Use of HSC-targeted LNP to generate a mouse model of lethal α-thalassemia and treatment via lentiviral gene therapy
Bibliographic record
Abstract
ABSTRACT: α-Thalassemia (AT) is one of the most commonly occurring inherited hematological diseases. However, few treatments are available, and allogeneic bone marrow transplantation is the only available therapeutic option for patients with severe AT. Research into AT has remained limited because of a lack of adult mouse models, with severe AT typically resulting in in utero lethality. By using a lipid nanoparticle (LNP) targeting the receptor CD117 and delivering a Cre messenger RNA (mRNACreLNPCD117), we were able to delete floxed α-globin genes at high efficiency in hematopoietic stem cells (HSC) ex vivo. These cells were then engrafted in the absence or presence of a novel α-globin-expressing lentiviral vector (ALS20αI). Myeloablated mice infused with mRNACreLNPCD117-treated HSC showed a complete knock out (KO) of α-globin genes. They showed a phenotype characterized by the synthesis of hemoglobin H (HbH; also known as β-tetramers or β4), aberrant erythropoiesis, and abnormal organ morphology, culminating in lethality ∼8 weeks after engraftment. Mice infused with mRNACreLNPCD117-treated HSC with at least 1 copy of ALS20αI survived long term with normalization of erythropoiesis, decreased production of HbH, and amelioration of the abnormal organ morphology. Furthermore, we tested ALS20αI in erythroid progenitors derived from α-globin-KO CD34+ cells and cells isolated from patients with both deletional and nondeletional HbH disease, demonstrating improvement in α-globin/β-globin mRNA ratio and reduction in the formation of HbH by high-performance liquid chromatography. Our results demonstrate the broad applicability of LNP for disease modeling, characterization of a novel mouse model of severe AT, and the efficacy of ALS20αI for treating AT.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".