Browsing by Subject "Gene knockdown techniques"

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    DSCN: Double-target selection guided by CRISPR screening and network
    (Public Library of Science, 2022-08-19) Liu, Enze; Wu, Xue; Wang, Lei; Huo, Yang; Wu, Huanmei; Li, Lang; Cheng, Lijun; Medicine, School of Medicine
    Cancer is a complex disease with usually multiple disease mechanisms. Target combination is a better strategy than a single target in developing cancer therapies. However, target combinations are generally more difficult to be predicted. Current CRISPR-cas9 technology enables genome-wide screening for potential targets, but only a handful of genes have been screend as target combinations. Thus, an effective computational approach for selecting candidate target combinations is highly desirable. Selected target combinations also need to be translational between cell lines and cancer patients. We have therefore developed DSCN (double-target selection guided by CRISPR screening and network), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network. In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target. Our analysis first demonstrated a high correlation of the DSCN sub-sampling-based gene knockdown model and its predicted differential gene expressions using observed gene expression in 22 pancreatic cell lines before and after MAP2K1 and MAP2K2 inhibition (R2 = 0.75). In DSCN algorithm, various scoring schemes were evaluated. The 'diffusion-path' method showed the most significant statistical power of differentialting known synthetic lethal (SL) versus non-SL gene pairs (P = 0.001) in pancreatic cancer. The superior performance of DSCN over existing network-based algorithms, such as OptiCon and VIPER, in the selection of target combinations is attributable to its ability to calculate combinations for any gene pairs, whereas other approaches focus on the combinations among optimized regulators in the network. DSCN's computational speed is also at least ten times fast than that of other methods. Finally, in applying DSCN to predict target combinations and drug combinations for individual samples (DSCNi), DSCNi showed high correlation between target combinations predicted and real synergistic combinations (P = 1e-5) in pancreatic cell lines. In summary, DSCN is a highly effective computational method for the selection of target combinations.
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    Mesenchyme Homeobox 2 Enhances Migration of Endothelial Colony Forming Cells Exposed to Intrauterine Diabetes Mellitus
    (Wiley, 2017-07) Gohn, Cassandra R.; Blue, Emily K.; Sheehan, BreAnn M.; Varberg, Kaela M.; Haneline, Laura S.; Cellular and Integrative Physiology, School of Medicine
    Diabetes mellitus (DM) during pregnancy has long-lasting implications for the fetus, including cardiovascular morbidity. Previously, we showed that endothelial colony forming cells (ECFCs) from DM human pregnancies have decreased vasculogenic potential. Here, we evaluate whether the molecular mechanism responsible for this phenotype involves the transcription factor, Mesenchyme Homeobox 2 (MEOX2). In human umbilical vein endothelial cells, MEOX2 upregulates cyclin-dependent kinase inhibitor expression, resulting in increased senescence and decreased proliferation. We hypothesized that dysregulated MEOX2 expression in neonatal ECFCs from DM pregnancies decreases network formation through increased senescence and altered cell cycle progression. Our studies show that nuclear MEOX2 is increased in ECFCs from DM pregnancies. To determine if MEOX2 is sufficient and/or required to induce impaired network formation, MEOX2 was overexpressed and depleted in ECFCs from control and DM pregnancies, respectively. Surprisingly, MEOX2 overexpression in control ECFCs resulted in increased network formation, altered cell cycle progression, and increased senescence. In contrast, MEOX2 knockdown in ECFCs from DM pregnancies led to decreased network formation, while cell cycle progression and senescence were unaffected. Importantly, migration studies demonstrated that MEOX2 overexpression increased migration, while MEOX2 knockdown decreased migration. Taken together, these data suggest that altered migration may be mediating the impaired vasculogenesis of ECFCs from DM pregnancies. While initially believed to be maladaptive, these data suggest that MEOX2 may serve a protective role, enabling increased vessel formation despite exposure to a DM intrauterine environment. J. Cell. Physiol. 232: 1885-1892, 2017.