Cellular Consequences of Copper Complexes Used To Catalyze Bioorthogonal Click Reactions
Bibliographic record
Abstract
Copper toxicity is a critical issue in the development of copper-based catalysts for copper(I)-catalyzed azide-alkyne cycloaddition (CuAAC) reactions for applications in living systems. The effects and related toxicity of copper on mammalian cells are dependent on the ligand environment. Copper complexes can be highly toxic, can induce changes in cellular metabolism, and can be rapidly taken up by cells, all of which can affect their ability to function as catalysts for CuAAC in living systems. Herein, we have evaluated the effects of a number of copper complexes that are typically used to catalyze CuAAC reactions on four human cell lines by measuring mitochondrial activity based on the metabolism of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) to study toxicity, inductively coupled plasma mass spectrometry to study cellular uptake, and coherent anti-Stokes Raman scattering (CARS) microscopy to study effects on lipid metabolism. We find that ligand environment around copper influences all three parameters. Interestingly, for the Cu(II)-bis-L-histidine complex (Cu(his)(2)), cellular uptake and metabolic changes are observed with no toxicity after 72 h at micromolar concentrations. Furthermore, we show that under conditions where other copper complexes kill human hepatoma cells, Cu(I)-L-histidine is an effective catalyst for CuAAC labeling of live cells following metabolic incorporation of an alkyne-labeled sugar (Ac(4)ManNAl) into glycosylated proteins expressed on the cell surface. This result suggests that Cu(his)(2) or derivatives thereof have potential for in vivo applications where toxicity as well as catalytic activity are critical factors for successful bioconjugation reactions.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".