Project description:Most FDA approved drugs are not equally effective in all patients, suggesting that identification of biomarkers to predict responders to a chemoprevention agent will be needed to stratify patients and achieve maximum benefit. The goal of this study was to investigate both patient specific and cell-context specific heterogeneity of metformin response, using cancer cell lines fibroblast cell lines and induced pluripotent stem cells differentiated into lung epithelial lineages. We performed transcriptome analysis on both patient-derived fibroblast cell lines and cancer cell lines to assess differential metformin response and identify response genes. We found differences in response to metformin treatment across a variety of cell lines and cellular contexts, suggesting heterogeneity that may be patient and cell-type specific. Gene expression profiling and analysis of metformin sensitive and resistant cells identified differentially expressed genes that may be able to stratify patients into metformin responders and non-responders.
Project description:We have sequenced miRNA libraries from human embryonic, neural and foetal mesenchymal stem cells. We report that the majority of miRNA genes encode mature isomers that vary in size by one or more bases at the 3’ and/or 5’ end of the miRNA. Northern blotting for individual miRNAs showed that the proportions of isomiRs expressed by a single miRNA gene often differ between cell and tissue types. IsomiRs were readily co-immunoprecipitated with Argonaute proteins in vivo and were active in luciferase assays, indicating that they are functional. Bioinformatics analysis predicts substantial differences in targeting between miRNAs with minor 5’ differences and in support of this we report that a 5’ isomiR-9-1 gained the ability to inhibit the expression of DNMT3B and NCAM2 but lost the ability to inhibit CDH1 in vitro. This result was confirmed by the use of isomiR-specific sponges. Our analysis of the miRGator database indicates that a small percentage of human miRNA genes express isomiRs as the dominant transcript in certain cell types and analysis of miRBase shows that 5’ isomiRs have replaced canonical miRNAs many times during evolution. This strongly indicates that isomiRs are of functional importance and have contributed to the evolution of miRNA genes
Project description:Gene expression profiling of immortalized human mesenchymal stem cells with hTERT/E6/E7 transfected MSCs. hTERT may change gene expression in MSCs. Goal was to determine the gene expressions of immortalized MSCs.
Project description:Here we investigate the nature of cellular heterogeneity in the gene expression of aging and characterize the changes that metformin causes in mouse adipose and muscle at the single-cell resolution. The single cell transcriptomes of ~13,000 cells were captured using single-cell RNA-sequencing, resulting in gene expression data for 13 cell types from the visceral adipose tissue stromal-vascular fraction and ~5,000 cells from 12 cell types from the gastrocnemius muscle. The study design featured three groups of male C57BL/6J mice that corresponded to three-month-old controls (young), 18-month-old controls (old), and 18-month-olds treated with 1000 ppm (0.1% w/w) metformin for 6 weeks (treated). Our analyses demonstrate that metformin’s age-associated changes results in the modulation of an old to young expression profile via a cell type-specific manner. This reversion is demonstrated by significant changes observed in cell-type proportions and the degree of cell-cell heterogeneity. Additionally, metformin is known to restore the dysregulation due to age in autophagy and immune response in adipose, hypoxia in muscle, and inflammatory responses in both tissues. In our data, we detect evidence of these processes at play where endothelial cells, macrophages, and stem/progenitor cells respond most effectively to metformin’s gerotherapeutic response compared to other cell types. We further characterize metformin’s role in reverting age-associated cell-type-specific shifts in the transcriptional space by investigating changes in gene expression distributions and gene regulatory network dynamics.