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Plant seeds accumulate triacylglycerol (TAG) as a major storage reserve that supports post-germination growth and seedling establishment. Vegetable oils are also essential for human nutrition and provide renewable feedstocks for industrial and biotechnological applications. In 1998, Focks and Benning published a landmark study in Plant Physiology describing the Arabidopsis WRINKLED1 mutants (wri1), which display a distinctive wrinkled seed phenotype and a dramatic reduction in seed oil accumulation. The conceptual importance of this discovery was not simply the identification of a low-oil mutant, but the demonstration that seed oil accumulation depends on developmental control of carbon flux from carbohydrates into fatty acid precursors. Cloning of the Arabidopsis WRI1 (AtWRI1) gene in 2004 transformed this physiological phenotype into a molecular framework by identifying WRI1 as a member of the APETALA2 (AP2) family of transcription factors that activates late glycolytic and fatty acid biosynthetic genes. Subsequent work uncovered the AW-box cis-element, upstream seed-maturation regulators, WRI1-interacting partners, post-transcriptional and post-translational modification mechanisms controlling WRI1 stability and activity, and the structural basis of WRI1-DNA recognition. These discoveries established WRI1 as a central regulatory node linking seed development, carbohydrate metabolism, and seed oil accumulation. More recent studies have broadened WRI1 biology beyond canonical seed oil biosynthesis to include non-seed oil-storing tissues, hormone and nutrient-associated processes, environmental responses, and structure-guided crop engineering. Here, we revisit the original Plant Physiology classic and trace how one mutant phenotype reshaped modern understanding of plant carbon partitioning, transcriptional regulation, and metabolic engineering.
Precise quantification of the causative agent of syphilis, Treponema pallidum (T. pallidum) is critical for advancing research in pathogenesis, treatment response, and vaccine development. However, current methods have certain limitations. Dark-field microscopy (DFM) suffers from low sensitivity, poor reproducibility, and strong operator dependence, while quantitative PCR (qPCR) offers high precision but is time-consuming, technically demanding, and reliant on high-quality, consistent commercial reagents. This methodological bottleneck highlights the urgent need for a technique that integrates the speed and simplicity of direct detection with the precision, objectivity, and throughput of an automated assay. Herein, to bridge this gap, we propose a strategy for rapid, high-throughput quantification of T. pallidum using a novel, fluorescence-based flow cytometric assay implemented on an automated urine analyzer (the Sysmex UF-5000 analyzer). The assay demonstrated a limit of detection of 7.02 × 10³T. pallidum/mL and excellent precision (all coefficients of variation < 20%). It showed strong quantitative agreement with qPCR across a wide dynamic range (4.98 × 103-2.10 × 107T. pallidum/mL), with an excellent correlation (r = 0.9967), without significant proportional or constant bias (Passing-Bablok slope = 1.003). Bland-Altman analysis confirmed a close agreement (mean difference: -1.14 × 105T.pallidum/mL). In contrast, DFM exhibited substantially higher variability (CVs 15.19-83.52%) and failed to detect low-concentration samples. Operationally, the flow cytometric assay provides results within 30 s per sample at a low consumable cost (approximately $0.35 per test), outperforming DFM in objectivity and throughput and qPCR in both speed and cost-effectiveness. In summary, this novel flow cytometric assay effectively overcomes the historical challenges associated with T.pallidum quantification. This automated, precise, and rapid assay integrates the simplicity of direct detection with the accuracy of molecular quantification, offering a standardized and practical tool to enhance research in syphilis microbiology, pharmacology, and immunology, paving the way for more reproducible and translatable scientific discoveries.
Medicinal plants remain a vital source of new therapeutic agents, yet many species remain underexplored. Echinops niveus Wall. ex Royle, a member of the Asteraceae family, has not been comprehensively evaluated for multifunctional bioactivity. This study aimed to assess the phytochemical composition, antioxidant, antimicrobial, anti-leishmanial, and cytotoxic properties of E. niveus extracts.The aerial parts of E. niveus were extracted with six different solvents: aqueous, methanolic, ethanolic, chloroform, ethyl acetate and n-hexane. They were characterized by phytochemical profiling and FT-IR spectroscopy. The antioxidant activity was measured by DPPH free radical scavenging and reducing power assays. Antimicrobial activity was assessed against Gram-positive bacteria (Bacillus subtilis, Staphylococcus aureus), Gram-negative bacteria (Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae) and some fungal strains. The anti-leishmanial activity was assessed by the MTT assay and the cytotoxic activity was explored in the brine shrimp lethality assay and prostate cancer cell lines (PC3). The n-hexane extract showed the strongest DPPH scavenging activity (IC₅₀ = 104.76 ± 1.2 µg/mL). The chloroform extract demonstrated the highest reducing power (73.14 ± 1.47 mg AAE/g) and total antioxidant capacity (63.49 ± 1.46 mg AAE/g). The aqueous extract exhibited the best antibacterial potential against tested strains. Anti-leishmanial activity exceeded 50% inhibition for all extracts, with the aqueous extract showing 64% inhibition. The n-hexane extract was most cytotoxic against brine shrimp (LD₅₀ = 56.15 µg/mL) and PC3 cells.Echinops niveus possesses significant multifunctional pharmacological potential, supporting further investigation into its bioactive compounds and mechanisms as a candidate for drug discovery.
DNA methylation is a critical epigenetic mark across numerous species, and identifying differentially methylated regions (DMRs) is essential for understanding genome regulation. Most existing DMR detection methods require predefined sample conditions, limiting the discovery of new epigenetic patterns, especially when group identities are unknown or uncertain, as is common in clinical settings. Additionally, only a very few approaches enable comparisons across multiple conditions. To address this significant gap, we present metilene3, a method for rapid, multi-condition DMR detection that operates in both supervised and unsupervised modes, using user-provided labels or autonomously clustering unlabeled samples. By segmenting the genome based on multiple pairwise methylation difference signals, metilene3 enables sample classification and DMR-anchored inference of epigenetic relationships. Using simulated and diverse human datasets, we show that metilene3 accurately detects DMRs, robustly clusters samples, and holds the potential to reveal new regulatory elements and sample stratifications. Specifically, in a pancreatic tissue dataset, metilene3 identifies DMRs enriched for key transcription factors involved in pancreatic cancer development, hinting towards an altered NFKB-NFAT regulatory program. Together, metilene3 provides a fast, interpretable framework for exploring heterogeneous methylomes and discovering epigenetic patterns across complex biological and clinical datasets.
To describe the data analytic strategy used to develop new quality-of-life measures for the Limb Injury Measurement Battery for Quality of Life (LIMB-QOL). Several item pools were created and administered to a large sample of individuals with a history of major extremity injury or limb loss (n = 603). Item analyses adhered to modern psychometric standards (e.g., PROMIS®, COSMIN) and aimed to create several item response theory-based (IRT) item banks based on the graded response model. Items were removed iteratively based on pre-defined criteria and IRT model assumptions were met for the final item pools (monotonicity, unidimensionality, local item independence); differential item functioning, test-retest reliability, and convergent validity were then evaluated. Computer adaptive test and short-form versions of final item banks were created and examined using data simulation. Item analyses led to the development of 8 new item banks and two fixed-length scales. These 10 new LIMB-QOL measures demonstrated initial evidence of reliability (α range = 0.94-0.98, test-retest ICC range: 0.68-0.91) and convergent validity for use in individuals with a history of major extremity injury or limb loss, and abbreviated formats of the full item banks exhibited comparable performance. The new LIMB-QOL measures demonstrated strong psychometric properties and can be used to collect patient-reported assessments of quality of life following major extremity injury and limb loss. The analytic strategy described herein exemplifies how the PROMIS methodology can be utilized to design IRT-based patient-reported outcome measures to fill measurement gaps for specific clinical populations.
Parkinson's disease (PD) is a neurodegenerative disease marked by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and formation of misfolded protein aggregates. A growing body of research has implicated glial cell dysfunction in PD etiology, including the concentration of activated glial cells around protein aggregates in post-mortem tissue. A disruption in the balance of pro- and anti-inflammatory immune response functions of microglia and astrocytes is believed to contribute towards neuronal degeneration as the disease progresses. However, the molecular mechanisms remain unclear. To shed light on the role of microglia and astrocytes in PD, this study analyzes three public single nuclear RNA sequencing datasets of the SNpc from patient and control post-mortem brains to identify altered molecular pathways in PD. Both astrocytes and microglia show significant upregulation of heat shock binding and misfolded protein response pathways, likely reflecting a response to accumulating protein aggregates. Additionally, both cell types show decreased expression of genes associated with receptor functions; for microglia this included cytokine receptor genes such as IL21R, IL4R, and IFI44L. Genes associated with resting state microglia and non-inflammatory reactive state microglia were downregulated in PD microglia, including P2RY13, RSAD2, CSF2RA, CSF3R, and CX3CR1. Concurrently, astrocytes and microglia both show decreased expression of genes associated with neurotransmitter receptor functions that include glutamate and other ion channel receptors, suggesting a loss of neuron-glia communication in later disease stages. Taken together, our findings imply that astrocytes and microglia respond to protein misfolding pathology in PD by upregulating chaperone protein folding functions. Additionally, the profile of upregulated functions implies that both cell types are under increased energetic demand. The downregulation of neurotransmitter and channel receptor functions in both cell types indicates that neuron-glia communication and other supportive functions may be lost in favour of autophagic, protein-clearance mechanisms in PD microglia and astrocytes.
This study aimed to develop polylactic-co-glycolic acid (PLGA) microspheres encapsulating dezocine and evaluate their efficacy in managing postoperative pain and mitigating cognitive impairment. Dezocine-loaded PLGA microspheres (DEZ@PLGA MS) were fabricated using a single-emulsion solvent evaporation technique and were characterized by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and in vitro release studies. A rat model of open reduction and internal fixation for right hindlimb tibial plateau fracture was established in healthy male rats. Following modeling, rats were randomly divided into four groups (n = 6 per group) and received intramuscular injections of equal volumes of normal saline, blank PLGA microsphere suspension, dezocine injection (5 mg/kg), or DEZ@PLGA MS suspension (5 mg/kg). Pain thresholds of the left hind paw and cognitive behavioral tests were assessed at specified time points post-administration. After testing, rats were euthanized. Muscle tissue at the injection site was collected for hematoxylin and eosin (H&E) staining to assess inflammatory responses, and hippocampal tissue was harvested for Western blot analysis. The SEM results revealed spherical microspheres with a smooth surface and uniform particle size. FTIR and DSC analyses confirmed stable encapsulation of dezocine (DEZ) in the PLGA matrix, with potential intermolecular interactions and amorphous drug dispersion. In vitro release studies demonstrated a sustained release profile of DEZ from the microspheres. Animal experimental results indicated that the DEZ@PLGA MS group exhibited a significantly prolonged duration of effective analgesia compared to the dezocine injection group. Additionally, DEZ@PLGA MS improved cognitive function, as evidenced by cognitive behavioral tests and Western blot results. H&E staining confirmed no significant inflammatory responses in muscle tissue across all groups, highlighting good biocompatibility of the formulation. Our study demonstrated that DEZ@PLGA MS effectively manages acute postoperative pain over an extended duration and improves postoperative cognitive function.
Chimeric RNAs, formed by the fusion of exons from two or more distinct genes, represent a significant class of noncanonical transcripts with increasing implications in cancer biology, development, and other biological processes. Their inherent novelty and the potential for sequence similarity with parental transcripts pose significant challenges for accurate detection and validation. While next-generation sequencing (NGS) has become the primary tool for chimeric RNA discovery, orthogonal validation methods are crucial to confirm their existence, delineate their precise structure, and quantify their abundance. Mass spectrometry (MS)-based approaches offer a powerful and complementary strategy for the robust validation of chimeric RNAs. This chapter will delve into the principles and applications of MS-based techniques for the definitive characterization of these fusion transcripts, highlighting their strengths in providing direct evidence of the chimeric junction at the peptide level, confirming the reading frame, and offering quantitative insights. We will explore various MS workflows, including targeted and untargeted peptidomics, and discuss the critical considerations for sample preparation, data acquisition, and bioinformatic analysis to ensure reliable and high-confidence validation of chimeric RNAs.
Systemic lupus erythematosus is a chronic autoimmune disease in which vascular and microcirculatory disorders can play a significant role in the clinical picture. The aim of this pilot study was to evaluate the dynamic thermal response of the tongue to a controlled cooling stimulus in patients, compared with that of healthy individuals. This observational study was conducted in 106 healthy individuals and 10 patients. Measurements were taken using a thermal imaging camera. After the baseline measurement, participants held cold water in their mouths for 60 s. Thermal images of the tongue were recorded immediately after the stimulus and after 2, 5, and 10 min. Mean temperature was analysed. Differences between groups were assessed using the Mann-Whitney U test. No significant differences were found between groups in most absolute temperature values. In the unadjusted analysis, at 2 min after stimulation, patients with systemic lupus erythematosus had significantly higher temperatures than healthy controls in the apex and central region of the tongue. Significant differences were also observed at 10 min in the apex and central region. The most consistent between-group differences were observed in dynamic recovery parameters. The T2-T0 parameter differed significantly between groups in all analysed tongue regions. In the central region, additional significant differences were observed for T0-T, T5-T0, and T10-T0. After false discovery rate adjustment, significant differences remained for T2-T0 and T10-T0 in the central region of the tongue. Overall, patients with SLE demonstrated a tendency toward greater post-cooling temperature recovery, particularly in the central region and apex of the tongue. Dynamic thermography of the tongue following controlled cold stimulation may reveal differences in thermal response between SLE patients and healthy individuals that are not apparent from baseline temperature alone. Due to the pilot nature of the study, the results should be considered preliminary and require confirmation in larger, well-characterised cohorts.
Chronic lymphocytic leukemia (CLL) progression critically depends on bidirectional communication between malignant B cells and non-malignant bystander cells within the tumor microenvironment, including stromal cells, macrophages, and T lymphocytes. Among the key regulators of this cross talk is the Src-family kinase Lyn, whose activity in stromal cells controls extracellular matrix remodeling, fibrotic niche formation, and the release of tumor-supportive extracellular vesicles (EVs). This chapter presents a comprehensive proteomics-based strategy to identify Lyn-dependent effector molecules conveyed by stromal-cell-derived EVs and to evaluate their functional relevance for leukemic cell survival. State-of-the-art EV purification methods compliant with the Minimal information for studies of extracellular vesicles (MISEV) guidelines, nanoparticle tracking analysis, and high-resolution Orbitrap mass spectrometry are integrated with CRISPR-mediated Lyn knockout models and functional co-culture assays using primary CLL cells. Comparative metaproteomic profiling of EVs from Lyn-proficient and Lyn-deficient stromal cells reveals profound alterations in extracellular matrix proteins, adhesion molecules, redox regulators, and signaling mediators, highlighting downstream pathways through which Lyn programs a tumor-supportive microenvironment. Bioinformatic network analysis and pathway enrichment further identify druggable candidate proteins, including matricellular components and integrin-associated anchors such as CD248, as potential therapeutic nodes. Collectively, this chapter illustrates how EV-focused proteomics enables the systematic dissection of microenvironment-driven oncogenic signaling and provides a translational framework for discovering actionable stromal targets in CLL and other Lyn-dependent malignancies.
Chimeric RNAs (chiRNAs), generated via genomic rearrangements or splicing events, are increasingly recognized as biomarkers and therapeutic targets in cancer and neurodegenerative disorders. This chapter introduces an integrative framework for high-confidence chiRNA identification leveraging the ChiTaRS 8.0 database and the ChiTaH pipeline. ChiTaRS 8.0 encompasses 47,445 human chiRNAs, 1,055 Hi-C breakpoints, and 1,598 drug targets, while ChiTaH facilitates disease-specific analysis of RNA-seq data from 250 peripheral blood mononuclear cell (PBMC) samples-including glioblastoma and oral squamous cell carcinoma-and 199 healthy controls. Our approach combines reference-based fusion detection, BLAT validation against GRCh38, gene-pair compatibility checks, and protein domain conservation analysis. Functional annotation and protein-protein interaction modeling uncovered oncogenic chiRNAs absent from existing databases, exhibiting tissue-specific patterns. In Alzheimer's disease, liquid biopsy analyses identified unique chimeras-such as ENO1-MCUR1 and APOE-APOE-in cerebrospinal fluid, linked to neurotransmitter pathways and amyloid processing, and absent in healthy samples, highlighting their potential as early biomarkers. We describe a scalable digital hospital framework integrating AI-driven fusion detection, relational databases, and clinical metadata for real-time diagnostics and patient monitoring. This system supports fusion-targeted drug discovery and patient stratification, bridging translational gaps in oncology and neurodegeneration. By coupling computational pipelines with multiomics data, our approach advances personalized medicine while addressing challenges in artifact filtering and functional validation. Ultimately, the ChiTaRS-ChiTaH platform offers a versatile tool for chiRNA discovery and annotation across diverse disease contexts, providing insights into molecular mechanisms and clinical applications.
Cancer vaccines inducing immunogenic responses to tumor-specific neoantigens are rapidly emerging into a new frontier of cancer therapy. Chimeric RNAs encoding fusion proteins are a rich source of novel neoantigens. Here, we present a straightforward bioinformatic pipeline to identify immunogenic peptides produced from chimeric RNAs. We apply a bespoke script to identify fusion-specific peptide regions from chimeric transcript predictions and leverage the netMHCpan program to identify immunogenic peptides. In this paper, we provide a guide for installation and running of the program as well as discuss the rationale behind its design.
Seasonally migrating animals must navigate environments where resources shift predictably but are increasingly perturbed by climate change and human activities. Empirical work highlights the importance of cognition for these movements, yet the joint roles of perception and memory in sustaining stable seasonal migration remain poorly understood. We develop and analyze a novel PDE (partial differential equation) model that couples random dispersal with two taxis processes: perception-driven movement toward a nonlocally sensed, periodically shifting resource, and memory-driven movement guided by a spatiotemporal map of past foraging successes over seasonal time windows. We first establish global well-posedness of the system, proving existence, uniqueness, and uniform boundedness of classical solutions. Using Leray-Schauder degree theory, we then show that the model admits at least one time-periodic solution synchronized with the seasonal resource. By constructing a Lyapunov-Krasovskii functional, we further derive sufficient conditions under which this periodic migratory pattern is unique and globally asymptotically stable, revealing a key trade-off between perception- and memory-driven taxis strengths and diffusive spreading. Numerical simulations corroborate the analytical results and demonstrate how the balance of perception and memory, the precision of memory, and its match or mismatch with environmental periodicity jointly govern migration efficiency and persistence. Together, these results provide a rigorous theoretical framework linking individual-level cognitive processes to the emergence, stability, and breakdown of seasonal migration routes in changing environments.
The disparity between the complex structures of synthesized materials and their simplified computational models leads to deviations between theoretically calculated and experimental performance. To narrow this gap, we introduce the statistical descriptor φ, which is defined as the proportion of high-activity configurations in a given element combination. By considering the activity distribution of multiple structures rather than relying on a single model structure, φ can more accurately quantify macroscopic catalytic activity. Using the Seq-Equiformer model, a graph neural network we developed by augmenting EquiformerV2 with LSTM to capture dynamic structural changes during oxygen evolution reaction, we predict overpotentials for 250 million structures of 3d transition metal doped CoOOH. Based on these predictions, the value of φ for each element combination is calculated, and six optimal dopant combinations with the highest φ values are determined. For the leading MnFeNiCu combination, Bayesian optimization-driven AI experiments further optimize the elemental ratios. After only 40 experimental iterations, exploring 0.44% of the search space, the catalyst Mn0.07Fe0.09Ni0.14Cu0.01Co0.69OOH is identified, delivering an overpotential of 246.5 mV at 100 mA cm-2 and retaining 98.5% activity over 1000 h at 1 A cm-2. In validation, the statistical descriptor achieves 80% accuracy in identifying the top catalysts, a 30% improvement over single-structure screening, which evaluates the element combination based on the best configuration. The integration of statistical modeling, machine learning, and autonomous experimentation offers a powerful strategy to accelerate catalyst discovery and enhance prediction accuracy.
The discovery of highly active polyethylene (PE) catalysts demands a systematic understanding of structure-condition-activity relationships in a vast chemical space. In this Letter, we present a data-driven framework combining explainable machine learning (ML) with large-scale virtual library generation. From a curated data set of 507 catalysts (bis(phenoxyimine) and bis(imino)pyridine ligands, seven metals), a gradient boosting regression (GBR) model achieves a test R2 of 0.91, outperforming convolutional and graph neural networks. SHAP analysis identifies topological (Chi2v), electronic (EState_VSA), and hydrophobic (SlogP_VSA) descriptors as governing activity and reveals a classical volcano-type temperature dependence, fundamentally governed by the Sabatier principle. A virtual library of 665 685 structures, constructed via combinatorial fragment assembly, extends the known chemical space substantially. High-throughput screening, coupled with SCscore filtering, yields 1090 synthetically accessible candidates with predicted activities exceeding 2 × 107 g mol-1 h-1. Substructure analysis uncovers metal-dependent design rules, in which early transition metals favor electron-deficient aromatics while late metals profit from moderately sized alkyls. This work establishes a practical route from experimental data to actionable catalyst designs.
Knowledge of family functioning (FF) for those with eating disorders (EDs) is driven by research with females, resulting in an overly gendered perception of FF. The current study: (1) descriptively examined FF among male adolescents with EDs, (2) compared FF among males with anorexia nervosa-restricting subtype (AN-R), AN-binge/purge subtype (AN-BP), and avoidant/restrictive food intake disorder (ARFID), and (3) compared FF between males and females with these EDs. Participants were 175 males and 175 females who completed the Family Assessment Device (FAD). Males scored above the clinical cutoffs on most FAD subscales. No differences in FF were found among males across ED diagnoses. Significant differences were found between males and females with AN-R on four FAD subscales (affective involvement [OR = 4.70], affective responsiveness [OR = 2.52], communication [OR = 2.78], and general functioning [OR = 2.22]), with males reporting worse FF (all ps < 0.03). Differences between males and females with AN-BP or ARFID were not large enough to meet statistical significance. This study increases understanding of FF in EDs from a more diverse standpoint. Male adolescents with EDs experience poor FF. Qualitative studies could clarify possible reasons behind poor FF for adolescent males with EDs and help to identify specific targets for treatment.
The Dame Barbara Windsor Dementia Goals Programme was launched by the UK Government to accelerate the development and delivery of new treatments for dementia. We present the recommendations from the Scientific Advisory Board, to enable timely access to therapies for the wider population, reducing health system burden while improving patient outcomes. The recommendations focus on three areas: (i) establishing a new dynamic national patient registry for clinical trial recruitment; (ii) the use of biomarkers to improve early and accurate diagnosis; and (iii) a framework for end-to-end implementation across the landscape of healthcare, research and regulators. A Brain Aging Registry for Biomarkers, Access to trials, Research and Adoption would support recruitment, monitoring, and personalized care. Embedding digital and biomarker innovations into routine care would improve personalized and equitable dementia services, with earlier diagnosis and more effective prevention. Robust patient and public involvement is required, to ensure transparency, trustworthiness, and meaningful participation.
Prior biological knowledge and phenotype information can help identify disease genes from whole genome/exome sequencing studies, but how best to incorporate external knowledge with variant data remains challenging. We developed a machine learning algorithm called RankVar to prioritize causative variants for rare diseases, based on clinical notes and genome/exome sequencing profiles. RankVar uses a random forest classifier trained on ~ 1 million variants from the 1000 Genomes Project with spiked-in pathogenic variants. For testing, we compiled sequencing data and phenotype information from several independent datasets: 260 subjects from the Children's Hospital of Philadelphia (CHOP) with positive genetic diagnosis of various Mendelian diseases, 135 subjects from Birth Defects Biorepository (BDB), as well as 356 and 97 subjects with candidate causal variants for autism spectrum disorders from the Simons Simplex Collection (SSC) and the Simons Foundation Powering Autism Research for Knowledge (SPARK), respectively. RankVar achieves a top 10 variant accuracy of 90.0%, 81.5%, 46.1%, and 76.3% for CHOP, BDB, SSC, and SPARK, respectively, with improved performance over existing approaches. Notably, RankVar successfully identified X-linked and Y-linked disease-causal variants, such as KDM6A (p.N915Kfs5*) and SRY (p.W98X), as the top candidate variants. Moreover, we evaluated RankVar for genomic reinterpretation of 130 unsolved CHOP cases with hearing loss and successfully identified 61 candidate causal variants after manual review. In summary, RankVar performed favorably relative to existing methods in our evaluation, accommodated different genetic models and X/Y chromosome variants, and may provide a useful framework for prioritizing variants in monogenic or oligogenic diseases. We anticipate that RankVar may aid in primary genetic diagnosis, genome reinterpretation of previously unsolved cases, and the discovery of novel disease genes.
Eosinophilic chronic rhinosinusitis (ECRS) is a chronic inflammatory disease. It is characterized by type 2 inflammation and olfactory dysfunction. Although olfactory dysfunction in patients with ECRS may have sensorineural and central components, the underlying mechanism remains to be elucidated. We aimed to investigate changes in the olfactory bulb (OB) and olfactory epithelium (OE) associated with sinonasal type 2 inflammation, using a murine model of ECRS. We developed a murine model of ECRS based on the topical application of calcipotriol and ovalbumin for 14 days and subsequent daily intranasal challenge with ovalbumin for 5 days. Histopathological analyses were performed to assess glial cells and periglomerular cells (PGCs) in the OB. Bulk RNA sequencing was performed to determine the impact of sinonasal type 2 inflammation on the OB and OE. Differentially expressed genes were identified using a false discovery rate < 0.05 (Benjamini-Hochberg adjustment). For molecules exhibiting marked fluctuations at the gene expression level, the protein expression was evaluated using enzyme-linked immunosorbent assay (ELISA). In the ECRS group, a significant increase was noted in the number of microglia/macrophages and astrocytes in the OB. In addition, the number of tyrosine hydroxylase-positive PGCs was significantly reduced in the OB of mice with ECRS. Bulk RNA sequencing analysis revealed a significant decrease in the gene expression levels of the lipocalin family in both the OB and OE. Subsequent ELISA confirmed a significant reduction in the protein levels of lipocalin 4 and major urinary protein 5 in the OE; conversely, lipocalin 3 levels in the OE were significantly increased. No significant differences were observed in the protein expression levels of these molecules within the OB. Sinonasal type 2 inflammation causes various changes in the olfactory system. These changes may be involved in the pathogenesis of sensorineural and central olfactory dysfunction in ECRS.