dataesr/scientific-paragraphs-categorization huggingface dataset:
Nodes represent specific interacting partners and edges connect pairs of nodes for which the P-value associated with the Pearson correlation coefficient exceeded a set threshold. Edge width is coded according to Pearson correlation coefficient (R < 0. 8 or R > 0. 8). The identified edges are depicted in red if they have been already reported as direct interaction partners in the BioGRID database. D, E Venn diagrams showing overlaps between protein-protein interactions deduced from the CBL (D) or CBLB (E) CNs and those reported in the BioGRID database for Homo sapiens and Mus musculus. Two sets of analysis are shown according to Pearson correlation coefficient values (R < 0. 8 or R > 0. 8). Histograms below the Venn diagrams compare the probabilities to predict existing interactions with a random network (Random), the complete CN, or the high-confidence CN (R > 0.
Acknowledgements We thank  H. Gu  and  J. Penninger  for CBL-and CBLB-deficient mice,  F. Lozano  and  Marc Orta  for CD5-deficient mice, and  Ingrid Hinchliffe  and  Fabien Danjan  for the construction of CBL OST and CBLB OST mice. This work was supported by  CNRS ,  INSERM, European Research Council  (FP7/2007-2013 grant no. 322465  ("Integrate") to B. M),  Agence Nationale de la Recherche  ( PHENOMIN  project to B. M. and  Basilic  project to M. M. ), the  Investissement d'Avenir program ProFI  ( Proteomics French Infrastructure  project;  ANR-10-INBS-08 ) and  PHENOMIN (French National Infrastructure for Mouse Phenogenomics) ,  Région Midi-Pyrénées , and by fellowships from the  Integrate  project (G. V. and E. B. ), the  NANOASIT   Euronanomed  project (A. G. B. ), and the  PHENOMIN  project (L.
F Pie charts showing the percentages of CBL and CBLB interactors detected in both the present study and the BioGRID public database. The size of the pie charts is proportional to the numbers of interactors per bait. Molecular Systems Biology 12: 876 | 2016 ª 2016 The Authors Molecular Systems Biology Assembly dynamics of the CBL and CBLB signalosomes Guillaume Voisinne et al overrepresentation of annotations related to "signaling adaptor activity" and "role in protein degradation" was observed (Fig EV3).
Analytical Chemistry Technical Note MALDI-FTICR-MSI Analysis. Optical images scanned (Canon LiDE-20, Canon, UK). MSI analysis was performed using constant accumulation of selected ions (CASI) using a 80 Da isolation window centered at 435 Da yielding a 2 Mword time-domain transient. Laser spot diameter was 100 μm, and raster spacing was 100-300 μm. Laser shots were 800 and power optimized for consistent ion production. MSI data were subject to window normalization to m/z of 468. 3064 (GirT-d 8 -CORT). Mass precision was typically ±0. 0005 Da. Average abundances were assigned from the summed spectra within ROIs. Neutral testosterone and DHT were analyzed (without derivatization) in positive mode at m/z 289. 2098 and m/z 291. 2112. Ions of derivatives were monitored in positive mode: m/z 402.
3115 (GirT-testosterone) and 404. 3271 (GirT-DHT). The CHCA matrix ions at positive m/z 417. 0483, along with GirT-d 8 -CORT, were monitored to assess uniformity of matrix application. Higher Spatial Resolution Analysis. Using a MALDI q-TOF MS, the ROI was defined and the spatial resolution set (50 μm). Positive ion data were acquired in sensitivity mode (target enhancement at m/z 402) with 350 laser shots/raster position using a 1 kHz laser. Optimization was achieved by tuning acquisition settings while collecting data from a control spot of analytes (0. 5 μL, 0. 1 g/mL of standard at ∼2 μm manually spotted with an equal volume of derivatization solution/matrix). Ions formed by derivatives were monitored in positive mode: m/ z 402. 31 (GirT-testosterone) and m/z 404. 33 (GirT-DHT). Mass filter windows were selected with a precision of ±0.
Mitochondrial transmembrane potential measurement in neonatal cardiomyocytes. Isolated rat cardiomyocytes were loaded with 100 nM TMRM at 37 °C for 15 min. Afterwards, the sarcolemmal membrane was permeabilized by perfusion of digitonin (5 μg/ml) in a Ca 2+ -free internal solution that contained 50 mM KCl, 80 mM potassium aspartate, 4 mM sodium pyruvate, 20 mM HEPES, 3 mM MgCl 2 , 3 mM Na 2 ATP, 5. 8 mM glucose and 0. 5 mM EGTA (pH 7. 3 with KOH). Then, the free Ca 2+ concentration in the internal solution was increased to 200 nM. The Ca 2+ was calculated using the Maxchelator program from the Stanford University (Stanford, CA, USA). Images were acquired with a Leica (SP5) confocal microscope (Mannheim, Germany). Excitation was achieved by a white light laser fitted at 549 nm and emission collected at 570 nm.
Analyses were made with Image J program (Wayne Rasband, National Institutes of Health, USA). Measurement of mitochondrial Ca 2+ in cardiomyocytes. Isolated neonatal or adult rat cardiomyocytes were loaded with 5 μM Rhod-2 at 37 °C for 30 min. To remove cytosolic Rhod-2, the sarcolemmal membrane was permeabilized by perfusion of digitonin (5 μg/ml) in a Ca 2+ -free internal solution that contained 50 mM KCl, 80 mM potassium aspartate, 4 mM sodium pyruvate, 20 mM HEPES, 3 mM MgCl 2 , 3 mM Na 2 ATP, 5. 8 mM glucose and 0. 5 mM EGTA (pH 7. 3 with KOH). After the sarcolemmal membrane was permeabilized, the free Ca 2+ concentration in the internal solution was increased to 200 nM. The Ca 2+ was calculated using the Maxchelator program from Stanford University. Images were acquired with a Leica (SP5) confocal microscope.
Excitation was achieved by a white light laser fitted at 552 nm and emission collected at 575 nm. Analyses were made with Image J program. FRET measurements of cAMP levels. FRET imaging experiments were performed 48 h after infection of neonatal cardiomyocytes. Cells were bathed in Hepes-buffered Ringer's solution containing: 125 mM NaCl, 25 mM HEPES, 10 mM glucose, 5 mM K 2 HPO 4 , 1 mM MgSO 4 and 1 mM CaCl 2 , pH 7. 4. For sAC activation by HCO 3 -, the medium was the Krebs-Henseleit solution containing: 120 mM NaCl, 2. 09 mM K 2 HPO 4 , 0. 34 mM KH 2 PO 4 , 24 mM NaHCO 3 , 1 mM MgSO 4 , 1 mM CaCl 2 and 10 mM D-glucose. Krebs-Henseleit solution was gassed continuously with 95% O 2 /5% CO 2 to maintain a pH of 7. 4. 35 Real-time FRET experiments were performed at room temperature. Images were captured every 5 s using the × 40 oil-immersion objective of an inverted microscope (Nikon, Champigny sur Marne, France) connected to a Cool SNAP HQ2 camera (Photometrics, Tucson, AZ, USA) controlled by the Metafluor software (Molecular Devices, Sunnyvale, CA, USA).
Acknowledgements. This work has been funded by  INSERM (AMG, CB, GV, RF) , the  Investment for the Future program   ANR-11-IDEX-0003-01  within the LABEX  ANR-10-LABX-0033  (CB, GV,  RF ) and  ANR  ( ANR-13-ISV1-0001-01 , CB). ZW and DL are supported by a fellowship from the  China Scholarship Council. The Leica microscope was funded by  CORDDIM (Investissement 2010 ,  COD100296 ). We thank  Florence Lefebvre  for her assistance in adult cardiomyocyte isolation and  Valérie Domergue  for animal housing and care at the  Animex facility, IPSIT, Châtenay-Malabry, France. We also thank  Christine Longin  for  Transmission Electron Microscopy at INRA, Jouy-en-Josas, France. We are also thankful to  Jessica Sabourin ,  Cécile Martel ,  Delphine Mika  and  Jerôme Leroy  for helpful discussions.
In addition to interpreting observations, understanding the chemical evolution of discs will also have astrobiological implications in the connection to the chemical composition of planets themselves. To date, almost 200 molecules have been detected in interstellar or circumstellar environments 2. The abundances of these molecules can be subject to change via a large number of chemical reactions (see  Caselli 2005; Henning & Semenov 2013, for reviews). In order to accurately model the evolution of even a small number of these molecules, complex computational networks of chemical reactions are needed in the form of coupled ordinary differential equations (ODEs). Several research groups have compiled publicly-available databases of both these chemical reaction networks, and data on the rates of individual chemical reactions themselves -including the UMIST Database for Astrochemistry 3  (UDfA;  Millar et al.
1997; Woodall et al. 2007; McElroy et al. 2013) , the Ohio State University networks 4  , and the Kinetic Database for Astrochemistry 5  (KIDA;  Wakelam et al. 2012). Databases either contain these rates explicitly, or include how such a rate depends on local properties in the form of a parametrised expression (often via the Arrhenius-Kooij equation, Arrhenius 1889; Kooij 1893). Chemical reactions fall into several categories and can involve a variety of reactants. Table  1  lists the common types of astrophysical reactions. While the majority of reactions are concerned with gas phase species or their interaction with photons, dust grain surfaces provide a location for further chemistry to occur. Gas phase molecules attach themselves to the surfaces of dust grains (a process known as adsorption) via two mechanisms: physisorption (involving weak van der Waals forces) or chemisorption (due to chemical valence bonds).
Hardware developments It is also important to assess new and projected hardware developments. We are approaching a time in which access to large numbers of processors increasingly outweighs the developments in performance of the processors themselves. Efficiently scalable numerical methods, such as Monte Carlo radiation transport and discontinuous Galerkin hydrodynamics solvers, will therefore be extremely advantageous in the near future. Another significant realisation (only recently for astronomers) is that graphics processings units (GPUs) can offer significant speedup per core. A relatively small (but growing) fraction of astrophysical codes have a GPU implementation, and those that do are often those used for cosmological applications (e. g. Schive et al. 2010; Bryan et al. 2014b ). However, a GPU implementation of the fargo disc-modelling code was developed by  Benítez-Llambay & Masset (2016) , where they quote a typical speedup per core of a factor 40.
ACKNOWLEDGMENTS We thank the anonymous referee for their comments, and positive review of the manuscript. We would like to acknowledge the Protoplanetary Discussions conference, including the members of the local and scientific organising committees. We also thank the attendees of the conference (Table 3) and those who took part in the discussion sessions chaired by  John Ilee  and  Daniel Price , all of whom helped inform many of the statements made here. We also particularly thank  Phil Armitage  for comments on the manuscript. Through most of this work TJH was funded by the  STFC  consolidated grant  ST/K000985/1  and is now funded by an  Imperial  Junior research fellowship. JDI gratefully acknowledges support from the  DISCSIM  project, grant agreement  341137 , funded by the  European Research Council  under ERC-2013-ADG.
The column was washed for 20 minutes with 100% mobile phase B after each separation. All mass spectra were collected in positive ion mode with the nanoflow electrospray ionization source held at a potential of 1850 V, an S-Lens RF value of 60% and an ion transfer tube temperature of 275 degrees Celsius. The data acquisition cycle time was fixed at 3 seconds and consisted of a single 120,000 resolution precursor ion scan from m/z 400 to 2,000 on the Orbitrap mass analyzer followed by MS/MS scans on the linear ion trap and Orbitrap MS 3 scans from m/z 100 to 500 at 60,000 resolution. Only ions with charge states between 2 and 7 having intensities over 5,000 counts were selected for MS/MS. Dynamic exclusion was applied for 6,000 seconds to precursor ions having been analyzed by MS/MS. Peptide sequences, post-translational modifications and protein identities were determined from LC-MS data using both the Mascot server (version 2.
4. 1) and Sequest HT search engines operated in Proteome Discoverer (version 1. 4. 0. 288). All MS/MS data were searched against version 3. 87 of the Mus musculus IPI database, which contained 26,627,161 amino acid residues within 59,534 protein entries (release date  September 27, 2011). False discovery rate (FDR) estimation was conducted using the Proteome Discoverer Percolator algorithm with validation based on q-value. Precursor and product ion mass tolerances were set to 20 ppm and 0. 6 Da, respectively. Dynamic modifications considered were asparagine and glutamine deamidation, methionine oxidation, cysteine pyridylethylation and phosphorylation of serine, threonine or tyrosine. The TMT tag (229. 163 Da) was set as a fixed modification at lysine and amino-terminal residues. Only tryptic peptides between 400 and 6,000 Da in mass, with up to one missed cleavage site, were considered in the search.
Peptide assignments were restricted to sequences of 6 to 144 residues. Peak areas from the six TMT reporter ions in MS 3 data were used for peptide quantification. Isotopic purity correction factors for each reporter ion were used in accordance with the manufacturer's recommendation. All mass spectrometry proteomics data generated in this study have been deposited (identifier PXD003398) to the Proteo-meXchange Consortium  [14]  via the PRIDE partner repository  [15]  and can be accessed through the following link: http://www. ebi. ac. uk/pride/archive/projects/PXD003398.
Cluster analysis Hierarchical clustering was conducted with Cluster (Version 3. 0) and Java TreeView (Version 1. 26) open source software, following published instructions  [16, 17]. More specifically, prior to clustering, data columns (representing samples depicting the three biological replicates for each of the five mouse models) were centered on their means (protein level data represented in rows were not further adjusted). Subsequently, steady-state protein level data (rows) were clustered using a 'city-block' algorithm. The unbiased comparison of samples (columns) was based on a Spearman Rank Correlation, a non-parametric alternative to the Pearson correlation coefficient, which was selected to protect the results from the influence of outliers. Data were visualized in Java TreeView and individual components exported in postscript format for assembly of the figure in Adobe Illustrator.
Statistics To evaluate the mass accuracy of peptide-to-spectrum matches (PSMs) from searches against the forward and reversed IPI mouse FASTA database, the mass accuracy of precursor ions was plotted against the relative number of peptide identifications. Calculated precursor ion m/z errors were grouped into ranges of 0. 5 parts per million (ppm) within the mass accuracy interval specified in the Proteome Discoverer workflow search parameters (-20 to 20 ppm). The number of PSMs for each precursor ion m/z error range was counted. Whereas the analysis was restricted to PSMs from the forward IPI database search exceeding 0. 05 FDR estimation cut-off scores, all PSMs from the reversed IPI database search were included. To generate the chart which bins proteins on the basis of the number of peptides underlying their identification, protein level search results (based on peptide sequence assignments having scores exceeding a 0.
As negative controls served wild-type mice (obtained as littermates of Prnp -/-mice by breeding heterozygote Prnp +/-mice) and parental cell lines expressing endogenous levels of PrP. Western blot analyses of these models confirmed PrP-deficiency as expected and revealed in wild-type control samples from each model the expression of varying levels of endogenous Prnp gene products. The latter migrated during electrophoresis in denaturing sodium dodecyl sulfate (SDS) polyacrylamide gels with the characteristic relative speeds expected for unglycosylated, mono-and di-glycosylated PrP, or were observed as faster migrating bands, presumably indicating the presence of the prominent C1 cleavage products, known to exist in these models (Fig 1B)  [7, 18, 19]. To generate global proteomics datasets, three biological replicates (cell culture dishes or mouse brains) were collected from the aforementioned cell clones or mice and subjected to rapid lysis in pre-heated SDS, using bead-beating for efficient homogenization and to promote the shearing of DNA.
The post-acquisition data analyses against the international protein index (IPI) mouse database centered on the Percolator algorithm, which uses a semi-supervised machine learning approach to discriminate confident identifications  [21] , and was conducted with search engines Mascot and Sequest embedded in the Proteome Discoverer platform. This first analysis was used to determine the composition of the global proteome in each of the models. To limit the influence of outlier MS3 data for the subsequent comparative quantitations, we stipulated that only proteins would be considered in this analysis, whose identifications were based on a minimum of three PSMs in a given dataset. We further required that the corresponding low mass TMT signature ion distributions for relative quantitation had not only been obtained for these PSMs but were collected at a time when no other precursor co-isolated with the precursor ion of interest and were of sufficient intensity to pass the respective Proteome Discoverer stringency filter (Fig  1D).
Although we have not further investigated the underlying causes for these relative shifts in the global proteome of brain-derived samples, it did not escape us that brain tissue might exhibit them because a large subset of its cells are non-proliferating and equipped with neuritic extensions expected to require, relative to nonneuronal cells, additional investments in energy-consuming transport processes. The inventory of intermediate filaments (IFs) in a given cell is well-known to depend on the cell type and its differentiation state  [22]. Because the objective of this study was to identify global proteome differences that may underlie divergent molecular phenotypes in distinct cell models, a positive internal control was sought to test if the methodology applied had led to global proteome datasets that exhibited expected cell type-specific differences.
Indeed, when we extracted IF data from the five datasets, we observed that IFs known to be present in a broad range of cell types (e. g. , vimentin and lamin) had been detected in all samples, IFs known to be present in epithelial cells were restricted to the NMuMG and 1C11 models (keratins), and, at the other end of the spectrum, muscle-specific IFs (syncoilin and desmin) were only observed in C2C12 myoblasts (Fig  2E ). Note that because all datasets provided comparable proteome coverage, the relative expression level of a given protein across the five dataset can be inferred by comparing the spectral counts underlying its respective identifications  [23]. Taken together, these data established that the coverage achieved in the global proteome analyses and the relative distributions of sub-proteomes across the five datasets were comparable, yet expected cell-type specific differences were preserved.
Hierarchical clustering reveals surprising disparity in the consequence of PrP-deficiency on global proteomes We next turned to investigating the specific consequences of PrP-deficiency in the various models, which relied on the TMT-based relative quantitations. A direct comparison of the datasets revealed 4,260 proteins (S1  in at least one of the datasets on the basis of a minimum of three PSMs that were accompanied by informative TMT signature ion profiles. To ensure a meaningful comparison between the models was possible, we initially limited analyses to the subset of 1,559 proteins, whose levels were quantified in all four cell lines (a strict requirement for the quantitation of proteins in the brain dataset was waived for reasons described below). Thus, individual proteins were represented by steady-state abundance profiles that consisted of 12 (cell models only) to 15 (all models) data points, based on the 4 (cell models only) to 5 (all models) x 3 (biological replicates) samples.
doi:10. 1371/journal. pone. 0156779. g003 relative lack of intensely red or blue colored protein ratio values in columns associated with these cells in S3 and S4 Figs) suggested that signaling downstream of PrP that can cause global proteome shifts might be relatively dormant in these cells. In contrast, 1C11 and N2a cells seemed to exhibit relatively stronger proteome shifts in response to PrP-deficiency. To investigate this aspect further, we next waived the requirement of a protein to be quantified in all four cell models, as inclusion of this filter might have masked critical differences between cell models, and asked instead, which of the 4,260 proteins quantified in all datasets underwent the most pronounced changes in their abundance levels in any of the four models (S5 Fig). This analysis revealed a total of 56 proteins, whose levels changed more than two-fold on the basis of relative quantitations of a minimum of three unique PSMs.
To address this question, we turned toward a previously reported PrP interactome dataset generated in the N2a cell model  [26]  and investigated how the relative abundance levels of candidate PrP interactors were affected in the N2a and 1C11 model in response to PrP deficiency. This analysis revealed that candidate PrP interactors were significantly overrepresented amongst the proteins most affected by PrP-deficiency in their steady-state levels (S3-S5 Figs), corresponding to a probability of 1. 50E-03 (Chi-square = 10. 07) if this overlap was due to chance alone. Moreover, the subset of candidate PrP interactors shortlisted in this manner (Fig  5C ) also shared the aforementioned opposite trend of their abundance levels in the two cell models (with the exception of the protein calreticulin, known to bind to PrP in the endoplasmic reticulum), as evidenced reagents) comprised higher (or lower) relative levels of MARCKS (MARCKSL1)-derived peptides than the corresponding peptide mixtures from PrP-deficient cells (labeled with odd-numbered TMT reagents).
Note the different ordinate scales on individual plots. The blue dots represent signature ion mass spectra selected for quantitation while the red dots represent those excluded. The largely overlapping distribution of blue and red dots is the consequence of a data processing configuration that relied on two complementary algorithms, Mascot and Sequest, for the assignment of spectra to peptides. To avoid artificial inflation of the number of peptides identified and quantified, the program identifies these occurrences as duplicate hits and rejects one of the replicates when tallying quantified peptides for a given protein. This was a puzzling finding that provoked a search for a plausible explanation. It was then noticed that the relationship in abundance levels for these proteins was not, in fact, simply inverse (note, for example, that protein entries whose abundance ratios triggered a dark blue shading in N2a cells did not give rise to a dark red shading of their abundance ratios in the 1C11 cell model, as would be expected for a truly inverse relationship) but manifested in some other way that also generated a highly negative correlation coefficient.
Discussion To our knowledge, the current study represents the first investigation to compare side-by-side in several models the consequences of a specific protein deficiency on the global proteome. It made use of a previously optimized workflow that incorporated the tagging of peptides with isobaric reagents to facilitate the direct comparison of samples in a high-end mass spectrometer. The analysis of three biological and three technical replicates from wild-type and PrP-deficient mouse models generated a repository that combines data from >150 hrs of tandem MS analysis time. Post-acquisition investigations of these datasets revealed a profound contribution of the cellular differentiation state to proteome shifts observed. We document that the deficiency of a single protein can affect abundance levels of sub-proteomes in opposite directions in different cell types.