dataesr/scientific-paragraphs-categorization huggingface dataset:
2 stem-loop (Fig. 4B ) in order to investigate whether the 6-nt recognition sequence of Seq9110 required a secondary structure context to hybridize with the internal loop of 5BSL3. 2. Clear binding was observed, but again, the data could not be fitted with a 1:1 model of interaction. Since 5BSL3. 2 has two binding sites for recognizing other RNA sequences of the HCV genome, the next step was to investigate the effect of the 5BSL3. 2-SL2 interaction on 5BSL3. 2-Seq9110 binding and whether these interactions were simultaneous or mutually exclusive. The experiments were performed by the single cycle kinetics on a decaying surface (SCKODS) method that was developed to analyze FIGURE 3. Native acrylamide gels of SL2 hairpins. Two µg of SL2 WT , SL2 kistem , SL2 GC , and SL2 AAU were prepared and were loaded on 15% (w/v) 75:1 acrylamide/bis(acrylamide) native gels as described in Materials and Methods.
Seq9110 was first injected over the 5BSL3. 2-coated surface at 1 µM (first arrow from the left). During the dissociation phase of the formed Seq9110-5BSL3. 2 complex, SL2 kistem was injected sequentially in the order of increasing concentrations, 62. 5 nM (second arrow), 250 nM (third arrow), and 1000 nM (fourth arrow). Experiments were performed with injections of Seq9110 only (referred to as Seq9110 alone). The regeneration of the surface was achieved with a 2-min pulse of a mixture of 40% formamide, 3. 6 M urea, and 30 mM EDTA prepared in milli-Q water. The gray curves represent the experimental data (two overlaid sensorgrams for SL2 kistem and Seq9110 alone). The black line for Seq9110 alone represents the fit of the dissociation phase according to Equation (2). The black lines for SL2 kistem represent the fit to a Langmuir 1:1 model of interaction according to Equations (  1 ) and (  2 ) by the SCKODS method.
ACKNOWLEDGMENTS We thank the structural biology facility ( UMS 3033/US001 ) of the  Institut Européen de Chimie et Biologie (Pessac, France ) for access to the Biacore  T200  instrument that was acquired with the support of the  Conseil Régional d'Aquitaine , the  GIS-IBiSA , and the  Cellule Hôtels à Projets  of the CNRS. We thank  Dr. Andrew Goldsborough  for reading the manuscript. This work was supported by the  Agence Nationale de Recherches sur le SIDA et les hépatites virales (ANRS) , grant # ASA12007GRA  and the  Institut National de la Santé et de la Recherche Médicale. C. M. and W. P. are supported by a fellowship from the  ANRS and the Ministère de l'Enseignement Supérieur et de la Recherche , respectively. Received December 7, 2012; accepted March 11, 2013.
Patients We used data from CASCADE in EuroCoord (www. EuroCoord. net), a collaboration of cohorts of patients with wellestimated dates of HIV seroconversion in Europe, Australia, Canada, and sub-Saharan Africa  [13]. The data for this study were pooled in 2006 and, unlike most recent updates, included information on reported seroconversion illness and specific symptoms. The date of HIV seroconversion, used to approximate the date of HIV infection, was estimated by various methods: most frequently as the midpoint between dates of the last negative and first positive HIV antibody test results, the date of laboratory evidence of seroconversion or the date of a seroconversion illness (and an earlier documented negative HIV test result). For this study we restricted to patients with laboratory evidence of seroconversion or an HIV test interval #6 months.
Sensitivity analyses As CD4 count is known to drop transiently at the time of HIV seroconversion, analyses of immunological definitions were also conducted excluding all CD4 counts collected within 4 weeks of the estimated date of seroconversion. Moreover, since patients with missing information on seroconversion illness symptoms or CD4 count within 6 months of HIV diagnosis were excluded from the analyses of the clinical and immunological definitions and this missing data mechanism may not be completely at random, we repeated the analyses imputing the trait for those with missing values  [17].
Finally, we used data collected in the pre-cART era to avoid selective drop at cART initiation for patients with worse prognosis. Recent studies have reported lower initial CD4 cell counts in individuals who have seroconverted more recently  [29]  and faster disease progression  [30]. It is possible, therefore, that our conclusions about the predictive value of clinical and immunological characteristics are not generalized to more recently-infected individuals. Initiation of cART in PHI could be beneficial to prevent rapid progression in patients with severe PHI. The best practice for the clinical management of patients diagnosed in PHI or early HIV infection remains unknown and there is ongoing debate on the value, timing and optimal duration of cART in PHI. In three recent randomised controlled trials (RCTs) patients who received transient cART in PHI presented longer times to CD4,350 cells/ mm 3 , lower viral set-point or delayed initiation of cART according to recommendations for chronic infection  [31, 32, 33]  compared with patients who did not receive cART in PHI.
The research leading to these results has received funding from the  European Union Seventh Framework Programme  (FP7/2007-2013) under EuroCoord grant agreement no. 260694. Sara Lodi  is holder of a  Juan de la Cierva Fellowship  (grant number  JCI 2010-08151 ). There are no current external funding sources for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. KP has received an honorarium from  Tibotec. AP has received fees for consultancy for  Gilead Sciences, GSK Biologicals , KP 360; speaker fees from  Gilead ; funds for research from  BMS ;  Advisory Board membership with Abbvie. JG has received fees for consulting from  Gilead Sciences, Bristol Myers Squibb ,  Merck Sharp Dohme Chibret ,  Janssen ,  Merck Sharp Dohme Chibret and Viiv.
The nanoLC method consisted in a 115-min gradient ranging from 4% to 45% acetronitrile in 0. 1% formic acid at a flow rate of 300 nL/min. Peptides were sampled on a 300 m ϫ 5 mm PepMap C18 precolumn and separated on a 75 m ϫ 250 mm C18 PepMap column (Thermofisher). MS and MS/MS data were acquired using Xcalibur (Thermofisher). Spray voltage and heated capillary were respectively set at 1. 4 kV and 200 °C. Survey full-scan MS spectra (m/z ϭ 400 -1600) were acquired in the Orbitrap with a resolution of 60,000 after accumulation of 10 6 ions (maximum filling time: 500 ms). The 20 most intense ions from the preview survey scan delivered by the Orbitrap were fragmented by collision induced dissociation (collision energy 35%) in the LTQ after accumulation of 10 4 ions (maximum filling time: 100 ms).
(  4 ) Data analyses. RAW files were processed using MaxQuant (27) version 1. 3. 0. 5. Spectra were searched against the Uniprot database (Mus musculus taxonomy 10090, 78810 sequences, July 2012 version) and the frequently observed contaminants database embedded in MaxQuant. Trypsin was chosen as the enzyme and 2 missed cleavages were allowed. Precursor mass error tolerances were set respectively at 20 ppm and 6 ppm for first and main searches. Fragment mass error tolerance was set to 0. 5 Da. Peptide modifications allowed during the search were: carbamidomethylation (C, fixed), acetyl (Protein N-ter, variable), oxidation (M, variable) and deamidation (NQ, variable). Minimum peptide length was set to seven amino acids. Minimum number of peptides, razor ϩ unique peptides and unique peptides were set to 1.
Maximum false discovery rates -calculated by employing a reverse database strategy -were set to 0. 01 at peptide and protein levels. Raw MS data files, unfiltered protein groups and peptides tables have been uploaded in ProteomeXchange server (www. proteomexchange. org accession PXD000052). Identification and quantification data are also provided for proteins in supplemental Table S7 and for peptides in supplemental Table  S8. Proteins identified as "contaminants," "reverse," and "only identified by site" were discarded from the list of identified proteins. iBAQ values were calculated using MaxQuant as previously described  (28) , from MS intensity of unique peptides. Data coming from technical MS replicates were summed automatically. (  5 ) Statistical analyses. Normalization of protein iBAQ values was performed on each sample using median calculated from proteins with values in all 16 samples.
Only proteins quantified with a minimum of two unique peptides and for which a quantitative value could be measured for the four affinity purifications replicates in at least one condition were considered for the statistical analysis. The following steps were performed using the Perseus toolbox (version 1. 2. 0. 16) available in the MaxQuant environment. After log2 transformation of iBAQ values and data imputation (replacing missing values by normal distribution as described in  (29) , protein expression differences were identified using t-testing with a Permutation-based FDR to correct for multiple hypothesis testing, S0 value set to 1  (30)  and p value threshold value set to 10 Ϫ3. Only proteins with a significant t test result and log2 ratio above 2 for enrichment in respectively nonactivated (Ϫ) and activated (ϩ) mast cells from Slp76 OST/OST versus Slp76 ϩ/ϩ mice were validated in resting and stimulated complex, respectively.
Sources of Information and Software Used for Protein Network Analyses-Information on protein interactions was extracted from the STRING (http://string-db. org) and PINA (http://cbg. garvan. unsw. edu. au/pina/) databases. The software Cytoscape was used to construct and visualize the SLP76 network (http://www. cytoscape. org). The LAT2 and Bcr protein-protein interaction networks were obtained from STRING database. We integrated experimentally verified interactions and those described in databases. Information regarding protease-coding genes has been extracted from Expasy web service (http://expasy. org).
RABE1 and RHG27 have never been described before as SLP76 interactors. The SLP76 Interactome is Markedly Enriched in Activated Mast Cells-To determine the activation-induced SLP76 interactome in mast cells, we performed two sets of comparative MS analyses of proteins copurified with SLP76. First, we compared the SLP76 interactome in Slp76 OST/OST BMMC and in Slp76 ϩ/ϩ BMMC, sensitized with IgE antibodies and challenged with antigen. The statistical treatment of iBAQ values coming from MaxQuant processing of LC-MS/MS data cor-FIG. 4. Strep-Tactin affinity purification of functional SLP76 protein complexes from BMMC lysates. A, StrepTactin affinity-purification in LM lysates of BMMC from Slp76 ϩ/ϩ (upper panel) and Slp76 OST/OST (lower panel) mice. Aliquots from each step of the purification procedure were Western blotted with anti-SLP76 antibodies (Input 1%, FT; flowthrough 1%, washes 1%; eluates 10%).
DISCUSSION We report here the first proteomic analysis of FcRI signaling in primary mouse mast cells. Specifically, we describe the SLP76 interactome in resting and in activated cultured mast cells, using affinity purification coupled to label-free quantitative proteomics. Data presented in this paper are the first results of a large-scale program aiming at assembling a comprehensive dynamic map of signaling events triggered by FcRI engagement in mast cells. SLP76 has been extensively investigated following TCR engagement in T cells, but much less following FcRI engagement in mast cells. SLP76 deletion was however found to profoundly affect IgE-induced mast cell activation  (20). As uncovered in the present study, one reason of this poor knowledge on an adapter molecule that is essential for FcRI signaling may be linked to its unexpected sensitivity to proteolysis by mast cell granular proteases.
Some proteins previously reported to associate with SLP76 in T cells were not found with a high enough statistical significance in our MS-based proteomic analysis of the SLP76 interactome in mast cells. Lyn and Nck-1 could however be detected by Western blot analysis among molecules that were affinity-purified with SLP 0ST (not shown). The reason for the discrepancy lies in the severe filters used for establishing statistical significance in our proteomic analysis (MS data and statistical analysis of these results are shown in supplemental Table  S6 ). Fifteen molecules that were inducibly recruited by SLP76 on FcRI engagement in mast cells have not been reported to be recruited by SLP76 on TCR engagement in T cells. These comprised adapter molecules (LAT2, 14 -3-3F, CSTF1 and Dok-3), a Tec family protein tyrosine kinase (Btk), Src kinase regulators (SKAP1 and SKAP2), GTPase-activating molecules (RABE1 and RHG37), a serine/threonine kinase with a guanine-nucleotide exchange factor activity for Rho GTPases (Bcr)  (41) , and molecules with poorly defined or unknown functions (HIG1A, SUCA, MSH2, PACS1 and DC1I2).
One notices several other interesting molecular partners in the Bcr interactome that can be constructed from published databases (Fig. 5D ). These include Kit, which critically determines the proliferation and differentiation of mast cells, molecules that control cytokine receptor signaling such as Jak2 and STAT5A/B, the exchange factors Sos and Vav, and, strikingly, a whole cluster of small G proteins of the Rho and Rac families that exert major cytoskeletal effects by controlling the dynamics of actin. Bcr has itself a serine-threonine kinase activity. Bcr is also a GTPase-activating protein for Rac1 and cdc42 and an exchange factor for Rho GTPases. Normally, the Bcr exchange factor activity is auto-inhibited by flanking sequences  (49). It is constitutively activated in the Bcr-Abl fusion protein (41) that accounts for the transformation of myeloid cells in chronic myeloid leukemia.
SLP76 interactome in resting BMMC. Proteins were identified in four affinity purifications of SLP76 OST complexes in resting cells. Listed molecules were enriched at least fourfold in nonactivated Slp OST/OST compared to Slp ϩ/ϩ BMMC (OSTϪ vs WTϪ). The significance (p Ͻ 10 Ϫ3 ) of enrichment (OSTϪ/WTϪ ratio) was determined using student's t-test. UniProt Accession numbers, protein symbols and molecular function are also shown.
SLP76 interactome in activated BMMC. The proteins listed were recurrently identified in four affinity purifications of SLP76 OST complexes in activated cells. Listed molecules were enriched at least fourfold in activated Slp OST/OST (OSTϩ) compared to Slp ϩ/ϩ BMMC (WTϩ). The enrichment was measured by the OSTϩ/WTϩ ratio and its significance (p Ͻ 10 Ϫ3 ) determined using student's t-test. The enrichment existing between activated (OSTϩ) and nonactivated (OSTϪ) BMMC is also shown and its significance was determined using student's t-test (at least fourfold enrichment: **p Ͻ 10 Ϫ2 , ***p Ͻ 10 Ϫ3 ). UniProt Accession numbers, protein symbols, and molecular function are also shown.
Acknowledgments-We thank  Dr. Eleanora Heisterkamp  ( Childrens Hospital of Los Angeles, Los Angeles CA ) for having generated Bcr-deficient mice;  Dr. Eunjoon Kim , ( Center for Synaptic Brain Dysfunctions, Institute for Basic Science, and Department of Biological Sciences, Korea Advanced Institute of Science and Technology , Daejeon  305-601 ,  Korea ) for Bcr-deficient bone marrow;  Dr. Koon-Guan Lee , ( Immunology Group, Bioprocessing Technology Institute, A * STAR, Singapore ) for Dok-3-deficient bone marrow;  Dr. Frederic Fiore  ( Centre d'Immunophenomique, Marseille ) for supervising the construction of Slp76 OST/OST mice,  Dr. Sylvain Latour  ( Inserm U768, Hopital Necker, Paris ) for 4G10 antibodies;  Dr. Vincenzo di Bartolo  ( Unitede Biologie Cellulaire des Lymphocytes, Departement d'Immunologie, Institut Pasteur, Paris ) for helpful advice and for mouse anti-SLP76 antibodies;  Dr.
Validation of selected miRNAs Based on the data obtained from the initial, discovery step of the study, we performed a validation step for the most promising miRNAs. Validation of miRNAs was done using individual miRNA assays from Exiqon. For this step, the set of analyzed samples was extended: in addition to samples from 32 patients used for profiling, we tested samples from 18 more patients with the same diagnosis and an additional 6 patients without NSCLC. For validation, quantitative PCR (qPCR) was performed in triplicate, and mean values of C t were used for calculation. Table  1. List of miRNA probes used to profile plasma samples from lung SCC patients.
The highest signal was detected for miR-451, -223, -15a, -486-5p, -16, and -21, which is in agreement with literature data  [27]. A small portion of analyzed miRNAs (including miR-206, -422a, and -202) have rarely been detected in our samples, and these miRNAs are likely to be present in small quantities or completely absent in plasma. For further analysis, global expression levels for each sample were normalized using the BRB-ArrayTools statistical software package  [28]  (Figure  S2 ).
Implementation We used both fastFVA  (Gudmundsson and Thiele, 2010)  and ILOG CPLEX (version 11. 2) (http://www. ilog. com/products/cplex) to solve the LP instances of the LP-based method, and CellNetAnalyzer which is a free software running under MATLAB to compute the EMs of a network  (Klamt et al. , 2007). All computations were performed on a PC with a Xeon CPU 3. 33 GHz and 10 GB RAM running under the LINUX OS.
The E. coli metabolic networks We use three versions of the E. coli metabolic network, as summarized in Table  1. The central network is from the KEGG database  (Kanehisa and Goto, 2000; Kanehisa et al. , 2012) , and iJE660 and iJO1366 are from the BiGG database  (Orth et al. , 2011; Schellenberger et al. , 2010) , stored as METATOOL and SBML formats, respectively. iJO1366 is the latest version of E. coli network, while we keep the older iJE660 in our experiments to allow comparison with previous work  (Edwards and Palsson, 2000a,b; Reed et al. , 2003). We should notice that these networks are obtained as closed systems, and thus, additional information like sources and biomass synthetics is needed to make these systems open  (Edwards and Palsson, 2000a; Reed et al. , 2003; Wunderlich and Mirny, 2006).
Phenotypic data To compare our in silico impact predictions with experimental data, we consider five datasets used in previous studies to assess the phenotypic consequences of gene knock-out. The first dataset, collected from literature by  Edwards and Palsson (2000a) , measures the growth capability of 79 gene deletion mutants, among which 41 are essential, 36 are non-essential and 2 have been observed as either essential or non-essential. Following  Wunderlich and Mirny (2006) , we consider the predictions of any method on the later 2 genes as always correct to compute the accuracy of the prediction, while we remove them to compute receiver operating characteristic (ROC) curves. The second dataset (insertional mutants), collected by  Badarinarayana et al. (2001)  and further used by  Wunderlich and Mirny (2006) , gives the growth rate of 481 mutants obtained by knock-out of single genes, among which 222 with 450% decrease in growth rate are considered essential.
While all genes are available in the iJE660 network, only 461 (including 218 essentials) are present in the iJO1366 network. The third dataset is the combination of the first two ones. Although they contain genes in common, we follow  Wunderlich and Mirny (2006)  and consider them all different because they are part of different networks specific to each dataset. The fourth dataset, collected by  Gerdes et al. (2003)  and further used by  Wunderlich and Mirny (2006) , evaluates the gene variability of 598 mutants, among which 120 are considered essential. While all genes are available in the iJE660 network, only 571 (including 117 essentials) are present in the iJO1366 network. The fifth dataset is the KEIO collection, collected by  Baba et al. (2006) , which partitions 4288 mutants into 317 (including 14 newly added by  Yamamoto et al.
, 2009)  essential and 3971 nonessential genes. Among them, 81 (respectively 144) essential and 554 (respectively 1222) nonessential genes are present in the iJE660 (respectively iJO1366) model. Because these experimental datasets are under different conditions, we used different input sources and output biomass in the networks for the different datasets, as listed in Supplementary Files. In short, for the mutants collected from literature, the minimal medium set is used to reconstruct four distinct networks, each of which includes only one of the energy sources. For the insertional mutants, we reconstruct the network by adding the minimal medium input with all energy sources. We use the rich source set when analyzing the Gerdes dataset and KEIO collection. As for outputs, all analyses with iJE660 share the same biomass output (Supplementary Table  S3 ), while for iJO1366, we use the core growth biomass proposed by  Orth et al.
Phenotypic prediction The FBIDs computed on each network vary significantly between different genes. For example, Figure  2  shows the distribution of FBIDs for the 1366 genes of the KEIO collection dataset estimated on the iJO1366 model. While 480% of all genes have an FBID smaller than 10, it increases to 540 for the msbA (b0914) gene, a bacterial lipid flippase whose knock-out blocks ATP synthesis by oxidative phosphorylation, or 448 for acpP (b1094), a acyl carrier protein that catalyzes polyketide biosynthesis of holo-ACPS; unsurprisingly, both are essential genes. To assess more quantitatively how predictive the FBID is for gene essentiality, we systematically compared the FBID corresponding to each gene deletion with the corresponding experimental phenotypic data, for both versions of the large E.
coli metabolic network (iJE660 and iJO1366). In each experimental data, the genes are separated in two classes corresponding to genes with a large or small phenotypic impact. By thresholding the FBID to some level, we can predict that genes with an FBID above the threshold should have large phenotypic impact, while those below the threshold should not. Figure  3  shows the ROC curve for each dataset and each network, corresponding to the sensitivity plotted as a function of 1-specificity when we vary the FBID threshold. In addition, we show on Table  3  the area under the ROC curve (AUC) and the accuracy reached in each case, when the FBID threshold is set for each phenotypic dataset to maximize the accuracy as in  Wunderlich and Mirny (2006). We can see that phenotype prediction for the iJE660 and iJO1366 E.
coli networks are overall similar, with an advantage for the former on all phenotype datasets. The performance on the first dataset (collected from literature) is rather disappointing. This can be explained, to some extent, by the fact that we had to modify the network by using the minimal inputs together with distinct carbon sources, which resulted in many metabolites and reactions being always inactive at steady state. The performance on the insertional mutants dataset is also not good and may also be due in part to the particular context of using minimal inputs. For the Gerdes dataset and KEIO collection, FBID performs pretty well on both networks, reaching an AUC of 0. 66 and 0. 78 for iJE660 and 0. 6 and 0. 72 for iJO1366, respectively. To compare the performance of FBID with existing approaches, we first focus on the iJE660 E.
2182 well with growth rate, our approach meets difficulties in finding important fluctuations in the number of reactions that become completely inhibited when a gene is deleted. On the insertional mutant dataset, all methods reach a similar level of accuracy, with a slight advantage for SA and MOMA over FBA and FBID. On the larger Gerdes dataset, FBID and FBA reach the same level, and clearly outperform SA. To further investigate the performance of FBID on large networks, we compare it with FBA on the largest iJO1366 network for the prediction of gene essentiality as defined in the KEIO collection. Figure  4  shows the ROC and precision-recall curves of both methods. We see that FBID (AUC ¼ 0. 72, accuracy ¼ 93%) outperforms FBA (AUC ¼ 0. 68, accuracy ¼ 89%) on this experiment, confirming the potential of FBID on large networks.