Multi-Pronged Effects of Cohesin Mutations in the Myeloid Leukemia of Down Syndrome
Bibliographic record
Abstract
Children with Down syndrome (DS) are at a 150-fold risk of developing acute megakaryoblastic leukemia. Known as the myeloid leukemia of Down syndrome (ML-DS), this disease is characterized by defined progressive stages that make it an ideal model to study leukemic transformation. While trisomy 21 alters steady state hematopoiesis, the addition of a GATA1 mutation, detected in nearly 30% of children with DS, frequently promotes transient abnormal myelopoiesis (TAM), a self-limiting pre-leukemia. Although TAM typically resolves within the first few months of life, 10-20% of individuals with TAM acquire an additional mutation that transforms the disease into ML-DS. Among these secondary mutations, cohesin alterations are seen in more than 50% of cases, far more than the incidence of these mutations in the general population of AML patients. Cohesin is a ring-shaped protein complex consisting of proteins SMC1, SMC3, RAD21, and either STAG1 or STAG2. Recent reports have implicated the cohesin complex in numerous functions related to chromatin architecture regulating gene expression including but not limited to domain insulation, recruitment of transcription factors, and enhancer-promoter interactions. Due to the genome wide binding of cohesin, elucidating how its mutation leads to transformation has remained elusive. Leveraging the ML-DS patient derived cell line CMY which has a patient relevant RAD21 Y3* mutation, we generated two RAD21 mutant corrected isogenic clones by CRISPR/Cas9 editing. In vitro the corrected clones grow significantly slower than the parental line, and NSG mice injected with the corrected clones had improved overall survival. To investigate the mechanism by which cohesin contributes to transformation, we performed multi-omics studies, including RNA-seq, ATAC-seq, CUT&RUN, and HI-C. ATAC motif enrichment revealed that the clones had increased accessibility of CTCF motifs and decreased accessibility of GATA and RUNT motifs. CUT&RUN for GATA1s, RAD21 and CTCF showed a striking increase in differential occupancy upon correction. We then compared our RNA-seq results with those from a primary human fetal liver model of ML-DS. Strikingly, GSEA of the RNA-seq data revealed strong enrichment for antigen processing and presentation, underpinned by increased HLA class II gene expression in the corrected clones. Utilizing the pan HLA class II antibody HLA-DR,DP,DQ we found that the corrected clones had a 2.5 fold increased cell surface expression compared to parental CMY cells. These results mirror the decrease in HLA-DR expression in T21, GATA1 mutant human fetal liver cells upon the introduction of a STAG2 mutation. These results suggest that one action of cohesin mutations is to affect HLA Class II expression and raise the possibility that cohesin mutations promote immune response evasion. Next, we searched for cell intrinsic drivers of leukemia progression mediated by cohesin mutations. Gene sets enriched in the RNA-seq data included metabolic terms such as electron transport chain, mitochondria and lipids. To validate an effect on metabolism, we induced oxidative stress in the cells with hydrogen peroxide and subsequently measured mitochondrial ROS (mitoROS). Upon treatment, both corrected clones showed significantly less induction of mitoROS compared to the RAD21 mutant parental line. These results prompted us to perform unbiased metabolomics, which led to the identification of 45 significantly altered metabolites including serine, phosphoethanolamine, and CDP-choline that were enriched in the corrected clones. These three metabolites comprise a node of the Kennedy pathway that feeds into phospholipid metabolism. We then performed unbiased lipidomics where 159 significantly changed lipid species were identified. Lipid ontology analysis revealed significant enrichment of glycerophospholipids and fatty acids with >3 double bonds; 62 phospholipids (PLs) were enriched in CMY and 32 were enriched in corrected clones. Saturation levels were drastically different between the enriched PLs, where 46.8% were saturated in parental cells but only 12.5% in the corrected clones. Interestingly, GSEA of the CUT&RUN data revealed enrichment of adipogenesis and biosynthesis of unsaturated fatty acids pathways. Together, these results suggest a role for differential phospholipid metabolism in leukemia transformation in DS.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".