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BACKGROUND: Hypertrophic response to pathological stimuli is a complex biological process that involves transcriptional and epigenetic regulation of the cardiac transcriptome. Although previous studies have implicated transcriptional factors and signaling molecules in pathological hypertrophy, the role of RNA-binding protein in this process has received little attention. METHODS: Here we used transverse aortic constriction and in vitro cardiac hypertrophy models to characterize the role of an evolutionary conserved RNA-binding protein Lin28a in pathological cardiac hypertrophy. Next-generation sequencing, RNA immunoprecipitation, and gene expression analyses were applied to identify the downstream targets of Lin28a. Epistatic analysis, metabolic assays, and flux analysis were further used to characterize the effects of Lin28a and its downstream mediator in cardiomyocyte hypertrophic growth and metabolic remodeling. RESULTS: Cardiac-specific deletion of Lin28a attenuated pressure overload-induced hypertrophic growth, cardiac dysfunction, and alterations in cardiac transcriptome. Mechanistically, Lin28a directly bound to mitochondrial phosphoenolpyruvate carboxykinase 2 ( Pck2) mRNA and increased its transcript level. Increasing Pck2 was sufficient to promote hypertrophic growth similar to that caused by increasing Lin28a, whereas knocking down Pck2 attenuated norepinephrine-induced cardiac hypertrophy. Epistatic analysis demonstrated that Pck2 mediated, at least in part, the role of Lin28a in cardiac hypertrophic growth. Furthermore, metabolomic analyses highlighted the role for Lin28a and Pck2 in promoting cardiac biosynthesis required for cell growth. CONCLUSIONS: Our study demonstrates that Lin28a promotes pathological cardiac hypertrophy and glycolytic reprograming, at least in part, by binding to and stabilizing Pck2 mRNA.

Direct lineage conversion offers a new strategy for tissue regeneration and disease modelling. Despite recent success in directly reprogramming fibroblasts into various cell types, the precise changes that occur as fibroblasts progressively convert to the target cell fates remain unclear. The inherent heterogeneity and asynchronous nature of the reprogramming process renders it difficult to study this process using bulk genomic techniques. Here we used single-cell RNA sequencing to overcome this limitation and analysed global transcriptome changes at early stages during the reprogramming of mouse fibroblasts into induced cardiomyocytes (iCMs). Using unsupervised dimensionality reduction and clustering algorithms, we identified molecularly distinct subpopulations of cells during reprogramming. We also constructed routes of iCM formation, and delineated the relationship between cell proliferation and iCM induction. Further analysis of global gene expression changes during reprogramming revealed unexpected downregulation of factors involved in mRNA processing and splicing. Detailed functional analysis of the top candidate splicing factor, Ptbp1, revealed that it is a critical barrier for the acquisition of cardiomyocyte-specific splicing patterns in fibroblasts. Concomitantly, Ptbp1 depletion promoted cardiac transcriptome acquisition and increased iCM reprogramming efficiency. Additional quantitative analysis of our dataset revealed a strong correlation between the expression of each reprogramming factor and the progress of individual cells through the reprogramming process, and led to the discovery of new surface markers for the enrichment of iCMs. In summary, our single-cell transcriptomics approaches enabled us to reconstruct the reprogramming trajectory and to uncover intermediate cell populations, gene pathways and regulators involved in iCM induction.

Direct lineage conversion offers a new strategy for tissue regeneration and disease modelling. Despite recent success in directly reprogramming fibroblasts into various cell types, the precise changes that occur as fibroblasts progressively convert to the target cell fates remain unclear. The inherent heterogeneity and asynchronous nature of the reprogramming process renders it difficult to study this process using bulk genomic techniques. Here we used single-cell RNA sequencing to overcome this limitation and analysed global transcriptome changes at early stages during the reprogramming of mouse fibroblasts into induced cardiomyocytes (iCMs). Using unsupervised dimensionality reduction and clustering algorithms, we identified molecularly distinct subpopulations of cells during reprogramming. We also constructed routes of iCM formation, and delineated the relationship between cell proliferation and iCM induction. Further analysis of global gene expression changes during reprogramming revealed unexpected downregulation of factors involved in mRNA processing and splicing. Detailed functional analysis of the top candidate splicing factor, Ptbp1, revealed that it is a critical barrier for the acquisition of cardiomyocyte-specific splicing patterns in fibroblasts. Concomitantly, Ptbp1 depletion promoted cardiac transcriptome acquisition and increased iCM reprogramming efficiency. Additional quantitative analysis of our dataset revealed a strong correlation between the expression of each reprogramming factor and the progress of individual cells through the reprogramming process, and led to the discovery of new surface markers for the enrichment of iCMs. In summary, our single-cell transcriptomics approaches enabled us to reconstruct the reprogramming trajectory and to uncover intermediate cell populations, gene pathways and regulators involved in iCM induction.

Researchers have applied a series of global models to investigate the link between the built environment and ride-hailing ridership based on ride-hailing data from one specific transportation network company (TNC). However, these research designs inadequately represent real ride-hailing demand within a specific spatial range and cannot reflect spatiotemporal heterogeneity in the link. For the first time, this study collects all demand data of TNCs in Nanjing and analyzes their relationship with the built environment. The effect of taxi demand is considered. We adopt a multiscale geographically weighted regression model to account for the spatial non-stationarity and the multiscale effect of each built environment variable. The findings reveal spatiotemporal heterogeneous relationships of the built environment with ride-hailing ridership. Although the relationship between taxi and ride-hailing ridership varies across spatial locations, ride-hailing always acts as a cooperator for traditional taxis. The findings provide implications for policy making, urban planning, and travel demand management of ride-hailing. [ABSTRACT FROM AUTHOR]

With the development of information and communication technologies (ICTs), considerable attention is being paid to the relationship between ICT use and travel behavior. However, it is unclear whether the effects of ICT use on trip generation vary across travel modes. Based on the data of 1022 respondents collected by a web-based questionnaire survey in Beijing, this study used a zero-inflated Poisson model to investigate the effects of ICT use on trip generation in different travel modes, in which ICT use was measured by both the time spent online and the Internet use frequency. The results indicated that the effects of ICT use on trip generation vary across auto, transit, and active trips. Moreover, two measurements of ICT use play essential roles in influencing a trip generation. Specifically, only the frequency of ordering food online showed a positive association with the likelihood of generating any transit trips. These findings demonstrate the importance of considering the differences across travel modes when analyzing the relationship between ICT use and trip generation., Author(s): Chaoying Yin [1]; Xiaoquan Wang [2]; Chunfu Shao (corresponding author) [2] 1. Introduction In the past few decades, information and communication technologies (ICTs) have attracted increasing attention. The smartphone [...]

The crucial role that the built environment plays in affecting commuting behavior has been widely examined with a primary focus on the residential built environment. Moreover, the spatial heterogeneities at residential and work locations are rarely considered simultaneously in the literature. Based on data from 2012 Changchun household travel survey, we employed a hierarchical cross-classified structural equation model to explore the relationship between the residential and workplace built environment and commuting mode choice. Additionally, the spatial heterogeneities at both residential and work locations are recognized. The estimation results provide evidence on the necessity of accommodating spatial heterogeneities at both locations. Apart from the residential built environment, all built environment attributes at work locations show significant associations with commuting mode choice. These findings suggest that urban planners should pay more attention to the industrial and commercial areas where most employment opportunities concentrate because of the role that the built environment around workplaces plays. [ABSTRACT FROM AUTHOR]