a gait recognition task, employing a vision transformer as a feature extractor and training several HE-compatible classifiers. Through a comprehensive statistical analysis, we evaluate the impact of HE on accuracy and computational complexity, especially with different activation functions and their polynomial approximations. Our results demonstrate the feasibility of secure and accurate gait recognition using HE, while highlighting the trade-off between security and performance.

PMID:40796590 | DOI:10.1038/s41598-025-14047-3

Can J Ophthalmol. 2025 Aug 9:S0008-4182(25)00357-6. doi: 10.1016/j.jcjo.2025.07.009. Online ahead of print.

ABSTRACT

OBJECTIVE: To compare the efficacy of aflibercept, bevacizumab, and ranibizumab for resolution of diabetic macular edema-associated hard exudates (HEs).

DESIGN: Post hoc analysis of the Diabetic Retinopathy Clinical Research Network Protocol T trial.

PARTICIPANTS: Two hundred and forty-eight subjects with 84 eyes were treated with aflibercept, 71 eyes were treated with bevacizumab, and 93 eyes were treated with ranibizumab.

METHODS: The volume of HEs was measured by automatically quantifying hyperreflective foci on optical coherence tomography volume scans using a deep-learning algorithm. HEs were quantified within the total macula (6 × 6 mm scan area), as well as separately within the central subfield (CSF), inner ring (IR), and outer ring (OR) of the Early Treatment Diabetic Retinopathy Study grid at baseline, 4, 12, 24, and 52 weeks (w) after treatment. The extent of HEs at baseline and the change over time were compared among the groups.

RESULTS: Baseline HEs in the total macula were 0.0211 ± 0.0275, 0.0215 ± 0.0266, and 0.0223 ± 0.0272 mm3 for the aflibercept, bevacizumab, and ranibizumab groups, respectively. At 1 year, HEs significantly decreased with aflibercept and ranibizumab in the total macula and in all subregions (CSF, OR, and IR), but they only decreased with evacizumab in the CSF. Over 1 year, the reduction in HEs was greater for aflibercept and ranibizumab than for bevacizumab within the total macular region, and anibizumab was superior to bevacizumab for the IR as well.

CONCLUSIONS: HEs decreased significantly with aflibercept and ranibizumab treatment over 1 year in all regions, but not with bevacizumab, highlighting the differential efficacy among agents in resolving HEs.

PMID:40796006 | DOI:10.1016/j.jcjo.2025.07.009

Bioinformatics. 2025 Aug 9:btaf446. doi: 10.1093/bioinformatics/btaf446. Online ahead of print.

ABSTRACT

MOTIVATION: A pivotal area of research in antibody engineering is to find effective modifications that enhance antibody-antigen binding affinity. Traditional wet-lab experiments assess mutants in a costly and time-consuming manner. Emerging deep learning solutions offer an alternative by modeling antibody structures to predict binding affinity changes. However, they heavily depend on high-quality complex structures, which are frequently unavailable in practice. Therefore, we propose ProtAttBA, a deep learning model that predicts binding affinity changes based solely on the sequence information of antibody-antigen complexes.

RESULTS: ProtAttBA employs a pre-training phase to learn protein sequence patterns, following a supervised training phase using labeled antibody-antigen complex data to train a cross-attention-based regressor for predicting binding affinity changes. We evaluated ProtAttBA on three open benchmarks under different conditions. Compared to both sequence- and structure-based prediction methods, our approach achieves competitive performance, demonstrating notable robustness, especially with uncertain complex structures. Notably, our method possesses interpretability from the attention mechanism. We show that the learned attention scores can identify critical residues with impacts on binding affinity. This work introduces a rapid and cost-effective computational tool for antibody engineering, with the potential to accelerate the development of novel therapeutic antibodies.

AVAILABILITY AND IMPLEMENTATION: Source codes and data are available at https://github.com/code4luck/ProtAttBA.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40795828 | DOI:10.1093/bioinformatics/btaf446

J Clin Endocrinol Metab. 2025 Aug 12:dgaf462. doi: 10.1210/clinem/dgaf462. Online ahead of print.

ABSTRACT

The pathophysiological mechanisms underlying type 2 diabetes mellitus (T2DM) remain incompletely understood, and the disease continues to impose a substantial burden on global health. In this study, we integrated the data from the largest genome-wide association study (GWAS; N = 898,130) of T2DM with human plasma protein quantitative trait locus (pQTL; N = 53,022) data to conduct the first proteome-wide association study (PWAS) of T2DM. Following Mendelian randomization and colocalization analyses, we identified nine independent putatively causal proteins. Among these, three were successfully replicated in other independent pQTL datasets, including two (HYOU1 and FLT3) that were novel and not identified in the original GWAS. Further integration with expression quantitative trait locus (eQTL) data from three diabetes- related tissues (blood, adipose tissue, and pancreas) revealed that five of the causal proteins also showed significant associations with T2DM at their cis-regulatory mRNA levels. Subsequent functional annotation supported potential pathogenic roles of the causal proteins. Notably, drug repurposing analysis identified 29 candidate drugs for T2DM treatment by targeting four causal proteins. In conclusion, our findings provide new insights into the pathogenesis of T2DM and highlight promising targets for future mechanistic and therapeutic investigations.

PMID:40796333 | DOI:10.1210/clinem/dgaf462

Biochem Pharmacol. 2025 Aug 10:117219. doi: 10.1016/j.bcp.2025.117219. Online ahead of print.

ABSTRACT

Several neurodegenerative diseases including amyotrophic lateral sclerosis (ALS) are characterized by toxic aggregates accumulation due to autophagy blockade, prompting researchers to identify new autophagy-activating drugs. Here we tested, in an in vitro ALS/PDC model, the neuroprotective effects of the antipsychotic Chlorpromazine (CPZ) and the antidepressant Clomipramine (CMI), chosen by drug repurposing approach for their ability to stimulate TPC2 lysosomal channel. Patch-clamp electrophysiology on enlarged lysosomes in NSC-34 motor neurons showed that CPZ and CMI induced large inwardly-rectifying currents, that were inhibited by TPC2 synthetic blocker trans-Ned-19. The same currents were evoked by TPC2 endogenous agonist NAADP and its mimetic agent TPC2-A1-N, and inhibited by trans-Ned-19 and siRNAs against TPC2 (siTPC2). CPZ and CMI elicited a significant [Ca2+]i increase that rapidly induced nuclear translocation of TFEB (transcription factor EB), the master regulator of autophagy. Accordingly, TPC2 stimulation by both the drugs boosted autophagy, as revealed by the activation of autophagy initiators ULK and AMPK α and modification of LC3-II/p62(SQSTM1) ratio. Furthermore, by normalizing autophagy markers, CPZ and CMI counteracted the detrimental effects induced by L-BMAA, a neurotoxin mimicking ALS/PDC. Notably, siTPC2 partially reverted CMI- and CPZ-induced neuroprotection as well as that produced by NAADP. At mitochondrial level, these drugs prevented ATP reduction and ROS overproduction in motor neurons exposed to L-BMAA for 24 h. For a longer L-BMAA exposure, CPZ and CMI counteracted LDH, cytochrome C and SMAC/DIABLO release, thus preventing cell demise. These findings suggest that TPC2 activation by drug repurposing could provide novel therapeutic options for ALS via autophagy regulation.

PMID:40796055 | DOI:10.1016/j.bcp.2025.117219

Nat Struct Mol Biol. 2025 Aug 12. doi: 10.1038/s41594-025-01614-5. Online ahead of print.

ABSTRACT

Pregnane X receptor (PXR) is a nuclear receptor considered to be a master transcription factor of xenobiotic metabolism. Here, using enhanced ultraviolet crosslinking and immunoprecipitation, we show that PXR can bind mRNAs in different cancer cell lines and normal liver tissues. PXR-bound mRNAs include genes related to metabolic reprogramming and lipid metabolism. Separately from its known nuclear transcriptional function, cytoplasmic PXR binds and stabilizes mature mRNA containing C+G-enriched sequences through its zinc-finger domain. Mechanistically, cytoplasmic PXR interacts with RNH1, an RNase inhibitor, to regulate RNA stability. In colorectal cancer cells, cytoplasmic PXR facilitates glucose uptake by stabilizing SLC2A1 mRNA. This process further promotes cell proliferation and cancer development. Our study unveils a previously unknown dimension of PXR-mediated gene regulation by characterizing PXR as an RNA-binding protein important for mRNA stability in the cytoplasm.

PMID:40797049 | DOI:10.1038/s41594-025-01614-5