PLoS Biol. 2025 Apr 2;23(4):e3003072. doi: 10.1371/journal.pbio.3003072. eCollection 2025 Apr.

ABSTRACT

An alga that abandoned photosynthesis? This Primer explores a PLOS Biology study showing that a single horizontal gene transfer event allowed the diatom Nitzschia sing1 to evolve a complete enzymatic machinery to break down alginate from brown algae, unlocking a new ecological niche.

PMID:40173128 | DOI:10.1371/journal.pbio.3003072

J Agric Food Chem. 2025 Apr 2. doi: 10.1021/acs.jafc.4c12671. Online ahead of print.

ABSTRACT

Methanol is an ideal one-carbon (C1) feedstock for bioconversion into multicarbon value-added compounds. Biocatalytic approaches to methanol conversion provide sustainable and environmentally friendly alternatives to conventional methods. This process is facilitated by methanol-oxidizing enzymes, including alcohol oxidase (AOx). Here, we report an AOx from Pestalotiopsis fici (PfAOx) with the highest methanol oxidation activity and efficient heterologous expression compared to other AOxs. To investigate the bioconversion of a multicarbon compound (C4 chemical, 2-keto-4-hydroxybutyrate, 2-KHB) from cost-effective methanol, we developed a one-pot enzyme system including PfAOx and pyruvate aldolase from Deinococcus radiodurans (DrADL) with catalase from Bos taurus (BtCAT, commercially available enzyme) to remove toxic H2O2. The optimal reaction conditions for 2-KHB production using PfAOx, DrADL, and BtCAT were determined as pH 8.0, 35 °C, 0.9 mg mL-1 PfAOx, 0.3 mg mL-1 DrADL, 1.5 mg mL-1 BtCAT, 150 mM methanol, 100 mM pyruvate, and 5 mM Mg2+ with shaking at 200 rpm. Under these reaction conditions, 88.8 mM (10.4 g L-1) of 2-KHB was produced for 75 min, representing a 74.0-fold higher yield compared to previously reported 2-KHB production systems from methanol and pyruvate. This study demonstrates a promising multi-enzyme cascade approach for the bioconversion of methanol into valuable compounds.

PMID:40173089 | DOI:10.1021/acs.jafc.4c12671

Proc Natl Acad Sci U S A. 2025 Apr 8;122(14):e2412310122. doi: 10.1073/pnas.2412310122. Epub 2025 Apr 2.

ABSTRACT

High tumor mutational burden (TMB-H) is a predictive biomarker for the responsiveness of cancer to immune checkpoint inhibitor (ICI) therapy that indicates whether immune cells can sufficiently recognize cancer cells as nonself. However, about 30% of all cancers from The Cancer Genome Atlas (TCGA) are classified as immune-desert tumors lacking T cell infiltration despite TMB-H. Since the underlying mechanism of these immune-desert tumors has yet to be unraveled, there is a pressing need to transform such immune-desert tumors into immune-inflamed tumors and thereby enhance their responsiveness to anti-PD1 therapy. Here, we present a systems framework for identifying immuno-oncotargets, based on analysis of gene regulatory networks, and validating the effect of these targets in transforming immune-desert into immune-inflamed tumors. In particular, we identify DEAD-box helicases 54 (DDX54) as a master regulator of immune escape in immune-desert lung cancer with TMB-H and show that knockdown of DDX54 can increase immune cell infiltration and lead to improved sensitivity to anti-PD1 therapy.

PMID:40172969 | DOI:10.1073/pnas.2412310122

Elife. 2025 Apr 2;13:RP92536. doi: 10.7554/eLife.92536.

ABSTRACT

In eukaryotes, protein kinase signaling is regulated by a diverse array of post-translational modifications, including phosphorylation of Ser/Thr residues and oxidation of cysteine (Cys) residues. While regulation by activation segment phosphorylation of Ser/Thr residues is well understood, relatively little is known about how oxidation of cysteine residues modulate catalysis. In this study, we investigate redox regulation of the AMPK-related brain-selective kinases (BRSK) 1 and 2, and detail how broad catalytic activity is directly regulated through reversible oxidation and reduction of evolutionarily conserved Cys residues within the catalytic domain. We show that redox-dependent control of BRSKs is a dynamic and multilayered process involving oxidative modifications of several Cys residues, including the formation of intramolecular disulfide bonds involving a pair of Cys residues near the catalytic HRD motif and a highly conserved T-loop Cys with a BRSK-specific Cys within an unusual CPE motif at the end of the activation segment. Consistently, mutation of the CPE-Cys increases catalytic activity in vitro and drives phosphorylation of the BRSK substrate Tau in cells. Molecular modeling and molecular dynamics simulations indicate that oxidation of the CPE-Cys destabilizes a conserved salt bridge network critical for allosteric activation. The occurrence of spatially proximal Cys amino acids in diverse Ser/Thr protein kinase families suggests that disulfide-mediated control of catalytic activity may be a prevalent mechanism for regulation within the broader AMPK family.

PMID:40172959 | DOI:10.7554/eLife.92536

Bioinformatics. 2025 Apr 2:btaf149. doi: 10.1093/bioinformatics/btaf149. Online ahead of print.

ABSTRACT

SUMMARY: In single-cell transcriptomics, inconsistent cell type annotations due to varied naming conventions and hierarchical granularity impede data integration, machine learning applications, and meaningful evaluations. To address this challenge, we developed the unified Hierarchical Annotation Framework (uHAF), which includes organ-specific hierarchical cell type trees (uHAF-T) and a mapping tool (uHAF-Agent) based on large language models. uHAF-T provides standardized hierarchical references for 38 organs, allowing for consistent label unification and analysis at different levels of granularity. uHAF-Agent leverages GPT-4 to accurately map diverse and informal cell type labels onto uHAF-T nodes, streamlining the harmonization process. By simplifying label unification, uHAF enhances data integration, supports machine learning applications, and enables biologically meaningful evaluations of annotation methods. Our framework serves as an essential resource for standardizing cell type annotations and fostering collaborative refinement in the single-cell research community.

AVAILABILITY AND IMPLEMENTATION: uHAF is publicly available at: https://uhaf.unifiedcellatlas.org and https://github.com/SuperBianC/uhaf.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40172934 | DOI:10.1093/bioinformatics/btaf149

Mycorrhiza. 2025 Apr 2;35(2):26. doi: 10.1007/s00572-025-01188-6.

ABSTRACT

Like other plant-microbe symbioses, the establishment of orchid mycorrhiza (ORM) is likely to require specific communication and metabolic adjustments between the two partners. However, while modulation of plant and fungal metabolism has been investigated in fully established mycorrhizal tissues, the molecular changes occurring during the pre-symbiotic stages of the interaction remain largely unexplored in ORM. In this study, we investigated the pre-symbiotic responses of the ORM fungus Tulasnella sp. SV6 to plantlets of the orchid host Serapias vomeracea in a dual in vitro cultivation system. The fungal mycelium was harvested prior to physical contact with the orchid roots and the fungal transcriptome and metabolome were analyzed using RNA-seq and untargeted metabolomics approaches. The results revealed distinct transcriptomic and metabolomic remodelling of the ORM fungus in the presence of orchid plantlets, as compared to the free-living condition. The ORM fungus responds to the presence of the host plant with a significant up-regulation of genes associated with protein synthesis, amino acid and lipid biosynthesis, indicating increased metabolic activity. Metabolomic analysis supported the RNA-seq data, showing increased levels of amino acids and phospholipids, suggesting a remodelling of cell structure and signalling during the pre-symbiotic interaction. In addition, we identified an increase of transcripts of a small secreted protein that may play a role in early symbiotic signalling. Taken together, our results suggest that Tulasnella sp. SV6 may perceive information from orchid roots, leading to a readjustment of its transcriptomic and metabolomic profiles.

PMID:40172721 | DOI:10.1007/s00572-025-01188-6

Philos Trans A Math Phys Eng Sci. 2025 Apr 2;383(2293):20240412. doi: 10.1098/rsta.2024.0412. Epub 2025 Apr 2.

ABSTRACT

During infectious disease outbreaks, the time-dependent reproduction number ([Formula: see text]) can be estimated to monitor pathogen transmission. In previous work, we developed a simulation-based method for estimating [Formula: see text] from temporally aggregated disease incidence data (e.g. weekly case reports). While that approach is straightforward to use, it assumes implicitly that all cases are reported and the computation can be slow when applied to large datasets. In this article, we extend our previous approach and develop a computationally efficient simulation-based method for estimating [Formula: see text] in real-time accounting for both temporal aggregation of incidence data and under-reporting (with a fixed reporting probability per case). Using simulated data, we show that failing to consider stochastic under-reporting can lead to inappropriately precise estimates, including scenarios in which the true [Formula: see text] value lies outside inferred credible intervals more often than expected. We then apply our approach to data from the 2018 to 2020 Ebola outbreak in the Democratic Republic of the Congo (DRC), again exploring the effects of case under-reporting. Finally, we show how our method can be extended to account for temporal variations in reporting. Given information about the level of case reporting, our framework can be used to estimate [Formula: see text] during future outbreaks with under-reported and temporally aggregated case data.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.

PMID:40172553 | DOI:10.1098/rsta.2024.0412

Infect Immun. 2025 Apr 2:e0033324. doi: 10.1128/iai.00333-24. Online ahead of print.

ABSTRACT

Babesia microti is the primary cause of human babesiosis in North America. Despite the emergence of the disease in recent years, the pathogenesis and immune response to B. microti infection remain poorly understood. Studies in laboratory mice have shown a critical role for macrophages in the elimination of parasites and infected red blood cells (iRBCs). Importantly, the underlying mechanisms that activate macrophages are still unknown. Recent evidence identified the release of extracellular vesicles (EVs) from Babesia iRBCs. EVs are spherical particles released from cell membranes under natural or pathological conditions that have been suggested to play roles in host-pathogen interactions among diseases caused by protozoan parasites. The present study examined whether EVs released from cultured Babesia iRBCs could activate macrophages and alter cytokine secretion. An analysis of vesicle size in EV fractions from Babesia iRBCs showed diverse populations in the <100 nm size range compared to EVs from uninfected RBCs. In co-culture experiments, EVs released by B. microti iRBCs appeared to be associated with macrophage membranes and cytoplasm, indicating uptake of these vesicles in vitro. Interestingly, the incubation of macrophages with EVs isolated from Babesia iRBC culture supernatants resulted in the activation of NF-κB and modulation of pro-inflammatory cytokines. These results support a role for Babesia-derived EVs in macrophage activation and provide new insights into the mechanisms involved in the induction of the innate immune response during babesiosis.

PMID:40172538 | DOI:10.1128/iai.00333-24

Adv Sci (Weinh). 2025 Apr 2:e2412775. doi: 10.1002/advs.202412775. Online ahead of print.

ABSTRACT

Chronic noncommunicable diseases (NCDS) are often characterized by gradual onset and slow progression, but the difficulty in early prediction remains a substantial health challenge worldwide. This study aims to explore the interconnectedness of disease occurrence through multi-omics studies and validate it in large-scale electronic health records. In response, the research examined multi-omics data from 160 sub-healthy individuals at high altitude and then a deep learning model called Omicsformer is developed for detailed analysis and classification of routine blood samples. Omicsformer adeptly identified potential risks for nine diseases including cancer, cardiovascular conditions, and psychiatric conditions. Analysis of risk trajectories from 20 years of large clinical patients confirmed the validity of the group in preclinical risk assessment, revealing trends in increased disease risk at the time of onset. Additionally, a straightforward NCDs risk prediction system is developed, utilizing basic blood test results. This work highlights the role of multiomics analysis in the prediction of chronic disease risk, and the development and validation of predictive models based on blood routine results can help advance personalized medicine and reduce the cost of disease screening in the community.

PMID:40171841 | DOI:10.1002/advs.202412775

iScience. 2025 Mar 4;28(4):112146. doi: 10.1016/j.isci.2025.112146. eCollection 2025 Apr 18.

ABSTRACT

Epidermal growth factor receptor (Egfr)-driven signaling regulates fundamental homeostatic processes. Dysregulated signaling via Egfr is implicated in numerous disease pathologies and distinct Egfr-associated disease etiologies are known to be tissue-specific. The molecular basis of this tissue-specificity remains poorly understood. Most studies of Egfr signaling to date have been performed in vitro or in tissue-specific mouse models of disease, which has limited insight into Egfr signaling patterns in healthy tissues. Here, we carried out integrated phosphoproteomic, proteomic, and transcriptomic analyses of signaling changes across various mouse tissues in response to short-term stimulation with the Egfr ligand Egf. We show how both baseline and Egf-stimulated signaling dynamics differ between tissues. Moreover, we propose how baseline phosphorylation and total protein levels may be associated with clinically relevant tissue-specific Egfr-associated phenotypes. Altogether, our analyses illustrate tissue-specific effects of Egf stimulation and highlight potential links between underlying tissue biology and Egfr signaling output.

PMID:40171493 | PMC:PMC11960661 | DOI:10.1016/j.isci.2025.112146

RSC Adv. 2025 Apr 1;15(13):9884-9890. doi: 10.1039/d4ra07801b. eCollection 2025 Mar 28.

ABSTRACT

This study focuses on the creation and application of an advanced impedimetric immunosensor designed for the sensitive detection of lymphoma cancer cells. The sensor was developed by modifying a glassy carbon electrode (GCE) with gold nanoparticles (AuNPs) and 3,3'-dithiodipropionic acid di(N-hydroxysuccinimide ester) boronic acid (AuNPs@DTSP-BA), followed by the attachment of rituximab monoclonal antibody. Incorporating the boronic acid (BA) component enabled effective oriented immobilization of the antibody, thereby improving the performance of the biosensor. Various spectroscopic techniques were used to characterize the immunosensor. The developed immunosensor demonstrated the ability to detect lymphoma cancer cells across a wide linear range of 100 to 50 000 cells per mL, with a detection sensitivity of 64 cells per mL.

PMID:40171289 | PMC:PMC11959537 | DOI:10.1039/d4ra07801b

Cancer Discov. 2025 Apr 2;15(4):685-701. doi: 10.1158/2159-8290.CD-24-0861.

ABSTRACT

Viewing the hallmarks as a sequence of adaptations captures the "why" behind the "how" of the molecular changes driving cancer. This eco-evolutionary view distils the complexity of cancer progression into logical steps, providing a framework for understanding all existing and emerging hallmarks of cancer and developing therapeutic interventions.

PMID:40170539 | DOI:10.1158/2159-8290.CD-24-0861

Sheng Wu Gong Cheng Xue Bao. 2025 Mar 25;41(3):1152-1178. doi: 10.13345/j.cjb.250065.

ABSTRACT

Biomanufacturing has emerged as a crucial driving force for efficient material conversion through engineered cells or cell-free systems. However, the intrinsic spatiotemporal heterogeneity, complexity, and dynamic characteristics of these processes pose significant challenges to systematic understanding, optimization, and regulation. This review summarizes essential methodologies for multi-omics data acquisition and analyses for bioprocesses and outlines modelling approaches based on multi-omics data. Furthermore, we explore practical applications of multi-omics and modelling in fine-tuning process parameters, improving fermentation control, elucidating stress response mechanisms, optimizing nutrient supplementation, and enabling real-time monitoring and adaptive adjustment. The substantial potential offered by integrating multi-omics with computational modelling for precision bioprocessing is also discussed. Finally, we identify current challenges in bioprocess optimization and propose the possible solutions, the implementation of which will significantly deepen understanding and enhance control of complex bioprocesses, ultimately driving the rapid advancement of biomanufacturing.

PMID:40170317 | DOI:10.13345/j.cjb.250065

Sheng Wu Gong Cheng Xue Bao. 2025 Mar 25;41(3):1112-1132. doi: 10.13345/j.cjb.240966.

ABSTRACT

The metabolic reactions in cells, whether spontaneous or enzyme-catalyzed, form a highly complex metabolic network closely related to cellular physiological metabolic activities. The reconstruction of cellular physiological metabolic network models aids in systematically elucidating the relationship between genotype and growth phenotype, providing important computational biology tools for precisely characterizing cellular physiological metabolic activities and green biomanufacturing. This paper systematically introduces the latest research progress in different types of cellular physiological metabolic network models, including genome-scale metabolic models (GEMs), kinetic models, and enzyme-constrained genome-scale metabolic models (ecGEMs). Additionally, our paper discusses the advancements in the automated construction of GEMs and strategies for condition-specific GEM modeling. Considering artificial intelligence offers new opportunities for the high-precision construction of cellular physiological metabolic network models, our paper summarizes the applications of artificial intelligence in the development of kinetic models and enzyme-constrained models. In summary, the high-quality reconstruction of the aforementioned cellular physiological metabolic network models will provide robust computational support for future research in quantitative synthetic biology and systems biology.

PMID:40170315 | DOI:10.13345/j.cjb.240966

Sheng Wu Gong Cheng Xue Bao. 2025 Mar 25;41(3):1052-1078. doi: 10.13345/j.cjb.250061.

ABSTRACT

Biomanufacturing harnesses engineered cells for the large-scale production of biochemicals, biopharmaceuticals, biofuels, and biomaterials, playing a vital role in mitigating global environmental crises, achieving carbon peaking and neutrality, and driving the green transformation of the economy and society. The effective design and construction of these engineered cells require precise and comprehensive computational models. Recent technological breakthroughs including high-throughput sequencing, mass spectrometry, spectroscopy, and microfluidic devices, coupled with advances in data science, artificial intelligence, and automation, have enabled the rapid acquisition of large-scale biological datasets, thereby facilitating a deeper understanding of cellular dynamics and the construction of mechanism-based models with enhanced accuracy. This review systematically summarises the mathematical frameworks employed in cellular modelling. It begins by evaluating prevalent mathematical paradigms, such as network topology analyses, stochastic processes, and kinetic equations, critically assessing their applicability across various contexts. The discussion then categorises modelling strategies for specific cellular processes, including cellular growth and division, morphogenesis, DNA replication, transcriptional regulation, metabolism, signal transduction, and quorum sensing. We also examine the recent progress in developing whole-cell models through the integration of diverse cellular processes. The review concludes by addressing key challenges such as data scarcity, unknown mechanisms, multi-dimensional data integration, and exponentially escalating computational complexity. Overall, this work consolidates the mathematical models for the precise simulation of cellular processes, thereby enhancing our understanding of the molecular mechanisms governing cellular functions and contributing to the future design and optimisation of engineered organisms.

PMID:40170312 | DOI:10.13345/j.cjb.250061

Sheng Wu Gong Cheng Xue Bao. 2025 Mar 25;41(3):901-916. doi: 10.13345/j.cjb.240653.

ABSTRACT

Biomanufacturing is an advanced manufacturing method that integrates biology, chemistry, and engineering. It utilizes renewable biomass and biological organisms as production media to scale up the production of target products through fermentation. Compared with petrochemical routes, biomanufacturing offers significant advantages in reducing CO2 emissions, lowering energy consumption, and cutting costs. With the development of systems biology and synthetic biology and the accumulation of bioinformatics data, the integration of information technologies such as artificial intelligence, large models, and high-performance computing with biotechnology is propelling biomanufacturing into a data-driven era. This paper reviews the latest research progress on databases, knowledge bases, and large language models for biomanufacturing. It explores the development directions, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for scientific research in related areas.

PMID:40170304 | DOI:10.13345/j.cjb.240653

Sheng Wu Gong Cheng Xue Bao. 2025 Mar 25;41(3):I-VIII. doi: 10.13345/j.cjb.250197.

ABSTRACT

Biomanufacturing is one of important strategies for sustainable development, China places significant emphasis on the development of biomanufacturing, and the national and local governments have successively introduced special policies for biomanufacturing, and vigorously developing biomanufacturing has become an unstoppable trend. At present, with the rapid development of systems biology and synthetic biology, biological big data and information technology are deeply integrating with biotechnology. Novel theories, methods and technologies for the design, creation and application of biological systems are constantly emerging, which promoted the development of biomanufacturing into the era of artificial intelligence (AI). In order to grasp the innovation and development of AI-driven biomanufacturing, we publish this special issue to review the opportunities, challenges, and development status of AI-driven biomanufacturing from aspects such as AI-driven enabling technologies, intelligent design and construction of biological parts, circuits and artificial cells, as well as intelligent bioprocess control and optimization, and look forward to the future developments. This will provide valuable references for effectively promoting technological innovation and industrial development in the field of biomanufacturing.

PMID:40170303 | DOI:10.13345/j.cjb.250197

Clin Transl Med. 2025 Apr;15(4):e70290. doi: 10.1002/ctm2.70290.

ABSTRACT

Single-cell omics has emerged as a powerful tool for elucidating cellular heterogeneity in health and disease. Parallel advances in artificial intelligence (AI), particularly in pattern recognition, feature extraction and predictive modelling, now offer unprecedented opportunities to translate these insights into clinical applications. Here, we propose single-cell -omics-based Disease Predictor through AI (scDisPreAI), a unified framework that leverages AI to integrate single-cell -omics data, enabling robust disease and disease-stage prediction, alongside biomarker discovery. The foundation of scDisPreAI lies in assembling a large, standardised database spanning diverse diseases and multiple disease stages. Rigorous data preprocessing, including normalisation and batch effect correction, ensures that biological rather than technical variation drives downstream models. Machine learning pipelines or deep learning architectures can then be trained in a multi-task fashion, classifying both disease identity and disease stage. Crucially, interpretability techniques such as SHapley Additive exPlanations (SHAP) values or attention weights pinpoint the genes most influential for these predictions, highlighting biomarkers that may be shared across diseases or disease stages. By consolidating predictive modelling with interpretable biomarker identification, scDisPreAI may be deployed as a clinical decision assistant, flagging potential therapeutic targets for drug repurposing and guiding tailored treatments. In this editorial, we propose the technical and methodological roadmap for scDisPreAI and emphasises future directions, including the incorporation of multi-omics, standardised protocols and prospective clinical validation, to fully harness the transformative potential of single-cell AI in precision medicine.

PMID:40170267 | DOI:10.1002/ctm2.70290

Epigenetics Chromatin. 2025 Apr 1;18(1):17. doi: 10.1186/s13072-025-00580-y.

ABSTRACT

BACKGROUND: DNA methylation plays a crucial role in gene regulation and epigenetic inheritance across diverse organisms. Daphnia magna, a model organism in ecological and evolutionary research, has been widely used to study environmental responses, pharmaceutical toxicity, and developmental plasticity. However, its DNA methylation landscape and age-related epigenetic changes remain incompletely understood.

RESULTS: In this study, we characterized DNA methyltransferases (DNMTs) and mapped DNA methylation across the D. magna genome using whole-genome bisulfite sequencing. Our analysis identified three DNMTs: a highly expressed but nonfunctional de novo methyltransferase (DNMT3.1), alongside lowly expressed yet functional de novo methyltransferase (DNMT3.2) and maintenance methyltransferase (DNMT1). D. magna exhibits overall low DNA methylation, targeting primarily CpG dinucleotides. Methylation is sparse at promoters but elevated in the first exons downstream of transcription start sites, with these exons showing hypermethylation relative to adjacent introns. To examine age-associated DNA methylation changes, we analyzed D. magna individuals across multiple life stages. Our results showed no significant global differences in DNA methylation levels between young, mature, and old individuals, nor any age-related clustering in dimensionality reduction analyses. Attempts to construct an epigenetic clock using machine learning models did not yield accurate age predictions, likely due to the overall low DNA methylation levels and lack of robust age-associated methylation changes.

CONCLUSIONS: This study provides a comprehensive characterization of D. magna's DNA methylation landscape and DNMT enzymes, highlighting a distinct pattern of exon-biased CpG methylation. Contrary to prior studies, we found no strong evidence supporting age-associated epigenetic changes, suggesting that DNA methylation may have a limited role in aging in D. magna. These findings enhance our understanding of invertebrate epigenetics and emphasize the need for further research into the interplay between DNA methylation, environmental factors, and gene regulation in D. magna.

PMID:40170124 | DOI:10.1186/s13072-025-00580-y

Pediatr Rheumatol Online J. 2025 Apr 1;23(1):36. doi: 10.1186/s12969-025-01084-5.

ABSTRACT

BACKGROUND: Systemic lupus erythematosus (SLE) is a multi-systemic autoimmune disease that causes inflammation of the serosa (serositis). This retrospective study aimed to evaluate the clinical characteristics of serositis in childhood-onset SLE (cSLE) and analyze its association with long-term outcomes.

METHODS: We retrospectively reviewed the medical records of patients with cSLE diagnosed at a medical center in Taiwan, analyzing data collected from January 2002 to December 2022. We analyzed the clinical features of patients with serositis as pleuritis and/or pericarditis with at least a small effusion (> 0.5 cm in depth) on sonography or chest radiography. Cox proportional hazards regression was used to calculate the hazard ratios (HR) and 95% confidence intervals (CI) for the association between serositis and all-cause mortality.

RESULTS: 185 patients with cSLE were enrolled, of whom 38 (20.54%) had serositis. Patients with serositis had a younger age at SLE diagnosis, a higher SLE Disease Activity Index 2000 score at serositis diagnosis, and an increased prevalence of lupus nephritis, central nervous system manifestations, end-stage renal disease (ESRD), a higher Systemic Lupus International Collaborating Clinics (SLICC)/American College of Rheumatology (ACR) damage index score, and a higher mortality than that of patients without serositis. Multivariate Cox regression analysis showed that both serositis (hazard ratio [HR]: 5.585, confidence interval [CI]: 1.853-17.80) and ESRD (HR: 13.956; CI: 3.822-50.964) were associated with mortality risk. Kaplan-Meier survival curve analysis revealed that patients with both serositis and ESRD had the poorest 20-year survival rate. Patients with late-onset serositis (occurring 1 year after SLE diagnosis) had higher mortality rates than those with early-onset serositis.

CONCLUSION: Children with lupus serositis had higher disease activity, a higher prevalence of comorbidities, and mortality. Patients with both serositis, especially late-onset serositis, and ESRD had an increased risk of poor long-term survival.

PMID:40170018 | DOI:10.1186/s12969-025-01084-5