Omics analysis reveals galectin-3 to be a potential key regulator of allergic inflammation in hereditary angioedema
Bibliographic record
Abstract
Background: Hereditary angioedema (HAE) is a rare inherited disorder that predisposes an individual to develop vasogenic edema. Bradykinin release, which increases vascular permeability, results in angioedema. C1 esterase inhibitor (C1-INH) is a major regulator of critical enzymes involved in bradykinin generation and mutations in genes that encode the C1 inhibitor of complement factor 1, which prevent its synthesis (type I HAE), form a dysfunctional protein (type II HAE), or have normal functioning C1-INH (type III HAE, aka HAE-III). Objectives: The goals of this study were to use a systems biology analysis to identify novel biomarkers to aid in the diagnosis of HAE-III and to elucidate its underlying pathogenic mechanisms. Methods: Blood samples were obtained from HAE-III subjects and age- and sex-matched healthy controls. DNA, RNA, and protein purified from the samples were subjected to multiomics analysis using a 1-shot liquid chromatography-mass spectrometry-based multiomics platform (Omni-MS, Dalton Bioanalytics) to profile proteins, lipids, electrolytes, and metabolites enabling concurrent analysis of diverse analyte classes. Results: A total of 1647 novel identifications that included genes, proteins, and metabolites were made when comparing HAE-III samples to control samples. Our identification library included MSFragger for protein identification, LipiDex for lipid identification, and Compound Discoverer for metabolite identification, enabling differential expression analysis. Key findings included a significant increase in the expression levels of galectin-3, lysosomal α-glucosidase, platelet factor 4, and platelet-derived growth factor subunit A in HAE-III subjects compared to controls, all of which generate an immunomodulatory response. Conclusion: Galectin-3 plays a critical role in eosinophil recruitment and airway allergic inflammation. It may contribute to chronic inflammation and fibrosis resulting in leaky vasculature, and it could be a potential therapeutic target in HAE-III.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".