Chirurgie (Heidelb). 2025 Mar 25. doi: 10.1007/s00104-025-02266-3. Online ahead of print.

ABSTRACT

BACKGROUND: The aim of the present study is to discuss the benefits of intraoperative frozen sections (FS) for the surgical management of endocrine tumors.

METHODS: A systematic search of the literature of the last ten years on FS in the field of oncologic endocrine surgery was carried out and a discussion based on the available evidence and experience of the authors is provided.

RESULTS: A group of publications focused on the role of intraoperative FS in thyroid surgery in identifying the malignant potential of thyroid nodules. The detection of lymph node metastasis and extrathyroidal growth in differentiated thyroid cancer (DTC) were also two other topical groups as well as the diagnosis of lymph node involvement based on stromal desmoplasia in medullary thyroid cancer (MTC). A further group investigated the possibilities of deep learning to overcome technical problems and another investigated the cost-benefit analyses. There is no relevant literature on the role of FS in the surgical treatment of parathyroid and adrenal cancers.

DISCUSSION: The synthesis of the available evidence suggests that FS investigations of the thyroid glands should be restricted to Bethesda V nodules. The technical limitations in the exclusion of vascular and capsular invasion make the FS unsuitable for follicular neoplasms and oncocytic lesions. The Delphi lymph node seems to be suitable for investigation using FS and when positive represents an indication for lymphadenectomy in cN0 patients. Larger studies are necessary in the future to confirm if the absence of desmoplasia with an intact tumor capsule can reliably justify omitting lymph node resection in MTC, independent of the calcitonin level. The costs and benefits depend on the individual context so that generalization is difficult. Deep learning models could generally improve the performance of FS analysis in the future.

CONCLUSION: In thyroid surgery awareness of the technical limitations of FS is crucial for correct implementation and thus to optimize its performance. A preoperative fine needle biopsy and surgical experience help in selecting the nodules that can benefit from FS. Deep image learning could help to overcome current problems in the future. In adrenal and parathyroid oncologic surgery FS do not play a relevant role.

PMID:40131405 | DOI:10.1007/s00104-025-02266-3

PLoS One. 2025 Mar 25;20(3):e0320219. doi: 10.1371/journal.pone.0320219. eCollection 2025.

ABSTRACT

Neuroinflammation is a key feature of human neurodisease including neuropathy and neurodegenerative disease and is driven by the activation microglia, immune cells of the nervous system. During activation microglia release pro-inflammatory cytokines as well as reactive oxygen species (ROS) that can drive local neuronal and glial damage. Phytocannabinoids are an important class of naturally occurring compounds found in the cannabis plant (Cannabis sativa) that interact with the body's endocannabinoid receptor system. Cannabidiol (CBD) is a prototype phytocannabinoid with anti-inflammatory properties observed in cells and animal models. We measured ROS in human microglia (HMC3) cells using CellROX, a fluorescent dynamic ROS indicator. We tested the effect of CBD on ROS level in the presence of three known immune activators: lipopolysaccharide (LPS), amyloid beta (Aβ42), and human immunodeficiency virus (HIV) glycoprotein (GP120). Confocal microscopy images within microglia were coupled to a deep learning model using a convolutional neural network (CNN) to predict ROS responses. Our study demonstrates a deep learning platform that can be used in the assessment of CBD effect in immune cells using ROS image measure.

PMID:40131976 | DOI:10.1371/journal.pone.0320219

STAR Protoc. 2025 Mar 24;6(2):103712. doi: 10.1016/j.xpro.2025.103712. Online ahead of print.

ABSTRACT

In Alzheimer's disease, there is an imbalance in neurotransmitter release and altered neuronal activation. Here, we present a protocol approach to analyze neuronal activity by combining local field potential (LFP) recording with microdialysis within the same animal. We describe steps for measuring glutamate and GABA levels following hippocampal amyloid-beta oligomer (Aβo) injections in rats. We then detail procedures for assembling the electrode and cannula, surgical implantation and simultaneous in vivo LFP recording, interstitial fluid collection, and Aβo injections.

PMID:40131934 | DOI:10.1016/j.xpro.2025.103712

PLoS One. 2025 Mar 25;20(3):e0320910. doi: 10.1371/journal.pone.0320910. eCollection 2025.

ABSTRACT

[This corrects the article DOI: 10.1371/journal.pone.0314754.].

PMID:40131880 | DOI:10.1371/journal.pone.0320910

Acta Biotheor. 2025 Mar 25;73(2):6. doi: 10.1007/s10441-025-09495-3.

ABSTRACT

To bring clarity, the term 'information' is resolved into three distinct meanings: physical pattern, statistical relations and knowledge about things. In parallel, three kinds of 'causation' are resolved: the action of physical force constrained by physical pattern (efficient cause), cybernetic (formal cause) and statistical inference. Cybernetic causation is an expression of fundamental (necessary) logical relations, statistical inference is phenomenological, but physical information and causation are proposed as what actually happens in the physical world. Examples of the latter are given to illustrate the underlying material dynamics in a range of biological systems from the appearance of 'synergistic information' among multiple variables (mainly in neuroscience); positional information in multicellular development; and the organisational structure of ecological communities, especially incorporating niche construction theory. A rigorous treatment of multi-level causation is provided as well as an explanation of the causal power of non-physical information structure, especially of interaction networks. The focus on physical information as particular pattern, echoing the insights of Howard Pattee, provides a more physically grounded view of emergence, downward causation and the concept of 'closure to efficient causation', all now prevalent in the organisational approach to biology.

PMID:40131488 | DOI:10.1007/s10441-025-09495-3

J Med Internet Res. 2025 Mar 25;27:e65872. doi: 10.2196/65872.

ABSTRACT

BACKGROUND: Torsades de pointes (TdP) is a rare yet potentially fatal cardiac arrhythmia that is often drug-induced. Drug-drug interactions (DDIs) are a major risk factor for TdP development, but the specific drug combinations that increase this risk have not been extensively studied.

OBJECTIVE: This study aims to identify clinically significant, high-priority DDIs to provide a foundation to minimize the risk of TdP and effectively manage DDI risks in the future.

METHODS: We used the following 4 frequency statistical models to detect DDI signals using the Food and Drug Administration Adverse Event Reporting System (FAERS) database: Ω shrinkage measure, combination risk ratio, chi-square statistic, and additive model. The adverse event of interest was TdP, and the drugs targeted were all registered and classified as "suspect," "interacting," or "concomitant drugs" in FAERS. The DDI signals were identified and evaluated using the Lexicomp and Drugs.com databases, supplemented with real-world data from the literature.

RESULTS: As of September 2023, this study included 4313 TdP cases, with 721 drugs and 4230 drug combinations that were reported for at least 3 cases. The Ω shrinkage measure model demonstrated the most conservative signal detection, whereas the chi-square statistic model exhibited the closest similarity in signal detection tendency to the Ω shrinkage measure model. The κ value was 0.972 (95% CI 0.942-1.002), and the Ppositive and Pnegative values were 0.987 and 0.985, respectively. We detected 2158 combinations using the 4 frequency statistical models, of which 241 combinations were indexed by Drugs.com or Lexicomp and 105 were indexed by both. The most commonly interacting drugs were amiodarone, citalopram, quetiapine, ondansetron, ciprofloxacin, methadone, escitalopram, sotalol, and voriconazole. The most common combinations were citalopram and quetiapine, amiodarone and ciprofloxacin, amiodarone and escitalopram, amiodarone and fluoxetine, ciprofloxacin and sotalol, and amiodarone and citalopram. Although 38 DDIs were indexed by Drugs.com and Lexicomp, they were not detected by any of the 4 models.

CONCLUSIONS: Clinical evidence on DDIs is limited, and not all combinations of heart rate-corrected QT interval (QTc)-prolonging drugs result in TdP, even when involving high-risk drugs or those with known risk of TdP. This study provides a comprehensive real-world overview of drug-induced TdP, delimiting both clinically significant DDIs and negative DDIs, providing valuable insights into the safety profiles of various drugs, and informing the optimization of clinical practice.

PMID:40132181 | DOI:10.2196/65872

Acta Neuropsychiatr. 2025 Mar 25:1-30. doi: 10.1017/neu.2025.11. Online ahead of print.

ABSTRACT

BACKGROUND: Non-adherence and negative attitudes towards medication are major problems in treating psychotic disorders. Cytochrome P450 2D6 (CYP2D6) contributes to the metabolism of aripiprazole and risperidone. Variations in CYP2D6 activity may affect treatment response or adverse effects. The impact of these variations on adherence and medication attitudes is unclear.

AIMS: This study investigates the relationships between CYP2D6 phenotype, self-reported adherence, adverse effects, and attitudes among aripiprazole and risperidone users.

METHODS: This study analyzed data from the SUPER-Finland cohort of 10,474 adults with psychotic episodes, including 1,429 aripiprazole and 828 risperidone users. The Attitudes towards neuroleptic treatment (ANT) questionnaire assessed adherence and adverse effects in all patients, while medication-related attitudes were examined in a subgroup of 1,000 participants. Associations between CYP2D6 phenotypes and outcomes were analyzed using logistic regression and beta regression in aripiprazole and risperidone groups separately.

RESULTS: Among risperidone users, we observed no association between CYP2D6 phenotypes and adherence, adverse effects, or attitudes. Similarly, no link was found between adherence and CYP2D6 phenotypes among aripiprazole users. However, aripiprazole users with the ultrarapid CYP2D6 phenotype had more adverse effects (OR = 1.71, 95 % CI 1.03-2.90, p = 0.041). Among aripiprazole users, CYP2D6 ultrarapid phenotype was associated with less favorable attitudes towards antipsychotic treatment (β = -0.48, p = 0.023).

CONCLUSIONS: We found preliminary evidence that the ultrarapid CYP2D6 phenotype is associated with increased adverse effects and negative attitudes towards antipsychotic medication among aripiprazole users. CYP2D6 phenotype did not influence adherence, adverse effects, or attitudes among risperidone users.

PMID:40130908 | DOI:10.1017/neu.2025.11

Brief Bioinform. 2025 Mar 4;26(2):bbaf065. doi: 10.1093/bib/bbaf065.

ABSTRACT

One of the main goals of metagenomic studies is to describe the taxonomic diversity of microbial communities. A crucial step in metagenomic analysis is metagenomic binning, which involves the (supervised) classification or (unsupervised) clustering of metagenomic sequences. Various machine learning models have been applied to address this task. In this review, the contributions of artificial neural networks (ANN) in the context of metagenomic binning are detailed, addressing both supervised, unsupervised, and semi-supervised approaches. 34 ANN-based binning tools are systematically compared, detailing their architectures, input features, datasets, advantages, disadvantages, and other relevant aspects. The findings reveal that deep learning approaches, such as convolutional neural networks and autoencoders, achieve higher accuracy and scalability than traditional methods. Gaps in benchmarking practices are highlighted, and future directions are proposed, including standardized datasets and optimization of architectures, for third-generation sequencing. This review provides support to researchers in identifying trends and selecting suitable tools for the metagenomic binning problem.

PMID:40131312 | DOI:10.1093/bib/bbaf065

Brief Bioinform. 2025 Mar 4;26(2):bbaf131. doi: 10.1093/bib/bbaf131.

ABSTRACT

DNA methylation plays a crucial role in human diseases pathogenesis. Substantial experimental evidence from clinical and biological studies has confirmed numerous methylation-disease associations, which provide valuable prior knowledge for advancing precision medicine through biomarker discovery and disease subtyping. To systematically mine reliable methylation prior knowledge from known DNA methylation-disease associations and develop robust computational methods for precision medicine applications, we propose MethPriorGCN. By integrating layer attention mechanisms and feature weighting mechanisms, MethPriorGCN not only identified reliable methylation digital biomarkers but also achieved superior disease subtype classification accuracy.

PMID:40131311 | DOI:10.1093/bib/bbaf131

Brief Bioinform. 2025 Mar 4;26(2):bbaf128. doi: 10.1093/bib/bbaf128.

ABSTRACT

Single-cell sequencing technology has enabled researchers to study cellular heterogeneity at the cell level. To facilitate the downstream analysis, clustering single-cell data into subgroups is essential. However, the high dimensionality, sparsity, and dropout events of the data make the clustering challenging. Currently, many deep learning methods have been proposed. Nevertheless, they either fail to fully utilize pairwise distances information between similar cells, or do not adequately capture their feature correlations. They cannot also effectively handle high-dimensional sparse data. Therefore, they are not suitable for high-fidelity clustering, leading to difficulties in analyzing the clear cell types required for downstream analysis. The proposed scSAMAC method integrates contrastive learning and negative binomial losses into a variational autoencoder, extracting features via contrastive unit similarity while preserving the intrinsic characteristics. This enhances the robustness and generalization during the clustering. In the contrastive learning, it constructs a mask module by adopting a negative sample generation method with gene feature saliency adjustment, which selects features more influential in the clustering phase and simulates data missing events. Additionally, it develops a novel loss, which consists of a soft k-means loss, a Wasserstein distance, and a contrastive loss. This fully utilizes data information and improves clustering performance. Furthermore, a multi-head attention mechanism module is applied to the latent variables at each layer of autoencoder to enhance feature correlation, integration, and information repair. Experimental results demonstrate that scSAMAC outperforms several state-of-the-art clustering methods.

PMID:40131310 | DOI:10.1093/bib/bbaf128

Chaos. 2025 Mar 1;35(3):033156. doi: 10.1063/5.0224768.

ABSTRACT

Detection of chaos in dynamical signals is an important and popular research area. Traditionally, the chaotic behavior is evaluated by calculating the Largest Lyapunov Exponent (LLE). However, calculating the LLE is sometimes difficult and requires specific data. Moreover, it introduces some subjective assumptions and is sometimes called a "manual" method. Therefore, there are many attempts to provide alternative ways to assess the dynamical signal as chaotic or deterministic. Some of them use deep learning methods. In this paper, we present a novel method of signal classification that is based on the assumption that it is easier to learn deterministic behavior than a chaotic one. We show that based on this assumption, it is possible to calculate the "amount of chaos" in the signal with the help of a simple LSTM (Long Short-Term Memory) neural network. The main advantage of this method is that-contrary to other deep learning-based methods-it does not require prior data to train the network as the results of the training process for a signal being classified are taken into account as the result of this evaluation. We confirm the method's validity using the publicly available dataset of chaotic and deterministic signals.

PMID:40131283 | DOI:10.1063/5.0224768

Expert Rev Anti Infect Ther. 2025 Mar 25. doi: 10.1080/14787210.2025.2484284. Online ahead of print.

ABSTRACT

INTRODUCTION: Traditional microbiological diagnostics face challenges in pathogen identification speed and antimicrobial resistance (AMR) evaluation. Artificial intelligence (AI) offers transformative solutions, necessitating a comprehensive review of its applications, advancements, and integration challenges in clinical microbiology.

AREAS COVERED: This review examines AI-driven methodologies, including machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs), for enhancing pathogen detection, AMR prediction, and diagnostic imaging. Applications in virology (e.g. COVID-19 RT-PCR optimization), parasitology (e.g. malaria detection), and bacteriology (e.g. automated colony counting) are analyzed. A literature search was conducted using PubMed, Scopus, and Web of Science (2018-2024), prioritizing peer-reviewed studies on AI's diagnostic accuracy, workflow efficiency, and clinical validation.

EXPERT OPINION: AI significantly improves diagnostic precision and operational efficiency but requires robust validation to address data heterogeneity, model interpretability, and ethical concerns. Future success hinges on interdisciplinary collaboration to develop standardized, equitable AI tools tailored for global healthcare settings. Advancing explainable AI and federated learning frameworks will be critical for bridging current implementation gaps and maximizing AI's potential in combating infectious diseases.

PMID:40131188 | DOI:10.1080/14787210.2025.2484284

Rev Sci Instrum. 2025 Mar 1;96(3):035117. doi: 10.1063/5.0231737.

ABSTRACT

According to a publication by the World Health Organization, depression is projected to emerge as the leading mental health issue. In the domain of affective computing, deep learning techniques are frequently employed to represent facial dynamics using both local and global perspectives for the purpose of automatic depression detection (ADD). Yet, current models overlook the crucial interplay between local and global dynamics in discerning the significant features essential for ADD. Addressing this oversight, a novel hybrid computational architecture, named LOGLFormer, has been introduced. This architecture integrates CNN-derived local attributes and transformer-sourced global patterns tailored for ADD. Within LOGLFormer, the design philosophies of ResNet and ViT inspire the CNN and transformer branches, respectively. The synergy of these branches encompasses local convolutional mechanisms, self-attention strategies, and multilayer perceptron entities. Furthermore, the intricacies arising from disparities in CNN and transformer feature sets are reconciled through the specially devised feature alignment module. Rigorous comparative analysis underscores the distinctive efficacy of the LOGLFormer in recognizing depression, notably outperforming several state-of-the-art techniques on two dedicated depression databases: AVEC2013 and AVEC2014. Code will be available at https://github.com/helang818/LOGLFormer.

PMID:40130984 | DOI:10.1063/5.0231737

Protein Sci. 2025 Apr;34(4):e70105. doi: 10.1002/pro.70105.

ABSTRACT

AlphaFold2 (AF2), a deep-learning-based model that predicts protein structures from their amino acid sequences, has recently been used to predict multiple protein conformations. In some cases, AF2 has successfully predicted both dominant and alternative conformations of fold-switching proteins, which remodel their secondary and/or tertiary structures in response to cellular stimuli. Whether AF2 has learned enough protein folding principles to reliably predict alternative conformations outside of its training set is unclear. Previous work suggests that AF2 predicted these alternative conformations by memorizing them during training. Here, we use CFold-an implementation of the AF2 network trained on a more limited subset of experimentally determined protein structures-to directly test how well the AF2 architecture predicts alternative conformations of fold switchers outside of its training set. We tested CFold on eight fold switchers from six protein families. These proteins-whose secondary structures switch between α-helix and β-sheet and/or whose hydrogen bonding networks are reconfigured dramatically-had not been tested previously, and only one of their alternative conformations was in CFold's training set. Successful CFold predictions would indicate that the AF2 architecture can predict disparate alternative conformations of fold-switched conformations outside of its training set, while unsuccessful predictions would suggest that AF2 predictions of these alternative conformations likely arise from association with structures learned during training. Despite sampling 1300-4300 structures/protein with various sequence sampling techniques, CFold predicted only one alternative structure outside of its training set accurately and with high confidence while also generating experimentally inconsistent structures with higher confidence. Though these results indicate that AF2's current success in predicting alternative conformations of fold switchers stems largely from its training data, results from a sequence pruning technique suggest developments that could lead to a more reliable generative model in the future.

PMID:40130805 | DOI:10.1002/pro.70105

Radiology. 2025 Mar;314(3):e242292. doi: 10.1148/radiol.242292.

ABSTRACT

Background The clinical, radiologic, and prognostic implications of interstitial pneumonia with autoimmune features (IPAF) in patients with idiopathic interstitial pneumonia and pathologic usual interstitial pneumonia (UIP) have not been fully evaluated. Purpose To compare autoimmune features according to CT patterns for the diagnosis of idiopathic pulmonary fibrosis (IPF) and to assess the diagnostic and prognostic implications of IPAF in patients with IPF-UIP. Materials and Methods This retrospective study included patients with UIP confirmed by surgical lung biopsy between January 2013 and February 2020. Data regarding clinical, radiologic, and pathologic autoimmune features were collected, and patients were diagnosed with IPAF according to current guidelines. CT signs for connective tissue disease (CTD; anterior upper lobe, straightedge, and exuberant honeycombing signs) were also evaluated. Overall survival (OS) was evaluated using Cox proportional hazards models. Results Among 210 patients included (median age, 64 years; IQR, 60-68 years; 158 male patients), 23 (11.0%) had IPAF. Patients with an alternative diagnosis or CT pattern indeterminate for UIP showed a higher prevalence of autoimmune features that were pathologic (38% [33 of 87] vs 20.3% [25 of 123]; P = .005) and serologic (20% [17 of 87] vs 9.8% [12 of 123]; P = .04) and IPAF (4.1% [five of 123] vs 21% [18 of 87]; P < .001) compared with patients with UIP or probable UIP pattern. However, IPAF was not predictive of OS (hazard ratio [HR], 0.81; 95% CI: 0.38, 1.72; P = .58). Lymphoid follicles (HR, 0.59; 95% CI: 0.37, 0.93; P = .02), CT signs for CTD (HR, 0.31; 95% CI: 0.09, 0.99; P = .047), and use of an antifibrotic agent (HR, 0.31; 95% CI: 0.19, 0.51; P < .001) were independently associated with higher OS, and greater extent of fibrosis on CT scans was associated with worse OS (HR, 1.08; 95% CI: 1.05, 1.11; P < .001). Conclusion In patients with IPF-pathologic UIP, serologic and pathologic autoimmune features were associated with indeterminate or alternative CT patterns. Certain histopathologic and radiologic autoimmune features, but not current IPAF criteria, were associated with survival. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Ackman in this issue.

PMID:40131107 | DOI:10.1148/radiol.242292

J Biomech Eng. 2025 Mar 25:1-53. doi: 10.1115/1.4068290. Online ahead of print.

ABSTRACT

Multiscale coupling between cell scale biology and tissue-scale mechanics is a promising approach for modeling disease growth. In such models, tissue-level growth and remodeling (G&R) is driven by cell-level signaling pathways and systems biology models, where each model operates at different scales. Herein, we generate multiscale G&R models to capture the associated multiscale connections. At the cell-scale, we consider systems biology models in the form of systems of ordinary differential equations (ODEs) and partial differential equations (PDEs) representing the reactions between the biochemicals causing the growth based on mass-action or logic-based Hill-type kinetics. At the tissue-scale, we employ kinematic growth in continuum frameworks. Two illustrative test problems (a tissue graft and aneurysm growth) are examined with various chemical signaling networks, boundary conditions, and mechano-chemical coupling strategies. We extend two open-source software frameworks - FEBio and FEniCS - to disseminate examples of multiscale growth and remodeling simulations. One-way and two-way coupling between the systems biology and the growth models are compared and the effect of biochemical diffusivity and ODE vs. PDE based systems biology modeling on the G&R results are studied. The results show that growth patterns emerge from reactions between biochemicals, the choice between ODEs and PDEs systems biology modeling, and the coupling strategy. Cross-verification confirms that results for FEBio and FEniCS are nearly identical. We hope that these open-source tools will support reproducibility and education within the biomechanics community.

PMID:40131342 | DOI:10.1115/1.4068290

Minerva Pediatr (Torino). 2025 Mar 25. doi: 10.23736/S2724-5276.24.07715-2. Online ahead of print.

ABSTRACT

Immunoglobulin A (IgA) vasculitis (IgAV, also known as Henoch-Schoenlein purpura, HSP) is a small vessel vasculitis, most commonly presenting in childhood. In most, it has a straightforward, self-limiting disease course, however some children may develop kidney involvement (IgAV-N) which occurs 4-12 weeks following disease onset and remains the biggest contributor to long-term morbidity. Therefore, children undergo a six-month period of kidney monitoring to identify nephritis via surrogate markers including urinalysis and blood pressure measurements. On-going efforts aim at earlier identification and prevention of nephritis during the window of opportunity between disease onset and established nephritis. By identifying those at highest risk of developing poorer kidney outcomes, the number of children developing chronic kidney disease stage 5 (CKD5) as a result of IgAV-N may be reduced. This review summarizes the latest scientific evidence that support the use of novel biomarkers which may allow nephritis to be identified earlier compared to traditional markers, as well as the risk stratification of children with established IgAV-N. These biomarkers may also enhance the evolving understanding of underlying inflammatory pathways. Promising novel urinary markers of early nephritis include angiotensinogen, Gd-IgA1, various complement proteins, and MCP-1, and serum markers such as α-SMA, C-Met, PTX-3, MMP-9, MRP 8/14, and adiponectin may help identify those at risk of developing CKD5. Prospective, longitudinal, international validation studies are required to investigate these markers further, including exploration of implementation into clinical practice.

PMID:40131233 | DOI:10.23736/S2724-5276.24.07715-2

mBio. 2025 Mar 25:e0045025. doi: 10.1128/mbio.00450-25. Online ahead of print.

ABSTRACT

DNA viruses at once elicit and commandeer host pathways, including DNA repair pathways, for virus replication. Despite encoding its own DNA polymerase and processivity factor, human cytomegalovirus (HCMV) recruits the cellular processivity factor, proliferating cell nuclear antigen (PCNA) and specialized host DNA polymerases involved in translesion synthesis (TLS) to replication compartments (RCs) where viral DNA (vDNA) is synthesized. While the recruitment of TLS polymerases is important for viral genome stability, the role of PCNA is poorly understood. PCNA function in DNA repair is regulated by monoubiquitination (mUb) or SUMOylation of PCNA at lysine 164 (K164). We find that mUb-PCNA increases over the course of infection, and modification of K164 is required for PCNA-mediated restriction of virus replication. mUb-PCNA plays important known roles in recruiting TLS polymerases to DNA, which we have shown are important for viral genome integrity and diversity, represented by structural variants and single nucleotide variants (SNVs), respectively. We find that PCNA drives SNVs on vDNA similar to Y-family TLS polymerases, but this did not require modification at K164. Unlike TLS polymerases, depeletion of PCNA did not result in large-scale rearrangements on vDNA. These striking results suggest separable PCNA-dependent and -independent functions of TLS polymerases on vDNA. By extension, these results imply roles for TLS polymerase beyond their canonical function in TLS in host biology. These findings highlight PCNA as a complex restriction factor for HCMV infection, likely with multiple distinct roles, and provide new insights into the PCNA-mediated regulation of DNA synthesis and repair in viral infection.IMPORTANCEGenome synthesis is a critical step of virus life cycles and a major target of antiviral drugs. Human cytomegalovirus (HCMV), like other herpesviruses, encodes machinery sufficient for viral DNA synthesis and relies on host factors for efficient replication. We have shown that host DNA repair factors play important roles in HCMV replication, but our understanding of this is incomplete. Building on previous findings that specialized host DNA polymerases contribute to HCMV genome integrity and diversity, we sought to determine the importance of proliferating cell nuclear antigen (PCNA), the central polymerase regulator. PCNA is associated with nascent viral DNA and restricts HCMV replication. While PCNA is dispensable for genome integrity, it contributes to genome diversity. Our findings suggest that host polymerases function on viral genomes by separable PCNA-dependent and -independent mechanisms. Through revealing complex roles for PCNA in HCMV replication, this study expands the repertoire of host DNA synthesis and repair proteins hijacked by this ubiquitous herpesvirus.

PMID:40130902 | DOI:10.1128/mbio.00450-25

J Virol. 2025 Mar 25:e0201824. doi: 10.1128/jvi.02018-24. Online ahead of print.

ABSTRACT

PRO 140, a humanized anti-HIV monoclonal antibody targeting the cell coreceptor CCR5, is currently under clinical trials, but it only affects CCR5-tropic viruses. In this study, we have engineered two tandem fusion proteins (2P23-PRO140SC and 2P23-PRO140-Fc) with bifunctional activity by adding short fusion-inhibitory peptide 2P23 to the single-chain fragment variable (scFv) of PRO 140 (PRO140SC) with or without the Fc domain of human IgG4. We first demonstrated that 2P23-PRO140SC and 2P23-PRO140-Fc could efficiently bind to the cell membranes through CCR5 anchoring, which did not affect the expression level of CCR5 on the cell surface. We then verified that the addition of 2P23 peptide to PRO140SC enabled a very potent activity against CXCR4-tropic HIV-1 isolates. As expected, the bispecific fusion proteins exhibited highly potent activities in inhibiting divergent HIV-1 subtypes and viral mutants that were resistant to the fusion inhibitors 2P23 and T20, and they displayed relatively low in vitro cytotoxicity. Furthermore, both the fusion proteins had robust in vivo anti-HIV activities in rats, with 2P23-PRO140-Fc much better than 2P23-PRO140SC. In conclusion, our studies have provided bispecific HIV-1 inhibitors that overcome the drawbacks of PRO 140 antibody and offered novel tools for studying the mechanisms of HIV-1 infection.IMPORTANCEGiven that HIV-1 evolves with high variability and drug resistance, the development of novel antivirals is important. CCR5-directed antibody PRO 140 is currently under clinical trials, but it only inhibits CCR5-tropic HIV-1 isolates. The designed fusion proteins by adding a minimum fusion-inhibitory peptide to PRO 140 enable dramatically increased activities in inhibiting both CCR5-tropic and CXCR4-tropic viruses, thus offering novel antiviral agents with a bispecific functionality that can overcome the drawbacks of PRO 140 antibody.

PMID:40130879 | DOI:10.1128/jvi.02018-24

Plant Cell Environ. 2025 Mar 25. doi: 10.1111/pce.15493. Online ahead of print.

ABSTRACT

In most angiosperms, female gametogenesis originates from a specifically selected haploid megaspore, as three out of the four megaspores produced by meiosis degenerate without undergoing further division or differentiation. The remaining megaspore acquires functional megaspore (FM) identity, becoming the FM, which is essential for plant reproductive development. However, the molecular mechanisms governing FM development (or megaspore degeneration) remain largely unexplored, with current studies focusing on only a limited number of genes or regulatory networks. To date, no comprehensive review has systematically introduced advances in this field. This review aims to highlight recent progress in understanding FM development, discuss its critical role in female reproductive development and prospect the mechanism of FM development in environmental adaptation. By offering new insights, this review enriches existing knowledge of FM development and provides fresh perspectives for future research in plant reproduction and its adaptation to the environment.

PMID:40130504 | DOI:10.1111/pce.15493