J Invertebr Pathol. 2025 Mar 17:108313. doi: 10.1016/j.jip.2025.108313. Online ahead of print.

ABSTRACT

Entomopathogenic nematodes (EPNs) are soil-dwelling organisms essential for controlling pest populations across diverse crops and regions worldwide. Over the past century, significant advancements have been made in isolating, identifying, and quantifying EPNs, enhancing our understanding of their natural distribution and influencing factors. This review outlines major milestones in EPN population dynamics research and highlights emerging molecular and biophysical tools that offer new research directions. Here, we examine the factors shaping EPN occurrence in agroecosystems, including interactions between hosts, EPNs, and their resource competitors (viewing EPNs as scavengers) and the biotic and abiotic drivers affecting their spatial and temporal patterns. Additionally, the review explores EPN interactions with plant rhizospheres and the impact of agricultural practices on their efficacy as biological control agents. Understanding EPN population dynamics is crucial for optimizing their use as sustainable pest management tools. By combining traditional insights with innovative methods, we can expand EPN applications in agroecosystems, fostering more resilient and eco-friendly agricultural practices.

PMID:40107567 | DOI:10.1016/j.jip.2025.108313

Curr Biol. 2025 Mar 13:S0960-9822(25)00198-8. doi: 10.1016/j.cub.2025.02.034. Online ahead of print.

ABSTRACT

Discrimination between different signals is crucial for animals' survival. Inhibition that suppresses weak neural activity is crucial for pattern decorrelation. Our understanding of alternative mechanics that allow efficient signal classification remains incomplete. We show that Drosophila olfactory receptor neurons (ORNs) have numerous intraglomerular axo-axonal connections mediated by the G protein-coupled receptor (GPCR), muscarinic type B receptor (mAChR-B). Contrary to its usual inhibitory role, mAChR-B participates in ORN excitation. The excitatory effect of mAChR-B only occurs at high ORN firing rates. A computational model demonstrates that nonlinear intraglomerular or global excitation decorrelates the activity patterns of ORNs of different types and improves odor classification and discrimination, while acting in concert with the previously known inhibition. Indeed, knocking down mAChR-B led to increased correlation in odor-induced ORN activity, which was associated with impaired odor discrimination, as shown in behavioral experiments. Furthermore, knockdown (KD) of mAChR-B and the GABAergic GPCR, GABAB-R, has an additive behavioral effect, causing reduced odor discrimination relative to single-KD flies. Together, this study unravels a novel mechanism for neuronal pattern decorrelation, which is based on nonlinear intraglomerular excitation.

PMID:40107267 | DOI:10.1016/j.cub.2025.02.034

J Pharm Biomed Anal. 2025 Mar 12;260:116806. doi: 10.1016/j.jpba.2025.116806. Online ahead of print.

ABSTRACT

The leaves of Annona muricata Linn. have long been utilized in traditional medicine for diabetes treatment, and there is no study that has employed a metabolomics approach to investigate the plant's effects in managing the disease. We aimed to explore the antidiabetic effects of the standardised A. muricata leaf extract on diabetes-induced rats by alloxan monohydrate (Ax) and nicotinamide (NA) using a proton nuclear magnetic resonance (¹H-NMR)-based metabolomics approach. Absolute quantification was performed on the leaf extract using ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Two different doses of the extract were administered orally for four weeks to diabetic rats induced with Ax + NA, and physical evaluations, biochemical analyses, and ¹H-NMR metabolomics of urine and serum were assessed. The results showed that quercetin 3-rutinoside was the most abundant compound in the 80 % ethanolic extract of A. muricata leaf. The induction of type 2 diabetes mellitus (T2DM) in the rat model was confirmed by the clear metabolic distinction between normal rats, diabetic rats, and metformin-treated diabetic rats. The low-dose of A. muricata leaf extract (200 mg/kg) was found to exhibit better results, significantly reducing serum urea levels in diabetic rats, with effects comparable to those of metformin. Additionally, metabolite analysis from ¹H-NMR metabolomics of serum and urine showed a slight shift toward normal metabolic profiles in the treated diabetic rats. Pathway analysis revealed alterations in the tricarboxylic acid cycle (TCA), pyruvate metabolism, and glycolysis/gluconeogenesis pathways in the diabetic rat model, which were improved following treatment with the A. muricata leaf extract. Overall, this study provides scientific support for its traditional use in diabetes management and offers new insights into the underlying molecular mechanisms.

PMID:40106911 | DOI:10.1016/j.jpba.2025.116806

Invest Radiol. 2025 Mar 20. doi: 10.1097/RLI.0000000000001179. Online ahead of print.

ABSTRACT

Over the past decade, Investigative Radiology has published numerous studies that have fundamentally advanced the field of thoracic imaging. This review summarizes key developments in imaging modalities, computational tools, and clinical applications, highlighting major breakthroughs in thoracic diseases-lung cancer, pulmonary nodules, interstitial lung disease (ILD), chronic obstructive pulmonary disease (COPD), COVID-19 pneumonia, and pulmonary embolism-and outlining future directions.Artificial intelligence (AI)-driven computer-aided detection systems and radiomic analyses have notably improved the detection and classification of pulmonary nodules, while photon-counting detector CT (PCD-CT) and low-field MRI offer enhanced resolution or radiation-free strategies. For lung cancer, CT texture analysis and perfusion imaging refine prognostication and therapy planning. ILD assessment benefits from automated diagnostic tools and innovative imaging techniques, such as PCD-CT and functional MRI, which reduce the need for invasive diagnostic procedures while improving accuracy. In COPD, dual-energy CT-based ventilation/perfusion assessment and dark-field radiography enable earlier detection and staging of emphysema, complemented by deep learning approaches for improved quantification. COVID-19 research has underscored the clinical utility of chest CT, radiographs, and AI-based algorithms for rapid triage, disease severity evaluation, and follow-up. Furthermore, tuberculosis remains a significant global health concern, highlighting the importance of AI-assisted chest radiography for early detection and management. Meanwhile, advances in CT pulmonary angiography, including dual-energy reconstructions, allow more sensitive detection of pulmonary emboli.Collectively, these innovations demonstrate the power of merging novel imaging technologies, quantitative functional analysis, and AI-driven tools to transform thoracic disease management. Ongoing progress promises more precise and personalized diagnostic and therapeutic strategies for diverse thoracic diseases.

PMID:40106831 | DOI:10.1097/RLI.0000000000001179

JMIR Aging. 2025 Mar 19;8:e63686. doi: 10.2196/63686.

ABSTRACT

BACKGROUND: Sarcopenia (loss of muscle mass and strength) increases adverse outcomes risk and contributes to cognitive decline in older adults. Accurate methods to quantify muscle mass and predict adverse outcomes, particularly in older persons with dementia, are still lacking.

OBJECTIVE: This study's main objective was to assess the feasibility of using deep learning techniques for segmentation and quantification of musculoskeletal tissues in magnetic resonance imaging (MRI) scans of the head in patients with neurocognitive disorders. This study aimed to pave the way for using automated techniques for opportunistic detection of sarcopenia in patients with neurocognitive disorder.

METHODS: In a cross-sectional analysis of 53 participants, we used 7 U-Net-like deep learning models to segment 5 different tissues in head MRI images and used the Dice similarity coefficient and average symmetric surface distance as main assessment techniques to compare results. We also analyzed the relationship between BMI and muscle and fat volumes.

RESULTS: Our framework accurately quantified masseter and subcutaneous fat on the left and right sides of the head and tongue muscle (mean Dice similarity coefficient 92.4%). A significant correlation exists between the area and volume of tongue muscle, left masseter muscle, and BMI.

CONCLUSIONS: Our study demonstrates the successful application of a deep learning model to quantify muscle volumes in head MRI in patients with neurocognitive disorders. This is a promising first step toward clinically applicable artificial intelligence and deep learning methods for estimating masseter and tongue muscle and predicting adverse outcomes in this population.

PMID:40106819 | DOI:10.2196/63686

Sci Adv. 2025 Mar 21;11(12):eadt3803. doi: 10.1126/sciadv.adt3803. Epub 2025 Mar 19.

ABSTRACT

Viral hepatitis remains a major global health issue, with chronic hepatitis B (HBV) and hepatitis C (HCV) causing approximately 1 million deaths annually, primarily due to liver cancer and cirrhosis. More than 1.5 million people contract HCV each year, disproportionately affecting vulnerable populations, including American Indians and Alaska Natives (AI/AN). While direct-acting antivirals (DAAs) are highly effective, timely and accurate HCV diagnosis remains a challenge, particularly in resource-limited settings. The current two-step HCV testing process is costly and time-intensive, often leading to patient loss before treatment. Point-of-care (POC) HCV antigen (Ag) testing offers a promising alternative, but no FDA-approved test meets the required sensitivity and specificity. To address this, we developed a fully automated, smartphone-based POC HCV Ag assay using platinum nanoparticles, deep learning image processing, and microfluidics. With an overall accuracy of 94.59%, this cost-effective, portable device has the potential to reduce HCV-related health disparities, particularly among AI/AN populations, improving accessibility and equity in care.

PMID:40106555 | DOI:10.1126/sciadv.adt3803

PLoS One. 2025 Mar 19;20(3):e0319828. doi: 10.1371/journal.pone.0319828. eCollection 2025.

ABSTRACT

This paper focuses on the evaluation and recommendation of healthcare applications in the mHealth field. The increase in the use of health applications, supported by an expanding mHealth market, highlights the importance of this research. In this study, a data set including app descriptions, ratings, reviews, and other relevant attributes from various health app platforms was selected. The main goal was to design a recommendation system that leverages app attributes, especially descriptions, to provide users with relevant contextual suggestions. A comprehensive pre-processing regime was carried out, including one-hot encoding, standardisation, and feature engineering. The feature, "Rating_Reviews", was introduced to capture the cumulative influence of ratings and reviews. The variable 'Category' was chosen as a target to discern different health contexts such as 'Weight loss' and 'Medical'. Various machine learning and deep learning models were evaluated, from the baseline Random Forest Classifier to the sophisticated BERT model. The results highlighted the efficiency of transfer learning, especially BERT, which achieved an accuracy of approximately 90% after hyperparameter tuning. A final recommendation system was designed, which uses cosine similarity to rank apps based on their relevance to user queries.

PMID:40106462 | DOI:10.1371/journal.pone.0319828

PLoS Genet. 2025 Mar 19;21(3):e1011420. doi: 10.1371/journal.pgen.1011420. Online ahead of print.

ABSTRACT

Deep learning techniques are increasingly utilized to analyze large-scale single-cell RNA sequencing (scRNA-seq) data, offering valuable insights from complex transcriptome datasets. Geneformer, a pre-trained model using a Transformer Encoder architecture and human scRNA-seq datasets, has demonstrated remarkable success in human transcriptome analysis. However, given the prominence of the mouse, Mus musculus, as a primary mammalian model in biological and medical research, there is an acute need for a mouse-specific version of Geneformer. In this study, we developed a mouse-specific Geneformer (mouse-Geneformer) by constructing a large transcriptome dataset consisting of 21 million mouse scRNA-seq profiles and pre-training Geneformer on this dataset. The mouse-Geneformer effectively models the mouse transcriptome and, upon fine-tuning for downstream tasks, enhances the accuracy of cell type classification. In silico perturbation experiments using mouse-Geneformer successfully identified disease-causing genes that have been validated in in vivo experiments. These results demonstrate the feasibility of analyzing mouse data with mouse-Geneformer and highlight the robustness of the Geneformer architecture, applicable to any species with large-scale transcriptome data available. Furthermore, we found that mouse-Geneformer can analyze human transcriptome data in a cross-species manner. After the ortholog-based gene name conversion, the analysis of human scRNA-seq data using mouse-Geneformer, followed by fine-tuning with human data, achieved cell type classification accuracy comparable to that obtained using the original human Geneformer. In in silico simulation experiments using human disease models, we obtained results similar to human-Geneformer for the myocardial infarction model but only partially consistent results for the COVID-19 model, a trait unique to humans (laboratory mice are not susceptible to SARS-CoV-2). These findings suggest the potential for cross-species application of the Geneformer model while emphasizing the importance of species-specific models for capturing the full complexity of disease mechanisms. Despite the existence of the original Geneformer tailored for humans, human research could benefit from mouse-Geneformer due to its inclusion of samples that are ethically or technically inaccessible for humans, such as embryonic tissues and certain disease models. Additionally, this cross-species approach indicates potential use for non-model organisms, where obtaining large-scale single-cell transcriptome data is challenging.

PMID:40106407 | DOI:10.1371/journal.pgen.1011420

Proc Natl Acad Sci U S A. 2025 Mar 25;122(12):e2409685122. doi: 10.1073/pnas.2409685122. Epub 2025 Mar 19.

ABSTRACT

PAS domains are ubiquitous sensory modules that transduce environmental signals into cellular responses through tandem PAS folds and PAS-associated C-terminal (PAC) motifs. While this conserved architecture underpins their regulatory roles, here we uncover a structural divergence in the metazoan PAS domain-regulated kinase (PASK). By integrating evolutionary-scale domain mapping with deep learning-based structural models, we identified two PAS domains in PASK, namely PAS-B and PAS-C, in addition to the previously known PAS-A domain. Unlike canonical PAS domains, the PAS fold and PAC motif in the PAS-C domain are spatially segregated by an unstructured linker, yet a functional PAS module is assembled through intramolecular interactions. We demonstrate that this assembly is nutrient responsive and serves to remodel the quaternary structure of PASK that positions the PAS-A domain near the kinase activation loop. This nutrient-sensitive spatial arrangement stabilizes the activation loop, enabling catalytic activation of PASK. These findings revealed an alternative mode of regulatory control in PAS sensory proteins, where the structural assembly of PAS domains links environmental sensing to enzymatic activity. By demonstrating that PAS domains integrate signals through dynamic structural rearrangements, this study broadens the understanding of their functional and regulatory roles and highlights potential opportunities for targeting PAS domain-mediated pathways in therapeutic applications.

PMID:40106358 | DOI:10.1073/pnas.2409685122

JMIR Form Res. 2025 Mar 19;9:e54803. doi: 10.2196/54803.

ABSTRACT

BACKGROUND: Medical abstract sentence classification is crucial for enhancing medical database searches, literature reviews, and generating new abstracts. However, Chinese medical abstract classification research is hindered by a lack of suitable datasets. Given the vastness of Chinese medical literature and the unique value of traditional Chinese medicine, precise classification of these abstracts is vital for advancing global medical research.

OBJECTIVE: This study aims to address the data scarcity issue by generating a large volume of labeled Chinese abstract sentences without manual annotation, thereby creating new training datasets. Additionally, we seek to develop more accurate text classification algorithms to improve the precision of Chinese medical abstract classification.

METHODS: We developed 3 training datasets (dataset #1, dataset #2, and dataset #3) and a test dataset to evaluate our model. Dataset #1 contains 15,000 abstract sentences translated from the PubMed dataset into Chinese. Datasets #2 and #3, each with 15,000 sentences, were generated using GPT-3.5 from 40,000 Chinese medical abstracts in the CSL database. Dataset #2 used titles and keywords for pseudolabeling, while dataset #3 aligned abstracts with category labels. The test dataset includes 87,000 sentences from 20,000 abstracts. We used SBERT embeddings for deeper semantic analysis and evaluated our model using clustering (SBERT-DocSCAN) and supervised methods (SBERT-MEC). Extensive ablation studies and feature analyses were conducted to validate the model's effectiveness and robustness.

RESULTS: Our experiments involved training both clustering and supervised models on the 3 datasets, followed by comprehensive evaluation using the test dataset. The outcomes demonstrated that our models outperformed the baseline metrics. Specifically, when trained on dataset #1, the SBERT-DocSCAN model registered an impressive accuracy and F1-score of 89.85% on the test dataset. Concurrently, the SBERT-MEC algorithm exhibited comparable performance with an accuracy of 89.38% and an identical F1-score. Training on dataset #2 yielded similarly positive results for the SBERT-DocSCAN model, achieving an accuracy and F1-score of 89.83%, while the SBERT-MEC algorithm recorded an accuracy of 86.73% and an F1-score of 86.51%. Notably, training with dataset #3 allowed the SBERT-DocSCAN model to attain the best with an accuracy and F1-score of 91.30%, whereas the SBERT-MEC algorithm also showed robust performance, obtaining an accuracy of 90.39% and an F1-score of 90.35%. Ablation analysis highlighted the critical role of integrated features and methodologies in improving classification efficiency.

CONCLUSIONS: Our approach addresses the challenge of limited datasets for Chinese medical abstract classification by generating novel datasets. The deployment of SBERT-DocSCAN and SBERT-MEC models significantly enhances the precision of classifying Chinese medical abstracts, even when using synthetic datasets with pseudolabels.

PMID:40106267 | DOI:10.2196/54803

IEEE Trans Pattern Anal Mach Intell. 2025 Mar 19;PP. doi: 10.1109/TPAMI.2025.3553051. Online ahead of print.

ABSTRACT

Point cloud semantic segmentation can enhance the understanding of the production environment and is a crucial component of vision tasks. The efficacy and generalization prowess of deep learning-based segmentation models are inherently contingent upon the quality and nature of the data employed in their training. However, it is often challenging to obtain data with inter-class balance, and training an intelligent segmentation network with the imbalanced data may cause cognitive bias. In this paper, a network framework InvSpaceNet is proposed, which generates an inverse feature space to alleviate the cognitive bias caused by imbalanced data. Specifically, we design a dual-branch training architecture that combines the superior feature representations derived from instance-balanced sampling data with the cognitive corrections introduced by the proposed inverse sampling data. In the inverse feature space of the point cloud generated by the auxiliary branch, the central points aggregated by class are constrained by the contrastive loss. To refine the class cognition in the inverse feature space, features are used to generate point cloud class prototypes through momentum update. These class prototypes from the inverse space are utilized to generate feature maps and structure maps that are aligned with the positive feature space of the main branch segmentation network. The training of the main branch is dynamically guided through gradients back propagated from different losses. Extensive experiments conducted on four large benchmarks (i.e., S3DIS, ScanNet v2, Toronto-3D, and SemanticKITTI) demonstrate that the proposed method can effectively mitigate point cloud imbalance issues and improve segmentation performance.

PMID:40106253 | DOI:10.1109/TPAMI.2025.3553051

IEEE Trans Med Imaging. 2025 Mar 19;PP. doi: 10.1109/TMI.2025.3552692. Online ahead of print.

ABSTRACT

Photoacoustic imaging (PAI) is a high-resolution biomedical imaging technology for the non-invasive detection of a broad range of chromophores at multiple scales and depths. However, limited by low chromophore concentration, weak signals in deep tissue, or various noise, the signal-to-noise ratio of photoacoustic images may be compromised in many biomedical applications. Although improvements in hardware and computational methods have been made to address this problem, they have not been readily available due to either high costs or an inability to generalize across different datasets. Here, we present a self-supervised deep learning method to increase the signal-to-noise ratio of photoacoustic images using noisy data only. Because this method does not require expensive ground truth data for training, it can be easily implemented across scanning microscopic and computed tomographic data acquired with various photoacoustic imaging systems. In vivo results show that our method makes the vascular details that were completely submerged in noise become clearly visible, increases the signal-to-noise ratio by up to 12-fold, doubles the imaging depth, and enables high-contrast imaging of deep tumors. We believe this method can be readily applied to many preclinical and clinical applications.

PMID:40106247 | DOI:10.1109/TMI.2025.3552692

IEEE J Biomed Health Inform. 2025 Mar 19;PP. doi: 10.1109/JBHI.2025.3552819. Online ahead of print.

ABSTRACT

Cancer therapy-related cardiac dysfunction (CTRCD) is a potential complication associated with cancer treatment, particularly in patients with breast cancer, requiring monitoring of cardiac health during the treatment process. Tissue Doppler imaging (TDI) is a remarkable technique that can provide a comprehensive reflection of the left ventricle's physiological status. We hypothesized that the combination of TDI features with deep learning techniques could be utilized to predict CTRCD. To evaluate the hypothesis, we developed a temporal-multimodal pattern network for efficient training (TPNET) model to predict the incidence of CTRCD over a 24-month period based on TDI, function, and clinical data from 270 patients. Our model achieved an area under curve (AUC) of 0.83 and sensitivity of 0.88, demonstrating greater robustness compared to other existing visual models. To further translate our model's findings into practical applications, we utilized the integrated gradients (IG) attribution to perform a detailed evaluation of all the features. This analysis has identified key pathogenic signs that may have remained unnoticed, providing a viable option for implementing our model in preoperative breast cancer patients. Additionally, our findings demonstrate the potential of TPNET in discovering new causative agents for CTRCD.

PMID:40106240 | DOI:10.1109/JBHI.2025.3552819

Sci Transl Med. 2025 Mar 19;17(790):eadp0675. doi: 10.1126/scitranslmed.adp0675. Epub 2025 Mar 19.

ABSTRACT

The diverse T cell receptor (TCR) repertoire confers the ability to recognize an almost unlimited array of antigens. Characterization of antigen specificity of tumor-infiltrating lymphocytes (TILs) is key for understanding antitumor immunity and for guiding the development of effective immunotherapies. Here, we report a large-scale comprehensive examination of the TCR landscape of TILs across the spectrum of pediatric brain tumors, the leading cause of cancer-related mortality in children. We show that a T cell clonality index can inform patient prognosis, where more clonality is associated with more favorable outcomes. Moreover, TCR similarity groups' assessment revealed patient clusters with defined human leukocyte antigen associations. Computational analysis of these clusters identified putative tumor antigens and peptides as targets for antitumor T cell immunity, which were functionally validated by T cell stimulation assays in vitro. Together, this study presents a framework for tumor antigen prediction based on in situ and in silico TIL TCR analyses. We propose that TCR-based investigations should inform tumor classification and precision immunotherapy development.

PMID:40106578 | DOI:10.1126/scitranslmed.adp0675

Sci Adv. 2025 Mar 21;11(12):eadp8100. doi: 10.1126/sciadv.adp8100. Epub 2025 Mar 19.

ABSTRACT

Exposure to airborne particulate matter (PM) has been attributed to millions of deaths annually. However, the PM components responsible for observed health effects remain unclear. Oxidative potential (OP) has gained increasing attention as a key property that may explain PM toxicity. Using online measurement methods that impinge particles for OP quantification within seconds, we reveal that 60 to 99% of reactive oxygen species (ROS) and OP in secondary organic aerosol and combustion-generated PM have a lifetime of minutes to hours and that the ROS activity of ambient PM decays substantially before offline analysis. This implies that current offline measurement methods substantially underestimate the true OP of PM. We demonstrate that short-lived OP components activate different toxicity pathways upon direct deposition onto reconstituted human bronchial epithelia. Therefore, we suggest that future air pollution and health studies should include online OP quantification, allowing more accurate assessments of links between OP and health effects.

PMID:40106561 | DOI:10.1126/sciadv.adp8100

STAR Protoc. 2025 Mar 18;6(2):103681. doi: 10.1016/j.xpro.2025.103681. Online ahead of print.

ABSTRACT

As genes tend to be co-regulated as gene modules, feature selection in machine learning (ML) on gene expression data can be challenged by the complexity of gene regulation. Here, we present a protocol for reconciling differences in classifier features identified using different ML approaches. We describe steps for loading the PathwaySpace R package, preparing input for analysis, and creating density plots of gene sets. We then detail procedures for testing whether apparently distinct feature sets are related in pathway space. For complete details on the use and execution of this protocol, please refer to Ellrott et al.1.

PMID:40106435 | DOI:10.1016/j.xpro.2025.103681

Am J Physiol Renal Physiol. 2025 Mar 19. doi: 10.1152/ajprenal.00285.2024. Online ahead of print.

ABSTRACT

We have devised a user-friendly downloadable, standalone application that solves a set of ordinary differential equations describing steady-state mass balance for salt (NaCl), urea and water in a single renal tubule with axial flow. The model was programmed in Python using an explicit ordinary differential equation solver. The standalone version allows users to interact with a GUI to insert parameter values and initiate the calculations. It outputs volume flow rate and solute concentrations as a function of position along the tubule. We illustrate the use of the model to address questions about the roles of the mammalian distal convoluted tubule (DCT) in water balance. The simulations suggest an important role for the DCT as a second diluting segment beyond the cortical thick ascending limb (CTAL), consistent with a critical function in excretion of water loads. Simulation of the effect of thiazide diuretics, which inhibit active salt absorption in the DCT, provides an explanation for the observation that these agents can produce hyponatremia when used clinically. The simulations also indicate that the DCT may transport salt in either direction (in accord with micropuncture findings), depending on the salt concentration in the fluid entering from the CTAL. Salt reabsorption by active transport is balanced by passive salt secretion as the luminal salt concentration approaches an asymptotic 'static head' level. The tool will allow users with no mathematical modeling experience to simulate transport in renal tubules, working toward the goal of expanding the use of mathematical modeling in physiology.

PMID:40106383 | DOI:10.1152/ajprenal.00285.2024

Proc Natl Acad Sci U S A. 2025 Mar 25;122(12):e2417588122. doi: 10.1073/pnas.2417588122. Epub 2025 Mar 19.

ABSTRACT

Plants produce diverse specialized metabolites with important ecological functions. It has recently become apparent that the genes for many of these pathways are not dispersed in plant genomes, but rather are arranged like beads on a string in biosynthetic gene clusters (BGCs). Pathways encoded by BGCs are as a rule dedicated linear pathways that do not form parts of wider metabolic networks. In contrast, the genes for the biosynthesis of widely distributed more ancestral metabolites such as carotenoids and anthocyanins are not clustered. Little is known about how these more recently evolved clustered pathways interact with general plant metabolism. We recently characterized a 12-gene BGC for the biosynthesis of the antimicrobial defense compound avenacin A-1, a triterpene glycoside produced by oats. Avenacin A-1 is acylated with the fluorophore N-methyl anthranilate and confers bright blue fluorescence of oat root tips under ultraviolet light. Here, we exploit a suite of >100 avenacin-deficient mutants identified by screening for reduced root fluorescence to identify genes required for the function of this paradigm BGC. Using a combination of mutant chemotyping, biochemical and molecular analysis, and genome resequencing, we identify two nonclustered genes (Sad4 and Pal2) encoding enzymes that synthesize the donors required for avenacin glycosylation and acylation (recruited from the phenylpropanoid and tryptophan pathways). Our finding of these Cluster Auxiliary Enzymes (CAEs) provides insights into the interplay between general plant metabolism and a newly evolved lineage-specific BGC.

PMID:40106352 | DOI:10.1073/pnas.2417588122

Curr Genet. 2025 Mar 19;71(1):7. doi: 10.1007/s00294-025-01311-2.

ABSTRACT

There is great interest in establishing microalgae as new platforms for the sustainable production of high-value products such as recombinant proteins. Many human therapeutic proteins must be glycosylated, which requires their passage through the secretory pathway into the culture medium. While the low complexity of proteins in the culture medium should facilitate affinity purification of secreted recombinant proteins, this has proven challenging for proteins secreted by the unicellular green alga Chlamydomonas reinhardtii. In Leishmania tarentulae, we observed that C-terminally exposed affinity tags are frequently truncated, presumably due to proteolytic activity. We wondered whether this might also occur in Chlamydomonas and contribute to the difficulties in affinity purification of secreted proteins in this alga. Using the methionine-rich 2S albumin from Bertholletia excelsa and the ectodomain of the SARS-CoV-2 spike protein produced and secreted in Chlamydomonas, we demonstrate that they can be efficiently affinity-purified from the culture medium by Ni-NTA chromatography when the 8xHis affinity tag is internalized. This finding represents an important step towards further development of Chlamydomonas as a host for the sustainable production of high-value recombinant proteins.

PMID:40105958 | DOI:10.1007/s00294-025-01311-2

BioDrugs. 2025 Mar 19. doi: 10.1007/s40259-025-00712-6. Online ahead of print.

ABSTRACT

The development of immune checkpoint inhibitors has revolutionized the treatment of patients with cancer. Targeting the programmed cell death protein 1 (PD-1)/programmed cell death 1 ligand 1(PD-L1) interaction using monoclonal antibodies has emerged as a prominent focus in tumor therapy with rapid advancements. However, the efficacy of anti-PD-1/PD-L1 treatment is hindered by primary or acquired resistance, limiting the effectiveness of single-drug approaches. Moreover, combining PD-1/PD-L1 with other immune drugs, targeted therapies, or chemotherapy significantly enhances response rates while exacerbating adverse reactions. Multispecific antibodies, capable of binding to different epitopes, offer improved antitumor efficacy while reducing drug-related side effects, serving as a promising therapeutic approach in cancer treatment. Several bispecific antibodies (bsAbs) targeting PD-1/PD-L1 have received regulatory approval, and many more are currently in clinical development. Additionally, tri-specific antibodies (TsAbs) and tetra-specific antibodies (TetraMabs) are under development. This review comprehensively explores the fundamental structure, preclinical principles, clinical trial progress, and challenges associated with bsAbs targeting PD-1/PD-L1.

PMID:40106158 | DOI:10.1007/s40259-025-00712-6