Am J Med Genet B Neuropsychiatr Genet. 2025 Mar 24:e33028. doi: 10.1002/ajmg.b.33028. Online ahead of print.

ABSTRACT

Schizophrenia is a neurodevelopmental psychiatric disorder characterized by symptoms of psychosis, thought disorder, and flattened affect. Immune mechanisms are associated with schizophrenia, though the precise nature of this relationship (causal, correlated, consequential) and the mechanisms involved are not fully understood. To elucidate these mechanisms, we conducted a transcriptome-wide Mendelian randomization study using gene expression exposures from 29 human cis-eQTL data sets encompassing 11 unique immune cell types, available from the eQTL catalog. These analyses highlighted 196 genes, including 67 located within the human leukocyte antigen (HLA) region. Enrichment analyses indicated an overrepresentation of immune genes, which was driven by the HLA genes. Stringent validation and replication steps retained 61 candidate genes, 27 of which were the sole causal signals at their respective loci, thereby representing strong candidate effector genes at known risk loci. We highlighted L3HYPDH as a potential novel schizophrenia risk gene and DPYD and MAPK3 as candidate drug repurposing targets. Furthermore, we performed follow-up analyses focused on one of the candidate effectors, interferon regulatory transcription factor 3 (IRF3), which coordinates interferon responses to viral infections. We found evidence of shared genetic etiology between schizophrenia and autoimmune diseases at the IRF3 locus, and a significant enrichment of IRF3 chromatin binding at known schizophrenia risk loci. Our findings highlight a novel schizophrenia risk gene, potential drug repurposing opportunities, and provide support for IRF3 as a schizophrenia hub gene, which may play critical roles in mediating schizophrenia-autoimmune comorbidities and the impact of infections on schizophrenia risk.

PMID:40126059 | DOI:10.1002/ajmg.b.33028

Front Pharmacol. 2025 Mar 7;16:1583486. doi: 10.3389/fphar.2025.1583486. eCollection 2025.

NO ABSTRACT

PMID:40124782 | PMC:PMC11926139 | DOI:10.3389/fphar.2025.1583486

iScience. 2025 Feb 21;28(3):112081. doi: 10.1016/j.isci.2025.112081. eCollection 2025 Mar 21.

ABSTRACT

Viral genomes encode viral transcriptional regulators (vTRs) that manipulate host gene expression to facilitate replication and evade immune detection. Nevertheless, their role in non-cancerous diseases remains largely underexplored. Here, we unveiled 268 new candidate vTRs from 14 of the 20 viral families we investigated. We mapped vTRs' genome-wide binding profiles and identified their potential human targets, which were enriched in immune-mediated pathways, neurodegenerative disorders, and cancers. Through vTR DNA-binding preference analysis, 283 virus-specific and human-like motifs were identified. Prioritized Epstein-Barr virus (EBV) vTR target genes were associated with multiple sclerosis (MS), rheumatoid arthritis, and systemic lupus erythematosus. The partitioned heritability study among 19 diseases indicated significant enrichment of these diseases in EBV vTR-binding sites, implicating EBV vTRs' roles in immune-mediated disorders. Finally, drug repurposing analysis pinpointed candidate drugs for MS, asthma, and Alzheimer disease. This study enhances our understanding of vTRs in diverse human diseases and identifies potential therapeutic targets for future investigation.

PMID:40124487 | PMC:PMC11928865 | DOI:10.1016/j.isci.2025.112081

J Pediatr Hematol Oncol. 2025 Apr 1;47(3):109-114. doi: 10.1097/MPH.0000000000003008. Epub 2025 Mar 24.

ABSTRACT

BACKGROUND: Congenital CD59 deficiency is a rare genetic disorder marked by chronic hemolysis, recurrent cerebrovascular events, and chronic inflammatory demyelinating polyneuropathy (CIDP). In a specific clinic, 3 siblings from a consanguineously married family were diagnosed with this condition, suggesting a genetic predisposition in their village where endogamous marriages are common.

MATERIALS AND METHODS: Genetic screening was conducted on 71 individuals from the village, including relatives of the diagnosed siblings, to investigate the prevalence and genetic transmission of the disorder.

RESULTS: The screening identified 18 carriers of the genetic mutation and revealed 2 additional siblings of the index patient with the disease. A past case of a cousin with a similar clinical history was also uncovered.

CONCLUSION: The findings highlight the increased risk of genetic disorders like CD59 deficiency in populations with frequent consanguineous marriages. The study underscores the importance of genetic counseling and preventive measures in such communities to mitigate the risk of congenital disorders.

PMID:40126046 | DOI:10.1097/MPH.0000000000003008

Postgrad Med J. 2025 Mar 24:qgaf037. doi: 10.1093/postmj/qgaf037. Online ahead of print.

ABSTRACT

BACKGROUND: The COVID-19 pandemic has significantly impacted global health, making it essential to understand its genetic effects on various traits.

METHOD: Leveraging the extensive FinnGen dataset comprising 500 000 individuals, we performed a Mendelian randomization (MR) phenome-wide association study. COVID-19-related phenotypes obtained from the COVID-19 Host Genetics Initiative GWAS (release 7). We employed four distinct approaches, including MR-Egger, weighted median, random-effect inverse variance weighted (IVW), and weighted mode, to conduct the MR analysis.

RESULTS: Two hundred fifty-five potential causal effects of COVID-19 were observed for a diverse range of outcomes using the IVW method, including cardiovascular disorders, respiratory conditions, autoimmune diseases, and metabolic disorders. Apart from a few that can be classified as "other traits," the majority of the traits are disease-related traits. We have also identified 31 traits, wherein all four distinct MR analyses yielded a P-value of less than 0.05. Only one trait remained statistically significant after multiple testing correction using the conservative Bonferroni threshold (P < 2.2E-5).

CONCLUSIONS: This phenome-wide MR study provides valuable insights into the genetically predicted effects of COVID-19 on a comprehensive range of traits. The identified associations contribute to our understanding of the complex interplay between the impact of the post-COVID-19 era on healthcare and may have implications for the development of targeted therapeutic strategies and public health interventions. Key messages What is already known on this topic - COVID-19 has a high mortality rate, and patients often have many sequelae, including myocarditis, acute respiratory distress syndrome, and neurological and hematologic complications. What this study adds Most of the current relevant studies lack large-scale phenotype-group ranging Mendelian randomization (MR) studies on the outcome of COVID-19 due to their small sample sizes. Therefore, this study performed a full phenotypic group MR analysis in the FinnGen dataset to investigate the relationship between COVID-19 and thousands of outcome variables. How this study might affect research, practice or policy- The study identified a set of traits that are strongly associated with genetic susceptibility to the long-term effects of COVID-19.

PMID:40126442 | DOI:10.1093/postmj/qgaf037

Methods Protoc. 2025 Mar 4;8(2):27. doi: 10.3390/mps8020027.

ABSTRACT

Genetic profiling and molecular biology methods have made it possible to study the etiology of the end-stage organ disease that led to transplantation, the genetic factors of compatibility and tolerance of the transplant, and the pharmacogenetics of immunosuppressive drugs and allowed for the development of monitoring methods for the early assessment of allograft rejection. This study aims to report the design and baseline characteristics of an integrated personalized genetic approach in solid organ transplantation, including whole-exome sequencing (WES) and the monitoring of dd-cfDNA by dPCR. Preliminary results reported female recipients with male donors undergoing two pediatric and five adult kidney and three heart transplantations. WES revealed a pathogenic mutation in RBM20 and VUS in TTN and PKP2 in heart recipients, while kidney donors presented mutations in UMOD and APOL1 associated with autosomal-dominant kidney diseases, highlighting the risks requiring the long-term monitoring of recipients, donors, and their family members. %dd-cfDNA levels were generally stable but elevated in cadaveric kidney recipient and one pediatric patient with infectious complications and genetic variants in the ABCB1 and ABCC2 genes. These findings highlight the potential of combining genetic and molecular biomarker-based approaches to improve donor-recipient matching, predict complications, and personalize post-transplant care, paving the way for precision medicine in transplantation.

PMID:40126245 | DOI:10.3390/mps8020027

J Allergy Clin Immunol Glob. 2025 Feb 12;4(2):100439. doi: 10.1016/j.jacig.2025.100439. eCollection 2025 May.

ABSTRACT

BACKGROUND: Patients with severe asthma may demonstrate reduced sensitivity to steroid treatment. However, the implications of this reduced responsiveness for clinical outcomes and the underlying mechanisms remain unclear.

OBJECTIVE: The aim of this study was to investigate whether steroid sensitivity in patients with asthma is related to severity and clinical outcomes and to elucidate the role of inflammatory pathways in reducing steroid sensitivity.

METHODS: This observational study of 169 asthma patients, with 161 followed for 1 year, involved isolation of peripheral blood mononuclear cells. These cells were treated with dexamethasone, and the mRNA expression of FKBP5, which is a marker of steroid sensitivity, was measured. To explore the mechanism underlying the reduced steroid sensitivity, cells were exposed to PI3K and MAPK inhibitors in combination with dexamethasone.

RESULTS: A total of 53 patients diagnosed with severe asthma exhibited markedly diminished sensitivity to steroids compared with those with nonsevere asthma. Reduced steroid sensitivity has emerged as a critical risk factor for failure to experience clinical remission and exacerbation. This relationship between reduced steroid sensitivity and disease severity and adverse outcomes was confirmed at the 1-year follow-up. Mechanistic investigations revealed that the degree of recovery from steroid sensitivity after PI3Kδ/γ inhibitor treatment was significantly greater in patients with severe asthma than in those with nonsevere asthma, a finding confirmed at the 1-year follow-up.

CONCLUSIONS: Patients with severe asthma demonstrate reduced steroid sensitivity, which results in unfavorable clinical outcomes. Conversely, inhibition of the PI3K pathway significantly improves steroid sensitivity.

PMID:40125453 | PMC:PMC11928809 | DOI:10.1016/j.jacig.2025.100439

iScience. 2025 Feb 22;28(3):112076. doi: 10.1016/j.isci.2025.112076. eCollection 2025 Mar 21.

ABSTRACT

Antibiotic activity against intracellular pathogens is commonly evaluated in static models that do not reproduce plasma concentration fluctuations. However, efficacy is influenced by exposure conditions, related to drug pharmacokinetic profile. This study developed and validated an intracellular pharmacodynamic model using the hollow fiber system, the gold standard for evaluating extracellular antibiotic activity. The activity of fluoroquinolones, i.e., bactericidal antibiotics with intracellular tropism, was studied against intracellular Staphylococcus aureus, involved in persistence/recurrence of infections. In this model, moxifloxacin was more effective than in static conditions (0.87 log10 killing gain), while ciprofloxacin kill rate was slower (18 vs. 12 h to achieve 1 log10 killing). These differences were linked to the Cmax/MIC ratio, which was 2.5-fold higher for moxifloxacin but 3.4-fold lower for ciprofloxacin in dynamic vs. static conditions. This model could be applied to other drugs, cell types, or pathogens, offering a tool for optimizing dosing schemes and considering intracellular reservoirs.

PMID:40124509 | PMC:PMC11930174 | DOI:10.1016/j.isci.2025.112076

J Clin Endocrinol Metab. 2025 Mar 24:dgaf165. doi: 10.1210/clinem/dgaf165. Online ahead of print.

NO ABSTRACT

PMID:40126533 | DOI:10.1210/clinem/dgaf165

Am J Respir Crit Care Med. 2025 Mar 24. doi: 10.1164/rccm.202502-0338ED. Online ahead of print.

NO ABSTRACT

PMID:40126387 | DOI:10.1164/rccm.202502-0338ED

Physiol Rev. 2025 Mar 24. doi: 10.1152/physrev.00032.2024. Online ahead of print.

ABSTRACT

Nanosized extracellular vesicles (EVs) are released by all cells to convey cell-to-cell communication. EVs, including exosomes and microvesicles, carry an array of bioactive molecules, such as proteins and RNAs, encapsulated by a membrane lipid bilayer. Epithelial cells, endothelial cells, and various immune cells in the lung contribute to the pool of EVs in the lung microenvironment and carry molecules reflecting their cellular origin. EVs can maintain lung health by regulating immune responses, inducing tissue repair, and maintaining lung homeostasis. They can be detected in lung tissues and biofluids such as bronchoalveolar lavage fluid and blood, offering information about disease processes and can function as disease biomarkers. Here, we discuss the role of EVs in lung homeostasis and pulmonary diseases such as asthma, chronic obstructive pulmonary disease, cystic fibrosis, idiopathic pulmonary fibrosis, and lung injury. The mechanistic involvement of EVs in pathogenesis and their potential as disease biomarkers are discussed. Lastly, the pulmonary field benefits from EVs as clinical therapeutics in severe pulmonary inflammatory disease, as EVs from mesenchymal stem cells attenuate severe respiratory inflammation in multiple clinical trials. Further, EVs can be engineered to carry therapeutic molecules for enhanced and broadened therapeutic opportunities, such as the anti-inflammatory molecule CD24. Finally, we discuss the emerging opportunity of using different types of EVs for treating severe respiratory conditions.

PMID:40125970 | DOI:10.1152/physrev.00032.2024

F1000Res. 2025 Mar 6;11:1513. doi: 10.12688/f1000research.128260.2. eCollection 2022.

ABSTRACT

BACKGROUND: Vancomycin is an effective first-line therapy primarily in methicillin-resistant Staphylococcus aureus (MRSA) infection and Clostridium difficile, however, it has been shown that its effectiveness and the reduction of nephrotoxicity depend on maintaining adequate therapeutic levels. Population pharmacokinetic (PopPk) models attempt to parameterize the behavior of plasma concentrations in different target populations and scenarios such as renal replacement therapy, to successful therapeutic outcome and avoid these side effects.

METHODS: A scoping review was conducted following the guidelines of Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR), through a search in PubMed, LILACS, OVID Medline, Scopus, Web of Science, SAGE Journals, Google Scholar and previous known registers of PopPk models in non-critically ill adult patients, published between 1998 and 2024.

RESULTS: A total of 190 papers were fully screened, of which were included 36 studies conducted in different populations; 12 in general population, 23 in special populations (surgical, with impaired renal function, obese, elderly, with cancer and cystic fibrosis), and 1 in mixed population (general and with cancer). The main parameters in the models were renal clearance and volume of distribution. The principal covariables that affected the models were creatinine clearance and weight. All studies used internal evaluation and 4 of them used an external group.

DISCUSSION: The technology for the development and implementation of PopPk models requires experts in clinical pharmacology and is limited to university and research centers. The software is mostly expensive and, in most cases, the pharmacokinetic models and the heterogeneity in the parameters and evaluation methods depend on which compartmental model, parameters, covariates and software have been used.

CONCLUSIONS: These models require validation in the clinical context and conducting experiments to adapt them for precision dosing in different subpopulations.

PMID:40124851 | PMC:PMC11928783 | DOI:10.12688/f1000research.128260.2

Front Immunol. 2025 Mar 7;16:1508584. doi: 10.3389/fimmu.2025.1508584. eCollection 2025.

ABSTRACT

INTRODUCTION: Mycobacterium abscessus is a pathogen recently associated with patients with chronic lung conditions such as bronchiectasis and cystic fibrosis. M. abscessus is an environmental bacterium but recent evidence suggests that the pathogen is also transmitted from host-to-host. Because M. abscessus is known to form biofilms on the respiratory mucosa the release of bacteria from the biofilm becomes an important aspect on the transmission of the infection.

METHODS: A biofilm releasing system was established. A transposon library of M. abscessus was then screened to identify genes associated with the release from biofilms.

RESULTS: Several enzymes and genes of unidentified function were linked with the ability to detach from the biofilm. It was also shown that detached bacteria were increased capable of establish a new biofilm, attach to epithelial cells, and infect macrophages. To determine the surface molecules linked with the ability to infect new hosts, a surface proteomic was performed, showing that detaching bacteria express many proteins do not present in biofilm bacteria.

DISCUSSION: Detached M. abscessus, one of the possible infectious phenotypes, contains specific proteins and lipids in the surface that facilitate the infection of new hosts. In addition, we identified many small proteins that have the likelihood to be associated with the release of the biofilm bacteria.

PMID:40124375 | PMC:PMC11925935 | DOI:10.3389/fimmu.2025.1508584

ACS Sens. 2025 Mar 24. doi: 10.1021/acssensors.4c02789. Online ahead of print.

ABSTRACT

Gas sensor arrays designed for pattern recognition face persistent challenges in achieving high sensitivity and selectivity for multiple volatile organic compounds (VOCs), particularly under varying environmental conditions. To address these limitations, we developed multimodal intelligent MEMS gas sensors by precisely tailoring the nanocomposite ratio of NiO and ZnO components. These sensors demonstrate enhanced responses to ethylene glycol (EG) and limonene (LM) at different operating temperatures, demonstrating material-specific selectivity. Additionally, a multitask deep learning model is employed for real-time, quantitative detection of VOCs, accurately predicting their concentration and type. These results showcase the effectiveness of combining material optimization with advanced algorithms for real-world VOCs detection, advancing the field of odor analysis tools.

PMID:40126565 | DOI:10.1021/acssensors.4c02789

J Med Internet Res. 2025 Mar 24;27:e67922. doi: 10.2196/67922.

ABSTRACT

BACKGROUND: Emerging evidence underscores the potential application of artificial intelligence (AI) in discovering noninvasive blood biomarkers. However, the diagnostic value of AI-derived blood biomarkers for ovarian cancer (OC) remains inconsistent.

OBJECTIVE: We aimed to evaluate the research quality and the validity of AI-based blood biomarkers in OC diagnosis.

METHODS: A systematic search was performed in the MEDLINE, Embase, IEEE Xplore, PubMed, Web of Science, and the Cochrane Library databases. Studies examining the diagnostic accuracy of AI in discovering OC blood biomarkers were identified. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-AI tool. Pooled sensitivity, specificity, and area under the curve (AUC) were estimated using a bivariate model for the diagnostic meta-analysis.

RESULTS: A total of 40 studies were ultimately included. Most (n=31, 78%) included studies were evaluated as low risk of bias. Overall, the pooled sensitivity, specificity, and AUC were 85% (95% CI 83%-87%), 91% (95% CI 90%-92%), and 0.95 (95% CI 0.92-0.96), respectively. For contingency tables with the highest accuracy, the pooled sensitivity, specificity, and AUC were 95% (95% CI 90%-97%), 97% (95% CI 95%-98%), and 0.99 (95% CI 0.98-1.00), respectively. Stratification by AI algorithms revealed higher sensitivity and specificity in studies using machine learning (sensitivity=85% and specificity=92%) compared to those using deep learning (sensitivity=77% and specificity=85%). In addition, studies using serum reported substantially higher sensitivity (94%) and specificity (96%) than those using plasma (sensitivity=83% and specificity=91%). Stratification by external validation demonstrated significantly higher specificity in studies with external validation (specificity=94%) compared to those without external validation (specificity=89%), while the reverse was observed for sensitivity (74% vs 90%). No publication bias was detected in this meta-analysis.

CONCLUSIONS: AI algorithms demonstrate satisfactory performance in the diagnosis of OC using blood biomarkers and are anticipated to become an effective diagnostic modality in the future, potentially avoiding unnecessary surgeries. Future research is warranted to incorporate external validation into AI diagnostic models, as well as to prioritize the adoption of deep learning methodologies.

TRIAL REGISTRATION: PROSPERO CRD42023481232; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023481232.

PMID:40126546 | DOI:10.2196/67922

Invest Ophthalmol Vis Sci. 2025 Mar 3;66(3):49. doi: 10.1167/iovs.66.3.49.

ABSTRACT

PURPOSE: Optic nerve crush (ONC) is a model for studying optic nerve trauma. Unilateral ONC induces massive retinal ganglion cell (RGC) degeneration in the affected eye, leading to vision loss within a month. A common assumption has been that the non-injured contralateral eye is unaffected due to the minimal retino-retinal projections of the RGCs at the chiasm. Yet, recently, microglia, the brain-resident macrophages, have shown a responsive phenotype in the contralateral eye after ONC. Whether RGC loss accompanies this phenotype is still controversial.

METHODS: Using the available RGCode algorithm and developing our own RGC-Quant deep-learning-based tool, we quantify RGC's total number and density across the entire retina after ONC.

RESULTS: We confirm a short-term microglia response in the contralateral eye after ONC, but this did not affect the microglia number. Furthermore, we cannot confirm the previously reported RGC loss between naïve and contralateral retinas 5 weeks after ONC induction across the commonly used Cx3cr1creERT2 and C57BL6/J mouse models. Neither sex nor the direct comparison of the RGC markers Brn3a and RBPMS, with Brn3a co-labeling, on average, 89% of the RBPMS+-cells, explained this discrepancy, suggesting that the early microglia-responsive phenotype does not have immediate consequences on the RGC number.

CONCLUSIONS: Our results corroborate that unilateral optic nerve injury elicits a microglial response in the uninjured contralateral eye but without RGC loss. Therefore, the contralateral eye should be treated separately and not as an ONC control.

PMID:40126507 | DOI:10.1167/iovs.66.3.49

Noncoding RNA. 2025 Mar 7;11(2):23. doi: 10.3390/ncrna11020023.

ABSTRACT

(1) Background: MicroRNAs are non-coding RNA sequences that regulate cellular functions by targeting messenger RNAs and inhibiting protein synthesis. Identifying their target sites is vital to understanding their roles. However, it is challenging due to the high cost and time demands of experimental methods and the high false-positive rates of computational approaches. (2) Methods: We introduce a Multi-Input Neural Network (MINN) algorithm that integrates diverse biologically relevant features, including the microRNA duplex structure, substructures, minimum free energy, and base-pairing probabilities. For each feature derived from a microRNA target-site duplex, we create a corresponding image. These images are processed in parallel by the MINN algorithm, allowing it to learn a comprehensive and precise representation of the underlying biological mechanisms. (3) Results: Our method, on an experimentally validated test set, detects target sites with an AUPRC of 0.9373, Precision of 0.8725, and Recall of 0.8703 and outperforms several commonly used computational methods of microRNA target-site predictions. (4) Conclusions: Incorporating diverse biologically explainable features, such as duplex structure, substructures, their MFEs, and binding probabilities, enables our model to perform well on experimentally validated test data. These features, rather than nucleotide sequences, enhance our model to generalize beyond specific sequence contexts and perform well on sequentially distant samples.

PMID:40126347 | DOI:10.3390/ncrna11020023

Noncoding RNA. 2025 Feb 26;11(2):18. doi: 10.3390/ncrna11020018.

ABSTRACT

RNA inverse design is an essential part of many RNA therapeutic strategies. To date, there have been great advances in computationally driven RNA design. The current machine learning approaches can predict the sequence of an RNA given its 3D structure with acceptable accuracy and at tremendous speed. The design and engineering of RNA regulators such as riboswitches, however, is often more difficult, partly due to their inherent conformational switching abilities. Although recent state-of-the-art models do incorporate information about the multiple structures that a sequence can fold into, there is great room for improvement in modeling structural switching. In this work, a relational geometric graph neural network is proposed that explicitly incorporates alternative structures to predict an RNA sequence. Converting the RNA structure into a geometric graph, the proposed model uses edge types to distinguish between the primary structure, secondary structure, and spatial positioning of the nucleotides in representing structures. The results show higher native sequence recovery rates over those of gRNAde across different test sets (eg. 72% vs. 66%) and a benchmark from the literature (60% vs. 57%). Secondary-structure edge types had a more significant impact on the sequence recovery than the spatial edge types as defined in this work. Overall, these results suggest the need for more complex and case-specific characterization of RNA for successful inverse design.

PMID:40126342 | DOI:10.3390/ncrna11020018

Soft Matter. 2025 Mar 24. doi: 10.1039/d4sm01117a. Online ahead of print.

ABSTRACT

Bacteria aggregate through various intercellular interactions to build biofilms, but the effect of environmental changes on them remains largely unexplored. Here, by using an experimental device that overcomes past difficulties, we observed the collective response of Escherichia coli aggregates to dynamic changes in the growth conditions. We discovered that nutrient starvation caused bacterial cells to arrange themselves into bundle-shaped clusters, developing a structure akin to that of smectic liquid crystals. The degree of the smectic-like bundle order was evaluated by a deep learning approach. Our experiments suggest that both the depletion attraction by extracellular polymeric substances and the growth arrest are essential for the bundle formation. Since these effects of nutrient starvation at the single-cell level are common to many bacterial species, bundle formation might also be a common collective behavior that bacterial cells may exhibit under harsh environments.

PMID:40126189 | DOI:10.1039/d4sm01117a

Adv Sci (Weinh). 2025 Mar 24:e2502744. doi: 10.1002/advs.202502744. Online ahead of print.

ABSTRACT

The liver harbors complex cross-scale structures, and the fibrosis-related alterations to these structures have a severe impact on the diverse function of the liver. However, the hepatic anatomic structures and their pathological alterations in the whole-liver scale remain to be elucidated. Combining the micro-optical sectioning tomography (MOST) system and liver Nissl staining, a first high-precision whole mouse liver atlas is generated, enabling visualization and analysis of the entire mouse liver. Thus, a detailed 3D panorama of CCl4-induced liver fibrosis pathology is constructed, capturing the 3D details of the central veins, portal veins, arteries, bile ducts, hepatic sinusoids, and liver cells. Pathological changes, including damaged sinusoids, steatotic hepatocytes, and collagen deposition, are region-specific and concentrated in the pericentral areas. The quantitative analysis shows a significantly reduced diameter and increased length density of the central vein. Additionally, a deep learning tool is used to segment steatotic hepatocytes, finding that the volume proportion of steatotic regions is similar across liver lobes. Steatosis severity increases with proximity to the central vein, independent of central vein diameter. The approach allows the cross-scale visualization of multiple structural components in liver research and promotes pathological studies from a 2D to a 3D perspective.

PMID:40126158 | DOI:10.1002/advs.202502744