Analysis of gene expression and use of connectivity mapping to identify drugs for treatment of human glomerulopathies
Bibliographic record
Abstract
Background Human glomerulonephritis (GN)—membranous nephropathy (MN), focal segmental glomerulosclerosis (FSGS) and IgA nephropathy (IgAN), as well as diabetic nephropathy (DN) are leading causes of chronic kidney disease. In these glomerulopathies, distinct stimuli disrupt metabolic pathways in glomerular cells. Other pathways, including the endoplasmic reticulum (ER) unfolded protein response (UPR) and autophagy, are activated in parallel to attenuate cell injury or promote repair. Methods We used publicly available datasets to examine gene transcriptional pathways in glomeruli of human GN and DN and to identify drugs. Results We demonstrate that there are many common genes upregulated in MN, FSGS, IgAN, and DN. Furthermore, these glomerulopathies were associated with increased expression of ER/UPR and autophagy genes, a significant number of which were shared. Several candidate drugs for treatment of glomerulopathies were identified by relating gene expression signatures of distinct drugs in cell culture with the ER/UPR and autophagy genes upregulated in the glomerulopathies (“connectivity mapping”). Using a glomerular cell culture assay that correlates with glomerular damage in vivo , we showed that one candidate drug – neratinib (an epidermal growth factor receptor inhibitor) is cytoprotective. Conclusion The UPR and autophagy are activated in multiple types of glomerular injury. Connectivity mapping identified candidate drugs that shared common signatures with ER/UPR and autophagy genes upregulated in glomerulopathies, and one of these drugs attenuated injury of glomerular cells. The present study opens the possibility for modulating the UPR or autophagy pharmacologically as therapy for GN.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".