Distinct gene regulatory networks govern hematopoietic and leukemia stem cells
Bibliographic record
Abstract
Abstract The underlying gene regulatory networks (GRN) that govern leukemia stem cells (LSC) in acute myeloid leukemia (AML) and hematopoietic stem cells (HSC) are not well understood. Here, we identified GRNs by integrating gene expression (GE) and chromatin accessibility data derived from functionally defined cell populations enriched for HSC and LSC. We analyzed n=32 LSC+ and n=32 LSC-cell fractions from n=22 AML patients, along with n=7 stem and n=10 progenitor enriched cell populations sorted from human umbilical cord blood (hUCB), producing a database of n≈17,000 transcription factor (TF) regulatory interactions for hUCB-HSPC and AML. We developed an iterative algorithm that associates the degree of chromatin openness with TF binding preferences, and the GE of candidate TF and target genes within 100kb upstream of transcription start sites. A putative regulatory structure was found to be enriched in HSC-enriched cell populations, comprising TF-target gene interactions between ETS1, EGR1, RUNX2, and ZNF683 oriented in a self-reinforcing configuration. A regulatory loop comprising FOXK1 and MEIS1, rather than the 4-factor HSC subnetwork, was detected in the LSC-specific GRN. The core HSC and LSC TF networks were extended using protein-protein interaction (PPI) data to determine connectivity with interacting genes whose expression strongly associated with LSC/HSC frequency estimates, producing a database of n=103,516 PPI target pathways. The effect of perturbing genes along the identified pathways on functional HSC and LSC frequency was predicted based on statistical regression analyses. To validate GRN predictions, we used pharmacologic and CRISPR targeting, in addition to re-examining published functional data associated with several network nodes that were predicted to impact stemness. Notably, we found that inhibition of CDK6 in AML samples markedly reduced LSC numbers as assessed in de novo serial xenotransplantation studies (fold change ≈ 10), as predicted by the LSC GRN model. Additionally, in-house CRISPR-based knockdown of ETS1 resulted in a significant decrease in HSC quiescence-associated microRNA-126 expression, and increased HSC frequency. Taken together, our models provide a comprehensive view of the underlying regulatory structures governing functional human HSC and LSC. This approach has translational potential as it can be used as a high-throughput in-silico screening tool for the systematic identification of gene targets for LSC elimination and HSC expansion.
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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 | 0.001 | 0.001 |
| 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".