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Access status: Open Access ,
Sputum Biomarkers of Inflammation to Track Acute Respiratory Events in School-Age Children with Cystic Fibrosis
(2026-02-06) Ben-Meir, Elad; Perrem, Lucy; Nissen, Gyde; Shaw, Michelle; Ratjen, Felix; Grasemann, Hartmut
Cystic fibrosis (CF) is characterized by neutrophil-driven airway inflammation and acute respiratory events (AREs) that contribute to progressive lung damage. AREs are clinically heterogeneous and often occur without measurable changes in lung function. This study aimed to evaluate the utility of molecular airway inflammatory markers for detecting AREs in school-age children with CF. We performed a secondary analysis of a prospective observational study of children with CF (ages 6.7–16.8 years) followed for two years. Sputum samples were collected from 50 participants during stable visits and AREs. Concentrations of 14 inflammatory cytokines were measured using ELISA and multiplex assays. Associations with lung function (ppFEV1 and lung clearance index [LCI]) and time to next ARE were assessed. A total of 179 sputum samples were analyzed, including 64 collected during AREs. Calprotectin, interleukin-8 (IL-8), and IL-1β were increased during AREs compared with stable visits, although concentrations frequently remained within ranges observed at stable visits. Other cytokines, including GM-CSF, IL-17A, IL-1α, TNF-α, and SPLUNC-1, were predictive of shorter time to subsequent AREs. No biomarker correlated with lung function measures. These findings indicate that airway inflammatory cytokine changes are associated with clinically diagnosed AREs but not with pulmonary function, supporting their potential role as complementary biomarkers in CF care.
Access status: Open Access ,
Microbiome Signatures in Advanced Gastric Cancer: Emerging Biomarkers for Risk Stratification, Therapy Guidance, and Prognostic Insight
(2026-01-31) Kim, Kyung-il John; Zhong, Hannah; Tai, Derek; Shah, Pranati; Park, Daniel; Goes, Vitor; Li, Jianan; Jung, Claire; Kim, Lucas; Guzman, Sofia; Brar, Gagandeep; Castillo, Dani
Gastric cancer (GC), often diagnosed at advanced or metastatic stages, remains a significant clinical challenge requiring novel biomarkers for early detection, risk stratification, and effective, personalized treatment optimization. Emerging evidence underscores a strong association between gut microbiome dysbiosis and GC initiation, progression, and therapeutic outcomes. This review explores the potential of the advanced/metastatic gastric microbiome as a source of diagnostic and targetable biomarkers and its role in modulating responses to immunotherapy. Although Helicobacter pylori (H. pylori) is the most significant risk factor for GC, several other gastrointestinal taxa—including Fusobacterium nucleatum (F. nucleatum)—have been implicated in advanced GC (AGC). At its inception, microbial dysbiosis contributes to chronic inflammation and immune evasion, thereby influencing tumor behavior and treatment efficacy. Integrating microbiome-based biomarkers into risk stratification, GC staging, and targetable treatment frameworks may enhance early detection, inform immunotherapy strategies, and improve patient-specific treatment responses. Bifidobacterium and Lactobacillus rhamnosus GG have the potential to change the immunotherapy framework with their direct influence on dendritic cell (DC) and cytotoxic T cell (CTL) activity. However, clinical translation is impeded by methodological heterogeneity, causality limitations, and a lack of clinical trials. Nonetheless, the integration of microbiome profiling and the development of therapeutic microbiome modulation strategies, such as personalized probiotics regimens and fecal microbiota transplantation, hold substantial potential for improving clinical outcomes and reducing treatment-related toxicity in GC management.
Access status: Open Access ,
Rhythmic Mechanisms Governing CAM Photosynthesis in Kalanchoe fedtschenkoi: High-Resolution Temporal Transcriptomics
(2026-01-29) Hu, Rongbin; Jawdy, Sara; Sreedasyam, Avinash; Lipzen, Anna; Wang, Mei; Ng, Vivian; Daum, Christopher; Keymanesh, Keykhosrow; Liu, Degao; Hu, Alex; Pasha, Asher; Provart, Nicholas J.; Borland, Anne M.; Tschaplinski, Timothy J.; Tuskan, Gerald A.; Schmutz, Jeremy; Yang, Xiaohan
Crassulacean acid metabolism (CAM) is a specialized photosynthetic pathway that enhances water-use efficiency by temporally separating nocturnal CO2 uptake from daytime decarboxylation and carbon fixation. To uncover the regulatory mechanisms coordinating these temporal dynamics, we generated high-resolution, 48 h time-course transcriptomes for the CAM model Kalanchoe fedtschenkoi under both 12 h/12 h light/dark (LD) cycles and continuous light (LL). A rhythmicity analysis revealed that diel light cues are the dominant driver of transcript oscillations: 16,810 genes (54.3% of annotated genes) exhibited rhythmic expression only under LD, whereas just 399 genes (1.3%) remained rhythmic under LL. A smaller set of 3009 genes (9.7%) oscillated in both conditions, indicating that the intrinsic circadian clock sustains rhythmicity for a limited subset of the transcriptome. A gene co-expression network analysis revealed extensive integration between circadian clock components, core CAM pathway enzymes, and stomatal regulators, defining regulatory modules that coordinate metabolic and physiological timing. Notably, key hub genes associated with post-translational and post-transcriptional regulation, including the E3 ubiquitin ligase HUB2 and several pentatricopeptide repeat (PPR) proteins, act as central nodes in CAM-associated networks. This discovery implicates epigenetic and organellar regulation as previously unrecognized critical tiers of control in CAM. Together, our results support a regulatory model in which CAM rhythmicity is governed by both external light/dark cues and the endogenous circadian clock through multi-level control spanning transcriptional and protein-level regulation. To support community exploration, we also provide an interactive eFP (electronic Fluorescent Pictograph) browser for visualizing time-resolved gene expression profiles.
Access status: Open Access ,
Recombinant Human IgG1-Hexamer Reduces Pathogenic Autoantibodies in the K/BxN Mouse Model of Arthritis Independent of FcRn
(2026-01-27) Lewis, Bonnie J. B.; Almizraq, Ruqayyah J.; Cen, Selena; Binnington, Beth; Frias Boligan, Kayluz; Spirig, Rolf; Käsermann, Fabian; Dunn, Shannon E.; Branch, Donald R.
Arthritis in K/BxN mice is provoked by pathogenic autoantibodies to glucose-6-phosphate isomerase (G6PI), which is a ubiquitously expressed enzyme that is present in cells, in the circulation and on articular cartilage. When G6PI autoantibodies (auto-Abs) deposit on the articular cartilage of K/BxN mice, arthritis ensues due to the activation of various components of the innate immune system. Recent studies have investigated the in vivo efficacy of recombinant fragment-crystallizable (Fc) protein-based therapeutics. Many recombinant Fc proteins evaluated provide protection against inflammation in mouse models of arthritis, such as the K/BxN serum-transfer model. More recently, rFc-µTP-L309C, a recombinant human IgG1-Fc with an additional point mutation at position L309C fused to the human IgM tailpiece to form a hexamer, has been shown to ameliorate the arthritis in K/BxN mice. Additional studies have shown that rFc-µTP-L309C has multiple effects that work together to ameliorate the arthritis, including inhibition of neutrophil migration into the joint, inhibition of IL-1β production, downregulation of Th1 and Th17 cells, and increases in T regulatory cells and synovial fluid IL-10. In this work, rFc-µTP-L309C was shown to effectively prevent arthritis in the K/BxN serum-transfer model, significantly downregulate inflammatory cytokines/chemokines, and ameliorate the arthritis in the endogenous K/BxN model. This amelioration of the arthritis was associated with a significant decrease in autoantibody levels, which was independent of the neonatal Fc receptor (FcRn). rFc-µTP-L309C was shown to specifically inhibit G6PI autoantibody secretion from B-cells with a concomitant increase in TGFβ and decrease in B-cell activating factor (BAFF). These new findings suggest that rFc-µTP-L309C may provide a therapeutic benefit for other antibody-mediated autoimmune diseases through its effects on B-cells.
Access status: Open Access ,
A Machine Learning Pipeline for Cusp Height Prediction in Worn Lower Molars: Methodological Proof-of-Concept and Validation Across Homo
(2026-01-27) Napolitano, Rebecca; Alichane, Hajar; Martini, Petra; Di Domenico, Giovanni; Martin, Robert M. G.; Hublin, Jean-Jacques; Oxilia, Gregorio
Reconstructing original cusp dimensions in worn molars represents a fundamental challenge across dentistry, anthropology, and paleontology, as dental wear obscures critical morphological information. In this proof-of-concept study, we present a standardized machine learning pipeline for predicting original cusp height, specifically the horn tips of the enamel–dentine junction (EDJ), in worn lower molars using three-dimensional morphometric data from micro-computed tomography (micro-CT). We analyzed 40 permanent lower first (M1) and second (M2) molars from four hominin groups, systematically evaluated across three wear stages: original, moderately worn (worn1), and severely worn (worn2). Morphometric variables including height, area, and volume were quantified for each cusp, with Random Forest and multiple linear regression models developed individually and combined through ensemble methods. To mimic realistic reconstruction scenarios while preserving a known ground truth, models were trained on unworn specimens (original EDJ morphology) and tested on other teeth after digitally simulated wear (worn1 and worn2). Predictive performance was evaluated using root mean square error (RMSE) and coefficient of determination (R2). Our results demonstrate that under moderate wear (worn1), the ensemble models achieved normalized RMSE values between 11% and 17%. Absolute errors typically below 0.25 mm for most cusps, with R2 values up to ~0.69. Performance deteriorated under severe wear (worn2), particularly for morphologically variable cusps such as the hypoconid and entoconid, but generally remained within sub-millimetric error ranges for several structures. Random Forests and linear models showed complementary strengths, and the ensemble generally offered the most stable performance across cusps and wear states. To enhance transparency and accessibility, we provide a comprehensive, user-friendly software pipeline including pre-trained models, automated prediction scripts, standardized data templates, and detailed documentation. This implementation allows researchers without advanced machine learning expertise to explore EDJ-based reconstruction from standard morphometric measurements in new datasets, while explicitly acknowledging the limitations imposed by our modest and taxonomically unbalanced sample. More broadly, the framework represents an initial step toward predicting complete crown morphology, including enamel thickness, in worn or damaged teeth. As such, it offers a validated methodological foundation for future developments in cusp and crown reconstruction in both clinical and evolutionary dental research.