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opencc-by-4.0Nov 2021View details →

Depicting pseudotime-lagged causality across single-cell trajectories for accurate gene-regulatory inference [Datasets]

This repository contains processed single-cell dataset files for DELAY.

opencc-by-4.0Dec 2022View details →

Optical trapping of micro-particles and bacterial cells in single channel and flow-focusing microfluidic devices

Video 1 - The video shows the flow-focusing and trapping of 1.84 μm bacteria-sized particles flowing at a sample flow rate of 0.1 μL/min. The horizontal sheath flow rate 1 μL/min and the vertical sheath flow rate is 0.5 μL/min. Trapping is achieved using a maximum laser power of 250mW.  Video 2 - The video shows the flow and fluorescence trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 0.013 μL/min. The channel surface is treated with pluronic F-127 to prevent cell adhesion.  Video 3 - The video shows the flow and trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 1 μL/min. Increased flow rate results in continuous transient trapping of the cells is achieved at a trapping power of 250mW.  The microchannel surface is not treated with pluronic F-127, therefore lot of particles stick to the channel surface.  Video 4 - The video shows the flow and trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion.  Video 5 - The video shows the flow and trapping of E. coli MG1655 flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion.  Video 6 - The video shows the flow and trapping of S. aureus 6538 flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion.

opencc-by-4.0Dec 2022View details →

Diffusive Dynamics of Bacterial Proteome as a Proxy of Cell Death

This dataset accompanies the article entitled Diffusive Dynamics of Bacterial Proteome as a Proxy of Cell Death, published in ACS Cent. Sci. (<a href="https://doi.org/10.1021/acscentsci.2c01078">https://doi.org/10.1021/acscentsci.2c01078</a>). subboxes_overview.xlsx:  This spreadsheet contains information about the protein composition of each sub-box. BOX_GEOMETRIES.zip: Starting structures (saved in Gromos87 .gro format) used for the production runs of the all-atom sub-boxes. PROTEIN_COM_MSD.zip: Text files containing the mean square displacements (MSDs) of the centers of mass (COMs) of individual proteins, as obtained from the production runs of the all-atom sub-boxes. The time is shown in picoseconds while the MSD values are given in nm2. For each sub-box, the protein numbering corresponds to the subboxes_overview.xlsx spreadsheet. CHAIN_COM_MSD.zip: Text files containing the mean square displacements (MSDs) of the centers of mass (COMs) of individual polypeptide chains, as obtained from the production runs of the all-atom sub-boxes. The time is shown in picoseconds while the MSD values are given in nm2. For each sub-box, the chain numbering corresponds to the subboxes_overview.xlsx spreadsheet. CHAIN_ROTACF.zip: Text files containing the rotational autocorrelation functions for individual polypeptide chains, as obtained from the production runs of the all-atom sub-boxes. The time is shown in picoseconds; the autocorrelation functions were calculated using a second-order Legendre polynomial. For each sub-box, the chain numbering corresponds to the subboxes_overview.xlsx spreadsheet.

A longitudinal study of DNA and RNA viruses plasma detection in allogeneic hematopoietic stem cell transplant recipients

Background: Viral infections are among the most common complications after allogeneic hematopoietic stem cell transplantation (allo-HSCT) and can be associated with transient or sustained viremia. Besides viruses that are common causes of infection, metagenomics revealed the presence of several novel viruses and variants that are overlooked in clinical routine and represent potential sources of unrecognized systemic infections. Our aim was to describe the prevalence and the dynamics of 17 DNA and 3 RNA viral infections using (r(RT-)PCR) assays on plasma samples of adult allo-HSCT recipients over a one-year period after HSCT. Methods: 109 adult patients that received a first allo-HSCT from 1st March 2017 to 31st January 2019 we included in this longitudinal observational monocentric cohort study. 17 DNA and 3 RNA viral species were screened with qualitative and/or quantitative r(RT)-PCR assays performed on plasma samples collected at five time-points (day 0 and 30 days, 3 months, 6 months and one year after HSCT). Results: TTV was the most prevalent with an increasing prevalence to 96% of patients at 3 months. HPgV-1 prevalence ranged from 26 to 36% of patients. TTV and HPgV-1 plasma viral load peaked at month 3 (TTV: median 3.29E5 copies/ml [range, 3.37E2 to 4.06E9 copies/ml]; HPgV-1: median 1.18E6 copies/ml [range, 2.61E3 to 4.49E7 copies/ml]). Among Polyomaviridae, BKPyV, JCPyV, MCPyV, HPyV6 and 7 were detected in ≥10% of patients at ≥1 time-point. HPyV6 and HPyV7 prevalence reached 27% and 12% of patients at month 3. Among those, 41% and 63% had quantifiable viral loads, with median viral loads above 1E3copies/ml and results may suggest HPyV6 sustained viremia. Co-detections were frequent, in particular at 3 months with ≥2 viruses detected in 72% of patients. Conclusion: Our study confirms that TTV and HPgV-1 infections are highly prevalent and that infection may be sustained up to one year after allo-HSCT. Our systematic and large strategy of screening also revealed diverse and numerous co-detections, and that several novel Polyomaviridae (MCPyV, HPyV6/7) that are overlooked in clinical routine are as or more frequently detected compared to classical culprits. Our results underscores the need for further studies investigating the clinical impact of classical culprits together with other viruses in particular novel Polyomaviridae and HPgV-1.

Amacrine cells differentially balance zebrafish colour circuits in the central and peripheral retina

In vertebrate vision, the feature-extracting circuits of the inner retina are driven by photoreceptors whose outputs are already pre-processed. In zebrafish, for example, outer retinal circuits split "colour" from "greyscale" information across all four cone-photoreceptor types. How does the inner retina process this incoming spectral information while also combining cone signals to shape new greyscale functions? We address this question by imaging the light-driven responses of amacrine cells (ACs) and bipolar cells (BCs) in larval zebrafish, in the presence and pharmacological absence of inner retinal inhibition. We find that amacrine cells exert distinct effects on greyscale processing depending on retinal region, as well as contributing to the generation of colour opponency in the central retina. However, in the peripheral retina amacrine cells enhanced opponency in some bipolar cells while at the same time suppressing pre-existing opponency in others, such that the net change in the number of colour-opponent units was essentially zero. To achieve this 'dynamic balance' ACs counteracted intrinsic colour opponency of BCs via the On-channel. Consistent with these observations, Off-stratifying ACs were exclusively achromatic, while all colour opponent ACs stratified in the On-sublamina. This study reveals that the central and peripheral retina of larval zebrafish employ fundamentally distinct inhibitory circuits to control the interaction between greyscale- and colour-processing. Differential actions on the On- and Off-channels control the transmission of colour-opponent signals in the periphery.

Cell selectivity in succinate receptor SUCNR1/GPR91 signaling in skeletal muscle

Succinate is released by skeletal muscle during exercise and activates SUCNR1/GPR91. Signaling of SUCNR1 is involved in cell-cell communication in skeletal muscle. However, the specific cell types responding to succinate and the directionality of communication are unclear. De novo analysis of transcriptomic datasets demonstrated that SUCNR1 mRNA is expressed in immune, adipose, and liver tissues, but scarce in skeletal muscle. In human tissues, SUCNR1 mRNA was associated with macrophage markers. Single-cell RNA sequencing and fluorescent RNAscope demonstrated that in human skeletal muscle, SUCNR1 mRNA is not expressed in muscle fibers but coincided with macrophage populations. Human M2-polarized macrophages exhibit high levels of SUCNR1 mRNA and stimulation with selective agonists of SUCNR1 triggered Gq- and Gi-coupled signaling. Primary human skeletal muscle cells were unresponsive to SUCNR1 agonists. In conclusion, SUCNR1 is not expressed in muscle cells and its role in the adaptive response of skeletal muscle to exercise is most likely mediated via paracrine mechanisms involving M2-like macrophages within the muscle.

Combined in vitro and cell-based selection display method producing specific binders against IL-9 receptor in high yields

The FEBS Journal FJ-22-0728 Combined in vitro and cell-based selection display method producing specific binders against IL-9 receptor in high yields Maro&scaron; Huličiak, Lada Biedermanová, Daniel Berdár, &Scaron;těpán Herynek, Lucie Kolářová, Jakub Tomala, Pavel Mikulecký, and Bohdan Schneider Institute of Biotechnology of the Czech Academy of Sciences, Průmyslová 595, 252 50 Vestec, Czech Republic Correspondence: Bohdan Schneider, Institute of Biotechnology of the Czech Academy of Sciences, BIOCEV, CZ-252 50 Vestec, Czech Republic, Tel: +420 728 303 566, e-mail: bohdan.schneider@ibt.cas.cz Abstract We combined cell-free ribosome display and cell-based yeast display selection to build specific protein binders to the extracellular domain of the human interleukin 9 receptor alpha (IL-9Rα). The target, IL-9Rα, is the receptor involved in the signaling pathway of IL-9, a pro-inflammatory cytokine medically important for its involvement in respiratory diseases. The successive use of modified protocols of ribosome and yeast displays allowed us to combine their strengths - the virtually infinite selection power of ribosome display and the production of (mostly) properly folded and soluble proteins in yeast display. The described experimental protocol is optimized to produce binders highly specific to the target, including selectivity to common proteins such as BSA, and proteins potentially competing for the binder such as receptors of other cytokines. The binders were trained from DNA libraries of two protein scaffolds called 57aBi and 57bBi developed in our laboratory. We show that the described unconventional combination of ribosome and yeast displays is effective in developing selective small protein binders to the medically relevant molecular target.

Data for: The significance of Apis cerana cerana (Hymenoptera: Apidae) gnawing off the old brood cells

Apis cerana cerana has the biological characteristic of gnawing off the old brood cells for rearing multiple generations of workers. This study investigated the internal structure of newly built, old, and semi-rebuilt brood cells and their effects on the morphological development of workers to understand the significance of Apis cerana cerana gnawing off the old brood cells. The structural dimensions of the brood cells and the morphological characteristics of the newly emerged workers were measured. The results showed that Apis cerana cerana gnaw off the old brood cells mainly in two ways either by removing the whole cell or only the cell walls keeping the bases. The workers construct semi-rebuilt brood cells on the foundation of these old cell bases. The main shapes of the newly built, old, and semi-rebuilt brood cells are hexagonal prism, hexagonal prismatic table, and hexagonal prism having three rhombuses, hemisphere, and hemisphere-shaped bases, respectively. The average thickness of the newly built or semi-rebuilt brood comb was significantly smaller than that of the old brood comb (P<0.05), while it was almost the same for the newly built and semi-rebuilt brood combs (P>0.05). The depth of brood cells showed no significant difference between the three types of brood cells (P>0.05), but the brood cell volumes were significantly different (P<0.05). There was no significant difference among the three diameters (at the top, middle, and bottom positions) of newly built or semi-rebuilt brood cells (P>0.05), but these changed within the old brood cells (P<0.05). The top, middle, and bottom diameters of the newly built or semi-rebuilt brood cells were significantly larger than those of the old brood cells (P<0.05), but were almost the same between the newly built and the semi-rebuilt brood cells (P>0.05). The weight and base thickness of the cocoon were significantly greater in the old brood cells than those in the semi-rebuilt brood cells (P<0.05). Importantly, the birth weight, body length, and the tested 6 external morphological indices did not show a significant difference between the newly built and the semi-rebuilt brood cells (P>0.05), but were significantly larger than those of old brood cells (P<0.05). The size of brood cell and the external morphology of the workers showed a positive correlation. This study highlights the significance of Apis cerana cerana gnawing off the old brood cells providing a reference for its scientific breeding.

Data and Analysis from "Analysis of context-specific KRAS-effectors (sub)complexes in Caco-2 cells"

Data, data processing and data analysis for manuscript "Analysis of context-specific KRAS-effectors (sub)complexes in Caco-2 cells". (Preprint available <a href="https://doi.org/10.1101/2022.08.15.503960">here</a>) Analysis of AP-MS data: analysis.zip Contains the following scripts as well as their outputs: <ul> <li>01_preparation.R R script for filtering and processing our mass spec data.</li> <li>02_diffbinding.R R script for differential analysis followed by gene set enrichment.</li> <li>03_funcstats.R R script for statistical analysis over different ontology terms.</li> <li>04_semantic_analysis.R R script for the GO semantic analysis for the output of 02 and 03.</li> <li>05_1_random_walks.py Python script for performing random walks for specific functional terms.</li> <li>05_2_random_walks_analysis.R R script for the analysis and visualization of the output of 05_1.</li> </ul> The required input data is deposited in the "data" sub-folder, taken directly from the linked PRoteomics IDEntification database (PRIDE) <a href="https://www.ebi.ac.uk/pride/archive/projects/PXD035399">entry</a>. Interactive visualization of the results of most of this analysis is available on <a href="https://github.com/PhilippJunk/kras_apms_vis">GitHub </a>as a Shiny app.   Analysis of whole cell lysate: analysis_wholecelllysate.zip Contains the following script, as well as its output: <ul> <li>01_analysis.R R script for loading the data and extracting/visualizing KRAS and effector abundances.</li> </ul> The required data is deposited in the "data" sub-folder, taken directly from the linked PRoteomics IDEntification database (PRIDE) <a href="https://www.ebi.ac.uk/pride/archive/projects/PXD039404">entry</a>.

opencc-by-4.0Dec 2021View details →

Cancer-Associated Fibroblast Classification in Single-Cell and Spatial Proteomics Data

ometiff: Imaging Data Cell Masks: Masks generated with cellprofiler from ilastik segmentation training cp-output_config: All relevant cellprofiler output and additional configuration files (for example clinical data) necessary to generate the single cell experiments. IMC Data Objects: Single cell experiment RDS files.   scRNA-seq_dataobjects: .Rds files containing the clustered breast cancer, colon cancer, HNSCC, NSCLC and PDAC datasets as well as the integrated validation dataset.

Dataset related to the article "Purinergic Receptor P2Y2 Stimulation Averts Aortic Valve Interstitial Cell Calcification and Myofibroblastic Activation"

This record contains raw data related to the article "Purinergic Receptor P2Y2 Stimulation Averts Aortic Valve Interstitial Cell Calcification and Myofibroblastic Activation" Rationale-Calcific aortic valve stenosis (CAVS) is a pathological condition of the aortic valve with a prevalence of 3% in the general population. It is characterized by massive rearrangement of the extracellular matrix, mostly due to the accumulation of fibro-calcific deposits driven by valve interstitial cells (VIC), and no pharmacological treatment is currently available. The aim of this study was to evaluate the effects of P2Y2 receptor (P2RY2) activation on fibro-calcific remodeling of CAVS. Methods-We employed human primary VICs isolated from CAVS leaflets treated with 2-thiouridine-5'-triphosphate (2ThioUTP, 10 µM), an agonist of P2RY2. The calcification was induced by inorganic phosphate (2 mM) and ascorbic acid (50 µg/mL) for 7 or 14 days, while the 2ThioUTP was administered starting from the seventh day. 2ThioUTP was chronically administered for 5 days to evaluate myofibroblastic activation. Results-P2RY2 activation, under continuous or interrupted pro-calcific stimuli, led to a significant inhibition of VIC calcification potential (p < 0.01). Moreover, 2ThioUTP treatment was able to significantly reduce pro-fibrotic gene expression (p < 0.05), as well as that of protein α-smooth muscle actin (p = 0.004). Conclusions-Our data suggest that P2RY2 activation should be further investigated as a pharmacological target for the prevention of CAVS progression, acting on both calcification and myofibroblastic activation.

opencc-by-3.0Nov 2020View details →

Dataset related to the publication "An invar-based fabry-perot cavity refractometer with a gallium fixed-point cell for assessment of pressure"

The data set consists of; The published paper, all figures that present measurement or simulation data in .png and .fig format and the underlying data plotted in the figures in text format.  The published plots were generated from the fig files. The text files were generated by reading the plotted data from the fig files. The files are named Fig_XX were XX corresponds to the figure number in the publication.  The format of the text file is as follows. Before every data set there is a header consisting of; The number of the subplot where the data is plotted (Plot: XX), the number of the data set in the sub plot (DataSet: XX), and the color of the line or marker in the plot (Color: XX). The description of what each color represents can be found in the publication.

Frozen feather and motif files for Distinct stage-specific transcriptional states of B cells derived from human tonsillar tissue.

Frozen feather and motif files for manuscript "Distinct stage-specific transcriptional states of B cells derived from human tonsillar tissue" by Espinoza DA, et al. Data was downloaded from cisTarget database from aertslab.org.

Single-cell RNA sequencing of mouse embryonic cells from the oocyte, 2-cell, 4-cell, 8-cell, blastocyst, and morula stages

STRT-N is a newly optimized single-cell RNA sequencing method for studies of early genome activation in mammalian preimplantation development. Single embryos from the oocyte, 2-cell, 4-cell, 8-cell, blastocyst, and morula stages were sampled for experiments and were sequenced using STRT-N method. Here is the raw data from STRTN-seq. FASTQ files are available in <a href="https://www.ebi.ac.uk/biostudies/studies/S-BSST976">BioStudies database</a>.

opencc-by-4.0Nov 2021View details →

Numerical Investigation of Interface Passivation Strategies for Sb2Se3/CdS Solar Cells

Sb2Se3 is an emerging earth-abundant material praised for its promising optoelectronic properties, although the presence of interfacial defects at the vicinity of the p–n junction limit its performance as photovoltaic absorber. Using a device modeling approach and a realistic set of material parameters, it unravels pathways mitigating the impact of interfacial defects with a baseline Sb2Se3/CdS. Two straightforward strategies are devised and tested against the baseline. First, a thin front surface sulfurization of the Sb2Se3 absorber allowing a local lowering of the valence band and creating a “front surface field,” resulting in an increased carrier selectivity and limiting the density of holes available for interface recombination, leading to a significant efficiency improvement for optimized conditions. Second, the use of an ultrathin insulating Al2O3 layer between the absorber and the buffer layer is considered, helping in preventing detrimental chemical interdiffusion at the junction. This strategy provides a direct interface passivation, though the interlayer thickness needs a fine tuning to balance the benefits of reduced interface recombination and a detrimental Al2O3 low-conductivity layer. In each case, an analysis covering a broad range of parameters is presented, and conclusions are made in the frame of past numerical and experimental results.

opencc-by-4.0Nov 2020View details →

Cell-Cell Interaction Database

Overview This page describes the automated construction of a cell-cell interaction database by filtering existing curated protein-protein interaction (PPI) data. Cell-cell interactions are important for understanding tissue organization. We and others have built cell-cell interaction databases (1-5). The resource available from this website represents an automatically built set of protein-protein interactions that can mediate cell-cell communication that is expanded compared to previous databases we have built.   Receptors Receptor genes were defined based on the union of the annotations from <ol> <li> the set of <a href="http://www.geneontology.org/">Gene Ontology</a> (GO) terms (6,7): <ul> <li> GO:0043235 - receptor complex, </li> <li> GO:0008305 - integrin complex, </li> <li> GO:0072657 - protein localized to membrane </li> <li> GO:0043113 - receptor clustering </li> <li> GO:0004872 - receptor activity, </li> <li> GO:0009897 - external side of plasma membrane) </li> </ul> </li> <li> <a href="https://www.uniprot.org/">UniProt</a> annotations <ul> <li>search term -"Receptor [KW-0675]" go:0005886 organism:human.</li> </ul> </li> </ol> This created a set of 4364 receptor genes (prior to manual curation)   Ligands Ligand genes were defined based on the union of the below annotations <ol> <li> the GO terms (6,7): <ul> <li> GO:0005102 - receptor binding </li> </ul> </li> <li> the set of proteins labelled as secreted in the Secretome dataset (<a href="http://www.proteinatlas.org/humanproteome/secretome">http://www.proteinatlas.org/humanproteome/secretome</a>) (8). </li> </ol> This created a set of 3209 Ligand genes (prior to manual curation)   Extracellular Matrix Extracellular Matrix (ECM) genes were defined based on the union of the annotations from <ol> <li> the GO terms (6,7): <ul> <li> GO:0031012 - extracellular matrix </li> <li> GO:0005578 - proteinacious extracellular matrix </li> <li> GO:0005201 - extracellular matrix structural constituent </li> <li> GO:1990430 - extracellular matrix protein binding </li> <li> GO:0035426 - extracellular matrix cell signalling </li> </ul> </li> </ol> This created a set of 433 ECM genes (prior to manual curation)   Manual Curation ECM, Receptor and ligand lists were manually curated <ul> <li>genes that were neither receptors or ligands were removed</li> <li>misclassified genes were moved to the correct list (i.e. receptors found on the ligand list or vice versa)</li> </ul> After curation, the resulting ligand, receptor and ECM sets consisted of: <ul> <li>Receptors - 1851 genes</li> <li>Ligands - 1593 genes</li> <li>ECM - 433 genes</li> </ul> In each of the above sets there are genes that are part of other sets (e.g. a gene can be ECM and ligand at the same time)   Interaction Data The set of protein interactions were downloaded from: <ol> <li> <a href="http://irefindex.org/wiki/index.php?title=iRefIndex">iRefIndex</a> (<a href="http://irefindex.org/download/irefindex/data/archive/release_14.0/">version 14</a>) (9). - all BioGRID interactions were excluded from the iRefIndex set as we imported the original source. </li> <li> <a href="http://www.pathwaycommons.org/">Pathway Commons</a> (<a href="http://www.pathwaycommons.org/archives/PC2/v8/">version 8</a>) (10). </li> <li> <a href="https://thebiogrid.org/">BioGRID</a> (<a href="https://downloads.thebiogrid.org/BioGRID/Release-Archive/BIOGRID-3.4.147/">version 3.4.147</a>) (11). </li> </ol> The entire interaction set was filtered to only include interactions that contained receptor-ligand, receptor-receptor, ligand-ligand, receptor-ecm, ligand-ecm or ecm-ecm interactions where the receptor, ligands and ecm were defined by the above lists. The resulting Receptor-Ligand network contained 2,593 unique proteins and 38,446 unique interactions (115,900 interaction total)   Data files <ul> <li> ligands.txt - table of ligands. (contains HGNC symbol and classification (Ligand, Ligand/ECM, Ligand/Receptor, Ligand/ECM/Receptor) </li> <li> receptors.txt - table of receptors. (contains HGNC symbol and classification (Receptor, Receptor/ECM, Ligand/Receptor, Ligand/ECM/Receptor) </li> <li> ecm.txt - table of ECM. (contains HGNC symbol and classification (ECM, ECM/Receptor, ECM/Ligand, Ligand/ECM/Receptor) </li> <li> protein_types.txt - table of unique set of receptor, ligand and ECM genes (all of the above tables: contains HGNC symbol as well as classification (Receptor, Ligand, ECM, ECM/Receptor, ECM/Ligand, Receptor/Ligand, Ligand/ECM/Receptor) </li> <li> receptor_ligand_interactions_mitab_v1.0_April2017.txt(.zip/.gz) - tab delimited file in <a href="https://psicquic.github.io/MITAB25Format.html">mitab 2.5</a> format containing the following columns: <ul> <li> AliasA - main Alias for molecule A (often the recognized gene symbol) </li> <li> AliasB- main Alias for molecule B (often the recognized gene symbol) </li> <li> uidA - unique identifier for molecule A (depending on the source database this can be one of the following types uniprot, refseq, entrez gene id, ensembl) </li> <li> uidB - unique identifier for molecule A (depending on the source database this can be one of the following types uniprot, refseq, entrez gene id, ensembl) </li> <li> altA - list of alternate identifiers for molecule A. </li> <li> altB - list of alternate identifiers for molecule B. </li> <li> aliasA - list of alternate aliases for molecule A. </li> <li> aliasB - list of alternate aliases for molecule B. </li> <li> method - list of psi-mi terms indicating experimental methods used to discover interaction. </li> <li> author - text listing authors </li> <li> pmids - list of pmids associated with the interaction. </li> <li> taxa - taxon id for molecule A. </li> <li> taxb - taxon id for molecule B. </li> <li> interactionType - list of psi-mi terms indicating the type of interactions it is. </li> <li> sourcedb - source database. </li> <li> interactionIdentifier - source database interaction identifier </li> <li> confidence - confidence of interaction as supplied by database source </li> </ul> </li> </ul>   References <ol> <li> Qiao W, Wang W, Laurenti E, Turinsky AL, Wodak SJ, Bader GD, Dick JE, Zandstra PW Intercellular network structure and regulatory motifs in the human hematopoietic system <a href="http://www.ncbi.nlm.nih.gov/pubmed/25028490">Pubmed</a> </li> <li> Kirouac DC, Ito C, Csaszar E, Roch A, Yu M, Sykes EA, Bader GD, Zandstra PW. Dynamic interaction networks in a hierarchically organized tissue. Mol Syst Biol. 2010 Oct 5;6:417 <a href="http://www.ncbi.nlm.nih.gov/pubmed/20924352">Pubmed</a> </li> <li> Yuzwa SA, Yang G, Borrett MJ, Clarke G, Cancino GI, Zahr SK, Zandstra PW, Kaplan DR, Miller FD. Proneurogenic Ligands Defined by Modeling Developing Cortex Growth Factor Communication Networks. Neuron. 2016 Sep 7;91(5):988-1004 <a href="http://www.ncbi.nlm.nih.gov/pubmed/27545711">Pubmed</a> </li> <li> Ramilowski JA, Goldberg T, Harshbarger J, Kloppmann E, Lizio M, Satagopam VP, Itoh M, Kawaji H, Carninci P, Rost B, Forrest AR. A draft network of ligand-receptor-mediated multicellular signalling in human. Nat Commun. 2015 Jul 22;6:7866. <a href="http://www.ncbi.nlm.nih.gov/pubmed/26198319">Pubmed</a> </li> <li> Rieckmann JC, Geiger R, Hornburg D, Wolf T, Kveler K, Jarrossay D, Sallusto F, Shen-Orr SS, Lanzavecchia A, Mann M, Meissner F. Social network architecture of human immune cells unveiled by quantitative proteomics. Nat Immunol. 2017 May;18(5):583-593. PMID: 28263321. </li> <li> Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000 May;25(1):25-9 <a href="http://www.ncbi.nlm.nih.gov/pubmed/10802651">Pubmed</a> </li> <li> The Gene Ontology Consortium. Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res. 2017 Jan 4;45(D1):D331-D338 <a href="http://www.ncbi.nlm.nih.gov/pubmed/27899567">Pubmed</a> </li> <li> Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, Olsson I, Edlund K, Lundberg E, Navani S, Szigyarto CA, Odeberg J, Djureinovic D, Takanen JO, Hober S, Alm T, Edqvist PH, Berling H, Tegel H, Mulder J, Rockberg J, Nilsson P, Schwenk JM, Hamsten M, von Feilitzen K, Forsberg M, Persson L, Johansson F, Zwahlen M, von Heijne G, Nielsen J, Pontén F. Proteomics. Tissue-based map of the human proteome. Science. 2015 Jan 23;347(6220) <a href="http://www.ncbi.nlm.nih.gov/pubmed/25613900">Pubmed</a> </li> <li> Razick S, Magklaras G, Donaldson IM. iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinformatics. 2008 Sep 30;9:405 <a href="http://www.ncbi.nlm.nih.gov/pubmed/18823568">Pubmed</a> </li> <li> Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O, Anwar N, Schultz N, Bader GD, Sander C. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res. 2011 Jan;39(Database issue):D685-90.2010 Nov 10. <a href="http://www.ncbi.nlm.nih.gov/pubmed/21071392">Pubmed</a> </li> <li> Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006 Jan 1;34(Database issue):D535-9. <a href="http://www.ncbi.nlm.nih.gov/pubmed/16381927">Pubmed</a> </li> </ol>

HtrA1 in placental cell models: A possible role in preeclampsia

Background: The High temperature requirement A 1 (HtrA1) is a multidomain secretory protein with serine-protease activity involved in the regulation of many cellular processes in both physiological and pathological conditions. HtrA1 is normally expressed in human placenta, and its expression is higher in the first trimester compared to the third trimester, suggesting an important role of this serine protease in early phases of human placenta development. The aim of this study was to evaluate the functional role of HtrA1 in in vitro models of human placenta in order to define the role of this serine protease in preeclampsia (PE) Methods: BeWo, HTR8/SVneo and HUVEC cells expressing HtrA1 and showing a cytoplasm localization of HtrA1 were used as syncytiotrophoblast, cytotrophoblast and placental endothelium models respectively. Oxidative stress was induced treating BeWo and HTR8/SVneo cells with H2O2 to mimic PE conditions in order to evaluate its effect on HtrA1 expression. In addition, HtrA1 overexpression and silencing experiments were performed to evaluate the effects on syncytialization, cell mobility and invasion processes. Finally, human recombinant HtrA1 treatments were performed to study the effects of exogenous HtrA1 on Endoglin, VEGFA, ERK1/2, NF-kB and PCNA that are molecules involved in PE onset Results: Our main data showed that the oxidative stress significantly increased HtrA1 expression in both BeWo and HTR8/SVneo cells. In addition, we demonstrated that HtrA1 has a pivotal role in cell motility and invasion processes. In particular, HtrA1 overexpression increased while HtrA1 silencing decreased cell motility and invasion in HTR8/SVneo cell model. Finally, we found that hrHtrA1 treatments of HUVEC did not alter the expression of the listed above molecules involved in PE onset; (4) Conclusions: In conclusion, our results suggest an important role of HtrA1 in regulating extravillous cytotrophoblast invasion and motility during early stage of placentation in the first trimester of gestation suggesting a key role of this serine protease in PE onset.

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Data used in the paper "High Performance H2−Mn Regenerative Fuel Cells through an Improved Positive Electrode Morphology "

The data in this spreadsheet was used to produce the figures in the paper Authors:Javier Rubio-Garcia, Anthony Kucernak, Barun Kumar Chakrabarti , Dong Zhao , Danlei Li2, Yuchen Tang , Mengzheng Ouyang , Chee Tong John Low and Nigel Brandon  Title:High Performance H2−Mn Regenerative Fuel Cells through an Improved Positive Electrode Morphology  Journal:Batteries DOI: Please cite the above reference if you wish to use this data DOI of data:10.5281/zenodo.7599405

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STRA6 promotes thyroid carcinoma progression via activation of the ILK/AKT/mTOR axis in cells and female nude mice

Background: Metastasis has emerged to be an important cause for poor prognosis of thyroid carcinoma (TC) and its molecular mechanisms are not fully understood. STRA6 is a multifunctional membrane protein widely expressed in embryonic and adult tissues. The function and mechanism of STRA6 in TC remain elusive. Objective: We aimed to explore the role of STRA6 in TC progression and provide a therapeutic target for TC. Methods: The expression and clinicopathological relevance of STRA6 were explored in TC. Stable STRA6-knockdown TC cells were established and used to determine the biological function of STRA6 in vitro and in vivo. RNA-sequencing and Co-immunoprecipitation were performed to unveil the molecular mechanism of STRA6 in TC progression. The potential of STRA6 as a therapeutic target was evaluated by lipid nanoparticles (LNPs) containing siRNA. Results: STRA6 was upregulated in TC and correlated with aggressive clinicopathological features including extrathyroidal extension and lymph node metastasis, which contributed to the poor prognosis of TC. STRA6 facilitated TC progression by enhancing proliferation and metastasis in vitro and in vivo. Mechanistically, STRA6 could interact with ILK and subsequently activate the AKT/mTOR signaling pathway. We further unveiled that STRA6 reprogrammed lipid metabolism through SREBP1, which was crucial for the metastasis of TC. Moreover, STRA6 siRNA delivered by LNP significantly inhibited cell growth in xenograft tumor models. Conclusions: Our study demonstrates the critical roles of STRA6 contributing to TC progression via the ILK/AKT/mTOR axis, which may provide a novel prognostic marker as well as a promising therapeutic target for aggressive TC.