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Finding an antibody to detect EZH1 protein expression – Part 4

Follow up to previous posts where we determine that we have an antibody to detect EZH1 expression in AML patient cells.

Expression quantification from the killifish, Fundulus rathbuni (gill epithelium)

Files created with the salmon quantification tool using the reference de novo transcriptome assembly from the killifish, Fundulus rathbuni. Fish were acclimated to either brackish or fresh water then exposed to an acute brackish water challenge. Transcriptome data from gill epithelium tissue were collected. A reference transcriptome assembly was generated from all individuals then used to analyze transcriptional responses to salinity.

Checking the expression and localization of split-SCN1A constructs in HEK293 cells

Examine the functionality (expression, ability to reconstitute, and localization of re-assembled SCN1A) of split-SCN1A-GFP1-10 and split-SCN1A-GFP11 constructs

USP5 Expression and Purification & Development of a Mass Spectrometry USP5 Catalytic Activity Assay

Expression and purification of full length USP51-835 for optimization of a mass spectrometry (MS) based assay to detect polyubiquitin cleavage

Table with results of differential gene expression analysis in the controlled environment for fin tissues.

Differences between transcriptomes of three Cottus fish lineages were assessed under controlled, laboratory conditions. Two tissues were investigated: fins and livers. Present table shows results of the differential gene expression analysis performed on fin tissues of Cottus fish. Base-mean, Log-2-fold change. standard error, statistics and associated p-values and FDR-corrected p-values are given for every contrast possible in our experimental design.

Table with the results of the time series gene expression analysis for fins.

Differences between transcriptomes of two Cottus fish lineages were assessed in the natural environment. Sampling was performed in a time series covering whole year. Two tissues were investigated: fins and livers. Present table shows results of a time series differential gene expression analysis performed on fin tissues.

Table with results of differential gene expression analysis in the controlled environment for liver tissues.

Differences between transcriptomes of three Cottus fish lineages were assessed under controlled, laboratory conditions. Two tissues were investigated: fins and livers. Present table shows results of the differential gene expression analysis performed on liver tissues of Cottus fish. Base-mean, Log-2-fold change. standard error, statistics and associated p-values and FDR-corrected p-values are given for every contrast possible in our experimental design.

opencc-by-4.0Mar 2019View details →

Expression of miR-96 in Cancers

STC2, HTRA4, NDUFA4L2 and NPTX2 were up-regulated more than 15-fold. All these four genes showed a extremely significant difference on expression levels between tumor and normal samples. However, neither miR-96 nor the four genes were associated with RCC tumor stages.

Expression and purification of HTT79-3144 with and without HAP40 from Sf9 Insect cells - 2019/01/28

Project: Biophysical investigation of purified HTT protein samples Experiment: Expression and purification of HTT79-3144 with and without HAP40 from Sf9 Insect cells Date completed: 2019/01/28 Rationale: HTT lacking the unstructured region of exon1 (aa. 1-78 which are not resolved in the cryoEM model PDB ID 6EZ8) may serve as a useful control when determining exon1-dependent functionality of the HTT protein. Apo and HAP40-bound HTT79-3144 will be produced from Sf9 insect cell expression system.

opencc-by-4.0Feb 2019View details →

Cloning and test expression of HTT fragment clones - 2019/02/28

Project: High resolution structural analysis of purified HTT proteins Experiment: Cloning and test expression of HTT fragment clones Date completed: 2019/02/28 Rationale: I aim to complement our previous work on purifying stable regions of huntingtin by cloning further regions of huntingtin corresponding to discrete HEAT repeats for eukaryotic expression. Constructs will initially be screened by BVES in insect sf9 cells. High quality constructs will undergo extensive crystallization experiments and structure determination by X-ray crystallography using well established high-throughput and systematic protocols in place at the SGC. Any solved structures may allow generation of a pseudoatomic resolution (<4 Å) model of the HTT protein using the cryoEM model as a guide.

opencc-by-4.0Apr 2019View details →

Dataset - Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

Simulation data needed to reproduce the results from the manuscript “Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models” by V. Pandey, N. Hadadi and V. Hatzimanikatis "REMI manuscript - simData" folder contains all simulation data which can be used to generate results of the paper:   • Expression_data: This folder contains Transcriptomics data from both studies: Ishii et al (see test_expr.mat) and Holm et al. • Fluxdata: Fluxomics data can be found form the studies Ishii et al and Holm et al. • Metabolomics: This contains metabolomics data of aforementioned both studies. • ModelsSolutions: We generated different models using with thermodynamics (TGex, TGexM, TM) and without thermodynamics models (Gex, GexM, M). Gex indicates integration with only gene expression, GexM indicates gene expression and metabolite, and M indicates only metabolites. ‘T’ is used for thermodynamic models.  Models for different mutants and conditions (e.g. pgm, pgi) can be found in the corresponding folders (TGex, TGexM, TM, Gex, GexM, and M).  Variables with the ‘store’ tag comprises flux solutions, correlation values and percentage error between simulation and experiment fluxes. • AlternativeMCS: We generated alternative states for MCS and saved results. • FVAMM: This is the result flux variability analysis can be found in this folder. • Scatter_plot: Scatter plots indicates correlation between measured and model predicted fluxes.

opencc-by-4.0May 2019View details →

Supplementary materials for the manuscript entitled "Comprehensive Identification, Phylogenetic Analysis and Expression Profiling of Multicopper Oxidase Genes in Maize"

Table S1. Gene IDs and names of maize and Arabidopsis MCOs Figure S1. Phylogenetic tree of protein sequences of multicopper oxidase (MCO) of maize and Arabidopsis with rectangular layout. Branches are not cladogram-transformed. Shown in nodes are bootstrap support. The tree is polar layout, and branches are cladogram-transformed. Maize IDs are labeled in brown. Clades representative of SKS, LAC and AAO are labeled in blue, red and green, respectively. The tree is reconstructed by PhyML with 1000 bootstrap replicates. Figure S2. Chromosome map depicting location of maize MCOs, with gene IDs shown in the map. Figure legend is the same as Figure 2. Figure S3. Phylogenetic tree of maize and Arabidopsis AAOs. The trees are rectangular layout. The tree is reconstructed by PhyML with 1000 bootstrap replicates. Shown in nodes are bootstrap support.

openmit-licenseJan 2019View details →

Supplementary File S2: Plasmids for independently tunable, low-noise gene expression

Supplementary File S2 README 2019-April-26 "Plasmids for independently tunable, low-noise gene expression" (Version 2) João P. N. Silva, Soraia Vidigal Lopes, Diogo J. Grilo, Zach Hensel This file describes the contents of the supplementary file for this manuscript. Python scripts were run in a Python 3 environment on OSX with various scientific python packages updated as of April 2019. With minor modifications for any similar environment it should be possible to generate Figures 1, 2, and 4 in the manuscript from these scripts and raw data. Contents: \dna sequences     \pDG101.gb Annotated DNA sequence in genbank format of plasmid pDG101     \pJS101.gb Annotated DNA sequence in genbank format of plasmid pJS101 (AddGene #118280)     \pJS102.gb Annotated DNA sequence in genbank format of plasmid pJS102 (AddGene #118281)     \pZH501.gb Annotated DNA sequence in genbank format of plasmid pZH501     \pZH509.gb Annotated DNA sequence in genbank format of plasmid pZH509 (AddGene #102664)     \pZH713.gb Annotated DNA sequence in genbank format of plasmid pZH713     \ZHX99.gb Annotated DNA sequence in genbank format for E. coli MG1655 chromosome insertion mutant ZHX99      \data     \DG FCS Data: Raw flow cytometry data from BioRad S3 sorted by day and experimental condition; file name format: plasmid_inducer-concentration_inducer-units_inducer.fcs     \pJS101 pDG101 independence         \"quick scope intensity.ijm" Fiji macro used to extract average fluorescence intensities         \"Microscope Data\" Microscope data analyzed using the above Fiji macro; directory names indicate ATc and IPTG concentrations for each experimentation condition/replicate         \"intensity analysis\"             X_nM_ATc_Y_uM_IPTG.csv files: Exported CSV data from Fiji for each condition             backgrounds.csv: Average intensity for every condition, image frame, and color for background subtraction         \"images for Figure 4": Image stack with raw images used to generate Fig 4A and 4B \code     \fcsAnalysis_final_181228.py step-wise script for generating Figures 1 and 2 from FCS data     \fcsCalcDirectory181228.py script containing functions for FCS analysis and figure generation     \fcsImages PDF figures output by FCS analysis scripts     \FlowCal-master Distribution of the FlowCal library used in analysis for this manuscript; this is distributed under the MIT license     \scopeAnalysisWorkflow190430.py step-wise script for generating Figures 4C and 4D from cell fluorescence microscopy data in CSV format exported from Fiji     \scopeCalcDirectory190430.py script containing functions for microscopy data analysis and figure generation

RAB1B Expression and Purification

As a result of a BioID experiment done to identify possible interaction partners of Huntingtin, RAB1B was flagged. This purification aims to provide a RAB1B sample for future experiments to confirm the possible interaction between these proteins.

RAB1A Expression and Purification

As a result of a BioID experiment done to identify possible interaction partners of Huntingtin, RAB1A was flagged. This purification aims to provide a sample of RAB1A for future experiments to confirm the possible interaction between these proteins.

MECP2 Expression and Purification

As a result of a BioID experiment done to identify possible interaction partners of Huntingtin, MECP2 was flagged. This purification aims to provide a MECP2 sample for future experiments to confirm the possible interaction between these proteins.

DC-SIGN expression on myeloid population in Luminal Breast Cancer

DC-SIGN expression by the different populations of DC and macrophages found in breast tumor samples using flow cytometry. As previously described (doi:10.1038/s41590-018-0145-8.), we identified four major DC populations: CD11c − CD123+ plasmacytoid pre-DCs, CD11c+BDCA1+ CD14− DCs, CD11c+ BDCA1− CD14− DCs, and CD11c+ BDCA1+ CD14+ inflammatory DCs. Staining was performed using the following antibodies: anti-CD45 APC-Cy7 (BD), anti-CD14 Qdot 605 (Thermo), anti-CD3 Alexa700 (Biolegend), anti-CD 19 Alexa700 (Biolegend), anti-CD56 Alexa700 (Biolegend), 400 anti-HLA-DR BV711 (Biolegend), anti-CD11c PC5 (Beckman Coulter), anti-BDCA-1 PE (BD), anti-CD123 PCy7 (BD) and anti-C-SIGN FITC (BD). Cells were analyzed by a LSRFORTESSA X-20 instrument (BD Biosciences). The most prominent intratumoral myeloid cell population, however, was shown to be represented by macrophages, defined as CD11c+ BDCA1−CD14+ cells. We analyzed DC-SIGN expression on the surface of these populations by flow cytometry. DC-SIGN was only expressed on the membrane of macrophages from tumoral as well as juxtatumoral tissues, but not on the DC populations analyzed. These results are represented in Figure 4 from the publication "Aberrant fucosylation enables breast cancer clusterin to interact with dendritic cell-specific ICAM-grabbing non-integrin (DC-SIGN)".

opencc-by-4.0Sep 2019View details →

Serum response factor utilizes distinct promoter- and enhancer-based mechanisms to regulate cytoskeletal gene expression in macrophages.

Cells of the monocyte/macrophage lineage play essential roles in tissue homeostasis and immune responses, but mechanisms underlying the coordinated expression of cytoskeletal genes required for specialized functions of these cells, such as directed migration and phagocytosis, remain unknown. Here, using genetic and genomic approaches, we provide evidence that serum response factor (SRF) regulates both general and cell type-restricted components of the cytoskeletal gene expression program in macrophages. Genome-wide location analysis of SRF in macrophages demonstrates enrichment of SRF binding at ubiquitously expressed target gene promoters, as expected, but also reveals that the majority of SRF binding sites associated with cell type-restricted target genes are at distal inter- and intragenic locations. Most of these distal SRF binding sites are established by the prior binding of the macrophage- and the B cell-specific transcription factor PU.1 and exhibit histone modifications characteristic of enhancers. Consistent with this, representative cytoskeletal target genes associated with these elements require both SRF and PU.1 for full expression. These findings suggest that SRF uses two distinct molecular strategies to regulate programs of cytoskeletal gene expression: a promoter-based strategy for ubiquitously expressed target genes and an enhancer-based strategy at target genes that exhibit cell type-restricted patterns of expression.

mTagBFP2-expressing vectors for electroporation of marine protists

Plasmid maps and genbank sequence files for the plasmids used for electroporation of model organism Nannochloropsis oceanica and environmental samples following the protocol <a href="https://www.protocols.io/view/fabrication-of-dna-constructs-by-gibson-assembly-a-7r8hm9w">Matute et al.</a> EMS initiative from Gordon and Betty Moore foundation.