SummaryMesenchymal stem cells (MSCs) can donate mitochondria and rescue anthracycline-induced cardiomyocyte (CM) damage, although the underlying mechanisms remain elusive. We determined that the superior efficiency of mitochondrial transfer by human induced-pluripotent-stem-cell-derived MSCs (iPSC-MSCs) compared with bone marrow-derived MSCs (BM-MSCs) is due to high expression of intrinsic Rho GTPase 1 (MIRO1). Further, due to a higher level of TNFαIP2 expression, iPSC-MSCs are more responsive to tumor necrosis factor alpha (TNF-α)-induced tunneling nanotube (TNT) formation for mitochondrial transfer to CMs, which is regulated via the TNF-α/NF-κB/TNFαIP2 signaling pathway. Inhibition of TNFαIP2 or MIRO1 in iPSC-MSCs reduced the efficiency of mitochondrial transfer and decreased CMs protection. Compared with BM-MSCs, transplantation of iPSC-MSCs into a mouse model of anthracycline-induced cardiomyopathy resulted in more human mitochondrial retention and bioenergetic preservation in heart tissue. Efficacious transfer of mitochondria from iPSC-MSCs to CMs, due to higher MIRO1 expression and responsiveness to TNF-α-induced nanotube formation, effectively attenuates anthracycline-induced CM damage.

Transplantation of bone marrow mesenchymal stem cells (BM-MSCs) can protect cardiomyocytes against anthracycline-induced cardiomyopathy (AIC) through paracrine effects. Nonetheless the paracrine effects of human induced pluripotent stem cell-derived MSCs (iPSC-MSCs) on AIC are poorly understood. In vitro studies reveal that doxorubicin (Dox)-induced reactive oxidative stress (ROS) generation and cell apoptosis in neonatal rat cardiomyocytes (NRCMs) are significantly reduced when treated with conditioned medium harvested from BM-MSCs (BM-MSCs-CdM) or iPSC-MSCs (iPSC-MSCs-CdM). Compared with BM-MSCs-CdM, NRCMs treated with iPSC-MSCs-CdM exhibit significantly less ROS and cell apoptosis in a dose-dependent manner. Transplantation of BM-MSCs-CdM or iPSC-MSCs-CdM into mice with AIC remarkably attenuated left ventricular (LV) dysfunction and dilatation. Compared with BM-MSCs-CdM, iPSC-MSCs-CdM treatment showed better alleviation of heart failure, less cardiomyocyte apoptosis and fibrosis. Analysis of common and distinct cytokines revealed that macrophage migration inhibitory factor (MIF) and growth differentiation factor-15 (GDF-15) were uniquely overpresented in iPSC-MSC-CdM. Immunodepletion of MIF and GDF-15 in iPSC-MSCs-CdM dramatically decreased cardioprotection. Injection of GDF-15/MIF cytokines could partially reverse Dox-induced heart dysfunction. We suggest that the potent paracrine effects of iPSC-MSCs provide novel “cell-free” therapeutic cardioprotection against AIC, and that MIF and GDF-15 in iPSC-MSCs-CdM are critical for these enhanced cardioprotective effects.

Characterizing human mobility patterns is essential for understanding human behaviors and the interactions with socioeconomic and natural environment, and plays a critical role in public health, urban planning, transportation engineering and related fields. With the widespread of location-aware mobile devices and continuing advancement of Web 2.0 technologies, location-based social media (LBSM) have been gaining widespread popularity in the past few years. With an access to locations of hundreds of million users, profiles and the contents of the social media posts, the LBSM data provided a novel modality of data source for human mobility study. By exploiting the explicit location footprints and mining the latent demographic information implied in the LBSM data, the purpose of this paper is to investigate the spatiotemporal characteristics of human mobility with a particular focus on the impact of demography. To serve this purpose, we first collect geo-tagged Twitter feeds posted in the conterminous United States area, and organize the collection of feeds using the concept of space-time trajectory corresponding to each Twitter user. Commonly human mobility measures, including detected home and activity centers, are derived for each user trajectory. We then select a subset of Twitter users that have detected home locations in the city of Chicago as a case study, and apply name analysis to the names provided in user profiles to learn the implicit demographic information of Twitter users, including race/ethnicity, gender and age. Finally we explore the spatiotemporal distribution and mobility characteristics of Chicago Twitter users, and investigate the demographic impact by comparing the differences across three demographic dimensions (race/ethnicity, gender and age). We found that, although the human mobility measures of different demographic groups generally follow the generic laws (e.g., power law distribution), the demographic information, particular the race/ethnicity group, significantly affects the urban human mobility patterns.