Ivig Negatively Regulates LPS-Induced Monocytes Activation Through a Microrna-146a Related Mechanism
Bibliographic record
Abstract
Abstract Abstract 3274 Background: Cells from the monocytic lineage are known to play a central role in the immune defense against pathogens. In the adaptive immune response, they act as antigen presenting cells to trigger T and B cell responses. Monocytic cells also participate in innate immunity following recognition of pathogen-associated molecular patterns (PAMPs) such as bacterial lipopolysaccharides (LPS), which leads to their activation and release of very potent inflammatory mediators. The innate immune response thus needs to be tightly regulated to control not only its onset, but also its termination in order to avoid excessive inflammation. Recent studies have shown that the differentiation and functions of monocytic cells involve small RNA species, named microRNAs (miRNAs). MiRNAs are 21–23 nucleotide long single strand RNAs, which mainly cause gene silencing by degradation of target mRNAs or by inhibition of translation. Among them, miR-146a has captivated interest as it plays an important role in the negative regulation of acute inflammatory responses during activation of the innate immune system. In fact, it has been shown that miR-146a expression is gradually increased in THP-1 monocytic cells following stimulation with LPS or cytokines (e.g. IL-1β and TNF-α) via a NF-κB dependent pathway. MiR-146a inhibits the expression of IRAK1 and TRAF6 leading to the subsequent suppression of NF-κB activity. Consequently, the expression of NF-κB target genes such as IL-1β, TNF-α and PU.1 is suppressed. Therefore, miR146a controls NF-κB signaling via a negative feedback regulation loop and thus can be considered as an anti-inflammatory mediator. IVIg is a therapeutic preparation of polyclonal human IgG isolated from the plasma of thousands of healthy donors. IVIg is well known for its anti-inflammatory effects on a variety of immune cells and processes. More precisely, it has been shown to abrogate the capacity of monocyte-derived dendritic cells to secrete pro-inflammatory cytokines while increasing the expression of anti-inflammatory cytokines such as IL-10. We thus hypothesize that at least some of the anti-inflammatory effects of IVIg on monocytic cells could be triggered through the modulation of miR-146a expression. Objectives: To evaluate the involvement of miR-146a in the anti-inflammatory effects of IVIg following LPS stimulation of human monocytes. Methods: Human monocytes were obtained from the blood of healthy volunteers and treated with LPS (1 mg/mL) or IVIg (15 mg/mL) alone or alternatively, pretreated with LPS followed by addition of IVIg. Pre-treatment with LPS was done during for 4 h prior to addition of IVIg for 3, 6, 12 and 24 hours. Cells were then recovered and separated in two parts. The first part was used to extract the small RNA fraction of total RNA for miRNA analysis and the second part was used for protein isolation. The miR-146a level was measured by real time PCR while NF-kB and IRF4 protein levels were evaluated by western blotting. Finally, the expression of the transcription factor PU.1 was evaluated by flow cytometry. Results: Our preliminary data revealed that addition of IVIg to LPS-pretreated human monocytes resulted in a significant upregulation of miR-146a expression associated with a significant reduction in NF-κB expression. Furthermore, the expression of the PU.1/IRF4 transcriptional activator complex involved in the stimulation of inflammatory cytokine production was modulated. Indeed, we found that the expression PU.1 was reduced in IVIg-treated cells whereas IRF4 expression was increased, thus promoting the IRF4-mediated cytokine production inhibitory pathway. Conclusion: Our preliminary data suggest that in human monocytes, the anti-inflammatory effects of IVIg may involve miR-146a negative feedback loop regulation of NF-κB activity. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".