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The characterization of cancer stem-like cells (CSCs) has profound implications for elucidating cancer biology and developing treatment strategies. Although surface markers are already used to identify CSCs, the expression of these markers is controversially linked to the phenotypes in different types of tumors and does not represent all functionally relevant of CSCs. Very recently, hyperactive HIF-1α/glycolysis metabolic pathway is recognized as a master regulator of CSCs. In this study, a near-infrared fluorescent small-molecule, IR-780, is identified for the exclusive characterization of human CSCs through the HIF-1α/glycolysis dependent mitochondrial transporter ABCB10's activity. The results identified for the first time that ABCB10 is involved in the preferential uptake of IR-780 in CSCs, which is regulated by HIF-1α via the direct interaction with the binding site of ABCB10 gene promoter region. In addition, IR-780 is demonstrated to conjugate with anticancer drug 5-fluorouracil to act as a potential drug delivery carrier for CSC-targeted therapy. Thus, the studies provide a new rational approach independent of surface markers to characterize CSCs via small-molecule-based imaging of HIF-1α/glycolysis hyperactive metabolic pathway dependent mitochondrial transporter's activity, which holds promise for the further development of CSCs targeted diagnostic and therapeutic strategies.
Pubmed ID: 29593950
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Open source database of curated, non-redundant set of profiles derived from published collections of experimentally defined transcription factor binding sites for multicellular eukaryotes. Consists of open data access, non-redundancy and quality. JASPAR CORE is smaller set that is non-redundant and curated. Collection of transcription factor DNA-binding preferences, modeled as matrices. These can be converted into Position Weight Matrices (PWMs or PSSMs), used for scanning genomic sequences. Web interface for browsing, searching and subset selection, online sequence analysis utility and suite of programming tools for genome-wide and comparative genomic analysis of regulatory regions. New functions include clustering of matrix models by similarity, generation of random matrices by sampling from selected sets of existing models and a language-independent Web Service applications programming interface for matrix retrieval.
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