BMC Plant Biol. 2025 Feb 25;25(1):254. doi: 10.1186/s12870-025-06242-1.

ABSTRACT

BACKGROUND: Papaya exhibits three sex types: female (XX), male (XY), and hermaphrodite (XYh), making it an unusual trioecious model for studying sex determination. A critical aspect of papaya sex determination is the pistil abortion in male flowers. However, the regulatory networks that control the development of pistils and stamens in papaya remain incompletely understood.

RESULTS: In this study, we identified three organ-specific clusters involved in papaya pistils and stamens development. We found that pistil development is primarily characterized by the significant expression of auxin-related genes, while the pistil abortion genes in males is mainly associated with cytokinin, gibberellin, and auxin pathways. Additionally, we constructed expression regulatory networks for the development of female pistils, aborted pistils and stamens in male flowers, revealing key regulatory genes and signaling pathways involved in papaya organ development. Furthermore, we systematically identified 65 members of the MADS-box gene family and 10 ABCDE subfamily MADS-box genes in papaya. By constructing a phylogenetic tree of the ABCDE subfamily, we uncovered gene contraction and expansion in papaya, providing an improved understanding of the developmental mechanisms and evolutionary history of papaya floral organs.

CONCLUSIONS: These findings provide a robust framework for identifying candidate sex-determining genes and constructing the sex determination regulatory network in papaya, providing insights and genomic resources for papaya breeding.

PMID:39994552 | DOI:10.1186/s12870-025-06242-1

Nat Cardiovasc Res. 2025 Feb 24. doi: 10.1038/s44161-025-00612-6. Online ahead of print.

ABSTRACT

Myocarditis, characterized by inflammatory cell infiltration, can have multiple etiologies, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or, rarely, mRNA-based coronavirus disease 2019 (COVID-19) vaccination. The underlying cellular and molecular mechanisms remain poorly understood. In this study, we performed single-nucleus RNA sequencing on left ventricular endomyocardial biopsies from patients with myocarditis unrelated to COVID-19 (Non-COVID-19), after SARS-CoV-2 infection (Post-COVID-19) and after COVID-19 vaccination (Post-Vaccination). We identified distinct cytokine expression patterns, with interferon-γ playing a key role in Post-COVID-19, and upregulated IL16 and IL18 expression serving as a hallmark of Post-Vaccination myocarditis. Although myeloid responses were similar across all groups, the Post-Vaccination group showed a higher proportion of CD4+ T cells, and the Post-COVID-19 group exhibited an expansion of cytotoxic CD8+ T and natural killer cells. Endothelial cells showed gene expression changes indicative of vascular barrier dysfunction in the Post-COVID-19 group and ongoing angiogenesis across all groups. These findings highlight shared and distinct mechanisms driving myocarditis in patients with and without a history of SARS-CoV-2 infection or vaccination.

PMID:39994453 | DOI:10.1038/s44161-025-00612-6

Mol Syst Biol. 2025 Feb 24. doi: 10.1038/s44320-025-00092-7. Online ahead of print.

ABSTRACT

The circadian clock regulates key physiological processes, including cellular responses to DNA damage. Circadian-based therapeutic strategies optimize treatment timing to enhance drug efficacy and minimize side effects, offering potential for precision cancer treatment. However, applying these strategies in cancer remains limited due to a lack of understanding of the clock's function across cancer types and incomplete insights into how the circadian clock affects drug responses. To address this, we conducted deep circadian phenotyping across a panel of breast cancer cell lines. Observing diverse circadian dynamics, we characterized metrics to assess circadian rhythm strength and stability in vitro. This led to the identification of four distinct circadian-based phenotypes among 14 breast cancer cell models: functional, weak, unstable, and dysfunctional clocks. Furthermore, we demonstrate that the circadian clock plays a critical role in shaping pharmacological responses to various anti-cancer drugs and we identify circadian features descriptive of drug sensitivity. Collectively, our findings establish a foundation for implementing circadian-based treatment strategies in breast cancer, leveraging clock phenotypes and drug sensitivity patterns to optimize therapeutic outcomes.

PMID:39994450 | DOI:10.1038/s44320-025-00092-7

Commun Biol. 2025 Feb 24;8(1):297. doi: 10.1038/s42003-025-07743-3.

ABSTRACT

The NADase activity of the defense-associated sirtuins (DSRs) is activated by the phage tail tube protein (TTP). Herein, we report cryo-EM structures of a free-state Bacillus subtilis DSR2 tetramer and a fragment of the tetramer, a phage SPR tail tube, and two DSR2-TTP complexes. DSR2 contains an N-terminal SIR2 domain, a middle domain (MID) and a C-terminal domain (CTD). The DSR2 CTD harbors the α-solenoid tandem-repeats like the HEAT-repeat proteins. DSR2 assembles into a tetramer with four SIR2 clustered at the center, and two intertwined MID-CTD chains flank the SIR2 core. SPR TTPs self-assemble into a tube-like complex. Upon DSR2 binding, the D1 domain of SPR TTP is captured between the HEAT-repeats domains of DSR2, which conflicts with TTPs self-assembly. Binding of TTPs induces conformational changes in DSR2 tetramer, resulting in increase of the NAD+ pocket volume in SIR2, thus activates the NADase activity and leads to cellular NAD+ depletion.

PMID:39994439 | DOI:10.1038/s42003-025-07743-3

Nat Neurosci. 2025 Feb 24. doi: 10.1038/s41593-024-01865-3. Online ahead of print.

ABSTRACT

Enlargement of the cerebrospinal fluid (CSF)-filled brain ventricles (ventriculomegaly) is a defining feature of congenital hydrocephalus (CH) and an under-recognized concomitant of autism. Here, we show that de novo mutations in the autism risk gene PTEN are among the most frequent monogenic causes of CH and primary ventriculomegaly. Mouse Pten-mutant ventriculomegaly results from aqueductal stenosis due to hyperproliferation of periventricular Nkx2.1+ neural progenitor cells (NPCs) and increased CSF production from hyperplastic choroid plexus. Pten-mutant ventriculomegalic cortices exhibit network dysfunction from increased activity of Nkx2.1+ NPC-derived inhibitory interneurons. Raptor deletion or postnatal everolimus treatment corrects ventriculomegaly, rescues cortical deficits and increases survival by antagonizing mTORC1-dependent Nkx2.1+ NPC pathology. Thus, PTEN mutations concurrently alter CSF dynamics and cortical networks by dysregulating Nkx2.1+ NPCs. These results implicate a nonsurgical treatment for CH, demonstrate a genetic association of ventriculomegaly and ASD, and help explain neurodevelopmental phenotypes refractory to CSF shunting in select individuals with CH.

PMID:39994410 | DOI:10.1038/s41593-024-01865-3

Sci Rep. 2025 Feb 24;15(1):6658. doi: 10.1038/s41598-025-91022-y.

ABSTRACT

Metformin, widely used for the treatment of type 2 diabetes, has recently gained attention for its potential anticancer properties. Several studies have shown that metformin treatment inhibits cell viability in colon adenocarcinoma (COAD); however, the research related to the tumor-node-metastasis (TNM) stage is limited. As COAD is frequently diagnosed at an advanced stage, understanding the genetic factors that regulate the pathogenesis of COAD at each TNM stage and the effects of metformin for potential treatment. Therefore, we identified differentially expressed factors at the TNM stage in metformin-treated COAD cells and investigated their regulatory mechanisms using microRNAs (miRNAs). Through bioinformatics analyses, four-jointed box kinase 1 (FJX1) and hsa-miR-1306-3p were identified as differentially expressed in COAD upon metformin treatment. Metformin treatment significantly reduced cell viability, with an observed decrease of approximately 50%. Analysis using quantitative real-time PCR showed an increase in hsa-miR-1306-3p and a decrease in FJX1 expression upon metformin treatment compared to untreated cells. Luciferase assay confirmed the sequence-specific binding of hsa-miR-1306-3p to FJX1. These findings highlight the potential of metformin as a therapeutic agent for COAD by modulating FJX1 expression via upregulation of hsa-miR-1306-3p, revealing novel avenues for COAD treatment.

PMID:39994354 | DOI:10.1038/s41598-025-91022-y

Nat Commun. 2025 Feb 24;16(1):1942. doi: 10.1038/s41467-025-57170-5.

ABSTRACT

This study showcases an integrative mass spectrometry-based strategy combining systems antigenomics and systems serology to characterize human antibodies in clinical samples. This strategy involves using antibodies circulating in plasma to affinity-enrich antigenic proteins in biochemically fractionated pools of bacterial proteins, followed by their identification and quantification using mass spectrometry. A selected subset of the identified antigens is then expressed recombinantly to isolate antigen-specific IgG, followed by characterization of the structural and functional properties of these antibodies. We focused on Group A streptococcus (GAS), a major human pathogen lacking an approved vaccine. The data shows that both healthy and GAS-infected individuals have circulating IgG against conserved streptococcal proteins, including toxins and virulence factors. The antigenic breadth of these antibodies remains relatively constant across healthy individuals but changes considerably in GAS bacteremia. Moreover, antigen-specific IgG analysis reveals individual variation in titers, subclass distributions, and Fc-signaling capacity, despite similar epitope and Fc-glycosylation patterns. Finally, we show that GAS antibodies may cross-react with Streptococcus dysgalactiae (SD), a bacterial pathogen that occupies similar niches and causes comparable infections. Collectively, our results highlight the complexity of GAS-specific antibody responses and the versatility of our methodology to characterize immune responses to bacterial pathogens.

PMID:39994218 | DOI:10.1038/s41467-025-57170-5

Cancer Genomics Proteomics. 2025 Mar-Apr;22(2):285-305. doi: 10.21873/cgp.20502.

ABSTRACT

BACKGROUND/AIM: Oncogenic processes are delineated by metabolic dysregulation. Drug likeness is pharmacokinetically tested through the CYP450 enzymatic system, whose genetic aberrations under epigenetic stress could shift male organisms into prostate cancer pathways. Our objective was to predict the susceptibility to prostate neoplasia, focused on benign prostatic hyperplasia (BPH) and prostate cancer (PCa), based on the pharmacoepigenetic and the metabolic profile of Caucasians.

MATERIALS AND METHODS: Two independent cohorts of 47,389 individuals in total were assessed to find risk associations of CYP450 genes with prostatic neoplasia. The metabolic profile of the first cohort was statistically evaluated and frequencies of absorption-distribution-metabolism-excretion-toxicity (ADMET) properties were calculated. Prediction of miRNA pharmacoepigenetic targeting was performed.

RESULTS: We found that prostate cancer and benign prostatic hyperplasia patients of the first cohort shared common cardiometabolic trends. Drug classes C08CA, C09AA, C09CA, C10AA, C10AX of the cardiovascular, and G04CA, G04CB of the genitourinary systems, were associated with increased prostate cancer risk, while C03CA and N06AB of the cardiovascular and nervous systems were associated with low-risk for PCa. CYP3A4*1B was the most related pharmacogenetic polymorphism associated with prostate cancer susceptibility. miRNA-200c-3p and miRNA-27b-3p seem to be associated with CYP3A4 targeting and prostate cancer predisposition. Metabolomic analysis indicated that 11β-OHT, 2β-OHT, 15β-OHT, 2α-OHT and 6β-OHT had a high risk, and 16α-OHT, and 16β-OHT had an intermediate disease-risk.

CONCLUSION: These findings constitute a novel integrated signature for prostate cancer susceptibility. Further studies are required to assess their predictive value more fully.

PMID:39993800 | DOI:10.21873/cgp.20502

Hum Vaccin Immunother. 2025 Dec;21(1):2463194. doi: 10.1080/21645515.2025.2463194. Epub 2025 Feb 24.

ABSTRACT

Invasive meningococcal disease is an uncommon but serious disease predominantly affecting children. This phase 2b study evaluated MenABCWY in 6-month-old infants followed by MenB-fHbp and MenABCWY in 2-month-old infants, the latter being the target age and intervention. Participants were randomized to MenABCWY, 60 µg or 120 µg MenB-fHbp+MenACWY-TT, or 4CMenB+MenACWY-TT, administered as 2 primary and 1 booster dose. The primary safety objective was to describe the safety profile of MenABCWY in participants enrolled at 2 months. Primary immunogenicity objectives were the percentage of participants achieving seroprotective serum bactericidal antibody using human complement titers. Overall, 314 and 12 participants were randomized to sentinel cohort and open-label expanded-enrollment stages, respectively. Based on 2 reports of fever requiring invasive investigations and accompanied by cerebrospinal fluid pleocytosis and 1 report arising from a previous study, the Sponsor terminated the study. Local reactions and systemic events after primary vaccination were generally mild to moderate, and tended to be higher with MenABCWY versus 4CMenB+MenACWY-TT. Immunogenicity data suggest that 1 month after vaccination 2, MenABCWY responses for MenA/C/W/Y were robust and comparable with 4CMenB+MenACWY-TT in 2-month-old participants. Immune responses for MenB test strains were higher with MenABCWY versus 4CMenB+MenACWY-TT and generally similar with 60 µg and 120 µg MenB-fHbp+MenACWY-TT or MenABCWY. Based on the limited results, the consistency of MenB immune responses with 60 µg and 120 µg MenB-fHbp suggests doses < 60 µg could be investigated to assess whether a more acceptable safety profile in conjunction with beneficial immune responses is possible in 2-month-old infants.

PMID:39993937 | DOI:10.1080/21645515.2025.2463194

Drug Discov Ther. 2025 Feb 23. doi: 10.5582/ddt.2024.01071. Online ahead of print.

ABSTRACT

Numerous medications have been associated with an increased risk of vaginal hemorrhage in women. In this study, we analyzed data from the FDA Adverse Event Reporting System (FAERS), focusing on reports of drug-induced vaginal bleeding in women. Risk signals were assessed using disproportionality analyses, specifically the reporting odds ratio (ROR) and the proportional reporting ratio (PRR), to identify significant associations between drugs and adverse events. We found that anticoagulants, hormonal drugs, psychotropic drugs, hypoglycemic agents, antineoplastic agents, anti-inflammatory drugs, immunological agents, and some drugs for osteoporosis were significantly associated with the risk of vaginal hemorrhage. Hormonal drugs, anticoagulants, and particularly antifungal agents were attributed to a notably high proportion of vaginal hemorrhage cases, necessitating further investigation into the underlying mechanisms. Therefore, precise clinical management of medications and optimization of treatment regimens are necessary to reduce the risk of vaginal hemorrhage and improve safety.

PMID:39993768 | DOI:10.5582/ddt.2024.01071

PLoS One. 2025 Feb 24;20(2):e0318249. doi: 10.1371/journal.pone.0318249. eCollection 2025.

ABSTRACT

OBJECTIVE: The autosomal recessive disease congenital chloride diarrhea (CLD), caused by loss-of-function mutations in the solute carrier family 26 member 3 (SLC26A3) gene, shows association with inflammatory bowel disease (IBD). However, it is unclear whether IBD risk is associated with genetic or immune signatures. SLC26A3 interacts with several ion transporters linked to intestinal inflammation, such as cystic fibrosis transmembrane conductance regulator (CFTR) and solute carrier family 9 member 3 (SLC9A3) causing congenital sodium diarrhea. We hypothesized that other epithelial channels affecting intestinal salt balance might modulate CLD phenotype or IBD risk.

MATERIALS AND METHODS: We analyzed 495 gene variants within 33 ion transporters among 28 patients with CLD and 44,443 population controls.

RESULTS: We found three intronic variants at or near the CFTR locus (rs17132543, rs2283054 and rs76622533) showing statistically significant (P < 1.42x10-5) associations with CLD.

CONCLUSIONS: These data demonstrate enrichment of rare variants at the CFTR locus in chromosomes harboring the Finnish founder mutation for CLD.

PMID:39992989 | DOI:10.1371/journal.pone.0318249

Int J Med Inform. 2025 Feb 21;197:105846. doi: 10.1016/j.ijmedinf.2025.105846. Online ahead of print.

ABSTRACT

BACKGROUND: The increasing use of Deep Learning (DL) in healthcare has highlighted the critical need for improved transparency and interpretability. While Explainable Artificial Intelligence (XAI) methods provide insights into model predictions, reliability cannot be guaranteed by simply relying on explanations.

OBJECTIVES: This position paper proposes the integration of Uncertainty Quantification (UQ) with XAI methods to improve model reliability and trustworthiness in healthcare applications.

METHODS: We examine state-of-the-art XAI and UQ techniques, discuss implementation challenges, and suggest solutions to combine UQ with XAI methods. We propose a framework for estimating both aleatoric and epistemic uncertainty in the XAI context, providing illustrative examples of their potential application.

RESULTS: Our analysis indicates that integrating UQ with XAI could significantly enhance the reliability of DL models in practice. This approach has the potential to reduce interpretation biases and over-reliance, leading to more cautious and conscious use of AI in healthcare.

PMID:39993336 | DOI:10.1016/j.ijmedinf.2025.105846

Proc Natl Acad Sci U S A. 2025 Mar 11;122(10):e2413442122. doi: 10.1073/pnas.2413442122. Epub 2025 Feb 24.

ABSTRACT

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood-brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer's disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM's ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals' rates of adverse cognitive change with age.

PMID:39993207 | DOI:10.1073/pnas.2413442122

PLoS One. 2025 Feb 24;20(2):e0316136. doi: 10.1371/journal.pone.0316136. eCollection 2025.

ABSTRACT

Lung cancer remains a leading cause of cancer-related deaths worldwide, with low survival rates often attributed to late-stage diagnosis. To address this critical health challenge, researchers have developed computer-aided diagnosis (CAD) systems that rely on feature extraction from medical images. However, accurately identifying the most informative image features for lung cancer detection remains a significant challenge. This study aimed to compare the effectiveness of both hand-crafted and deep learning-based approaches for lung cancer diagnosis. We employed traditional hand-crafted features, such as Gray Level Co-occurrence Matrix (GLCM) features, in conjunction with traditional machine learning algorithms. To explore the potential of deep learning, we also optimized and implemented a Bidirectional Long Short-Term Memory (Bi-LSTM) network for lung cancer detection. The results revealed that the highest performance using hand-crafted features was achieved by extracting GLCM features and utilizing Support Vector Machine (SVM) with different kernels, reaching an accuracy of 99.78% and an AUC of 0.999. However, the deep learning Bi-LSTM network surpassed both methods, achieving an accuracy of 99.89% and an AUC of 1.0000. These findings suggest that the proposed methodology, combining hand-crafted features and deep learning, holds significant promise for enhancing early lung cancer detection and ultimately improving diagnosis systems.

PMID:39992919 | DOI:10.1371/journal.pone.0316136

PLoS One. 2025 Feb 24;20(2):e0317193. doi: 10.1371/journal.pone.0317193. eCollection 2025.

ABSTRACT

This study aims at the limitations of traditional methods in the evaluation of stroke sequelae and rehabilitation effect monitoring, especially for the accurate identification and tracking of brain injury areas. To overcome these challenges, we introduce an advanced neuroimaging technology based on deep learning, the SWI-BITR-UNet model. This model, introduced as novel Machine Learning (ML) model, combines the SWIN Transformer's local receptive field and shift mechanism, and the effective feature fusion strategy in the U-Net architecture, aiming to improve the accuracy of brain lesion region segmentation in multimodal MRI scans. Through the application of a 3-D CNN encoder and decoder, as well as the integration of the CBAM attention module and jump connection, the model can finely capture and refine features, to achieve a level of segmentation accuracy comparable to that of manual segmentation by experts. This study introduces a 3D CNN encoder-decoder architecture specifically designed to enhance the processing capabilities of 3D medical imaging data. The development of the 3D CNN model utilizes the ADAM optimization algorithm to facilitate the training process. The Bra2020 dataset is utilized to assess the accuracy of the proposed deep learning neural network. By employing skip connections, the model effectively integrates the high-resolution features from the encoder with the up-sampling features from the decoder, thereby increasing the model's sensitivity to 3D spatial characteristics. To assess both the training and testing phases, the SWI-BITR-Unet model is trained using reliable datasets and evaluated through a comprehensive array of statistical metrics, including Recall (Rec), Precision (Pre), F1 test score, Kappa Coefficient (KC), mean Intersection over Union (mIoU), and Receiver Operating Characteristic-Area Under Curve (ROC-AUC). Furthermore, various machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Adaptive Boosting (AdaBoost), and K-Nearest Neighbor (KNN), have been employed to analyze tumor progression in the brain, with performance characterized by Hausdorff distance. In From the performance of ML models, the SWI-BITR-Unet model was more accurate than other models. Subsequently, regarding DICE coefficient values, the segmentation maps (annotation maps of brain tumor distributions) generated by the ML models indicated the models's capability to autonomously delineate areas such as the tumor core (TC) and the enhancing tumor (ET). Moreover, the efficacy of the proposed machine learning models demonstrated superiority over existing research in the field. The computational efficiency and the ability to handle long-distance dependencies of the model make it particularly suitable for applications in clinical Settings. The results showed that the SNA-BITR-UNet model can not only effectively identify and monitor the subtle changes in the stroke injury area, but also provided a new and efficient tool in the rehabilitation process, providing a scientific basis for developing personalized rehabilitation plans.

PMID:39992898 | DOI:10.1371/journal.pone.0317193

ACS Appl Mater Interfaces. 2025 Feb 24. doi: 10.1021/acsami.4c22067. Online ahead of print.

ABSTRACT

Inspired by the natural mechanism of taste perception, artificial bionic electronic tongues have successfully enabled the detection and classification of various tastes. The liquid-solid contact electrification (LSCE) effect has emerged as a highly effective approach for developing self-powered electronic tongues. However, droplet-based sensing structures often face challenges related to internal and environmental interferences, compromising their stability and repeatability. In this work, we developed a monolithically integrated self-powered microfluidic bionic electronic tongue (SMET), combining the LSCE effect with deep learning algorithms to achieve highly reliable and intelligent sample identification and concentration detection. The incorporation of a multiplexed microchannel structure significantly reduced the required liquid sample volume while simultaneously increasing the electrical output amplitude (up to 10 V at multitone wave excitation), thereby enhancing sensitivity. Instead of micropumps, miniaturized exciters were employed as SMET drivers to generate multiple excitation waveforms, producing various signal types to improve specific algorithmic accuracy. The SMET achieved over 93% classification accuracy for five taste element samples (glacial acetic acid, anhydrous dextrose, quinine, edible chili essence, sodium chloride) and five concentrations of sodium chloride solutions using a single waveform signal, reaching 100% accuracy with the fusion of multiple waveform signals. Furthermore, the SMET was used to detect more than ten different taste samples, each exhibiting distinct signal variations. Thus, due to its ultrahigh sensitivity to the electrical properties of liquids, SMET enables accurate and rapid analysis of liquid samples with high reliability, positioning it as a promising tool in the field of rapid liquid detection.

PMID:39992874 | DOI:10.1021/acsami.4c22067

Proc Natl Acad Sci U S A. 2025 Mar 4;122(9):e2415475122. doi: 10.1073/pnas.2415475122. Epub 2025 Feb 24.

ABSTRACT

The most common chemotherapeutics induce DNA damage to eradicate cancer cells, yet defective DNA repair can propagate mutations, instigating therapy resistance and secondary malignancies. Structural variants (SVs), arising from copy-number-imbalanced and -balanced DNA rearrangements, are a major driver of tumor evolution, yet understudied posttherapy. Here, we adapted single-cell template-strand sequencing (Strand-seq) to a HER2+ breast cancer model to investigate the formation of doxorubicin-induced de novo SVs. We coupled this approach with nucleosome occupancy (NO) measurements obtained from the same single cell to enable simultaneous SV detection and cell-type classification. Using organoids from TetO-CMYC/TetO-Neu/MMTV-rtTA mice modeling HER2+ breast cancer, we generated 459 Strand-seq libraries spanning various tumorigenesis stages, identifying a 7.4-fold increase in large chromosomal alterations post-doxorubicin. Complex DNA rearrangements, deletions, and duplications were prevalent across basal, luminal progenitor (LP), and mature luminal (ML) cells, indicating uniform susceptibility of these cell types to SV formation. Doxorubicin further elevated sister chromatid exchanges (SCEs), indicative of genomic stress persisting posttreatment. Altered nucleosome occupancy levels on distinct cancer-related genes further underscore the broad genomic impact of doxorubicin. The organoid-based system for single-cell multiomics established in this study paves the way for unraveling the most important therapy-associated SV mutational signatures, enabling systematic studies of the effect of therapy on cancer evolution.

PMID:39993200 | DOI:10.1073/pnas.2415475122

Medicine (Baltimore). 2025 Feb 21;104(8):e41482. doi: 10.1097/MD.0000000000041482.

ABSTRACT

BACKGROUND: The phase 3 randomized, active-controlled GS-US-380-1844 (NCT02603120) study evaluated switching to the single-tablet regimen bictegravir, emtricitabine, and tenofovir alafenamide (B/F/TAF) from dolutegravir (DTG), abacavir (ABC), and lamivudine (3TC) among people with HIV-1. Previously, results from the 48-week double-blind phase showed that switching to B/F/TAF was noninferior to remaining on DTG/ABC/3TC and that B/F/TAF was well tolerated. Here, we show the long-term safety and efficacy of switching to B/F/TAF from DTG/ABC/3TC among people with HIV-1.

METHODS: Participants were virologically suppressed people with HIV-1 (HIV-1 RNA <50 copies/mL for ≥ 3 months prior to screening) receiving DTG/ABC/3TC at baseline. Participants were randomized 1:1 to switch to B/F/TAF or remain on DTG/ABC/3TC. Following 48 weeks of treatment with B/F/TAF or DTG/ABC/3TC in the double-blind phase, participants had the option to enter an open-label extension phase, during which they received B/F/TAF. Virologic, immunologic, and safety outcomes during treatment with B/F/TAF through the open-label extension up to 168 weeks, including preexisting and treatment-emergent resistance, were analyzed.

RESULTS: Among 547 participants in the all-B/F/TAF analysis set, virologic suppression (HIV-1 RNA < 50 copies/mL) was maintained in 99% to 100% of participants up to 168 weeks into B/F/TAF treatment, including in those with preexisting resistance; no treatment-emergent resistance was detected. CD4 cell counts remained stable during B/F/TAF treatment, with median (interquartile range) changes from baseline of -17 (-120, 65) cells/µL at week 48 and -9 (-100, 108) cells/µL at week 96. Safety and tolerability findings were consistent with previously reported findings up to week 48; most drug-related adverse events were grade 1 or 2 in severity; no new safety signals were identified.

CONCLUSION: Switching to B/F/TAF from DTG/ABC/3TC was associated with continued high rates of virologic suppression up to week 168, with no treatment-emergent resistance. B/F/TAF was well tolerated throughout the study period.

PMID:39993074 | DOI:10.1097/MD.0000000000041482

J Antimicrob Chemother. 2025 Feb 24:dkaf054. doi: 10.1093/jac/dkaf054. Online ahead of print.

ABSTRACT

BACKGROUND: The rise in fungal infections caused by multidrug-resistant pathogens like Candida haemulonii sensu stricto presents a significant global health challenge. The common resistance to current treatments underscores the urgency to explore alternative therapeutic strategies, including drug repurposing.

OBJECTIVES: To assess the potential of repurposing tafenoquine, an antimalarial agent, for antifungal use against C. haemulonii sensu stricto.

METHODS: The efficacy of tafenoquine was tested using in vitro assays for minimum inhibitory concentration (MIC), minimum fungicidal concentration, biofilm inhibition, cell damage, cell membrane integrity, nucleotide leakage, sorbitol protection assay, and efflux pump inhibition. The compound's cytotoxicity was assessed through a haemolysis assay, and in vivo safety and efficacy were tested using Tenebrio molitor larvae.

RESULTS: Tafenoquine exhibited potent fungicidal activity against C. haemulonii sensu stricto with an MIC of 4 mg/L and significantly inhibited biofilm formation by 60.63%. Tafenoquine also impaired mitochondrial functionality, leading to compromised cellular respiration. Despite these effects, tafenoquine did not cause significant protein leakage, indicating a distinct mechanism from membrane-targeting agents. In vivo study confirmed tafenoquine's non-toxic profile with no observed haemolysis or acute toxicity in the T. molitor model. During antifungal treatment with tafenoquine, a survival rate of approximately 60% was observed after 3 days.

CONCLUSIONS: The findings of this study highlight tafenoquine's potential as a promising candidate for antifungal drug repurposing, especially against C. haemulonii sensu stricto. Its effectiveness in inhibiting fungal growth and biofilm formation underscores its viability for further clinical development as a novel antifungal therapy.

PMID:39992314 | DOI:10.1093/jac/dkaf054

Innovation (Camb). 2025 Jan 18;6(2):100773. doi: 10.1016/j.xinn.2024.100773. eCollection 2025 Feb 3.

ABSTRACT

Pharmacogenomic landscapes and related databases are important for identifying the biomarkers of drug response and toxicity. However, these data are still lacking for the Chinese population. In this study, we constructed a pharmacogenomic landscape and an associated database using whole-genome sequencing data generated by non-invasive prenatal testing in 206,640 Chinese individuals. In total, 1,577,513 variants (including 331,610 novel variants) were identified among 3,538 pharmacogenes related to 2,086 drugs. We found that the variant spectrum in the Chinese population differed among the seven major regions. Regional differences also exist among provinces in China. The average numbers of drug enzyme, transporter, and receptor variants were 258, 557, and 632, respectively. Subsequent correlation analysis indicated that the pharmacogenes affecting multiple drugs had fewer variants. Among the 16 categories of drugs, we found that nervous system, cardiovascular system, and genitourinary system/sex hormone drugs were more likely to be affected by variants of pharmacogenes. Characteristics of the variants in the enzyme, transporter, and receptor subfamilies showed specificity. To explore the clinical utility of these data, a genetic association study was conducted on 1,019 lung cancer patients. Two novel variants, AKT2 chr19:40770621 C>G and SLC19A1 chr21:46934171 A>C, were identified as novel platinum response biomarkers. Finally, a pharmacogenomic database, named the Chinese Pharmacogenomic Knowledge Base (CNPKB: http://www.cnpkb.com.cn/), was constructed to collect all the data. In summary, a pharmacogenomic landscape and database for the Chinese population were constructed in this study, which could support personalized Chinese medicine in the future.

PMID:39991480 | PMC:PMC11846038 | DOI:10.1016/j.xinn.2024.100773