The use of human pluripotent stem cells for in vitro disease modeling and clinical applications requires protocols that convert these cells into relevant adult cell types. Here, we report the rapid and efficient differentiation of human pluripotent stem cells into vascular endothelial and smooth muscle cells. We found that GSK3 inhibition and BMP4 treatment rapidly committed pluripotent cells to a mesodermal fate and subsequent exposure to VEGF or PDGF-BB resulted in the differentiation of either endothelial or vascular smooth muscle cells, respectively. Both protocols produced mature cells with efficiencies over 80% within six days. Upon purification to 99% via surface markers, endothelial cells maintained their identity, as assessed by marker gene expression, and showed relevant in vitro and in vivo functionality. Global transcriptional and metabolomic analyses confirmed that the cells closely resembled their in vivo counterparts. Our results suggest that these cells could be used to faithfully model human disease.

Small molecule splicing modifiers have been previously described that target the general splicing machinery and thus have low specificity for individual genes. Several potent molecules correcting the splicing deficit of the SMN2 (survival of motor neuron 2) gene have been identified and these molecules are moving towards a potential therapy for spinal muscular atrophy (SMA). Here by using a combination of RNA splicing, transcription, and protein chemistry techniques, we show that these molecules directly bind to two distinct sites of the SMN2 pre-mRNA, thereby stabilizing a yet unidentified ribonucleoprotein (RNP) complex that is critical to the specificity of these small molecules for SMN2 over other genes. In addition to the therapeutic potential of these molecules for treatment of SMA, our work has wide-ranging implications in understanding how small molecules can interact with specific quaternary RNA structures.

The long-tailed macaque, also referred to as cynomolgus monkey (Macaca fascicularis), is one of the most important nonhuman primate animal models in basic and applied biomedical research. To improve the predictive power of primate experiments for humans, we determined the genome sequence of a Macaca fascicularis female of Mauritian origin using a whole-genome shotgun sequencing approach. We applied a template switch strategy that uses either the rhesus or the human genome to assemble sequence reads. The sixfold sequence coverage of the draft genome sequence enabled discovery of about 2.1 million potential single-nucleotide polymorphisms based on occurrence of a dimorphic nucleotide at a given position in the genome sequence. Homology-based annotation allowed us to identify 17,387 orthologs of human protein-coding genes in the M. fascicularis draft genome, and the predicted transcripts enabled the design of a M. fascicularisspecific gene expression microarray. Using liver samples from 36 individuals of different geographic origin we identified 718 genes with highly variable expression in liver, whereas the majority of the transcriptome shows relatively stable and comparable expression. Knowledge of the M. fascicularis draft genome is an important contribution to both the use of this animal in disease models and the safety assessment of drugs and their metabolites. In particular, this information allows high-resolution genotyping and microarray-based gene-expression profiling for animal stratification, thereby allowing the use of well-characterized animals for safety testing.

Identification of novel antibiotics remains a major challenge for drug discovery. The present study explores use of phenotypic readouts beyond classical antibacterial growth inhibition adopting a combined multiparametric high content screening and genomic approach. Deployment of the semi-automated bacterial phenotypic fingerprint (BPF) profiling platform in conjunction with a machine learning-powered dataset analysis, effectively allowed us to narrow down, compare and predict compound mode of action (MoA). The method identifies weak antibacterial hits allowing full exploitation of low potency hits frequently discovered by routine antibacterial screening. We demonstrate that BPF classification tool can be successfully used to guide chemical structure activity relationship optimization, enabling antibiotic development and that this approach can be fruitfully applied across species. The BPF classification tool could be potentially applied in primary screening, effectively enabling identification of novel antibacterial compound hits and differentiating their MoA, hence widening the known antibacterial chemical space of existing pharmaceutical compound libraries. More generally, beyond the specific objective of the present work, the proposed approach could be profitably applied to a broader range of diseases amenable to phenotypic drug discovery.

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