Characterization of hTERT Mutations in Bone Marrow Failure Syndromes
Bibliographic record
Abstract
Abstract Inherited bone marrow failure (IBMF) syndromes are a group of disorders associated with insufficient production of hematopoietic cells and are characterized by a predisposition for malignancies including myelodysplastic syndrome (MDS) and acute leukemia (AML). A majority of these disorders including aplastic anemia (AA) and dyskeratosis congenita (DKC) are characterized by defects in telomere maintenance and excessively short telomeres. Studies have demonstrated an association between shortened telomeres, advanced disease and increased risk of developing blood cancers. Heterozygous mutations in the gene encoding the telomerase protein component, hTERT, are seen in 5-15% of patients with IBMF, resulting in short telomeres and advanced disease. Loss of function of one autosomal copy of hTERT is sufficient to reduce telomerase levels and accelerate telomere attrition. The degree of inactivity subsequent disease phenotype among the mutations is variable. However, the impact of telomerase mutations and shortened telomeres on disease progression and response to therapeutics is not well understood. To understand the biochemical properties and cellular consequences of mutant hTERT expression we have generated expression constructs in various cellular models in order to investigate the function of 7 distinct hTERT mutants identified in patients with AA, DKC and MDS/AML . These mutations have been identified in patients with family history or clonal evolution of disease and are all located in functionally distinct regions of the protein. Using both primary BJ fibroblasts and an in vitro reconstitution system, we have determined that each of the mutants retain varying levels of telomerase activity and repeat addition processivity, with the exception of K570N, in which both biochemical properties are severely reduced. BJ cells transfected with each of the hTERT mutants alter the growth rate compared to parental BJ cells and with the exception of K570N, bypass senescence. Expression of mutant hTERT in the THP-1 leukemic cell line results in a variety of cellular phenotypes including distinct morphological and cell cycle changes. Most notably, A202T and A1062T mutant hTERT proteins results in a delay of the G1/S transition, which may have profound implications during hematopoiesis, negatively impacting the development and differentiation of mature blood cells. In addition, we have tested the response of each of our mutant hTERT THP-1 cell lines with common chemotherapeutic agents including decitabine, etoposide, cytarabine and topotecan. While some of our mutant lines exhibit partial resistance or sensitivity to a particular agent, we have found that P704S, K1050N and A1062T exhibit global resistance to all of the agents tested compared to WT-hTERT expressing cells. Interestingly, these 3 mutants exhibit similar morphological changes, losing their ability to adhere to the culture flask. We are currently investigating the ability of these mutant cells to adhere to a human stromal layer. Finally, to address the function of mutant telomerase in hematopoiesis we are currently utilizing a long term culture method using CD34+ hematopoietic stem cells transfected with either a control vector or specific hTERT variants. This model system allows us to study the differentiation capacity of each mutant as well as examine clonal evolution. To our knowledge, this is the first long term study in CD34+ stem cells to examine the impact of hTERT mutations. CD34 cells were transfected via electroporation, sorted using fluorescent activated cell sorting using a GFP positive co-transfection vector and grown on a murine stromal feeder layer. Our results indicate the expression level of TERT is significantly increased as compared to EV indicating successful over-expression of TERT. We have been able to assess the differentiation capacity using a methocult assay as the cells age as well as examine telomere length, telomerase activity, and chromosomal stability. These experiments will determine the consequence of these hTERT mutations and telomere shortening in hematopoietic cells to help understand the contribution to bone marrow failure. By defining the role of telomeres in hematological disorders, it may be possible to alter treatment strategies based on predicted outcomes from our investigations or lead to novel therapeutics for bone marrow failure syndromes. Disclosures No relevant conflicts of interest to declare.
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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 | 0.000 |
| 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) | 0.001 | 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".