MicroRNA silencing of tumor suppressor DLC-1 promotes efficient hepatitis C virus replication in primary human hepatocytes
Krishna BanaudhaMichael KaliszewskiTamara KorolnekLiliana FloreaMan Lung YeungKuan-Teh JeangAjit Kumar
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Abstract:
MicroRNAs (miRNAs) are approximately 22-nucleotide noncoding RNAs that constitute silencers of target gene expression. Aberrant expression of miRNA has been linked to a variety of cancers, including hepatocellular carcinoma (HCC). Hepatitis C virus (HCV) infection is considered a major cause of chronic liver disease and HCC, although the mechanism of virus infection–associated hepatocarcinogenesis remains unclear. We report a direct role of miRNAs induced in HCV-infected primary human hepatocytes that target the tumor suppressor gene DLC-1 (a Rho GTPase-activating protein), which is frequently deleted in HCC, and other solid human tumors. MicroRNA miR-141 that targets DLC-1 was accentuated in cells infected with HCV genotypes 1a, 1b, and 2a. We present several lines of evidence that efficient HCV replication requires miR-141–mediated suppression of DLC-1. An increase in miR-141 correlated with the inhibition of DLC-1 protein in HCV-infected cells. Depletion of miR-141 with oligonucleotides complementary to the miRNAs inhibited virus replication, whereas artificially increased levels of intracellular miR-141 enhanced HCV replication. HCV-infected hepatocytes showed enhanced cell proliferation that can be countered by overexpression of DLC-1. Conclusion: The collective results of this study suggest a novel mechanism of HCV infection–associated miRNA-mediated regulation of a tumor suppressor protein that has the ability to influence cell proliferation and HCV infection–mediated liver cancer. (Hepatology 2011)Keywords:
Replication
When a replication fails, scientists have to decide whether to make a second attempt or move on. Psychology researchers who attempt to replicate studies often face this decision, given the empirical rate of replication success in psychology, which is lower than desired. Here, we report 17 re-replications of experiments for which an original replication had failed. In 5/17 of these “rescue” projects (29%), the “rescue” study mostly or fully replicated the original results, albeit with a smaller effect size; in the other 12, the second replication was also judged to have failed. We speculate that successful rescue projects were due to larger sample sizes or methodological changes such as attention checks. In the absence of obvious weaknesses in a failed replication study’s sample or procedure, however, it may be most efficient to stop pursuing an effect after a single failed replication.
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Abstract Here we test the feasibility of using decision markets to select studies for replication and provide evidence about the replicability of online experiments. Social scientists ( n = 162) traded on the outcome of close replications of 41 systematically selected MTurk social science experiments published in PNAS 2015–2018, knowing that the 12 studies with the lowest and the 12 with the highest final market prices would be selected for replication, along with 2 randomly selected studies. The replication rate, based on the statistical significance indicator, was 83% for the top-12 and 33% for the bottom-12 group. Overall, 54% of the studies were successfully replicated, with replication effect size estimates averaging 45% of the original effect size estimates. The replication rate varied between 54% and 62% for alternative replication indicators. The observed replicability of MTurk experiments is comparable to that of previous systematic replication projects involving laboratory experiments.
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Throughout the last decade, the so-called replication crisis has stimulated many researchers to conduct large-scale replication projects. With data from four of these projects, we computed probabilistic forecasts of the replication outcomes, which we then evaluated regarding discrimination, calibration and sharpness. A novel model, which can take into account both inflation and heterogeneity of effects, was used and predicted the effect estimate of the replication study with good performance in two of the four data sets. In the other two data sets, predictive performance was still substantially improved compared to the naive model which does not consider inflation and heterogeneity of effects. The results suggest that many of the estimates from the original studies were inflated, possibly caused by publication bias or questionable research practices, and also that some degree of heterogeneity between original and replication effects should be expected. Moreover, the results indicate that the use of statistical significance as the only criterion for replication success may be questionable, since from a predictive viewpoint, non-significant replication results are often compatible with significant results from the original study. The developed statistical methods as well as the data sets are available in the R package ReplicationSuccess.
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Throughout the last decade, the so-called replication crisis has stimulated many researchers to conduct large-scale replication projects. With data from four of these projects, we computed probabilistic forecasts of the replication outcomes, which we then evaluated regarding discrimination, calibration and sharpness. A novel model, which can take into account both inflation and heterogeneity of effects, was used and predicted the effect estimate of the replication study with good performance in two of the four data sets. In the other two data sets, predictive performance was still substantially improved compared to the naive model which does not consider inflation and heterogeneity of effects. The results suggest that many of the estimates from the original studies were inflated, possibly caused by publication bias or questionable research practices, and also that some degree of heterogeneity between original and replication effects should be expected. Moreover, the results indicate that the use of statistical significance as the only criterion for replication success may be questionable, since from a predictive viewpoint, non-significant replication results are often compatible with significant results from the original study. The developed statistical methods as well as the data sets are available in the R package ReplicationSuccess.
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