Molecular pathways of kidney development and their applications to clinical research
Bibliographic record
Abstract
Congenital anomalies of the kidney and urinary tract (CAKUT) are the major cause of childhood chronic kidney disease and an antecedent cause of adult-onset cardiovascular and kidney failure. Both genetic and environmental factors have been implicated in human kidney malformations, with pathogenic variants or DNA copy number variations identified in ∼16% and 10% of cases, respectively. To date, >60 CAKUT-associated genes have been identified, most of which are established regulators of organogenesis. Although excellent reviews covering the genetic bases of CAKUT exist, new approaches for automated analysis and machine learning require formats that can be easily read and interpreted by computers. Here, we develop and describe fully machine-readable, well-annotated pathways to visualize and analyze key events during kidney development. Pathways include genes controlling nephrogenesis, including glomerulotubular development, the GDNF/RET signaling axis driving ureter branching, the development of the ureteric bud-derived collecting system, and lineage dependencies of all kidney cell types with marker gene expression. These pathways are published on the WikiPathways database. Furthermore, we provide 3 examples of how to apply these molecular pathways to translational clinical research. We demonstrate how they (i) inform the discovery of new CAKUT-associated candidate genes, (ii) illuminate the aberrant transcriptomic panorama in a specific genetic kidney malformation, and (iii) help understand how environmental perturbations may cause kidney malformations. Taken together, this review summarizes and visualizes current knowledge informing kidney maldevelopment and genetic causes of CAKUT and facilitates future advanced data analyses and data integration approaches.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".