Identification of a high incidence region for retroviral vector integration near exon 1 of the LMO2 locus

Therapeutic retroviral vector integration near the oncogene LMO2 is thought to be a cause of leukemia in X-SCID gene therapy trials. However, no published studies have evaluated the frequency of vector integrations near exon 1 of the LMO2 locus. We identified a high incidence region (HIR) of vector integration using PCR techniques in the upstream region close to the LMO2 transcription start site in the TPA-Mat T cell line. The integration frequency of the HIR was one per 4.46 × 10(4 )cells. This HIR was also found in Jurkat T cells but was absent from HeLa cells. Furthermore, using human cord blood-derived CD34(+ )cells we identified a HIR in a similar region as the TPA-Mat T cell line. One of the X-linked severe combined immunodeficiency (X-SCID) patients that developed leukemia after gene therapy had a vector integration site in this HIR. Therefore, the descriptions of the location and the integration frequency of the HIR presented here may help us to better understand vector-induced leukemogenesis

Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques

Abstract Background Cyclic peptide-based drug discovery is attracting increasing interest owing to its potential to avoid target protein depletion. In drug discovery, it is important to maintain the biostability of a drug within the proper range. Plasma protein binding (PPB) is the most important index of biostability, and developing a computational method to predict PPB of drug candidate compounds contributes to the acceleration of drug discovery research. PPB prediction of small molecule drug compounds using machine learning has been conducted thus far; however, no study has investigated cyclic peptides because experimental information of cyclic peptides is scarce. Results First, we adopted sparse modeling and small molecule information to construct a PPB prediction model for cyclic peptides. As cyclic peptide data are limited, applying multidimensional nonlinear models involves concerns regarding overfitting. However, models constructed by sparse modeling can avoid overfitting, offering high generalization performance and interpretability. More than 1000 PPB data of small molecules are available, and we used them to construct a prediction models with two enumeration methods: enumerating lasso solutions (ELS) and forward beam search (FBS). The accuracies of the prediction models constructed by ELS and FBS were equal to or better than those of conventional non-linear models (MAE = 0.167–0.174) on cross-validation of a small molecule compound dataset. Moreover, we showed that the prediction accuracies for cyclic peptides were close to those for small molecule compounds (MAE = 0.194–0.288). Such high accuracy could not be obtained by a simple method of learning from cyclic peptide data directly by lasso regression (MAE = 0.286–0.671) or ridge regression (MAE = 0.244–0.354). Conclusion In this study, we proposed a machine learning techniques that uses low-dimensional sparse modeling to predict the PPB value of cyclic peptides computationally. The low-dimensional sparse model not only exhibits excellent generalization performance but also improves interpretation of the prediction model. This can provide common an noteworthy knowledge for future cyclic peptide drug discovery studies

Seasonal differences in brown adipose tissue density and pulse rate variability in a thermoneutral environment

Abstract Background Brown adipose tissue (BAT) is sympathetically activated and induces thermogenesis during cold exposure, thereby influencing energy expenditure and body fat levels. The very low frequency (VLF) components of pulse rate variability could be a form of thermogenic sympathetic nervous activity, but no clear relationship has yet been reported between VLF activity and BAT density. We therefore aimed to evaluate the association between them. Methods We enrolled 20 adults in winter and 20 matched adults in summer. We assessed BAT densities based on total hemoglobin concentrations ([total-Hb]) measured with near-infrared time-resolved spectroscopy. We calculated VLF activity from pulse rate variability measurements. Results BAT density ([total-Hb]; winter 70.5 ± 17.0 μM, summer 57.8 ± 18.3 μM) and VLF activity (winter 6.7 ± 0.8, summer 6.1 ± 0.9) were significantly higher in winter than in summer (P < 0.05). However, there was no significant correlation between VLF activity and BAT density in either season. Conclusion Each parameter exhibited seasonal variation, but we failed to observe any significant correlations

Decellularized Extracellular Matrix as an In Vitro Model to Study the Comprehensive Roles of the ECM in Stem Cell Differentiation

Stem cells are a promising cell source for regenerative medicine. Stem cell differentiation must be regulated for applications in regenerative medicine. Stem cells are surrounded by extracellular matrix (ECM) in vivo. The ECM is composed of many types of proteins and glycosaminoglycans that assemble into a complex structure. The assembly of ECM molecules influences stem cell differentiation through orchestrated intracellular signaling activated by many ECM molecules. Therefore, it is important to understand the comprehensive role of the ECM in stem cell differentiation as well as the functions of the individual ECM molecules. Decellularized ECM is a useful in vitro model for studying the comprehensive roles of ECM because it retains a native-like structure and composition. Decellularized ECM can be obtained from in vivo tissue ECM or ECM fabricated by cells cultured in vitro. It is important to select the correct decellularized ECM because each type has different properties. In this review, tissue-derived and cell-derived decellularized ECMs are compared as in vitro ECM models to examine the comprehensive roles of the ECM in stem cell differentiation. We also summarize recent studies using decellularized ECM to determine the comprehensive roles of the ECM in stem cell differentiation