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In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts present in each cell are sequenced. This leads to unreliable quantification of genes with low or moderate expression, which hinders downstream analysis. To address this challenge, we developed SAVER (single-cell analysis via expression recovery), an expression recovery method for unique molecule index (UMI)-based scRNA-seq data that borrows information across genes and cells to provide accurate expression estimates for all genes.

Background. Mendelian randomization (MR) uses genetic variants as instrumental variables to estimate the causal effect of risk exposures in epidemiology. Two-sample summary-data MR that uses publicly available genome-wide association studies (GWAS) summary data have become a popular design in practice. With the sample size of GWAS continuing to increase, it is now possible to utilize genetic instruments that are only weakly associated with the exposure.Methods. To maximize the statistical power of MR, we propose a genome-wide design where more than a thousand genetic instruments are used. For the statistical analysis, we use an empirical partially Bayes approach where instruments are weighted according to their true strength, thus weak instruments bring less variation to the estimator. The final estimator is highly efficient in the presence of many weak genetic instruments and is robust to balanced and/or sparse pleiotropy.Results. We apply our method to estimate the causal effect of body mass index (BMI) and major blood lipids on cardiovascular disease outcomes. Compared to previous MR studies, we obtain much more precise causal effect estimates and substantially shorter confidence intervals. Some new and statistically significant findings are: the estimated causal odds ratio of BMI on ischemic stroke is 1.19 (95% CI:1.07-1.32, p-value ≤ 0.001); the estimated causal odds ratio of high-density lipoprotein cholesterol (HDL-C) on coronary artery disease (CAD) is 0.78 (95% CI 0.73-0.84, p-value ≤ 0.001). However, the estimated effect of HDL-C becomes substantially smaller and statistically non-significant when we only use the strong instruments.Conclusions. By employing a genome-wide design and robust statistical methods, the statistical power of MR studies can be greatly improved. Our empirical results suggest that, even though the relationship between HDL-C and CAD appears to be highly heterogeneous, it may be too soon to completely dismiss the HDL hypothesis. Further investigations are needed to demystify the observational and genetic associations between HDL-C and CAD.

Childhood obesity is an impending epidemic. This article is an over-

Melatonin exhibits anti-inflammatory and anticancer effects and could be a chemopreventive and chemotherapeutic agent against cancers, but the precise mechanisms involved remain largely unresolved. In this study, we evaluated the mechanism of action of melatonin in human MDA-MB-361 breast cancer cells. Melatonin at pharmacological concentrations (10(-3)  m) significantly suppressed cell proliferation and induced apoptosis in a dose-dependent manner. The observed suppression of proliferation was accompanied by the melatonin-mediated inhibition of COX-2, p300, and NF-κB signaling. Melatonin significantly inhibited COX-2 expression and prostaglandin E(2) (PGE2) production, abrogated p300 histone acetyltransferase activity and p300-mediated NF-κB acetylation, thereby blocking NF-κB binding and p300 recruitment to COX-2 promoter. Pretreatment with a COX-2- or p300-selective inhibitor abrogated the melatonin-induced inhibition of cell proliferation, whereas PGE2 treatment or COX-2 transfection reversed the inhibition by melatonin. Moreover, melatonin markedly inhibited phosphorylation of PI3K, Akt, PRAS40, and GSK-3 proteins, thereby inactivating the PI3K/Akt signaling pathway. Pretreatment with a PI3K- or an Akt-selective inhibitor or an Akt-specific siRNA blocked the melatonin-mediated inhibition of cell proliferation. Conversely, gene delivery of a constitutively active Akt effectively reversed the inhibition by melatonin. Furthermore, melatonin induced Apaf-1 expression, triggered cytochrome C release, and stimulated caspase-3 and caspase-9 activities and cleavage, leading to an activation of the Apaf-1-dependent apoptotic pathway. Pretreatment with an Apaf-1-specific siRNA effectively attenuated the melatonin-induced apoptosis. These results therefore indicate that melatonin inhibits cell proliferation and induces apoptosis in MDA-MB-361 breast cancer cells in vitro by simultaneously suppressing the COX-2/PGE2, p300/NF-κB, and PI3K/Akt/signaling and activating the Apaf-1/caspase-dependent apoptotic pathway.

We consider large-scale studies in which thousands of significance tests are performed simultaneously. In some of these studies, the multiple testing procedure can be severely biased by latent confounding factors such as batch effects and unmeasured covariates that correlate with both primary variable(s) of interest (e.g. treatment variable, phenotype) and the outcome. Over the past decade, many statistical methods have been proposed to adjust for the confounders in hypothesis testing. We unify these methods in the same framework, generalize them to include multiple primary variables and multiple nuisance variables, and analyze their statistical properties. In particular, we provide theoretical guarantees for and LEAPP [60], which correspond to two different identification conditions in the framework: the first requires a set of "negative controls" that are known a priori to follow the null distribution; the second requires the true non-nulls to be sparse. Two different estimators which are based on RUV-4 and LEAPP are then applied to these two scenarios. We show that if the confounding factors are strong, the resulting estimators can be asymptotically as powerful as the oracle estimator which observes the latent confounding factors. For hypothesis testing, we show the asymptotic z-tests based on the estimators can control the type I error. Numerical experiments show that the false discovery rate is also controlled by the Benjamini-Hochberg procedure when the sample size is reasonably large.

Quercetin, a polyphenolic bioflavonoid, possesses multiple pharmacological actions including anti-inflammatory and antitumor properties. However, the precise action mechanisms of quercetin remain unclear. Here, we reported the regulatory actions of quercetin on cyclooxygenase-2 (COX-2), an important mediator in inflammation and tumor promotion, and revealed the underlying mechanisms. Quercetin significantly suppressed COX-2 mRNA and protein expression and prostaglandin (PG) E(2) production, as well as COX-2 promoter activation in breast cancer cells. Quercetin also significantly inhibited COX-2-mediated angiogenesis in human endothelial cells in a dose-dependent manner. The in vitro streptavidin-agarose pulldown assay and in vivo chromatin immunoprecipitation assay showed that quercetin considerably inhibited the binding of the transactivators CREB2, C-Jun, C/EBPβ and NF-κB and blocked the recruitment of the coactivator p300 to COX-2 promoter. Moreover, quercetin effectively inhibited p300 histone acetyltransferase (HAT) activity, thereby attenuating the p300-mediated acetylation of NF-κB. Treatment of cells with p300 HAT inhibitor roscovitine was as effective as quercetin at inhibiting p300 HAT activity. Addition of quercetin to roscovitine-treated cells did not change the roscovitine-induced inhibition of p300 HAT activity. Conversely, gene delivery of constitutively active p300 significantly reversed the quercetin-mediated inhibition of endogenous HAT activity. These results indicate that quercetin suppresses COX-2 expression by inhibiting the p300 signaling and blocking the binding of multiple transactivators to COX-2 promoter. Our findings therefore reveal a novel mechanism of action of quercetin and suggest a potential use for quercetin in the treatment of COX-2-mediated diseases such as breast cancers.

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