TAK-242, a small-molecule inhibitor of Toll-like receptor 4 signalling, unveils similarities and differences in lipopolysaccharide- and lipidinduced inflammation and insulin resistance in muscle cells
Bibliographic record
Abstract
Emerging evidence suggests that TLR (Toll-like receptor) 4 and downstream pathways [MAPKs (mitogen-activated protein kinases) and NF-κB (nuclear factor κB)] play an important role in the pathogenesis of insulin resistance. LPS (lipopolysaccharide) and saturated NEFA (non-esterified fatty acids) activate TLR4, and plasma concentrations of these TLR4 ligands are elevated in obesity and Type 2 diabetes. Our goals were to define the role of TLR4 on the insulin resistance caused by LPS and saturated NEFA, and to dissect the independent contribution of LPS and NEFA to the activation of TLR4-driven pathways by employing TAK-242, a specific inhibitor of TLR4. LPS caused robust activation of the MAPK and NF-κB pathways in L6 myotubes, along with impaired insulin signalling and glucose transport. TAK-242 completely prevented the inflammatory response (MAPK and NF-κB activation) caused by LPS, and, in turn, improved LPS-induced insulin resistance. Similar to LPS, stearate strongly activated MAPKs, although stimulation of the NF-κB axis was modest. As seen with LPS, the inflammatory response caused by stearate was accompanied by impaired insulin action. TAK-242 also blunted stearate-induced inflammation; yet, the protective effect conferred by TAK-242 was partial and observed only on MAPKs. Consequently, the insulin resistance caused by stearate was only partially improved by TAK-242. In summary, TAK-242 provides complete and partial protection against LPS- and NEFA-induced inflammation and insulin resistance, respectively. Thus, LPS-induced insulin resistance depends entirely on TLR4, whereas NEFA works through TLR4-dependent and -independent mechanisms to impair insulin action.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".