Animal models are indispensable tools for probing physiological processes, pathological perturbations, and for developing new therapies. Ultimately, animal models must provide useful guideposts for developing gene therapy approaches that are eventually proven effective in humans. Significant differences in results using various gene therapy animal models have been described. However, little or no information exists regarding differences that exist among the available large animal models. Here we evaluated, in the hemopoietic cells of baboons, a system for selection that has previously demonstrated activity in mice, dogs and humans. This system employs a derivative of the thrombopoietin receptor (F36Vmpl), that is conditionally activated in the presence of a small molecule drug called a chemical inducer of dimerization (CID). Whereas cultured mouse, dog and human hemopoietic cells all respond to the mpl signal, we observed only a minor and variable response to the mpl signal in the cultured cells of baboons. Similarly, we have noted significant CID-dependent rises in the frequency of transduced hemopoietic cells in mice and in dogs, however here we show that responses to CID administration in three baboons were modest and variable. A number of factors may account for the disparity between baboons and other species. First, we have noted that GFP expression is attenuated in the red cells of baboons transplanted with the F36VmplGFP vector compared to red cells derived from mice, dogs and humans transduced with the same vector. Since red cells expressing F36Vmpl respond especially well to CID administration in the mouse and dog, an inadequate level of F36Vmpl expression may account for the absence of a red cell response in the baboons. Of note, placing F36Vmpl directly downstream of the LTR (rather than downstream of the IRES) failed to generate more prominent hematological responses to CID administration. A second issue pertains to the susceptibility of the baboon cells to proliferate in response to the murine mpl signal. As noted above, significant CID dependent growth occurs in transduced hemopoietic cells from mice, dogs and humans in response to the murine mpl signal. Whether baboon cells would have responded better to signals emanating from baboon mpl is unknown, however it is noteworthy that mouse and human mpl are indistinguishable with respect to their ability to induce growth of transduced human cord blood cells. A third set of considerations pertains to the optimal concentration of CID necessary to trigger cell growth. Head-to-head comparisons of transduced dog, baboon and human CD34+ cells point to differences in the relative thresholds for triggering mpl-induced growth, and differences between the species with respect to the concentrations at which excessive amounts of CID inhibit cell growth due to excessive occupancy of the drug binding domains. Finally, we have noted major differences in the pharmacokinetic profiles of AP20187 in baboons compared to dogs. These findings have general implications for the evaluation and development of new strategies for gene therapy.
Cancers that appear pathologically similar often respond differently to the same drug regimens. Methods to better match patients to drugs are in high demand. We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia (AML) by introducing: data from 30 AML patients including genome-wide gene expression profiles and in vitro sensitivity to 160 chemotherapy drugs, a computational method to identify reliable gene expression markers for drug sensitivity by incorporating multi-omic prior information relevant to each gene's potential to drive cancer. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone, and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents.
Abstract RUNX1 familial platelet disorder (RUNX1-FPD) is an autosomal dominant disorder caused by a monoallelic mutation of RUNX1, initially resulting in approximately half-normal RUNX1 activity. Clinical features include thrombocytopenia, platelet functional defects, and a predisposition to leukemia. RUNX1 is rapidly degraded through the ubiquitin-proteasome pathway. Moreover, it may autoregulate its expression. A predicted kinetic property of autoregulatory circuits is that transient perturbations of steady-state levels result in continued maintenance of expression at adjusted levels, even after inhibitors of degradation or inducers of transcription are withdrawn, suggesting that transient inhibition of RUNX1 degradation may have prolonged effects. We hypothesized that pharmacological inhibition of RUNX1 protein degradation could normalize RUNX1 protein levels, restore the number of platelets and their function, and potentially delay or prevent malignant transformation. In this study, we evaluated cell lines, induced pluripotent stem cells derived from patients with RUNX1-FPD, RUNX1-FPD primary bone marrow cells, and acute myeloid leukemia blood cells from patients with RUNX1 mutations. The results showed that, in some circumstances, transient expression of exogenous RUNX1 or inhibition of steps leading to RUNX1 ubiquitylation and proteasomal degradation restored RUNX1 levels, thereby advancing megakaryocytic differentiation in vitro. Thus, drugs retarding RUNX1 proteolytic degradation may represent a therapeutic avenue for treating bleeding complications and preventing leukemia in RUNX1-FPD.
Summary Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static phenotypic readouts. Transmitted light images, on the other hand, provide direct morphological readouts and can be acquired over time to provide a rich data source for dynamic cell phenotypic state identification. Here, we describe an end-to-end deep learning platform, UPSIDE (for Unsupervised Phenotypic State IDEntification), for discovering cell states and their dynamics from transmitted light movies. UPSIDE uses the variational auto-encoder architecture to learn latent cell representations, that are then clustered for state identification, decoded for feature interpretation, and linked across movie frames for transition rate inference. Using UPSIDE, we identified distinct blood cell types in a heterogeneous dataset. From acute myeloid leukemia cell movies, we then identified stem-cell associated morphological states and their inter-conversion rates. UPSIDE opens up use of transmitted light movies for systematic exploration of cell state heterogeneity and dynamics in biology and medicine.
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