Genetically engineered human embryonic kidney cells as a novel vehicle for dual patch clamp study of human gap junction channels
Bibliographic record
Abstract
Mutations in more than half of human connexin genes encoding gap junction (GJ) subunits have been linked to inherited human diseases. Functional studies of human GJ channels are essential for revealing mechanistic insights into the etiology of disease-linked connexin mutants. However, the commonly used Xenopus oocytes, N2A, HeLa, and other model cells for recombinant expression of human connexins have different and significant limitations. Here we developed a human cell line (HEK293) with each of the endogenous connexins (Cx43 and Cx45) knocked out using the CRISPR-Cas9 system. Double knockout HEK293 cells showed no background GJ coupling, were easily transfected with several human connexin genes (such as those encoding Cx46, Cx50, Cx37, Cx45, Cx26, and Cx36) which successfully formed functional GJs and were readily accessible for dual patch clamp analysis. Single knockout Cx43 or Cx45 HEK cell lines could also be used to characterize human GJ channels formed by Cx45 or Cx43, respectively, with an expression level suitable for studying macroscopic and single channel GJ channel properties. A cardiac arrhythmia linked Cx45 mutant R184G failed to form functional GJs in DKO HEK293 cells with impaired localizations. These genetically engineered HEK293 cells are well suited for patch clamp study of human GJ channels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 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".