Project description:We used phosphoproteomics to compare the responses of the ERK1/2 inhibitors, SCH772984 and GDC0994, and the MKK1/2 inhibitor, trametinib. These are compared with responses to the MKK1/2 inhibitor, selumetinib (AZD6244), previously measured by our lab in the same metastatic melanoma cell line. In three replicate experiments, we quantified a total of 12,805 class I phosphosites on 3,819 proteins in the trametinib-SCH772984-DMSO experiment, and 7,074 class I phosphosites on 2,453 in the GDC0994-SCH772984-DMSO experiment. This included 466 phosphosites that reproducibly decreased in response to at least one inhibitor in the trametinib-SCH772984-DMSO experiment and 414 phosphosites in the GDC0994-SCH772984-DMSO experiment. The results demonstrate linearity in signaling through the MAP kinase pathway. By comparing multiple inhibitors targeted to multiple tiers of protein kinases in the MAPK pathway, we gain insight into regulation and new targets of the oncogenic BRAF driver pathway in cancer cells, and a useful approach for evaluating the specificity of drugs and drug candidates. SILAC Experimental Design Experiment 1 Replicate 1: Heavy – DMSO, Medium – SCH772984, Light – Trametinib Replicate 2: Heavy – SCH772984, Medium – Trametinib, Light – DMSO Replicate 3: Heavy – Trametinib, Medium – DMSO, Light – SCH772984 SILAC Experimental Design Experiment 2 Replicate 1: Heavy – DMSO, Medium – SCH772984, Light – GDC0994 Replicate 2: Heavy – SCH772984, Medium – GDC0994, Light – DMSO Replicate 3: Heavy – GDC0994, Medium – DMSO, Light – SCH772984 File List 1. Zipped MaxQuant search results folder containing index and output folders for each raw file, ‘combined’ output folder, and mqpar.xml MaxQuant search parameters file 2. Individual raw files of phosphopeptide-enriched ERLIC fractions 3. Zipped MaxQuant version used for analysis 4. FASTA file containing Uniprot human identifications 5. Instructions for viewing annotated spectra
Project description:We used phosphoproteomics to compare the responses of the ERK1/2 inhibitors, SCH772984 and GDC0994, and the MKK1/2 inhibitor, trametinib. These are compared with responses to the MKK1/2 inhibitor, selumetinib (AZD6244), previously measured by our lab in the same metastatic melanoma cell line. In three replicate experiments, we quantified a total of 12,805 class I phosphosites on 3,819 proteins in the trametinib-SCH772984-DMSO experiment, and 7,074 class I phosphosites on 2,453 in the GDC0994-SCH772984-DMSO experiment. This included 466 phosphosites that reproducibly decreased in response to at least one inhibitor in the trametinib-SCH772984-DMSO experiment and 414 phosphosites in the GDC0994-SCH772984-DMSO experiment. The results demonstrate linearity in signaling through the MAP kinase pathway. By comparing multiple inhibitors targeted to multiple tiers of protein kinases in the MAPK pathway, we gain insight into regulation and new targets of the oncogenic BRAF driver pathway in cancer cells, and a useful approach for evaluating the specificity of drugs and drug candidates. SILAC Experimental Design Experiment 1 Replicate 1: Heavy – DMSO, Medium – SCH772984, Light – Trametinib Replicate 2: Heavy – SCH772984, Medium – Trametinib, Light – DMSO Replicate 3: Heavy – Trametinib, Medium – DMSO, Light – SCH772984 SILAC Experimental Design Experiment 2 Replicate 1: Heavy – DMSO, Medium – SCH772984, Light – GDC0994 Replicate 2: Heavy – SCH772984, Medium – GDC0994, Light – DMSO Replicate 3: Heavy – GDC0994, Medium – DMSO, Light – SCH772984 File List 1. Zipped MaxQuant search results folder containing index and output folders for each raw file, ‘combined’ output folder, and mqpar.xml MaxQuant search parameters file 2. Individual raw files of phosphopeptide-enriched ERLIC fractions and total protein fractions 3. Zipped MaxQuant version used for analysis 4. FASTA file containing Uniprot human identifications 5. Instructions for viewing annotated spectra
Project description:The summary file contains summary information for all the raw files processed with a single MaxQuant run. The summary information consists of some MaxQuant parameters, information of the raw file contents, and statistics on the peak detection. Based on this file a quick overview can be gathered on the quality of the data in the raw file.
Project description:The summary file contains summary information for all the raw files processed with a single MaxQuant run. The summary information consists of some MaxQuant parameters, information of the raw file contents, and statistics on the peak detection. Based on this file a quick overview can be gathered on the quality of the data in the raw file.
Project description:The summary file contains summary information for all theraw files processed with a single MaxQuant run. Thesummaryinformation consists of some MaxQuantparameters, information of the raw file contents, andstatistics on the peak detection. Based on this file a quickoverview can be gathered on the quality of the data in theraw file.
Project description:We developed and validated ‘HIT-MAP’ (High-resolution Informatics Toolbox in MALDI-MSI Proteomics), an open-source bioinformatics workflow using peptide mass fingerprint analysis and a dual scoring system to computationally assign peptide and protein annotations to high mass resolution MSI datasets, and generate customisable spatial distribution maps. The uploaded files are an example dataset for the HiTMaP proteomics search engine, designed for MALDI-imaging proteomics annotation. The example data files contain one bovine lens tissue section and one mouse brain tissue section. The ID folder contains the protein/peptide identification result for each tissue segment, and the summary folder contains the protein cluster images.
Project description:LncRNA expression profiling for liver tissues of mice fed for NFD, LSF and HSF groups Summary: An abstract of the experiment and the data analysis. Experiment Workflow: A workflow of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: Mouse 8 x 60K LncRNA expression array information. Summary Table of Files for Data Delivery: Contains summary table of files for data delivery and the recommended software programs for viewing the data. Data Analysis for LncRNAs 1. Raw LncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity LncRNAs were filtered (LncRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these LncRNAs can be found from the LncRNA Expression Profiling Data.xls file). 2. Quality assessment of LncRNA data after filtering: Contains Box Plot and Scatter Plot for LncRNAs after filtering (This data can be found from the LncRNA Expression Profiling Data.xls file). 3. Differentially expressed LncRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed LncRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed LncRNAs (The heat map can be found from the LncRNA Expression Profiling Data.xls file). Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity mRNAs were filtered (mRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these mRNAs can be found from the mRNA Expression Profiling Data.xls file). 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering (This data can be found from the mRNA Expression Profiling Data.xls file). 3. Differentially expressed mRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed mRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed mRNAs (The heat map can be found from the mRNA Expression Profiling Data.xls file). 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. LncRNA Classification and Subgroup Analysis 1. Rinn lincRNAs profiling: Contains profiling data of all lincRNAs based on John Rinn's papers (This data can be found from the Rinn lincRNAs profiling.xls file). 2. LincRNAs nearby coding gene data table: Contains the differentially expressed lincRNAs and nearby coding gene pairs (distance < 300 kb) (This data can be found from the LincRNAs nearby coding gene data table.xls file). Sample RNA Quality Control: Sample quality control data file from NanoDrop ND-1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction of methods for sample preparation, microarray design, experiment, and data analysis.
Project description:LncRNA expression profiling for liver tissues of mice fed for a normal diet (NFD, 3mice) and a high-fat diet (HFD, 3mice) Summary: An abstract of the experiment and the data analysis. Experiment Workflow: A workflow of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: Mouse 8 x 60K LncRNA expression array information. Summary Table of Files for Data Delivery: Contains summary table of files for data delivery and the recommended software programs for viewing the data. Data Analysis for LncRNAs 1. Raw LncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity LncRNAs were filtered (LncRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these LncRNAs can be found from the LncRNA Expression Profiling Data.xls file). 2. Quality assessment of LncRNA data after filtering: Contains Box Plot and Scatter Plot for LncRNAs after filtering (This data can be found from the LncRNA Expression Profiling Data.xls file). 3. Differentially expressed LncRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed LncRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed LncRNAs (The heat map can be found from the LncRNA Expression Profiling Data.xls file). Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity mRNAs were filtered (mRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these mRNAs can be found from the mRNA Expression Profiling Data.xls file). 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering (This data can be found from the mRNA Expression Profiling Data.xls file). 3. Differentially expressed mRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed mRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed mRNAs (The heat map can be found from the mRNA Expression Profiling Data.xls file). 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. LncRNA Classification and Subgroup Analysis 1. Rinn lincRNAs profiling: Contains profiling data of all lincRNAs based on John Rinn's papers (This data can be found from the Rinn lincRNAs profiling.xls file). 2. LincRNAs nearby coding gene data table: Contains the differentially expressed lincRNAs and nearby coding gene pairs (distance < 300 kb) (This data can be found from the LincRNAs nearby coding gene data table.xls file). Sample RNA Quality Control: Sample quality control data file from NanoDrop ND-1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction of methods for sample preparation, microarray design, experiment, and data analysis.
Project description:The evidence for the social modulation of reproduction in vertebrates is established but poorly defined for many taxa despite the link between social cues and endocrine function. It is unclear if physiological effects, such as increased sex hormone concentration or gamete maturation, impact reproductive outcomes, such as fertilization rates or viability of offspring. We investigated the effect of conspecific signals on the calling behaviour, reproductive output, and gonadal gene expression in boreal chorus frogs (Pseudacris maculata). Using breeding trials in a captive setting, we found that broadcasts of conspecific signals nearly doubled the duration of calling activity and increases the proportion of viable eggs by over 13%. We then used RNA-sequencing techniques to demonstrate exposure to broadcasts of chorus behaviour increased the expression of genes associated with steroidogenesis, spermatogenesis, and gonadal development in male chorus frogs within 6 hours. Several gene pathways were similarly affected in male chorus frogs receiving hormone injections to induce sperm development and release, implying there are parallels in hypothalamic-pituitary-gonadal axis stimulation produced by reception of conspecific cues and hormonal induction. We provide some of the first evidence of amphibian social signals causing rapid molecular linked to fertility regulation.