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Fig. 7 in Expression profile of two HSP70 chaperone proteins in response to extreme thermal acclimation in Xestia c-nigrum (Lepidoptera: Noctuidae)

Fig. 7. Expression levels of 2 HSP70s at different developmental stages relative to expression levels in 2nd instars at 25 °C. The data are denoted as the mean ± SEM (error bar), and the different lowercase or uppercase letters indicate a significant differenwce in the means as assessed using multi-comparison tests (P <0.05).

Fig. 4 in Expression profile of two HSP70 chaperone proteins in response to extreme thermal acclimation in Xestia c-nigrum (Lepidoptera: Noctuidae)

Fig. 4. Phylogenetic tree of Xc-HSC70 and Xc-HSP70 amino acid sequences from different species. A 3-letter code has been included to indicate the order name of the corresponding insect and vertebrate orders (COL = Coleoptera, LEP = Lepidoptera, DIP = Diptera, HYM = Hymenoptera, and VER =Vertebrata). The values indicated on the branches correspond to bootstrap percentages (BP).

Fig. 3 in Expression profile of two HSP70 chaperone proteins in response to extreme thermal acclimation in Xestia c-nigrum (Lepidoptera: Noctuidae)

Fig. 3. Schematic structure of the Xc-HSC70 gene. Exons are shown as boxes in which white boxes represent untranslated regions, whereas the black boxes are the protein-coding exons; introns are indicated as lines between the boxes. The numbers above and below the drawing represent the sizes (base pairs) of each exon and intron, respectively. The start codon (ATG) and stop codon (TAA) are also indicated. The genomic DNA sequence of Xc-HSC70 has been deposited in GenBank under accession no. KF731994.

Fig. 2 in Expression profile of two HSP70 chaperone proteins in response to extreme thermal acclimation in Xestia c-nigrum (Lepidoptera: Noctuidae)

Fig. 2. Nucleotide and deduced amino acid sequences of the Xc-HSP70 gene. The signature sequences of the HSP70 family are shown in boxes, the nuclear localization signal sequence is underlined, the consensus sequence EEVD at the C-terminus is indicated in italics, and the start and stop codons are in bold. The nucleotides and amino acids are numbered along the lef and right margins. The sequence encoding Xc-HSP70 has been deposited in GenBank under accession no. HQ698836.

Fig. 1 in Expression profile of two HSP70 chaperone proteins in response to extreme thermal acclimation in Xestia c-nigrum (Lepidoptera: Noctuidae)

Fig. 1. Nucleotide and deduced amino acid sequences of Xc-HSC70. The signature sequences of the HSP70 family are shown in boxes, the nuclear localization signal sequence is underlined, the consensus sequence EEVD at the C-terminus is indicated in italics, and the start and stop codons are in bold. The nucleotides and amino acids are numbered along the lef and right margins. The sequence encoding Xc-HSC70 has been deposited in GenBank under accession no. KC844151.

opencc-by-4.0Mar 2015View details →

Fig. 2 in Molecular cloning of heat shock protein gene HSP90 and effects of abamectin and double-stranded RNA on its expression in Panonychus citri (Trombidiformes: Tetranychidae)

Fig. 2. Alignment of HSP90 amino acid sequence of P. citri McGregor and its homologus amino acid sequences from other species (Frankliniella occidentalis, Trialeurodes vaporariorum, Pteromalus puparum, Tetranychus cinabarinus).

opencc-by-4.0Apr 2019View details →

Supplementing data and code for: "Correlation of mRNA delivery timing and protein expression in lipid-based transfection"

Supplementary data and code for Reiser et al.: Correlation of mRNA delivery timing and protein expression in lipid-based transfection. 2019, <a href="https://doi.org/10.1093/intbio/zyz030">doi:10.1093/intbio/zyz030</a>. See `README.pdf` for further details.

opencc-by-4.0Dec 2020View details →

Extracellular vesicles from Fusarium graminearum contain protein effectors expressed during infection of corn

Fusarium graminearum (Fgr) is a devastating filamentous fungal pathogen that causes diseases in cereals, while producing mycotoxins that are toxic for humans and animals, and render grains unusable. Low efficiency in managing Fgr poses a constant need for identifying novel control mechanisms. Evidence that fungal extracellular vesicles (EVs) from pathogenic yeast have a role in human disease led us to question whether this is also true for fungal plant pathogens. We separated EVs from Fgr and performed a proteomic analysis to determine if EVs carry proteins with potential roles in pathogenesis. We revealed that protein effectors, which are crucial for fungal virulence, were detected in EV preparations and some of them did not contain predicted secretion signals. Furthermore, a transcriptomic analysis of corn (Zea mays) plants infected by Fgr revealed that the genes of some of the effectors were highly expressed in vivo, suggesting that the Fgr EVs are a mechanism for the unconventional secretion of effectors and virulence factors. Our results expand the knowledge on fungal EVs in plant pathogenesis and cross-kingdom communication, and may contribute to the discovery of new antifungals. The following are available online at www.mdpi.com/xxx/s1, Figure S1. Controls for the separation of EVs from Fusarium graminearum (Fgr) by SEC. Figure S2. The superoxide dismutase [Cu-Zn] (SOD1) from F. graminearum (Fgr) contains a diacidic amino acid motif implicated in unconventional secretion. Figure S3. Sequence alignment of the chitinase GH18 domain.  Figure S4. Computational prediction of effector candidates detected in EV samples from Fusarium graminearum (Fgr). Table S1. List of proteins detected in EVs from Fusarium graminearum (Fgr). Table S2. List of proteins employed in the computational effector prediction analysis. Table S3. Proteins identified in the secretome from Fusarium graminearum (Fgr). Table S4. List of transcripts identified in corn (Zea mays) infected by Fusarium graminearum (Fgr). Table S5. Gene expression values per biological replicate.

opencc-by-4.0Feb 2023View details →

Sachs: Protein and Phospholipids Expressions

The Sachs dataset measures the expression level of different proteins and phospholipids in human cells. It includes the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions.   Task: The dataset can be used to study causal discovery algorithms.   Summary:  <ul> <li>Size of collection: 14 datasets on 11 features and 707 to 927 observations</li> <li>Task: Causal Discovery Problem</li> <li>Data Type: Continuous Data</li> <li>Dataset Scope: Collection of Datasets</li> <li>Ground Truth: Known Graph</li> <li>Temporal Structure: Static Data</li> <li>License: CC BY (granted by G. Nolan and D. Lauffenburger)</li> <li>Missing Values: No Missing Data</li> </ul>   Missingness Statement: There are no missing values.   Features: Each sample in each dataset consists of quantitative amounts of each of the following 11 phosphorylated molecules, simultaneously measured from single cells: <table> <tbody> <tr> <th>Measured molecule</th> <th>Antibody specificity</th> </tr> </tbody> <tbody> <tr> <td>Raf</td> <td>Phosphorylation at S259</td> </tr> <tr> <td>Erk1 and Erk2</td> <td>Phosphorylation at T202 and Y204</td> </tr> <tr> <td>P38</td> <td>Phosphorylation at T180 and Y182</td> </tr> <tr> <td>Jnk</td> <td>Phosphorylation at T183 and Y185</td> </tr> <tr> <td>Akt.</td> <td>Phosphorylation at S473</td> </tr> <tr> <td>Mek 1 and Mek2</td> <td>Phosphorylation at S217 and S221</td> </tr> <tr> <td>PKA substrates</td> <td>Detects proteins and peptides containing a phospho-Ser/Thr residue with arginine at the -3 position</td> </tr> <tr> <td>PKC</td> <td>Detects phosphorylated PKC-α, -βI, -βII, -δ, -ϵ, -η, and -θ isoforms only at C-terminal residue homologous to S660 of PKC-βII</td> </tr> <tr> <td>Plcg</td> <td>Phosphorylation at Y783</td> </tr> <tr> <td>PIP2</td> <td>Detects PIP2</td> </tr> <tr> <td>PIP3</td> <td>Detects PIP3</td> </tr> </tbody> </table>   Files: The datasets were collected after a series of stimulatory cues and inhibitory interventions, with cell reactions stopped at 15 minutes after stimulation by fixation, to profile the effects of each condition on the intracellular signaling networks of human primary naïve CD4+ T cells, downstream of CD3, CD28, and LFA-1 activation. The following conditions were used: <table> <tbody> <tr> <th>File</th> <th>Stimulation</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>cd3cd28.csv</td> <td>CD3, CD28</td> <td>T cell activation</td> </tr> <tr> <td>cd3cd28icam2.csv</td> <td>CD3, CD28, ICAM-2</td> <td>LFA-1 signaling induction</td> </tr> <tr> <td>cd3cd28_aktinhib.csv</td> <td>CD3, CD28, akt-inhibitor</td> <td>PKA activation</td> </tr> <tr> <td>cd3cd28_g0076.csv</td> <td>CD3, CD28, G0076</td> <td>AKT inhibition</td> </tr> <tr> <td>cd3cd28_psitect.csv</td> <td>CD3, CD28, Psitectorigenin</td> <td>MEK1/MEK2 inhibition</td> </tr> <tr> <td>cd3cd28_u0126.csv</td> <td>CD3, CD28, U0126</td> <td>PKC activation</td> </tr> <tr> <td>cd3cd28_ly.csv</td> <td>CD3, CD28, LY294002</td> <td>PKC inhibition</td> </tr> <tr> <td>pma.csv</td> <td>PMA</td> <td>PIP2 production inhibition</td> </tr> <tr> <td>b2camp.csv</td> <td>β2camp</td> <td>AKT inhibition</td> </tr> <tr> <td>cd3cd28icam2_aktinhib.csv</td> <td>CD3, CD28, ICAM-2, akt-inhib</td> <td>simulated dataset</td> </tr> <tr> <td>cd3cd28icam2_g0076.csv</td> <td>CD3, CD28, ICAM-2, G0076</td> <td>simulated dataset</td> </tr> <tr> <td>cd3cd28icam2_psit.csv</td> <td>CD3, CD28, ICAM-2, Psitectorigenin</td> <td>simulated dataset</td> </tr> <tr> <td>cd3cd28icam2_u0126.csv</td> <td>CD3, CD28, ICAM-2, U0126</td> <td>simulated dataset</td> </tr> <tr> <td>cd3cd28icam2_ly.csv</td> <td>CD3, CD28, ICAM-2, LY294002</td> <td>simulated dataset</td> </tr> </tbody> </table> <ul> <li>Ground_Truth.csv: Directed Graph derived in Sachs et al. (2005) through multiple interventional experiments.</li> </ul>

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Replicated differential expression analysis in a green-brown polymorphic grasshopper reveals role of beta-carotene-binding protein in body coloration

Open the record for dataset details and reuse information.

publicOct 2025View details →

dryad36/100

Ectopical expression of bacterial collagen-like protein supports its role as adhesin in host-parasite coevolution

<div> <div> <div> <div> For a profound understanding of antagonistic coevolution, it is necessary to identify the coevolving genes. The bacterium Pasteuria and its host, the microcrustacean Daphnia, are a well-characterized paradigm for co-evolution, but the underlying genes remain largely unknown. A genome-wide association study suggested a Pasteuria collagen-like protein 7 (Pcl7) as a candidate mediating parasite attachment and driving its coevolution with the host. Since Pasteuria ramosa cannot currently be genetically manipulated, we used Bacillus thuringiensis to express a fusion protein of a Pcl7 carboxy- terminus from P. ramosa and the amino-terminal domain of a B. thuringiensis collagen-like protein (CLP). Mutant B. thuringiensis (Pcl7-Bt) spores but not wild-type B. thuringiensis (WT-Bt) spores, attached to the same site of susceptible hosts as P. ramosa. Furthermore, Pcl7-Bt spores attached readily to susceptible host genotypes, but only slightly to resistant host genotypes. These findings indicated that the fusion protein was properly expressed and folded and demonstrated that indeed the C-terminus of Pcl7 mediates attachment in a host genotype-specific manner. These results provide strong evidence for the involvement of a CLP in the coevolution of Daphnia and P. ramosa and open new avenues for genetic epidemiological studies of host–parasite interactions. </div> </div> </div> </div>

opencc-zeroMar 2024View details →

opencc-by-4.0Apr 2024View details →

Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data

This repository contains raw and processed data of the original datasets generated for Lee et al. "Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data". The files Dilution_3_ROI02.ome.tiff, Dilution_3_ROI02_mask.ome.tiff, st_reclustered_expr_data.h5ad contain raw data (as ome tiff), a mesmer-generated segmentation mask, and single-cell quantification in anndata format of the cell pellet data generated for the study. The file tonsil-for-zenodo.tar.gz contains all raw and processed data of the IMC data of the 16 human tonsil ROIs: <ul> <li>imc contains raw tiff stacks of the acquisition</li> <li>mask contains mesmer-generated masks</li> <li>channel.yml contains channel info</li> <li>exp_mat/dc/ contains single cell quantifications using mesmer segmentation</li> </ul>

opencc-by-4.0May 2022View details →

Data from: Phylogenetic distribution and expression pattern analyses identified a divergent basal body assembly protein involved in land plant spermatogenesis

<pre>Data from: Phylogenetic distribution and expression pattern analyses identified a divergent basal body assembly protein involved in land plant spermatogenesis Author information Shizuka Koshimizu1, Naoki Minamino2, Tomoaki Nishiyama3, Emiko Yoro4, Mayuko Sato5, Mayumi Wakazaki5, Kiminori Toyooka5, Kazuo Ebine2,6, Keiko Sakakibara4, Takashi Ueda2,6, and Kentaro Yano1 1 School of Agriculture, Meiji University, Kawasaki 214-8571, Japan 2 Division of Cellular Dynamics, National Institute for Basic Biology, Okazaki 444-8585, Japan 3 Research Center for Experimental Modeling of Human Disease, Kanazawa University, Kanazawa 920-0934, Japan 4 Department of Life Science, Rikkyo University, Tokyo 171‐8501, Japan 5 RIKEN Center for Sustainable Resource Science, Yokohama 230-0045, Japan 6 Department of Basic Biology, SOKENDAI (The Graduate University for Advanced Studies), Okazaki 444-8585, Japan This directory contains 0_README (this file) 1_Marchantia_FL_data 2_Physcomitrella_FL_data 3_Physcomitrella_TEM_images 4_PAML 1_Marchantia_FL_data Raw image data using quantification of spermatid phenotypes in Marchantia. quantification.xlsx: Summary of quantification data. 211111 and 211210: Observation date. ∟Mpbld10-1, Mpbld10-2, and Tak-1: Observed lines. ∟raw data: *.lsm files are raw image data. ∟binary image: *.tif files are binarization images of the Hoechst33342 data. ∟DIC: *.tif files are maximum intensity projection images of the C2 channels (detection of DIC images) of the raw data. ∟Hoechst33342: *.tif files are maximum intensity projection images of the C1 channels (detection of Hoechst33342 signals) of the raw data. 2_Physcomitrella_FL_data Merged data of DIC and Hoechst33342 signal images using quantification of spermatid phenotypes in Physcomitrella. line22-*.png: The images of Ppbld10-22 mutant. line30-*.png: The images of Ppbld10-30 mutant. wt.png: The images of wild type. Number in the images 1: With flagella 2: Without flagella 3_Physcomitrella_TEM_images TEM images of spermatids in the Ppbld10-30 mutant, except for images shown in Supporting Information Fig. S11. 4_PAML Files using analysis by PALM. input.fasta: Input multi fasta file. species_tree.nwk: The gene tree file for the global clock model (rooted using chlorophytes as an outgroup). species_tree_marked.nwk: A gene tree file with marks specifying land plant stem and crown branches as category #1 (for a local clock model). clock1: A directory containing output files of a PAML run with the global clock model (clock = 1) clock2: A directory containing output files of a PAML run with a local clock model (clock = 2). The branches assumed to have a different rate (r1) than the default rate r0 are specified with #1 in species_tree_marked.nwk.</pre>

opencc-by-4.0Apr 2024View details →

Data for paper: Transfer learning for cross-context prediction of protein expression from 5'UTR sequence

This depsit contains data for the paper entitled: "Transfer learning for cross-context prediction of protein expression from 5'UTR sequence". The rebeca.zip file contains a snapshot of the rebeca package which can be used to train, fine tune and test the CONV-LSTM model used in this study. The datasets.zip file contains the compiled sequence to expression datasets from across all Flow-seq expressions considered in this study.  The analysis.zip file contains all data files and jupyter notebooks necessary to reproduce our analysis. Each Flow-seq study has a dedicated folder (e.g., `fepB') with two sub-folders: 1. The `data\_split' folder, which contains the steps necessary to split the Flow-seq data for our ML experiments (a `readme.txt' file describes the input and output files and a jupyter notebook is available to reproduce the data split); 2. The `data\_analysis' folder, which contains a jupyter notebook and the necessary input files to reproduce the analysis of our experiments.

opencc-by-4.0Oct 2017View details →

Supplementary Material v1: Multivariate entropy characterizes the gene expression and protein -protein networks in four types of cancer

Supplementary Material PDF Multivariate entropy characterizes the gene expression and protein -protein networks in four types of cancer

opencc-by-4.0Apr 2018View details →

Full-length huntingtin constructs for eukaryotic expression of huntingtin protein with different polyQ lengths 2018/05/01

Huntingtin structure-function open lab notebook project: Full-length huntingtin constructs for eukaryotic expression of huntingtin protein with different polyQ lengths 2018/05/01

opencc-by-4.0Aug 2018View details →

Finding an antibody to detect EZH1 protein expression – Part 2

Follow up to previous post of trying to find an antibody to determine EZH1 expression in AML patient cells.

opencc-by-4.0Sep 2018View details →

Finding an antibody to detect EZH1 protein expression – Part 3

Follow up to previous post of trying to find an antibody to determine EZH1 expression in AML patient cells.

opencc-by-4.0Nov 2018View details →

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.