cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1300001
title: Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model

description: We propose an asymptotically normal and efficient procedure to estimate every finite subgraph for covariate-adjusted Gaussian graphical model. As a consequence, a confidence interval as well as <i>p</i>-value can be obtained for each edge. The procedure is tuning-free and enjoys easy implementation and efficient computation through parallel estimation on subgraphs or edges. We apply the asymptotic normality result to perform support recovery through edge-wise adaptive thresholding. This support recovery procedure is called ANTAC, standing for asymptotically normal estimation with thresholding after adjusting covariates. ANTAC outperforms other methodologies in the literature in a range of simulation studies. We apply ANTAC to identify gene–gene interactions using an eQTL dataset. Our result achieves better interpretability and accuracy in comparison with a state-of-the-art method. Supplementary materials for the article are available online.
DOI: 10.6084/m9.figshare.1300001.v1
title: Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model

description: We propose an asymptotically normal and efficient procedure to estimate every finite subgraph for covariate-adjusted Gaussian graphical model. As a consequence, a confidence interval as well as <i>p</i>-value can be obtained for each edge. The procedure is tuning-free and enjoys easy implementation and efficient computation through parallel estimation on subgraphs or edges. We apply the asymptotic normality result to perform support recovery through edge-wise adaptive thresholding. This support recovery procedure is called ANTAC, standing for asymptotically normal estimation with thresholding after adjusting covariates. ANTAC outperforms other methodologies in the literature in a range of simulation studies. We apply ANTAC to identify gene–gene interactions using an eQTL dataset. Our result achieves better interpretability and accuracy in comparison with a state-of-the-art method. Supplementary materials for the article are available online.
DOI: 10.6084/m9.figshare.1301099
title: Downregulation of ATG14 by EGR1-MIR152 sensitizes ovarian cancer cells to cisplatin-induced apoptosis by inhibiting cyto-protective autophagy

description: Cisplatin is commonly used in ovarian cancer treatment by inducing apoptosis in cancer cells as a result of lethal DNA damage. However, the intrinsic and acquired resistance to cisplatin in cancer cells remains a big challenge for improving overall survival. The cyto-protective functions of autophagy in cancer cells have been suggested as a potential mechanism for chemoresistance. Here, we reported <i>MIR152</i> as a new autophagy-regulating miRNA that plays a role in cisplatin-resistance. We showed that <i>MIR152</i> expression was dramatically downregulated in the cisplatin-resistant cell lines A2780/CP70, SKOV3/DDP compared with their respective parental cells, and in ovarian cancer tissues associated with cisplatin-resistance. Overexpression of <i>MIR152</i> sensitized cisplatin-resistant ovarian cancer cells by reducing cisplatin-induced autophagy, enhancing cisplatin-induced apoptosis and inhibition of cell proliferation. A mouse subcutaneous xenograft tumor model using A2780/CP70 cells with overexpressing <i>MIR152</i> was established and displayed decreased tumor growth in response to cisplatin. We also identified that ATG14 is a functional target of <i>MIR152</i> in regulating autophagy inhibition. Furthermore, we found that EGR1 (early growth response 1) regulated the <i>MIR152</i> gene at the transcriptional level. Ectopic expression of EGR1 enhanced efficacy of chemotherapy in A2780/CP70 cells. More importantly, these findings were relevant to clinical cases. Both EGR1 and <i>MIR152</i> expression levels were significantly lower in ovarian cancer tissues with high levels of ERCC1 (excision repair cross-complementation group 1), a marker for cisplatin-resistance. Collectively, these data provide insights into novel mechanisms for acquired cisplatin-resistance. Activation of EGR1 and <i>MIR152</i> may be a useful therapeutic strategy to overcome cisplatin-resistance by preventing cyto-protective autophagy in ovarian cancer.
DOI: 10.6084/m9.figshare.1301099.v1
title: Downregulation of ATG14 by EGR1-<i>MIR152</i> sensitizes ovarian cancer cells to cisplatin-induced apoptosis by inhibiting cyto-protective autophagy

description: Cisplatin is commonly used in ovarian cancer treatment by inducing apoptosis in cancer cells as a result of lethal DNA damage. However, the intrinsic and acquired resistance to cisplatin in cancer cells remains a big challenge for improving overall survival. The cyto-protective functions of autophagy in cancer cells have been suggested as a potential mechanism for chemoresistance. Here, we reported <i>MIR152</i> as a new autophagy-regulating miRNA that plays a role in cisplatin-resistance. We showed that <i>MIR152</i> expression was dramatically downregulated in the cisplatin-resistant cell lines A2780/CP70, SKOV3/DDP compared with their respective parental cells, and in ovarian cancer tissues associated with cisplatin-resistance. Overexpression of <i>MIR152</i> sensitized cisplatin-resistant ovarian cancer cells by reducing cisplatin-induced autophagy, enhancing cisplatin-induced apoptosis and inhibition of cell proliferation. A mouse subcutaneous xenograft tumor model using A2780/CP70 cells with overexpressing <i>MIR152</i> was established and displayed decreased tumor growth in response to cisplatin. We also identified that ATG14 is a functional target of <i>MIR152</i> in regulating autophagy inhibition. Furthermore, we found that EGR1 (early growth response 1) regulated the <i>MIR152</i> gene at the transcriptional level. Ectopic expression of EGR1 enhanced efficacy of chemotherapy in A2780/CP70 cells. More importantly, these findings were relevant to clinical cases. Both EGR1 and <i>MIR152</i> expression levels were significantly lower in ovarian cancer tissues with high levels of ERCC1 (excision repair cross-complementation group 1), a marker for cisplatin-resistance. Collectively, these data provide insights into novel mechanisms for acquired cisplatin-resistance. Activation of EGR1 and <i>MIR152</i> may be a useful therapeutic strategy to overcome cisplatin-resistance by preventing cyto-protective autophagy in ovarian cancer.
DOI: 10.6084/m9.figshare.1301099.v2
title: Downregulation of ATG14 by EGR1-MIR152 sensitizes ovarian cancer cells to cisplatin-induced apoptosis by inhibiting cyto-protective autophagy

description: Cisplatin is commonly used in ovarian cancer treatment by inducing apoptosis in cancer cells as a result of lethal DNA damage. However, the intrinsic and acquired resistance to cisplatin in cancer cells remains a big challenge for improving overall survival. The cyto-protective functions of autophagy in cancer cells have been suggested as a potential mechanism for chemoresistance. Here, we reported <i>MIR152</i> as a new autophagy-regulating miRNA that plays a role in cisplatin-resistance. We showed that <i>MIR152</i> expression was dramatically downregulated in the cisplatin-resistant cell lines A2780/CP70, SKOV3/DDP compared with their respective parental cells, and in ovarian cancer tissues associated with cisplatin-resistance. Overexpression of <i>MIR152</i> sensitized cisplatin-resistant ovarian cancer cells by reducing cisplatin-induced autophagy, enhancing cisplatin-induced apoptosis and inhibition of cell proliferation. A mouse subcutaneous xenograft tumor model using A2780/CP70 cells with overexpressing <i>MIR152</i> was established and displayed decreased tumor growth in response to cisplatin. We also identified that ATG14 is a functional target of <i>MIR152</i> in regulating autophagy inhibition. Furthermore, we found that EGR1 (early growth response 1) regulated the <i>MIR152</i> gene at the transcriptional level. Ectopic expression of EGR1 enhanced efficacy of chemotherapy in A2780/CP70 cells. More importantly, these findings were relevant to clinical cases. Both EGR1 and <i>MIR152</i> expression levels were significantly lower in ovarian cancer tissues with high levels of ERCC1 (excision repair cross-complementation group 1), a marker for cisplatin-resistance. Collectively, these data provide insights into novel mechanisms for acquired cisplatin-resistance. Activation of EGR1 and <i>MIR152</i> may be a useful therapeutic strategy to overcome cisplatin-resistance by preventing cyto-protective autophagy in ovarian cancer.
DOI: 10.6084/m9.figshare.1301103
title: Impaired macrophage autophagy increases the immune response in obese mice by promoting proinflammatory macrophage polarization

description: Recent evidence that excessive lipid accumulation can decrease cellular levels of autophagy and that autophagy regulates immune responsiveness suggested that impaired macrophage autophagy may promote the increased innate immune activation that underlies obesity. Primary bone marrow-derived macrophages (BMDM) and peritoneal macrophages from high-fat diet (HFD)-fed mice had decreased levels of autophagic flux indicating a generalized impairment of macrophage autophagy in obese mice. To assess the effects of decreased macrophage autophagy on inflammation, mice with a <i>Lyz2</i>-<i>Cre</i>-mediated knockout of <i>Atg5</i> in macrophages were fed a HFD and treated with low-dose lipopolysaccharide (LPS). Knockout mice developed systemic and hepatic inflammation with HFD feeding and LPS. This effect was liver specific as knockout mice did not have increased adipose tissue inflammation. The mechanism by which the loss of autophagy promoted inflammation was through the regulation of macrophage polarization. BMDM and Kupffer cells from knockout mice exhibited abnormalities in polarization with both increased proinflammatory M1 and decreased anti-inflammatory M2 polarization as determined by measures of genes and proteins. The heightened hepatic inflammatory response in HFD-fed, LPS-treated knockout mice led to liver injury without affecting steatosis. These findings demonstrate that autophagy has a critical regulatory function in macrophage polarization that downregulates inflammation. Defects in macrophage autophagy may underlie inflammatory disease states such as the decrease in macrophage autophagy with obesity that leads to hepatic inflammation and the progression to liver injury.
DOI: 10.6084/m9.figshare.1301103.v2
title: Impaired macrophage autophagy increases the immune response in obese mice by promoting proinflammatory macrophage polarization

description: Recent evidence that excessive lipid accumulation can decrease cellular levels of autophagy and that autophagy regulates immune responsiveness suggested that impaired macrophage autophagy may promote the increased innate immune activation that underlies obesity. Primary bone marrow-derived macrophages (BMDM) and peritoneal macrophages from high-fat diet (HFD)-fed mice had decreased levels of autophagic flux indicating a generalized impairment of macrophage autophagy in obese mice. To assess the effects of decreased macrophage autophagy on inflammation, mice with a <i>Lyz2</i>-<i>Cre</i>-mediated knockout of <i>Atg5</i> in macrophages were fed a HFD and treated with low-dose lipopolysaccharide (LPS). Knockout mice developed systemic and hepatic inflammation with HFD feeding and LPS. This effect was liver specific as knockout mice did not have increased adipose tissue inflammation. The mechanism by which the loss of autophagy promoted inflammation was through the regulation of macrophage polarization. BMDM and Kupffer cells from knockout mice exhibited abnormalities in polarization with both increased proinflammatory M1 and decreased anti-inflammatory M2 polarization as determined by measures of genes and proteins. The heightened hepatic inflammatory response in HFD-fed, LPS-treated knockout mice led to liver injury without affecting steatosis. These findings demonstrate that autophagy has a critical regulatory function in macrophage polarization that downregulates inflammation. Defects in macrophage autophagy may underlie inflammatory disease states such as the decrease in macrophage autophagy with obesity that leads to hepatic inflammation and the progression to liver injury.
DOI: 10.6084/m9.figshare.1301103.v1
title: Impaired macrophage autophagy increases the immune response in obese mice by promoting proinflammatory macrophage polarization

description: Recent evidence that excessive lipid accumulation can decrease cellular levels of autophagy and that autophagy regulates immune responsiveness suggested that impaired macrophage autophagy may promote the increased innate immune activation that underlies obesity. Primary bone marrow-derived macrophages (BMDM) and peritoneal macrophages from high-fat diet (HFD)-fed mice had decreased levels of autophagic flux indicating a generalized impairment of macrophage autophagy in obese mice. To assess the effects of decreased macrophage autophagy on inflammation, mice with a <i>Lyz2</i>-<i>Cre</i>-mediated knockout of <i>Atg5</i> in macrophages were fed a HFD and treated with low-dose lipopolysaccharide (LPS). Knockout mice developed systemic and hepatic inflammation with HFD feeding and LPS. This effect was liver specific as knockout mice did not have increased adipose tissue inflammation. The mechanism by which the loss of autophagy promoted inflammation was through the regulation of macrophage polarization. BMDM and Kupffer cells from knockout mice exhibited abnormalities in polarization with both increased proinflammatory M1 and decreased anti-inflammatory M2 polarization as determined by measures of genes and proteins. The heightened hepatic inflammatory response in HFD-fed, LPS-treated knockout mice led to liver injury without affecting steatosis. These findings demonstrate that autophagy has a critical regulatory function in macrophage polarization that downregulates inflammation. Defects in macrophage autophagy may underlie inflammatory disease states such as the decrease in macrophage autophagy with obesity that leads to hepatic inflammation and the progression to liver injury.
DOI: 10.6084/m9.figshare.1301951
title: Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling

description: High-throughput DNA sequencing allows for the genotyping of common and rare variants for genetic association studies. At the present time and for the foreseeable future, it is not economically feasible to sequence all individuals in a large cohort. A cost-effective strategy is to sequence those individuals with extreme values of a quantitative trait. We consider the design under which the sampling depends on multiple quantitative traits. Under such trait-dependent sampling, standard linear regression analysis can result in bias of parameter estimation, inflation of Type I error, and loss of power. We construct a likelihood function that properly reflects the sampling mechanism and uses all available data. We implement a computationally efficient EM algorithm and establish the theoretical properties of the resulting maximum likelihood estimators. Our methods can be used to perform separate inference on each trait or simultaneous inference on multiple traits. We pay special attention to gene-level association tests for rare variants. We demonstrate the superiority of the proposed methods over standard linear regression through extensive simulation studies. We provide applications to the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study and the National Heart, Lung, and Blood Institute Exome Sequencing Project. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1301951.v1
title: Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling

description: High-throughput DNA sequencing allows for the genotyping of common and rare variants for genetic association studies. At the present time and for the foreseeable future, it is not economically feasible to sequence all individuals in a large cohort. A cost-effective strategy is to sequence those individuals with extreme values of a quantitative trait. We consider the design under which the sampling depends on multiple quantitative traits. Under such trait-dependent sampling, standard linear regression analysis can result in bias of parameter estimation, inflation of type I error, and loss of power. We construct a likelihood function that properly reflects the sampling mechanism and utilizes all available data. We implement a computationally efficient EM algorithm and establish the theoretical properties of the resulting maximum likelihood estimators. Our methods can be used to perform separate inference on each trait or simultaneous inference on multiple traits. We pay special attention to gene-level association tests for rare variants. We demonstrate the superiority of the proposed methods over standard linear regression through extensive simulation studies. We provide applications to the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study and the National Heart, Lung, and Blood Institute Exome Sequencing Project.
DOI: 10.6084/m9.figshare.1301951.v2
title: Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling

description: High-throughput DNA sequencing allows for the genotyping of common and rare variants for genetic association studies. At the present time and for the foreseeable future, it is not economically feasible to sequence all individuals in a large cohort. A cost-effective strategy is to sequence those individuals with extreme values of a quantitative trait. We consider the design under which the sampling depends on multiple quantitative traits. Under such trait-dependent sampling, standard linear regression analysis can result in bias of parameter estimation, inflation of Type I error, and loss of power. We construct a likelihood function that properly reflects the sampling mechanism and uses all available data. We implement a computationally efficient EM algorithm and establish the theoretical properties of the resulting maximum likelihood estimators. Our methods can be used to perform separate inference on each trait or simultaneous inference on multiple traits. We pay special attention to gene-level association tests for rare variants. We demonstrate the superiority of the proposed methods over standard linear regression through extensive simulation studies. We provide applications to the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study and the National Heart, Lung, and Blood Institute Exome Sequencing Project. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1301951.v4
title: Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling

description: High-throughput DNA sequencing allows for the genotyping of common and rare variants for genetic association studies. At the present time and for the foreseeable future, it is not economically feasible to sequence all individuals in a large cohort. A cost-effective strategy is to sequence those individuals with extreme values of a quantitative trait. We consider the design under which the sampling depends on multiple quantitative traits. Under such trait-dependent sampling, standard linear regression analysis can result in bias of parameter estimation, inflation of Type I error, and loss of power. We construct a likelihood function that properly reflects the sampling mechanism and uses all available data. We implement a computationally efficient EM algorithm and establish the theoretical properties of the resulting maximum likelihood estimators. Our methods can be used to perform separate inference on each trait or simultaneous inference on multiple traits. We pay special attention to gene-level association tests for rare variants. We demonstrate the superiority of the proposed methods over standard linear regression through extensive simulation studies. We provide applications to the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study and the National Heart, Lung, and Blood Institute Exome Sequencing Project. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1301951.v3
title: Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling

description: High-throughput DNA sequencing allows for the genotyping of common and rare variants for genetic association studies. At the present time and for the foreseeable future, it is not economically feasible to sequence all individuals in a large cohort. A cost-effective strategy is to sequence those individuals with extreme values of a quantitative trait. We consider the design under which the sampling depends on multiple quantitative traits. Under such trait-dependent sampling, standard linear regression analysis can result in bias of parameter estimation, inflation of Type I error, and loss of power. We construct a likelihood function that properly reflects the sampling mechanism and uses all available data. We implement a computationally efficient EM algorithm and establish the theoretical properties of the resulting maximum likelihood estimators. Our methods can be used to perform separate inference on each trait or simultaneous inference on multiple traits. We pay special attention to gene-level association tests for rare variants. We demonstrate the superiority of the proposed methods over standard linear regression through extensive simulation studies. We provide applications to the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study and the National Heart, Lung, and Blood Institute Exome Sequencing Project. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1304841
title: Effect of initial total solids concentration on volatile fatty acid production from food waste during anaerobic acidification

description: The effect of initial total solids (TS) concentration on volatile fatty acid (VFAs) production from food waste under mesophilic conditions (35 °C) was determined. VFAs concentration and composition, biogas production, soluble chemical oxygen demand concentration, TS and volatile solids (VS) reduction, and ammonia nitrogen release were investigated. The VFAs concentrations were 26.10, 39.68, 59.58, and 62.64 g COD/L at TS contents of 40, 70, 100, and 130 g/L, respectively. While the VFAs’ yields ranged from 0.467 to 0.799 g COD/g VS<sub>fed</sub>, decreased as initial TS increased. The percentage of propionate was not affected by TS concentration, accounting for 30.19–34.86% of the total VFAs, while a higher percentage of butyrate and lower percentage of acetate was achieved at a higher TS concentration. Biogas included mainly hydrogen and carbon dioxide and the maximum hydrogen yield of 148.9 ml/g VS<sub>fed</sub> was obtained at 130 g TS/L. concentration, TS and VS reductions increased as initial TS increased. Considering the above variables, we conclude that initial TS of 100 g/L shall be the most appropriate to VFAs production.