Post-transcriptional Inhibition of Luciferase Reporter Assays by the Nod-like Receptor Proteins NLRX1 and NLRC3
Bibliographic record
Abstract
Luciferase reporter assays (LRAs) are widely used to assess the activity of specific signal transduction pathways. Although powerful, rapid and convenient, this technique can also generate artifactual results, as revealed for instance in the case of high throughput screens of inhibitory molecules. Here we demonstrate that the previously reported inhibitory effect of the Nod-like receptor (NLR) protein NLRX1 on NF-κB- and type I interferon-dependent pathways in LRAs was a nonspecific consequence of the overexpression of the NLRX1 leucine-rich repeat (LRR) domain. By comparing luciferase activity and luciferase gene expression using quantitative PCR from the same samples, we showed that NLRX1 inhibited LRAs in a post-transcriptional manner. In agreement, NLRX1 also repressed LRAs if luciferase was expressed under the control of a constitutive promoter, although the degree of inhibition by NLRX1 seemed to correlate with the dynamic inducibility of luciferase reporter constructs. Similarly, we observed that overexpression of another NLR protein, NLRC3, also resulted in artifactual inhibition of LRAs; thus suggesting that the capacity to inhibit LRAs at a post-transcriptional level is not unique to NLRX1. Finally, we demonstrate that host type I interferon response to Sendai virus infection was normal in NLRX1-silenced human HEK293T cells. Our results thus highlight the fact that LRAs are not a reliable technique to assess the inhibitory function of NLRs, and possibly other overexpressed proteins, on signal transduction pathways.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".