Macrophage Polarization Leads to Differential Inflammatory Responses to Live and Dead <i>Mycobacterium leprae</i>
Bibliographic record
Abstract
Background: The clinical presentation of leprosy is widely varied along a spectrum, and where a patient lands on this spectrum largely depends on the individual's immune response to the infection. Several studies suggest that macrophage phenotypes have a role in the pathogenesis and recovery of disease states. We aimed to study how macrophage polarization affects the inflammatory response towards live and irradiated Mycobacterium leprae ( M. leprae ). Methods: Human monocytic cell line dual THP-1 was differentiated into four different macrophage polarization categories: phorbol-12-myristate-13-acetate (PMA)-differentiated, M1, M1-activated, and M2. Immunofluorescence assay was used to observe M. leprae phagocytosis by macrophages. Activity of transcription factors nuclear factor kappa B (NF-кB) and interferon regulatory factors (IRFs) were measured through reporter plasmids in these cells. A multiplex immunoassay panel was used to measure secreted cytokines upon infection. Results: M1 macrophage activation led to an increased inflammatory response, increased phagocytosis of dead M. leprae , and increased secretion of interferon (IFN)-β, thymic stromal lymphopoietin (TSLP), and soluble CD163 (sCD163) upon stimulation with live M. leprae. When activated, macrophages increased phagocytosis via upregulation of CD163, expressed on the macrophage surface, in response to bacterial shedding. An increased IRF-mediated response, representing an increased IFN-β secretion in M1 and M1-activated cells upon stimulation with live M. leprae along with an increase in sCD163 and TSLP secretion, suggests movement towards a resolution of the inflammatory state. Conclusions: Why certain individuals are more prone to be in a predominant M1 or M2 macrophage state is still unknown. As discoveries are made regarding the interplay and balance between pro- and anti-inflammatory responses, new treatment approaches can be designed to ameliorate and control disease severity in individuals suffering from leprosy. Clin Infect Immun. 2023;8(1):31-36 doi: https://doi.org/10.14740/cii163
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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 | 0.001 |
| 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) | 0.000 | 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".