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At least 757 records · Page 42

Multi-omics data compendium: Data package 7 (Pck007)

In type 1 diabetes (T1D), autoimmune response and inflammation cause the death of pancreatic ß cells, leading to the body’s inability to produce insulin and maintain glucose homeostasis. This process is at least in part mediated by pro-inflammatory cytokines, such as interferon (IFN)a, IFN?, interleukin (IL)-1ß, and tumor necrosis factor (TNF)a, which induce ß-cell dysfunction and apoptosis. A deep understanding of the ß-cell signaling and regulatory networks induced by these cytokines could lead to the identification of therapeutic targets to prevent T1D development. To study cytokine-mediated islets/ß-cell signaling and regulatory networks, a variety of omics experiments have been conducted, including transcriptomics, epigenomics (DNA methylation, UMI-4C, ATAC-seq & ChIP-seq), proteomics (bottom-up, top-down, post-translational modification analysis), lipidomics, and metabolomics. The combination of these datasets can be instrumental in identifying signaling components and regulatory factors involved in ß-cell stress/death. Here, we aggregated these multiple omics datasets into a centralized location, providing a quality-controlled and statistically rigorous resource for investigators seeking to holistically study ß-cell regulation by pro-inflammatory cytokines.

Sarkar, Soumyadeep [Pacific Northwest National Lab↗

Multi-omics data compendium: Data package 8 (Pck008)

In type 1 diabetes (T1D), autoimmune response and inflammation cause the death of pancreatic ß cells, leading to the body’s inability to produce insulin and maintain glucose homeostasis. This process is at least in part mediated by pro-inflammatory cytokines, such as interferon (IFN)a, IFN?, interleukin (IL)-1ß, and tumor necrosis factor (TNF)a, which induce ß-cell dysfunction and apoptosis. A deep understanding of the ß-cell signaling and regulatory networks induced by these cytokines could lead to the identification of therapeutic targets to prevent T1D development. To study cytokine-mediated islets/ß-cell signaling and regulatory networks, a variety of omics experiments have been conducted, including transcriptomics, epigenomics (DNA methylation, UMI-4C, ATAC-seq & ChIP-seq), proteomics (bottom-up, top-down, post-translational modification analysis), lipidomics, and metabolomics. The combination of these datasets can be instrumental in identifying signaling components and regulatory factors involved in ß-cell stress/death. Here, we aggregated these multiple omics datasets into a centralized location, providing a quality-controlled and statistically rigorous resource for investigators seeking to holistically study ß-cell regulation by pro-inflammatory cytokines.

Sarkar, Soumyadeep [Pacific Northwest National Lab↗

Multi-omics data compendium: Data package 9 (Pck009)

In type 1 diabetes (T1D), autoimmune response and inflammation cause the death of pancreatic ß cells, leading to the body’s inability to produce insulin and maintain glucose homeostasis. This process is at least in part mediated by pro-inflammatory cytokines, such as interferon (IFN)a, IFN?, interleukin (IL)-1ß, and tumor necrosis factor (TNF)a, which induce ß-cell dysfunction and apoptosis. A deep understanding of the ß-cell signaling and regulatory networks induced by these cytokines could lead to the identification of therapeutic targets to prevent T1D development. To study cytokine-mediated islets/ß-cell signaling and regulatory networks, a variety of omics experiments have been conducted, including transcriptomics, epigenomics (DNA methylation, UMI-4C, ATAC-seq & ChIP-seq), proteomics (bottom-up, top-down, post-translational modification analysis), lipidomics, and metabolomics. The combination of these datasets can be instrumental in identifying signaling components and regulatory factors involved in ß-cell stress/death. Here, we aggregated these multiple omics datasets into a centralized location, providing a quality-controlled and statistically rigorous resource for investigators seeking to holistically study ß-cell regulation by pro-inflammatory cytokines.

Sarkar, Soumyadeep [Pacific Northwest National Lab↗

Multi-omics data resource: Data package 22 (Pck022)

In type 1 diabetes (T1D), autoimmune response and inflammation cause the death of pancreatic ß cells, leading to the body’s inability to produce insulin and maintain glucose homeostasis. This process is at least in part mediated by pro-inflammatory cytokines, such as interferon (IFN)a, IFN?, interleukin (IL)-1ß, and tumor necrosis factor (TNF)a, which induce ß-cell dysfunction and apoptosis. A deep understanding of the ß-cell signaling and regulatory networks induced by these cytokines could lead to the identification of therapeutic targets to prevent T1D development. To study cytokine-mediated islets/ß-cell signaling and regulatory networks, a variety of omics experiments have been conducted, including transcriptomics, epigenomics (DNA methylation, UMI-4C, ATAC-seq & ChIP-seq), proteomics (bottom-up, top-down, post-translational modification analysis), lipidomics, and metabolomics. The combination of these datasets can be instrumental in identifying signaling components and regulatory factors involved in ß-cell stress/death. Here, we aggregated these multiple omics datasets into a centralized location, providing a quality-controlled and statistically rigorous resource for investigators seeking to holistically study ß-cell regulation by pro-inflammatory cytokines. The data package consists of isolated pancreatic islets from adult male C57BL6/J mice treated with IL-1β, IFNγ or IL-1β + IFNγ for 6 h and submitted for scRNA-seq. This study focused on understanding the heterogeneity of the cytokine-mediated response. Data contributors: Jennifer S Stancill & John A Corbett: Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA Data repository: GSE156175 Publication: 10.26508/lsa.202000949

Sarkar, Soumyadeep [Pacific Northwest National Lab↗

PNNL DataHub Project: Omics Lethal Human Viruses Project Profiling of the Host Response to Ebola Virus Infection, Processed Experimental Dataset Catalog

Ebola virus (EBOV) is high risk biological agent, classified as a Category A priority pathogen (Flaviviridae) by the National Institute of Allergy and Infectious Diseases (NIAID), known to cause hemorrhagic fever with high mortality rates in humans. Lethal host-pathogen invasion mechanisms and the cellular intricacies behind these fatal infections still remain unclear. The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013-2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. Herein, PNNL sub-projects provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open-access to viral Omics datasets and project lifecycle metadata. Secondary host-pathogen viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics (P), metabolomics (M), lipidomics (L), and transcriptomics (T) dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific Ebola virus [NCBITAXON:186536] (Zaire/Makona or Zaire/Mayinga) experimental infection study. Human host samples types include peripheral blood mononuclear cells isolated from blood plasma ["PBMC", BTO:0001025], human hepatoma carcinoma cells ["HUH", BTO:0001950], human umbilical vein endothelial cells ["HUVEC", BTO:0001949], immortalized human hepatocyte cells ["IHH", BTO:0006147], and human histiocytic lymphoma cells ["U937", BTO:0001412].

59 BASIC BIOLOGICAL SCIENCES↗

PNNL DataHub Project: Omics Lethal Human Viruses Project Profiling of the Host Response to Influenza A Virus Infection, Processed Experimental Dataset Catalog

Influenza A virus (IAV) is a high risk biological agent, classified as a Category C priority pathogen (Orthomyxoviridae) by the National Institute of Allergy and Infectious Diseases (NIAID), and is known to cause severe respiratory disease with high mortality rates in humans. Lethal host-pathogen invasion mechanisms and the cellular intricacies behind these fatal infections still remain unclear. The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013-2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. Herein, PNNL sub-projects provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open-access to viral Omics datasets and project lifecycle metadata. Secondary host-pathogen viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics (P), metabolomics (M), lipidomics (L), and transcriptomics (T) dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific Influenza A virus [NCBITAXON:11320] experimental infection study. Host sample types include human lung adenocarcinoma cells ["Calu-3", BTO:0002750] and whole mouse lung [BTO:0000763] tissue collections.

59 BASIC BIOLOGICAL SCIENCES↗

Omics-Lethal Human Viruses, Influenza A Experiment ICL102

The purpose of this experiment was to evaluate the human host cellular response to wild-type Influenza A/Anhui/1/2013 (H7N9; "AH1-WT") virus and mutant viruses NS1-L103F/I106M ("AH1-F/M") and partially ferret-adapted ("AH1-691") infection. Sample data was obtained from human lung adenocarcinoma cells (Calu-3) and processed for mRNA, miRNA, proteomics, lipidomics, and metabolomics expression analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics, metabolomics, lipidomics, and transcriptomics dataset download each have a direct relationship to a primary sample submission corresponding to a specific Influenza A virus infection.

59 BASIC BIOLOGICAL SCIENCES↗

Omics-Lethal Human Viruses, Influenza A Experiment ICL103

The purpose of this experiment was to evaluate the human host response to Influenza A wild-type (H5N1) virus and mutant viruses. Sample data was obtained for human lung adenocarcinoma cell line (Calu-3) infected with WT Influenza A/Vietnam/1203/2004 (H5N1, VN1203) and mutant viruses PB2-K627E and NS1-trunc124 for mRNA, miRNA, proteomics, lipidomics, and metabolomics data analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics, metabolomics, lipidomics, and transcriptomics dataset download each have a direct relationship to a primary sample submission corresponding to a specific Influenza A virus infection.

59 BASIC BIOLOGICAL SCIENCES↗

Omics-Lethal Human Viruses, Influenza A Experiment ICL104

The purpose of this experiment was to evaluate the host response to Influenza A pandemic wild-type H1N1 (A/California/04/2009), natural isolate virus. Sample data was obtained from human lung adenocarcinoma cells (Calu-3) with wild-type H1N1 virus (A/California/04/2009) and processed for mRNA, miRNA, proteomics, metabolomics, and lipidomics expression analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics, metabolomics, lipidomics, and transcriptomics dataset download each have a direct relationship to a primary sample submission corresponding to a specific Influenza A virus infection.

59 BASIC BIOLOGICAL SCIENCES↗

PNNL DataHub Project: Omics Lethal Human Viruses Project Profiling of the Host Interferon-Stimulated Response to Virus Infection, Processed Experimental Dataset Catalog

Human Interferon (IFN) alpha, beta, and gamma participate in the body's natural immune response to lethal virus infection and disease.The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013 - 2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013-2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. Herein, PNNL sub-projects provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open-access to viral Omics datasets and project lifecycle metadata. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Transcriptomics (T) dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific Human Interferon (IFN), interferon alpha (IFNα), interferon beta (IFNβ), and/or interferon gamma (IFNγ) stimulated response to an experimental virus infection treatment study. Host sample types include cerebellum ["CB", BTO:0000232], cortical neurons ["CN", BTO:0004102], cortex ["CT", BTO:0000233], dendritic cells ["DC", BTO:0002042], granule cell neurons ["GCN", BTO:0003393], lymph node ["LN", BTO:0000784], and serum ["SE", BTO:0001239] from mouse (Mus musculus) tissue collections.

59 BASIC BIOLOGICAL SCIENCES↗

Omics-Lethal Human Viruses, Interferon-Stimulated Response Experiment IFNaIHH001

The purpose of this experiment was to evaluate the host interferon-stimulated cellular response to interferon alpha (IFNα) treatment. Sample data was obtained from human immortalized human hepatocyte cells (IHH) treated with or without IFNα for mRNA and miRNA transcriptome expression analysis.

59 BASIC BIOLOGICAL SCIENCES↗

Omics-Lethal Human Viruses, Influenza A Experiment IM102

The purpose of this experiment was to evaluate the host response to wild-type Influenza A/Anhui/1/2013 (H7N9; "AH1-WT") virus and mutant viruses NS1-103F/106M ("AH1-F/M") and partially ferret-adapted ("AH1-691") infection. Sample data was obtained from mouse lung tissue and processed for mRNA, miRNA, proteomics, metabolomics, and lipidomics expression analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics, metabolomics, lipidomics, and transcriptomics dataset download each have a direct relationship to a primary sample submission corresponding to a specific Influenza A virus infection.

59 BASIC BIOLOGICAL SCIENCES↗

Omics-Lethal Human Viruses, Influenza A Experiment IM103

The purpose of this experiment was to evaluate the host response to wild-type Influenza A/Vietnam/1203/2004 (H5N1; "VN1203") virus infection and mock infection. Sample data was obtained for mouse lung tissue and processed for mRNA, miRNA, proteomics, lipidomics, and metabolomics expression analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics, metabolomics, lipidomics, and transcriptomics dataset download each have a direct relationship to a primary sample submission corresponding to a specific Influenza A virus infection.

59 BASIC BIOLOGICAL SCIENCES↗

Omics Lethal Human Viruses Project Profiling of the Host Response to MERS-CoV Infection, Processed Experimental Dataset Catalog

Middle East Respiratory Syndrome coronavirus (MERS-CoV) is classified as a Category C priority pathogen (Coronaviridae) by the National Institute of Allergy and Infectious Diseases (NIAID), and is known to cause severe respiratory disease with high mortality rates in humans. Lethal host-pathogen invasion mechanisms and the cellular intricacies behind these fatal infections still remain unclear. The NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program project (2013-2018) aimed to develop an improved comprehensive understanding of the host response to a suite of viruses causing lethal infections leveraging a systems biology approach. Herein, PNNL sub-projects provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open-access to viral Omics datasets and project lifecycle metadata. Secondary host-pathogen viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics (P), metabolomics (M), lipidomics (L), and transcriptomics (T) dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific MERS-CoV [NCBITAXON:1335626] experimental infection study. Host sample types include human lung adenocarcinoma cells ["Calu-3", BTO:0002750], human bronchial epithelial cells ["Calu-3 clone 2B4"; BTO:0002022], primary human fibroblasts ["FB"; BTO:0000452], primary human airway epithelial cells ["HAE"; BTO:0005571], human microvascular endothelial cells ["HMVE"; BTO:0003123], and whole mouse lung [BTO:0000763] tissue collections.

59 BASIC BIOLOGICAL SCIENCES↗

Omics-Lethal Human Viruses, MERS-CoV Experiment MFB001

The purpose of this experiment was to evaluate the human host response to wild-type MERS-CoV (icMERS-CoV) virus infection. Sample data was obtained from primary human fibroblasts and processed for mRNA, miRNA, proteomics, metabolomics, and lipidomics expression analysis. econdary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics, metabolomics, lipidomics, and transcriptomics dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific MERS-CoV virus infection.

59 BASIC BIOLOGICAL SCIENCES↗

Omics-Lethal Human Viruses, MERS-CoV Experiment MFB002

The purpose of this experiment was to evaluate the human host response to wild-type Middle Eastern Respiratory Syndrome coronavirus (icMERS-CoV, EMC2012) and mutant virus infection. Sample data was obtained from primary human fibroblasts for mRNA, miRNA, proteomics, lipidomics, and metabolomics expression analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics, metabolomics, lipidomics, and transcriptomics dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific MERS-CoV virus infection.

59 BASIC BIOLOGICAL SCIENCES↗

Omics-Lethal Human Viruses, MERS-CoV Experiment MFB003

The purpose of this experiment was to evaluate the human host response to wild-type Middle Eastern Respiratory Syndrome coronavirus (icMERS-CoV, EMC2012) virus infection. Sample data was obtained from primary human fibroblasts for mRNA, miRNA, proteomics, lipidomics, and metabolomics expression analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics, metabolomics, lipidomics, and transcriptomics dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific MERS-CoV virus infection.

59 BASIC BIOLOGICAL SCIENCES↗

Omics-Lethal Human Viruses, MERS-CoV Experiment MHAE001

The purpose of this experiment was to evaluate the human host response to wild-type Middle Eastern Respiratory Syndrome coronavirus (icMERS-CoV, EMC2012) virus infection. Sample data was obtained from human airway epithelial cells for mRNA, miRNA, proteomics, lipidomics, and metabolomics expression analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics, metabolomics, lipidomics, and transcriptomics dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific MERS-CoV virus infection.

59 BASIC BIOLOGICAL SCIENCES↗