5 research outputs found
The regulation of miRNAs by reconstituted high-density lipoproteins in diabetes-impaired angiogenesis
Diabetic vascular complications are associated with impaired ischaemia-driven angiogenesis. We recently found that reconstituted high-density lipoproteins (rHDL) rescue diabetes-impaired angiogenesis. microRNAs (miRNAs) regulate angiogenesis and are transported within HDL to sites of injury/repair. The role of miRNAs in the rescue of diabetes-impaired angiogenesis by rHDL is unknown. Using a miRNA array, we found that rHDL inhibits hsa-miR-181c-5p expression in vitro and using a hsa-miR-181c-5p mimic and antimiR identify a novel anti-angiogenic role for miR-181c-5p. miRNA expression was tracked over time post-hindlimb ischaemic induction in diabetic mice. Early post-ischaemia when angiogenesis is important, rHDL suppressed hindlimb mmu-miR-181c-5p. mmu-miR-181c-5p was not detected in the plasma or within HDL, suggesting rHDL specifically targets mmu-miR-181c-5p at the ischaemic site. Three known angiogenic miRNAs (mmu-miR-223-3p, mmu-miR-27b-3p, mmu-miR-92a-3p) were elevated in the HDL fraction of diabetic rHDL-infused mice early post-ischaemia. This was accompanied by a decrease in plasma levels. Only mmu-miR-223-3p levels were elevated in the hindlimb 3 days post-ischaemia, indicating that rHDL regulates mmu-miR-223-3p in a time-dependent and site-specific manner. The early regulation of miRNAs, particularly miR-181c-5p, may underpin the rescue of diabetes-impaired angiogenesis by rHDL and has implications for the treatment of diabetes-related vascular complications
Functional genomic landscape of acute myeloid leukaemia
The implementation of targeted therapies for acute myeloid leukaemia (AML) has been challenging because of the complex mutational patterns within and across patients as well as a dearth of pharmacologic agents for most mutational events. Here we report initial findings from the Beat AML programme on a cohort of 672 tumour specimens collected from 562 patients. We assessed these specimens using whole-exome sequencing, RNA sequencing and analyses of ex vivo drug sensitivity. Our data reveal mutational events that have not previously been detected in AML. We show that the response to drugs is associated with mutational status, including instances of drug sensitivity that are specific to combinatorial mutational events. Integration with RNA sequencing also revealed gene expression signatures, which predict a role for specific gene networks in the drug response. Collectively, we have generated a dataset-accessible through the Beat AML data viewer (Vizome)-that can be leveraged to address clinical, genomic, transcriptomic and functional analyses of the biology of AML
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Integrative analysis of drug response and clinical outcome in acute myeloid leukemia
Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML.
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•Acute myeloid leukemia patient cohort with clinical, molecular, drug response data•Validation and discovery of diverse biological features of drug response•Broad mapping of tumor cell differentiation state affecting response to drugs•Modeling reveals a strong and targetable determinant of clinical outcome
Bottomly et al. present a functional genomic resource composed of molecular, clinical, and drug response data on acute myeloid leukemia patient specimens. Through integration of all of these data, they identify genetic and cell differentiation state features that predict drug response, and they utilize modeling to identify targetable determinants of clinical outcome