Innate Immune Cell Expression of Pattern Recognition Receptors From β-Thalassemia Patients During Intensive Combination Chelation Therapy
Bibliographic record
Abstract
Abstract Abstract 1025 Introduction: Thalassemia major patients endure chronic RBC transfusions, high levels of tissue iron, iron chelation and organ injury. Patients with thalassemia also have reduced immune function and are at risk for infection. Infection is in fact the second most common cause of death in thalassemia. The innate immune system provides the first line of defense against infection and it specificity depends on pattern recognition receptors (PRRs) specific to microbial pathogens. One class of PRR called the toll-like receptors (TLRs) interact with CD14 on innate immune cells transducing the signal for bacterial Lipopolysaccharide (LPS), resulting in cytokine production. The role iron plays in thalassemia in determining expression level of PRRs is unknown. Thus, the goal in these studies is to investigate the relationship of iron overload and its chelation to innate immune cell expression of PRRs in thalassemia. Patients and Methods: Eighteen transfusion dependent thalassemia patients (11 – 29 yrs old) participating in the combination trial of deferasirox and deferoxamine (Novartis sponsored CICL670AUS24T) were enrolled in a substudy investigating innate immunology (Novartis sponsored CICL670AUS42T). Fasting blood samples were obtained i) at baseline after a 72 hr. washout of chelator, and ii) at 6 and 12 months on study. Fourteen healthy controls (10 – 35 yrs old) were also enrolled. Peripheral blood mononuclear cells (PBMCs) were isolated from blood samples and then from these cells monocytes and granulocytes were purified using antibody-linked magnetic microbeads (Miltenyi Biotec Inc). Highly enriched populations of CD14+ monocytes and CD15+ granulocytes were verified by flow cytometry. The expression level of CD14 and CD15 in PBMCs and TLR4 in purified cells were determined and reported as the median fluorescent intensity (MFI). Liver iron concentration (LIC) was determined by biomagnetic susceptibility (“SQUID”, Ferritometer®) in patients with thalassemia; healthy controls were shown to have normal ferritin. Results: In PBMCs from thalassemia patients at baseline, the expression of monocyte CD14 and TLR4 were significantly increased 22% and 6.5% respectively compared to healthy controls (p < 0.05). Granulocytes from patients with thalassemia at baseline were also found to have a 50% higher expression of TLR4 compared to controls. Markers of iron burden, such as LIC and ferritin also significantly correlated with the expression of monocyte TLR4. In longitudinal analysis markers of iron burden, the expression of TLR4 on monocytes and granulocytes all significantly decreased in the follow-up period in thalassemia patients receiving intensive combination chelator therapy (p<0.05). Conclusions: These studies support the hypothesis that iron burden influences the innate immune response in thalassemia as demonstrated by the increased monocyte expression of both CD14 and TLR4 at baseline, both of which likely contribute to the commonly observed susceptibility to infection. After intensive chelation, the levels of CD14 and TLR4 decreased, indicating that decreased iron overload with chelation may improve innate immune responsiveness. These changes in CD14 and TLR4 may be able to restore proper innate immune function in thalassemia patients. Disclosures: Walter: Novartis: Research Funding. Harmatz:Novartis: Research Funding; FerroKin BioSciences: Research Funding. Porter:Novartis: Consultancy, Research Funding. Vichinsky:ARUP Research Lab: Research Funding; ApoPharma: Consultancy, Research Funding; Novartis: Consultancy, Research Funding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".