D the Advanced Cell Classifier program [12] (www.cellclassifier.org), which allows

D the Advanced Cell Classifier program [12] (www.cellclassifier.org), which allows the user to assign predefined phenotypes to cells. The computer uses this training set to learn a model and to classify unassigned cells through several machine learning methods (Figure S5). To find the best method, we compared the 10-fold cross validation accuracy of the most commonly used classification methods i.e. Multilayer Perceptron ( = Artificial Neural Networks), Logit Boost ( = logistic regression with boosting), Support Vector Machine, Random Forest, and K-nearest Neighbor. Logit Boost with minor improvements was the most optimal method for all of the assays. We also tested the Naive Bayesian method and found that using advanced methods significantly increased accuracy [12] (Figure 2d, Figure S6a). The WEKA implementation of the machine learning methods was used with default parameters [17]. In Figure S6b we show the receiver operating characteristics (ROC) curves [22] for the EI assay. Both the cross validation and ROC analysis show high recognition rates (CV .95 and AUC .0.99), making the analysis robust.(TIF)Table S3 Sequences of siRNAs targeting ATP6V1B2, ATP6AP2, ATP6V1A, CUL3, and CSE1L genes.High-Content Analysis of IAV Entry Events(TIF)Author ContributionsConceived and designed the experiments: IB AH. Performed the experiments: IB YY. Analyzed the data: PH. Wrote the paper: IB YY AH PH.AcknowledgmentsThe authors are grateful to the Light Microscopy and Screening 18204824 Centre (LMSC) 1315463 at ETH Zurich for support in high-throughput microscopy.
Lipid homeostasis is tightly maintained by balanced lipogenesis, catabolism (b-oxidation), and uptake/secretion. Disruptions of lipid formation and catabolism have been DprE1-IN-2 site implicated in various metabolic diseases, such as obesity and diabetes. Liver is a major organ for lipogenesis, where most lipogenic genes, including the fatty acid synthase (FAS), stearoyl-CoA desaturase-1 (SCD1) and long chain free fatty acid elongase (FAE), are highly expressed. Several nuclear receptors have been implicated in lipid homeostasis, such as the liver X receptors (LXRs) [1], thyroid hormone receptor (TR) [2] and peroxisome proliferator-activated receptors (PPARs). Both LXRa and LXRb have been shown to promote lipogenesis though direct and indirect mechanism [1,3,4]. Upon activation, LXRs form a heterodimer with retinoid X receptor (RXR) and bind to its direct target lipogenic genes promoter, such as FAS, or up-regulate the sterol regulatory element binding protein (SREBP)-1c, a transcriptional factor known to regulate the expression of a battery of lipogenic enzymes [5,6,7]. TR can be activated by thyroid hormone and subsequently increase transcription of several genes involved in lipogenesis [8,9]. PPARs have distinct roles in lipid metabolism. PPARa enhances the metabolic usage of fatty acids by inducing enzymes involved in boxidation [10,11]. PPARc is a key SIS-3 regulator of adipocytedifferentiation and promotes lipid storage in mature adipocytes [12,13]. Overexpression of PPARc in liver of PPARa null mice induced the expression of lipogenic genes, leading to hepatic steatosis [14]. CD36, a membrane receptor capable of uptaking modified forms of low-density lipoproteins (LDL) and fatty acids from circulation [15,16], has been identified as a direct target of PPARc in liver [17]. While expression of an activated form of PPARd in the adipose tissues of transgenic mice was shown to activate fat metabolism and produce lean mice that.D the Advanced Cell Classifier program [12] (www.cellclassifier.org), which allows the user to assign predefined phenotypes to cells. The computer uses this training set to learn a model and to classify unassigned cells through several machine learning methods (Figure S5). To find the best method, we compared the 10-fold cross validation accuracy of the most commonly used classification methods i.e. Multilayer Perceptron ( = Artificial Neural Networks), Logit Boost ( = logistic regression with boosting), Support Vector Machine, Random Forest, and K-nearest Neighbor. Logit Boost with minor improvements was the most optimal method for all of the assays. We also tested the Naive Bayesian method and found that using advanced methods significantly increased accuracy [12] (Figure 2d, Figure S6a). The WEKA implementation of the machine learning methods was used with default parameters [17]. In Figure S6b we show the receiver operating characteristics (ROC) curves [22] for the EI assay. Both the cross validation and ROC analysis show high recognition rates (CV .95 and AUC .0.99), making the analysis robust.(TIF)Table S3 Sequences of siRNAs targeting ATP6V1B2, ATP6AP2, ATP6V1A, CUL3, and CSE1L genes.High-Content Analysis of IAV Entry Events(TIF)Author ContributionsConceived and designed the experiments: IB AH. Performed the experiments: IB YY. Analyzed the data: PH. Wrote the paper: IB YY AH PH.AcknowledgmentsThe authors are grateful to the Light Microscopy and Screening 18204824 Centre (LMSC) 1315463 at ETH Zurich for support in high-throughput microscopy.
Lipid homeostasis is tightly maintained by balanced lipogenesis, catabolism (b-oxidation), and uptake/secretion. Disruptions of lipid formation and catabolism have been implicated in various metabolic diseases, such as obesity and diabetes. Liver is a major organ for lipogenesis, where most lipogenic genes, including the fatty acid synthase (FAS), stearoyl-CoA desaturase-1 (SCD1) and long chain free fatty acid elongase (FAE), are highly expressed. Several nuclear receptors have been implicated in lipid homeostasis, such as the liver X receptors (LXRs) [1], thyroid hormone receptor (TR) [2] and peroxisome proliferator-activated receptors (PPARs). Both LXRa and LXRb have been shown to promote lipogenesis though direct and indirect mechanism [1,3,4]. Upon activation, LXRs form a heterodimer with retinoid X receptor (RXR) and bind to its direct target lipogenic genes promoter, such as FAS, or up-regulate the sterol regulatory element binding protein (SREBP)-1c, a transcriptional factor known to regulate the expression of a battery of lipogenic enzymes [5,6,7]. TR can be activated by thyroid hormone and subsequently increase transcription of several genes involved in lipogenesis [8,9]. PPARs have distinct roles in lipid metabolism. PPARa enhances the metabolic usage of fatty acids by inducing enzymes involved in boxidation [10,11]. PPARc is a key regulator of adipocytedifferentiation and promotes lipid storage in mature adipocytes [12,13]. Overexpression of PPARc in liver of PPARa null mice induced the expression of lipogenic genes, leading to hepatic steatosis [14]. CD36, a membrane receptor capable of uptaking modified forms of low-density lipoproteins (LDL) and fatty acids from circulation [15,16], has been identified as a direct target of PPARc in liver [17]. While expression of an activated form of PPARd in the adipose tissues of transgenic mice was shown to activate fat metabolism and produce lean mice that.

Leave a Reply