J Extracell Vesicles. 2025 Feb;14(2):e70046. doi: 10.1002/jev2.70046.

ABSTRACT

Up-regulation of Centrosomal Protein 55 (CEP55) in cancer cells increases malignancy, and the protein can be transferred via exosomes. However, the mechanism of how CEP55 is delivered to exosomes is unknown. In this study, we addressed this issue and analysed trafficking of EGFP-CEP55 from early to late endosomes by using high-resolution microscopy. Our data show that endogenous as well as EGFP-CEP55 appeared as dot-like structures in cancer cells. However, we did not find an internalization of CEP55 into early Rab5- and late Rab7-positive endosomes but only into secretory late CD63-positive endosomes. In addition, an association of the CEP55 dots with the endoplasmic reticulum and with ALG-2-interacting protein X (Alix) dots was detected. Moreover, mutation of the CEP55-Alix interaction site strongly reduced the formation of CEP55 dots as well as CEP55 localization in extracellular vesicles. In summary, our data indicate that delivery of CEP55 into exosomes does not occur by the canonical early-to-late endosome pathway but by Alix-mediated recruitment to secretory late secretory CD63 endosomes.

PMID:39976236 | DOI:10.1002/jev2.70046

Neurol Ther. 2025 Feb 20. doi: 10.1007/s40120-024-00697-4. Online ahead of print.

ABSTRACT

INTRODUCTION: Efficacy/tolerability of adjunctive brivaracetam (BRV) for focal-onset seizures (FOS) in patients aged ≥ 16 years was established in randomized controlled trials. This study aimed to evaluate the effectiveness of adjunctive BRV in patients (≥ 16 years) with FOS with/without focal to bilateral tonic-clonic seizures in daily clinical practice.

METHODS: A 12-month, prospective, real-world, noninterventional study in nine European countries (EP0077/NCT02687711). BRV was prescribed per clinical practice and European Summary of Product Characteristics. Eligible patients had never received BRV before inclusion. Treating physicians made the decision to prescribe BRV, independently of study participation. Primary effectiveness outcome: BRV retention rate at 12 months; secondary effectiveness outcomes: 50% responder rate, seizure freedom.

RESULTS: A total of 544 patients received ≥ 1 BRV dose (mean age: 43.6 years; 52.8% female; mean time since diagnosis: 22.7 years). Patients had a mean of 7.3 lifetime antiseizure medications (ASMs) and median of 3.7 FOS/28 days during 3-month retrospective baseline. Median total ASM drug load (including BRV) was 3.0 at BRV initiation (n = 539) and 3.3 at study end (n = 314). At 12 months, 57.7% of 541 patients remained on BRV, 60.4% of 230 were responders (≥ 50% seizure reduction since baseline), and 13.8% of 269 were seizure-free since BRV initiation. Historical levetiracetam use appeared not to impact retention rate (56.6% of 320 and 59.3% of 221 patients with and without historical levetiracetam use, respectively). 36.0% of 544 patients had drug-related treatment-emergent adverse events (TEAEs), mostly (≥ 5% of patients) drug ineffective (11.4%) and seizure (6.3%). The three most common drug-related TEAEs leading to permanent BRV discontinuation (of 544 patients) were drug ineffective (10.1%), seizure (5.1%), and behavior disorder (3.3%).

CONCLUSIONS: Adjunctive BRV was effective in clinical practice in patients with predominantly difficult-to-treat FOS, as shown by BRV retention rate of 57.7% at 12 months, which is in line with real-world retention rates for other new-generation ASMs.

PMID:39976891 | DOI:10.1007/s40120-024-00697-4

J Neuromuscul Dis. 2024 Dec 20:22143602241296226. doi: 10.1177/22143602241296226. Online ahead of print.

ABSTRACT

GNE myopathy is an autosomal recessive hereditary muscle disorder that has the following clinical characteristics: develops in early adulthood, gradually progresses from the distal muscles, and is relatively sparing of quadriceps until the advanced stages of the disease. With further progression, patients become non-ambulatory and need a wheelchair. There is growing concern about extra-muscular presentations such as thrombocytopenia, respiratory dysfunction, and sleep apnea syndrome. Pathologically, rimmed vacuoles and tubulofilamentous inclusions are observed in affected muscles. The cause of the disease is thought to be a sialic acid deficiency due to mutations of the GNE gene required for in vivo sialic acid biosynthesis. Sialic acid supplementation to a presymptomatic GNE myopathy mouse model was effective in preventing the development of the disease. Several clinical studies have been conducted to evaluate the safety and efficacy of sialic acid supplementation in humans. Based on the favorable results of these studies, an extended-release aceneuramic acid formulation was approved for treatment of GNE myopathy in Japan in March 2024. It is anticipated that it will be a significant step in the development of an effective treatment for GNE myopathy and other ultra-orphan diseases.

PMID:39973407 | DOI:10.1177/22143602241296226

medRxiv [Preprint]. 2025 Feb 2:2025.01.31.25321503. doi: 10.1101/2025.01.31.25321503.

ABSTRACT

We developed a simulated process to show a software implementation to facilitate an approach to integrate the Informed Consent Ontology, a reference ontology of informed consent information, to express implicit description and implement conceptual permission from informed consent life cycle. An early study introduced an experimental method to use Semantic Web Rule Language (SWRL) to describe and represent permissions to computational deduce more information from the Informed Consent Ontology (ICO), demonstrated by the use of the All of Us informed consent documents. We show how incomplete information in informed consent documents can be elucidated using a computational model of permissions toward health information technology that integrates ontologies. Future goals entail applying our computational approach for specific sub-domains of the informed consent life cycle, specifically for vaccine informed consent.

PMID:39974098 | PMC:PMC11838618 | DOI:10.1101/2025.01.31.25321503

Eur J Gastroenterol Hepatol. 2025 Jan 21. doi: 10.1097/MEG.0000000000002917. Online ahead of print.

ABSTRACT

BACKGROUND: Cystic fibrosis-related liver disease (CFRLD) is a health problem that can affect as many as 30-40% of cystic fibrosis patients by the age of 12 years. We studied the epidemiology of CFRLD thanks to the first exclusively pediatric CFRLD patient registry to date.

METHODS: Descriptive cross-sectional study. Information from medical records from January 2018 to December 2020 is collected. CFRLD was classified according to the European Society of Paediatric Gastroenterology, Hepatology and Nutrition 2017 criteria.

RESULTS: Data were collected from 168 pediatric patients diagnosed with CFRLD (90.5% liver involvement without cirrhosis and 8.5% multinodular cirrhosis).

CONCLUSION: In this national registry, including exclusively pediatric population, liver disease is diagnosed around 7 years of age. Liver involvement without cirrhosis is the most frequent finding among our patients but about 9% of the patients already had cirrhosis. CFRLD is one of the challenges faced by pediatric gastroenterologists in the future and national registries give us the opportunity to further study and broaden our knowledge.

PMID:39976013 | DOI:10.1097/MEG.0000000000002917

bioRxiv [Preprint]. 2025 Feb 8:2025.02.03.636268. doi: 10.1101/2025.02.03.636268.

ABSTRACT

Constipation causes significant morbidity in Cystic Fibrosis (CF) patients. Using CF patient (F508del) derived ex vivo ileal and distal colonic/rectal enteroids as a model and the Forskolin Induced Swelling Assay (FIS), we compared CFTR mediated fluid secretion in human enterocytes across the crypt-villus axis. CFTR expression and FIS decreased as enterocytes differentiated from crypt to become partially differentiated and then mature villus cells . While there was no FIS response in undifferentiated (crypt enterocytes) F508del-CF enteroids, partially differentiated F508del-CF enteroids had a swelling response to forskolin (cAMP) and linaclotide (cGMP) which was ∼48%, and ∼67% of the response in healthy enteroids, respectively and was prevented by a CFTR inhibitor. Also, linaclotide and a general PDE inhibitor independently enhanced combined CFTR-modulator-induced FIS response from partially differentiated F508del-CF enteroids. These findings demonstrate that partially differentiated ileal and distal colonic F508del-CFTR enteroids can be stimulated to secrete fluid by cAMP and cGMP.

PMID:39975121 | PMC:PMC11838475 | DOI:10.1101/2025.02.03.636268

bioRxiv [Preprint]. 2025 Jan 27:2025.01.24.634747. doi: 10.1101/2025.01.24.634747.

ABSTRACT

Single-cell RNA sequencing (scRNA-seq) studies identified a novel subpopulation of epithelial cells along the rostrocaudal axis of human intestine specifically marked by bestrophin 4 (BEST4) that are enriched for genes regulating pH, GPCR acid-sensing receptors, satiety, cGMP signaling, HCO3 - secretion, ion transport, neuropeptides, and paracrine hormones. Interestingly, BEST4+ cells in the proximal small intestine express CFTR but have not been linked to the previously described CFTR High Expresser Cell (CHE) subpopulation in rat and human intestine. ScRNA-seq studies in rat jejunum identified CHEs and a gene expression profile consistent with human small intestinal BEST4+ and neuropod cells. Protein immunolocalization confirmed that CHEs express CFTR, BEST4, neuropod proteins, high levels of intracellular uroguanylin (UGN), guanylyl cyclase-C (GC-C), and the proton channel otopetrin 2 (OTOP2), and display long basal processes connecting to neurons. OTOP2, GC-C, and CFTR traffic robustly into the apical domain of CHEs in response to acidic luminal conditions, indicating their roles in luminal pH regulation. In the ΔF508 cystic fibrosis (CF) rat jejunum, the loss of apical CFTR did not affect BEST4 protein expression in CHEs. However, there was an increased abundance of CHE cells in the ΔF508 rat jejunum compared to wild-type animals. Furthermore, ΔF508 rat CHEs expressed higher levels of GC-C at the apical domain compared to wild-type. These data implicate CHEs in intestinal CF disease pathogenesis.

NEW & NOTEWORTHY: This is the first study to identify CFTR High Expresser cells in the rat small intestine as neuropod cells capable of sensing and responding to luminal pH. This study also provides the first characterization of CFTR and relevant mRNA and proteins in CHEs in CF rat models that provide insights into the significance of CHEs to CF intestinal disease.

PMID:39974899 | PMC:PMC11838207 | DOI:10.1101/2025.01.24.634747

Lancet Reg Health Eur. 2024 Sep 15;45:101061. doi: 10.1016/j.lanepe.2024.101061. eCollection 2024 Oct.

NO ABSTRACT

PMID:39974772 | PMC:PMC11838078 | DOI:10.1016/j.lanepe.2024.101061

FEBS Lett. 2025 Feb 20. doi: 10.1002/1873-3468.70013. Online ahead of print.

ABSTRACT

The arachidonic acid (AA) pathway promotes tumor progression by modulating the complex interactions between cancer and immune cells within the microenvironment. In this Review, we summarize the knowledge acquired thus far concerning the intricate mechanisms through which eicosanoids either promote or suppress the antitumor immune response. In addition, we will discuss the impact of eicosanoids on immune cells and how they affect responsiveness to immunotherapy, as well as potential strategies for manipulating the AA pathway to improve anticancer immunotherapy. Understanding the molecular pathways and mechanisms underlying the role played by AA and its metabolites in tumor progression may contribute to the development of more effective anticancer immunotherapies.

PMID:39973474 | DOI:10.1002/1873-3468.70013

Int Forum Allergy Rhinol. 2025 Feb 19. doi: 10.1002/alr.23549. Online ahead of print.

NO ABSTRACT

PMID:39972960 | DOI:10.1002/alr.23549

ArXiv [Preprint]. 2025 Feb 1:arXiv:2501.14158v2.

ABSTRACT

Magnetic resonance imaging (MRI) is a non-invasive imaging modality and provides comprehensive anatomical and functional insights into the human body. However, its long acquisition times can lead to patient discomfort, motion artifacts, and limiting real-time applications. To address these challenges, strategies such as parallel imaging have been applied, which utilize multiple receiver coils to speed up the data acquisition process. Additionally, compressed sensing (CS) is a method that facilitates image reconstruction from sparse data, significantly reducing image acquisition time by minimizing the amount of data collection needed. Recently, deep learning (DL) has emerged as a powerful tool for improving MRI reconstruction. It has been integrated with parallel imaging and CS principles to achieve faster and more accurate MRI reconstructions. This review comprehensively examines DL-based techniques for MRI reconstruction. We categorize and discuss various DL-based methods, including end-to-end approaches, unrolled optimization, and federated learning, highlighting their potential benefits. Our systematic review highlights significant contributions and underscores the potential of DL in MRI reconstruction. Additionally, we summarize key results and trends in DL-based MRI reconstruction, including quantitative metrics, the dataset, acceleration factors, and the progress of and research interest in DL techniques over time. Finally, we discuss potential future directions and the importance of DL-based MRI reconstruction in advancing medical imaging. To facilitate further research in this area, we provide a GitHub repository that includes up-to-date DL-based MRI reconstruction publications and public datasets-https://github.com/mosaf/Awesome-DL-based-CS-MRI.

PMID:39975448 | PMC:PMC11838702

ArXiv [Preprint]. 2025 Jan 27:arXiv:2501.15712v1.

ABSTRACT

Computational modeling of cardiovascular function has become a critical part of diagnosing, treating and understanding cardiovascular disease. Most strategies involve constructing anatomically accurate computer models of cardiovascular structures, which is a multistep, time-consuming process. To improve the model generation process, we herein present SeqSeg (sequential segmentation): a novel deep learning based automatic tracing and segmentation algorithm for constructing image-based vascular models. SeqSeg leverages local U-Net-based inference to sequentially segment vascular structures from medical image volumes. We tested SeqSeg on CT and MR images of aortic and aortofemoral models and compared the predictions to those of benchmark 2D and 3D global nnU-Net models, which have previously shown excellent accuracy for medical image segmentation. We demonstrate that SeqSeg is able to segment more complete vasculature and is able to generalize to vascular structures not annotated in the training data.

PMID:39975447 | PMC:PMC11838707

ArXiv [Preprint]. 2025 Jan 24:arXiv:2311.11046v2.

ABSTRACT

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. Here, we used globally representative data from the ENIGMA-MDD working group containing 7,012 participants from 30 sites (N=2,772 MDD and N=4,240 HC), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of such features and classifiers is unfeasible. Perhaps more sophisticated integration of multimodal information may lead to a higher performance in this diagnostic task.

PMID:39975425 | PMC:PMC11838705

bioRxiv [Preprint]. 2025 Feb 8:2025.02.07.637149. doi: 10.1101/2025.02.07.637149.

ABSTRACT

As spatial molecular data grow in scope and resolution, there is a pressing need to identify key spatial structures associated with disease. Current approaches often rely on hand-crafted features such as local abundances of manually annotated, discrete cell types, which may overlook important signals. Here we introduce variational inference-based microniche analysis (VIMA), a method that combines deep learning with principled statistics to discover associated spatial features with greater flexibility and precision. VIMA uses a variational autoencoder to extract numerical "fingerprints" from small tissue patches that capture their biological content. It uses these fingerprints to define a large number of "microniches" - small, potentially overlapping groups of tissue patches with highly similar biology that span multiple samples. It then uses rigorous statistics to identify microniches whose abundance correlates with case-control status. We show in simulations that VIMA is well calibrated and more powerful and accurate than other approaches. We then apply VIMA to a 140-gene spatial transcriptomics dataset in Alzheimer's dementia, a 54-marker CO-Detection by indEXing (CODEX) dataset in ulcerative colitis (UC), and a 7-marker immunohistochemistry dataset in rheumatoid arthritis (RA), in each case recapitulating known biology and identifying novel spatial features of disease.

PMID:39975274 | PMC:PMC11839118 | DOI:10.1101/2025.02.07.637149

bioRxiv [Preprint]. 2025 Jan 27:2025.01.24.634753. doi: 10.1101/2025.01.24.634753.

ABSTRACT

Given the successful graft-versus-leukemia cell treatment effect observed with allogeneic hematopoietic stem cell transplant for patients with refractory or relapsed acute myeloid leukemia, immunotherapies have also been investigated in the nontransplant setting. Here, we use a multi-omic approach to investigate spatiotemporal interactions in the bone marrow niche between leukemia cells and immune cells in patients with refractory or relapsed acute myeloid leukemia treated with a combination of the immune checkpoint inhibitor pembrolizumab and hypomethylating agent decitabine. We derived precise segmentation data by extensively training nuclear and membrane cell segmentation models, which enabled accurate transcript assignment and deep learning-feature-based image analysis. To overcome read-depth limitations, we integrated the single-cell RNA sequencing data with single-cell-resolution spatial transcriptomic data from the same sample. Quantifying cell-cell distances between cell edges rather than cell centroids allowed us to conduct a more accurate downstream analysis of the tumor microenvironment, revealing that multiple cell types of interest had global enrichment or local enrichment proximal to leukemia cells after pembrolizumab treatment, which could be associated with their clinical responses. Furthermore, ligand-receptor analysis indicated a potential increase in TWEAK signaling between leukemia cells and immune cells after pembrolizumab treatment.

HIGHLIGHTS: Spatial transcriptomic analysis of R-AML bone marrow niches provides detailed information about intercellular interactions in the tumor microenvironment.Immunotherapy shifts the cell composition of the leukemia neighborhood.

PMID:39975227 | PMC:PMC11838223 | DOI:10.1101/2025.01.24.634753

bioRxiv [Preprint]. 2025 Jan 29:2025.01.29.634860. doi: 10.1101/2025.01.29.634860.

ABSTRACT

Identification of neuron type is critical to understand computation in neural circuits through extracellular recordings in awake, behaving animal subjects. Yet, modern recording probes have limited power to resolve neuron type. Here, we leverage the well-characterized architecture of the cerebellar circuit to perform expert identification of neuron type from extracellular recordings in behaving non-human primates. Using deep-learning classifiers we evaluate the information contained in readily accessible extracellular features for neuron identification. Waveform, discharge statistics, anatomical layer, and functional interactions each can inform neuron labels for a sizable fraction of cerebellar units. Together, as inputs to a deep-learning classifier, the features perform even better. Our tools and methodologies, validated during smooth pursuit eye movements in the cerebellar floccular complex of awake behaving monkeys, can guide expert identification of neuron type during cerebellar-dependent tasks in behaving animals across species. They lay the groundwork for characterization of information processing in the cerebellar cortex.

IMPACT STATEMENT: To understand how the brain performs computations in the service of behavior, we develop methods to link neuron type to functional activity within well-characterized neural circuits. Here, we show how features derived from extracellular recordings provide complementary information to disambiguate neuron identity in the cerebellar cortex.

PMID:39975199 | PMC:PMC11838295 | DOI:10.1101/2025.01.29.634860

bioRxiv [Preprint]. 2025 Feb 5:2025.01.31.635598. doi: 10.1101/2025.01.31.635598.

ABSTRACT

Deep learning excels at segmenting objects within noisy cryo-electron tomograms, but the approach is typically bottlenecked by access to ground truth training data. To address this issue we have developed CryoTomoSim (CTS), an open-source software package that builds coarse-grained models of macromolecular complexes embedded in vitreous ice and then simulates transmitted electron tilt series for tomographic reconstruction. Using CTS outputs, we demonstrate the effects of key microscope parameters (dose, defocus, and pixel size) on deep learning-based segmentation, and show that including both molecular crowding and diversity within synthetic datasets is key to training cellular segmentation networks from purely synthetic inputs. While very effective as initial models, the accuracy of these networks is currently limited, and real cellular data is necessary to train the most accurate and generalizable U-Nets. Using a co-training approach, we first segment over 100 tomograms from neuronal growth cones to quantify their cytoskeletal distributions and then we build a generalized cellular cryo-ET segmentation network called NeuralSeg that can segment a subset of cellular features in tomograms from all domains of life.

PMID:39975172 | PMC:PMC11838407 | DOI:10.1101/2025.01.31.635598

bioRxiv [Preprint]. 2025 Jan 30:2025.01.29.635570. doi: 10.1101/2025.01.29.635570.

ABSTRACT

Spatial correlation of functional connectivity profiles across matching anatomical locations in individuals is often calculated to delineate individual differences in functional networks. Likewise, spatial correlation is assessed across average functional connectivity profiles of groups to evaluate the maturity of functional networks during development. Despite its widespread use, spatial correlation is limited to comparing two samples at a time. In this study, we employed a variational autoencoder to embed functional connectivity profiles from various anatomical locations, individuals, and group averages for simultaneous comparison. We demonstrate that our variational autoencoder, with pre-trained weights, can project new functional connectivity profiles from the vertex space to a latent space with as few as two dimensions, yet still retain meaningful global and local structures in the data. Functional connectivity profiles from various functional networks occupy distinct compartments of the latent space. Moreover, the variability of functional connectivity profiles from the same anatomical location is readily captured in the latent space. We believe that this approach could be useful for visualization and exploratory analyses in precision functional mapping.

PMID:39975052 | PMC:PMC11838398 | DOI:10.1101/2025.01.29.635570

bioRxiv [Preprint]. 2025 Feb 5:2024.08.29.610300. doi: 10.1101/2024.08.29.610300.

ABSTRACT

The proto-oncogene Ras which governs diverse intracellular pathways has four major isoforms (KRAS4A, KRAS4B, HRAS, and NRAS) with substantial sequence homology and similar in vitro biochemistry. There is considerable interest in investigating the roles of these independently as their association with different cancers vary, but there are few Ras isoform-specific binding reagents as the only significant sequence differences are in their disordered and highly charged C-termini which have been difficult to elicit antibodies against. To overcome this limitation, we use deep learning-based methods to de novo design Ras isoform-specific binders (RIBs) for all major Ras isoforms that specifically target the Ras C-terminus. The RIBs bind to their target Ras isoforms both in vitro and in cells with remarkable specificity, disrupting their membrane localization and inhibiting Ras activity, and should contribute to dissecting the distinct roles of Ras isoforms in biology and disease.

PMID:39975043 | PMC:PMC11838417 | DOI:10.1101/2024.08.29.610300

bioRxiv [Preprint]. 2025 Feb 5:2025.02.04.636539. doi: 10.1101/2025.02.04.636539.

ABSTRACT

The development of therapies and vaccines targeting integral membrane proteins has been complicated by their extensive hydrophobic surfaces, which can make production and structural characterization difficult. Here we describe a general deep learning-based design approach for solubilizing native membrane proteins while preserving their sequence, fold, and function using genetically encoded de novo protein WRAPs ( W ater-soluble R Fdiffused A mphipathic P roteins) that surround the lipid-interacting hydrophobic surfaces, rendering them stable and water-soluble without the need for detergents. We design WRAPs for both beta-barrel outer membrane and helical multi-pass transmembrane proteins, and show that the solubilized proteins retain the binding and enzymatic functions of the native targets with enhanced stability. Syphilis vaccine development has been hindered by difficulties in characterizing and producing the outer membrane protein antigens; we generated soluble versions of four Treponema pallidum outer membrane beta barrels which are potential syphilis vaccine antigens. A 4.0 Å cryo-EM map of WRAPed TP0698 is closely consistent with the design model. WRAPs should be broadly useful for facilitating biochemical and structural characterization of integral membrane proteins, enabling therapeutic discovery by screening against purified soluble targets, and generating antigenically intact immunogens for vaccine development.

PMID:39975033 | PMC:PMC11838538 | DOI:10.1101/2025.02.04.636539