The benefits of adult stem cells for repair of the heart have been attributed to the repertoire of salutary paracrine activities they appear to exert. We previously isolated human W8B2+ cardiac stem cells (CSCs) and found they powerfully influence cardiomyocytes and endothelial cells to collectively promote cardiac repair and regeneration. Here, the complexity of the W8B2+ CSC secretomes was characterised and examined in more detail. Using ion exchange chromatography to separate soluble proteins based on their net surface charge, the secreted factors responsible for the pro-survival activity of W8B2+ CSCs were found within the low and medium cation fractions. In addition to the soluble proteins, extracellular vesicles generated from W8B2+ CSCs not only exhibited pro-survival and pro-angiogenic activities, but also promoted proliferation of neonatal cardiomyocytes. These extracellular vesicles contain a cargo of proteins, mRNA and primary microRNA precursors that are enriched in exosomes and are capable of modulating collectively many of the cellular pathways involved in protein metabolism, cell growth, as well as cellular responses to stress and organisation of the extracellular matrix. Thus the W8B2+ CSC secretome contains a multitude of bioactive paracrine factors we have now characterised, that might well be harnessed for therapeutic application for cardiac repair and regeneration.

Background: Melanoma patients who have detectable serum soluble NKG2D ligands either at the baseline or posttreatment of PD1/PDL1 blockade exhibit poor overall survival. Among families of soluble human NKG2D ligands, the soluble human MHC I chain-related molecule (sMIC) was found to be elevated in melanoma patients and mostly associated with poor response to PD1/PDL1 blockade therapy. Methods: In this study, we aim to investigate whether co-targeting tumor-released sMIC enhances the therapeutic outcome of PD1/PDL1 blockade therapy for melanoma. We implanted sMIC-expressing B16F10 melanoma tumors into syngeneic host and evaluated therapeutic efficacy of anti-sMIC antibody and anti-PDL1 antibody combination therapy in comparison with monotherapy. We analyzed associated effector mechanism. We also assessed sMIC/MIC prevalence in metastatic human melanoma tumors. Results: We found that the combination therapy of the anti-PDL1 antibody with an antibody targeting sMIC significantly improved animal survival as compared to monotherapies and that the effect of combination therapy depends significantly on NK cells. We show that combination therapy significantly increased IL-2Rα (CD25) on NK cells which sensitizes NK cells to low dose IL-2 for survival. We demonstrate that sMIC negatively reprograms gene expression related to NK cell homeostatic survival and proliferation and that antibody clearing sMIC reverses the effect of sMIC and reprograms NK cell for survival. We further show that sMIC/MIC is abundantly present in metastatic human melanoma tumors.

BackgroundIt has been observed that many transcription factors (TFs) can bind to different genomic loci depending on the cell type in which a TF is expressed in, even though the individual TF usually binds to the same core motif in different cell types. How a TF can bind to the genome in such a highly cell-type specific manner, is a critical research question. One hypothesis is that a TF requires co-binding of different TFs in different cell types. If this is the case, it may be possible to observe different combinations of TF motifs – a motif grammar – located at the TF binding sites in different cell types. In this study, we develop a bioinformatics method to systematically identify DNA motifs in TF binding sites across multiple cell types based on published ChIP-seq data, and address two questions: (1) can we build a machine learning classifier to predict cell-type specificity based on motif combinations alone, and (2) can we extract meaningful cell-type specific motif grammars from this classifier model.ResultsWe present a Random Forest (RF) based approach to build a multi-class classifier to predict the cell-type specificity of a TF binding site given its motif content. We applied this RF classifier to two published ChIP-seq datasets of TF (TCF7L2 and MAX) across multiple cell types. Using cross-validation, we show that motif combinations alone are indeed predictive of cell types. Furthermore, we present a rule mining approach to extract the most discriminatory rules in the RF classifier, thus allowing us to discover the underlying cell-type specific motif grammar.ConclusionsOur bioinformatics analysis supports the hypothesis that combinatorial TF motif patterns are cell-type specific.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4340-z) contains supplementary material, which is available to authorized users.