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Stable HIV Decoy Receptor Expression After In Vivo HSC Transduction in Mice and NHPs: Safety and Efficacy in Protection From SHIV

Li, Molecular Therapy. 2023.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10124088/

Autologous hematopoietic stem cell (HSC) gene therapy offers a promising HIV treatment strategy, but cost, complexity, and toxicity remain significant challenges. Using female mice and female nonhuman primates (NHPs) (i.e., rhesus macaques), researchers developed an approach based on the stable expression of eCD4-Ig, a secreted decoy protein for HIV and simian–human immunodeficiency virus (SHIV) receptors. Their goals were to (1) assess the kinetics and serum level of eCD4-Ig, (2) evaluate the safety of HSC transduction with helper-dependent adenovirus–eCD4-Ig, and (3) test whether eCD4-Ig expression has a protective effect against viral challenge. They found that stable expression of the decoy receptor was achieved at therapeutically relevant levels. These data will guide future in vivo studies. Supported by ORIP (P51OD010425) and NHLBI.

Intestinal Microbiota Controls Graft-Versus-Host Disease Independent of Donor–Host Genetic Disparity

Koyama et al., Immunity. 2023.

https://pubmed.ncbi.nlm.nih.gov/37480848/

Allogeneic hematopoietic stem cell transplantation is a curative therapy for hematopoietic malignancies and non-malignant diseases, but acute graft-versus-host disease (GVHD) remains a serious complication. Specifically, severe gut GVHD is the major cause of transplant-related mortality. Here, the authors show that genetically identical mice, sourced from different vendors, had distinct commensal bacterial compositions, which resulted in significantly discordant severity in GVHD. These studies highlight the importance of pre-transplant microbiota composition for the initiation and suppression of immune-mediated pathology in the gastrointestinal tract, demonstrating the impact of non-genetic environmental determinants to transplant outcome. Supported by ORIP (S10OD028685), NIA, NCI, and NHLBI.

A Deep Learning Platform to Assess Drug Proarrhythmia Risk

Serrano et al., Cell Stem Cell. 2023.

https://www.sciencedirect.com/science/article/pii/S1934590922004866?via%3Dihub=

Investigators trained a convolutional neural network (CNN) classifier to learn and ultimately identify features of in vitro action potential recordings of human induced pluripotent stem cell (iPSC)–derived cardiomyocytes (hiPSC-CMs) that are associated with lethal Torsade de Pointes arrhythmia. The CNN classifier accurately predicted the risk of drug-induced arrhythmia. The risk profiles of the test drugs were similar across hiPSC-CMs derived from different healthy donors. In addition, pathogenic mutations that cause arrhythmogenic cardiomyopathies in patients significantly increased the proarrhythmic propensity to certain intermediate and high‑risk drugs in the hiPSC-CMs. These data indicate that deep learning can identify in vitro arrhythmic features that correlate with clinical arrhythmia and discern the influence of patient genetics on the risk of drug-induced arrhythmia. Supported by ORIP (S10OD030264) and NHLBI.