Investigating Gene Expression of Bone-Marrow Derived Macrophages with GM-CSF Stimulation in vitro
Bibliographic record
Abstract
Macrophages are specialized immune cells that play a crucial role in engulfing and destroying harmful pathogens and debris within the body. When stimulated with specific cytokines, macrophages transition into subtypes with specialized functions. For example, macrophages can differentiate into classically activated macrophages (M1) when stimulated with interferon-γ (IFN-γ ) and lipopolysaccharide (LPS). M1 macrophages are a subtype of activated macrophages that exhibit pro-inflammatory properties and are essential for combating infections and promoting tissue inflammation and repair. When macrophages are stimulated with another cytokine, known as Granulocyte-Macrophage Colony-Stimulating Factor (GM-CSF), they tend to develop an M1-like phenotype. The altered gene expression that occurs when macrophages are cultured in GM-CSF remains a relatively unexplored area, with few studies having profiled this modified genotype of bone marrow-derived macrophages (BMDMs). This study aims to characterize the genotype of GM-CSF macrophages using an in vitro model as well as a genetic database. Expression data from M-CSF or GM-CSF BMDM was retrieved using microarrays, and differential gene analysis was performed using R studio. The analysis suggested an increase in pro-inflammatory gene expression in GM-CSF BMDMs compared to M-CSF-induced macrophages. The findings are reinforced by RT-PCR and qPCR analyses of specific key pro-inflammatory genes. For instance, interleukin-6 (IL-6), IL-1, as well as NLR family pyrin containing domain 3 (NLRP3) were all significantly upregulated in GM-CSF BMDMs. This project seeks to comprehensively characterize GM-CSF-induced macrophages as pro-inflammatory agents. Moreover, the cytokine's propensity to induce a pro-inflammatory response raises concerns about its applicability in treatment plans.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".