<i>TSC22D</i> , <i>WNK</i> and <i>NRBP</i> gene families exhibit functional buffering and evolved with Metazoa for macromolecular crowd sensing
Bibliographic record
Abstract
SUMMARY The ability to sense and respond to osmotic fluctuations is critical for the maintenance of cellular integrity. Myriad redundancies have evolved across all facets of osmosensing in metazoans, including among water and ion transporters, regulators of cellular morphology, and macromolecular crowding sensors, hampering efforts to gain a clear understanding of how cells respond to rapid water loss. In this study, we harness the power of gene co-essentiality analysis and genome-scale CRISPR-Cas9 screening to identify an unappreciated relationship between TSC22D2 , WNK1 and NRBP1 in regulating cell volume homeostasis. Each of these genes have paralogs and are functionally buffered for macromolecular crowd sensing and cell volume control. Within seconds of hyperosmotic stress, TSC22D, WNK and NRBP family members physically associate into cytoplasmic biocondensates, a process that is dependent on intrinsically disordered regions (IDRs). A close examination of these protein families across metazoans reveals that TSC22D genes evolved alongside a domain in NRBPs that specifically binds to TSC22D proteins, which we have termed NbrT ( N RBP b inding region with T SC22D), and this co-evolution is concomitant with rapid IDR length expansion in WNK family kinases. Our study identifies functions for unrecognized components of the cell volume sensing machinery and reveals that TSC22D , WNK and NRBP genes evolved as cytoplasmic crowding sensors in metazoans to co-regulate rapid cell volume changes in response to osmolarity.
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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) | 0.002 | 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".