An erythroid-specific lentiviral vector improves anemia and iron metabolism in a new model of XLSA
Bibliographic record
Abstract
ABSTRACT: X-linked sideroblastic anemia (XLSA) is a congenital anemia caused by mutations in ALAS2, a gene responsible for heme synthesis. Treatments are limited to pyridoxine supplements and blood transfusions, offering no definitive cure except for allogeneic hematopoietic stem cell transplantation, only accessible to a subset of patients. The absence of a suitable animal model has hindered the development of gene therapy research for this disease. We engineered a conditional Alas2-knockout (KO) mouse model using tamoxifen administration or treatment with lipid nanoparticles carrying Cre-mRNA and conjugated to an anti-CD117 antibody. Alas2-KOBM animals displayed a severe anemic phenotype characterized by ineffective erythropoiesis (IE), leading to low numbers of red blood cells, hemoglobin, and hematocrit. In particular, erythropoiesis in these animals showed expansion of polychromatic erythroid cells, characterized by reduced oxidative phosphorylation, mitochondria's function, and activity of key tricarboxylic acid cycle enzymes. In contrast, glycolysis was increased in the unsuccessful attempt to extend cell survival despite mitochondrial dysfunction. The IE was associated with marked splenomegaly and low hepcidin levels, leading to iron accumulation in the liver, spleen, and bone marrow and the formation of ring sideroblasts. To investigate the potential of a gene therapy approach for XLSA, we developed a lentiviral vector (X-ALAS2-LV) to direct ALAS2 expression in erythroid cells. Infusion of bone marrow (BM) cells with 0.6 to 1.4 copies of the X-ALAS2-LV in Alas2-KOBM mice improved complete blood cell levels, tissue iron accumulation, and survival rates. These findings suggest our vector could be curative in patients with XLSA.
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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) | 0.001 | 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 | 0.001 | 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".