dataesr/scientific-paragraphs-categorization huggingface dataset:
The fact that SARS-CoV doesn't kill cells for several days after infection may allow a better chance for recovery of proteome data influence. In the past, various methods have been employed to predict ''important'' vertices from network topology analysis. Multiple network centrality measures have been shown to identify high centrality vertices that are of greater interest than vertices with low centrality. To predict key regulators of respiratory virus infection, we chose to focus on betweenness and degree centrality, since these have been shown to be of interest in the study of inferred networks derived from high-throughput biological data  [2, [4] [5] [6] [20] [21] [22]. In addition to network topology, genes/proteins that are highly DE are of potential interest. Employing the ''wisdom of crowds'' concept  [14] , we hypothesized that some/all of these prioritization strategies could be combined to produce an optimized ranking, such that the most influential/critical genes Regulatory influence models for each gene cluster of both viruses were applied to comparable datasets from distinct model systems.
Further, the top 100 genes in the SARS-CoV list of regulators includes all three JNK/p38-specific MAPK phosphatases (DUSP8, DUSP10, and DUSP16), which is significant since SARS-CoV is known to up-regulate JNK  [44, 45]. All three DUSP genes are highly up-regulated (data not shown), perhaps representing a negative feedback loop as the host cell attempts to counteract the virus-induced JNK activation. Interestingly, the predicted conserved regulators for SARS-CoV identified the CREB/ATF family members CREB5 and ATF3 as highly ranked. We determined that the best ranking method for the SARS-CoV dataset was a combined DE/hub/bottleneck metric, such that designation as a conserved regulator for SARS-CoV required relatively high differential expression between strains of differing pathogenicity, high network degree ranking, and high network betweenness, in addition to exhibiting conserved behavior between Calu3 and the primary airway epithelial model.
Proteomics Detailed proteomics methodology, including sample preparation, processing and analysis, are provided in Supporting Information S1. Calu3 cells lysates were trypsin digested and fractionated by strong cation exchange (SCX) as previously described  [51, 52]. A novel accurate mass and time (AMT) tag database  [53]  was generated for each sample type by LC-MS/MS analysis  [51, 54]  of each SCX fraction and LC-MS analyses were subsequently performed on each individual unfractionated sample to generate quantitative data using identical chromatographic and electrospray conditions as for LC-MS/MS analyses. For quantitative analyses, the LC system was interfaced to an Exactive mass spectrometer (Thermo Scientific), and the temperature of the heated capillary and the ESI voltage were 250uC and 2.
2 kV, respectively. Data were collected over the mass range 400-2,000 m/z. Quantitative LC-MS datasets were processed using the PRISM Data Analysis system  [55] , which is a series of software tools developed in-house (e. g. Decon2LS  [56]  and VIPER  [57]  freely available at http://ncrr. pnl. gov/software/). Individual steps in this data processing approach are reviewed here  [53]. The peak intensity values (i. e. abundances) for the final peptide identifications were processed in a series of steps using MatLabH R2010b, including quality control  [58] , normalization  [59] , and quantification to protein level  [60]. Comparative statistical analyses of time-matched mock samples with infected samples per sample type were performed using a Dunnett adjusted t-test to assess differences in protein average abundance, and a G-test to assess associations among factors due to the presence/absence of response  [61].
Compendia We made compendia of all data for each virus type, such that data from all HP and LP strains were included. Data were included for each probe where at least one time point demonstrated differential expression with respect to time-matched mocks, using the criteria of a minimum fold change of 2. 0 and maximum FDR-corrected p-value of 0. 05. Compendia were assembled for transcriptome and proteome datasets. To deal with missing values in proteome data, a conservative approach was adapted, in which the missing values of a given experimental condition were filled in with the average value for the replicates present for that condition, only if at least half of the replicates were present in that condition. Proteins for which one or more conditions showed more than half missing values were discarded.
Network Inference Compendia were used as input to the Context Likelihood of Relatedness (CLR) software  [3] , which uses assessment of mutual information to generate a matrix representing the pairwise relationships between all genes. CLR runs were set with parameters: bins = 10; spline = 3. To determine an appropriate CLR cutoff value for incorporation of a gene vertex into the inferred network, we generated influenza networks using multiple cutoffs, calculated betweenness for all vertices in each network, and used several functional gene lists  [23] [24] [25] [26] [27] [28] [29]  to test the topscoring genes for functional enrichment. A CLR threshold of 2. 0 was chosen to generate all subsequent networks.
Edge Validation Transcription factor/target pairs were taken from msigdb subgroup C3:TFT, downloaded from http://www. broadinstitute. org/gsea/msigdb/collections. jsp. All network edges were compared to the database edges to determine overlap, similar to  [3]. To determine the background levels of accidental edge inference, one of the parent vertex columns in each network edge file was scrambled, thus generating a random network based on the same number of vertices and edges. These new edges were then compared with the database edges, and the permutation process was repeated 1000 times. P-values were calculated from z-scores derived from the size of database overlap, and the mean and standard deviation of the overlaps of random networks with the database. Based on a dataset of 400 different transcriptome profiles of HUVEC having been knocked down by siRNA transfection  [19]  available on the NCBI-GEO database via the ascension number GSE27871, we identified the list of significant differentially expressed genes (fold-change$1.
5, p,0. 05) compare to a control condition (''TNFa untreated'' condition) for each knocked-down condition. Moreover, for each gene identified in the influenza or SARS inferred co-expression networks for which a knocked-down profile was available, we identified the list of adjacent genes in the inferred network. Then a p-value representing the statistical significance of the overlap between the differentially expressed genes identified based on the knocked down profiles and the network neighbors was calculated using the right-tailed Fisher's exact test. P-values under. 01 were counted as significant. For both the influenza and SARS-CoV networks, we generated 500 random networks having the same topological structure by randomly permuting the vertices but by keeping the edges fixed. For each generated random network, we performed the same overlap test with the HUVEC dataset as before.
For influenza, we used the VN1203 data as HP, and the Ca04 and NL602 strains as LP. In contrast to the SARS-CoV experiments which were performed with wild type virus alongside mutants within each experiment, influenza strains were all used in separate experiments, thus making determination of fold change significance between strains problematic. We therefore simply took the sum of the absolute value of the HP and LP differences at all time points for each gene. With this approach, the DE behavior of each gene is collapsed into a single value for all temporal stages of the infection process. Since a large number of genes show a response to influenza virus infection in the first 12 hours, we only included these time points in the DE ranking to avoid incorporation of secondary regulatory effects in our analysis.
Network Integration Proteome vertices of edge file entries (from CLR-derived proteome networks, see above) were converted to probe IDs using the biomaRt package in R. For complete integration, converted proteome edge entries were simply added to transcriptome edge entries. These new combined edge files were used to build integrated networks; all vertices in the networks were then assessed for betweenness and degree centrality. For conservative integration, converted proteome edges were identified for which both parent vertices existed in the transcriptome network, but no edge between them existed. These edges were then combined with the transcriptome edges and all other proteome edges were discarded.
Inferelator-based Modeling The Inferelator software (May 2008, version 1. 2) was used to infer critical regulatory influences in our datasets. Required input to the program includes sets of candidate regulators and regulatory targets. Genes known to function as transcription factors (all mouse genes with the GO annotation ''transcription factor activity''; acquired from http://www. informatics. jax. org/) were provided as regulators to the Inferelator software  [32]. To ensure that a rich selection of candidate regulators was available for modeling, the top 500 ranked genes from the betweenness/ degree/DE combined ranking were chosen as additional SARS-CoV regulators, and the top 500 ranked genes from the betweenness/degree were chosen as additional influenza virus regulators. For regulatory targets, merged proteome/transcriptome network from both viruses were used to derive adjacency matrices, which were subsequently used for hierarchical clustering using Ward's minimum variance method  [62].
The average expression profiles of these clusters were used as the regulatory targets. Number of clusters was determined by running the Inferelator with cluster levels from 5 to 50, and the cluster level at which the predictive model could best replicate the observed data was chosen as the optimal cluster level (28 clusters for SARS-CoV, 30 for influenza virus). Inferelator was run with the max. inter. corr. cutoff parameter set to 21, so as to prevent calculation of the effect of dimeric regulators.
Model System Comparison Inferelator models derived from SARS-CoV and influenza virus-infected Calu3 datasets were compared to the observed expression levels in SARS-CoV-infected HAE cells and influenza virus-infected mice, respectively as described in  [18]. To summarize, the cluster expression levels in the HAE and mouse systems were predicted using the assigned regulatory weights of the Inferelator models from Calu3 and the regulator expression levels from the non-Calu3 data. To improve the comparability of the mouse dataset, only days 1 and 2 post-infection were used in the comparison, despite data from days 4 and 7 being also present. In this way we determined the applicability of regulatory influences in Calu3 to other systems. Individual genes were ranked by their cross-prediction (Xpred) score  [18]  to prioritize genes with high correlations in both the relationship between the predicted regulatory mechanisms in the Calu3 model and the target model (HAE or mouse), and between each gene and its parent clusters overall behavior.
Acknowledgments The authors would like to thank  Drs. Lynn Law  and  Thomas Weber  for their helpful review of this manuscript. Portions of this work were performed in the W. R. Wiley Environmental Molecular Science Laboratory , a national scientific user facility sponsored by the  U. S. Department of Energy's Office of Biological and Environmental Research  and located at  Pacific Northwest National Laboratory  (PNNL). PNNL is operated by  Battelle Memorial Institute for the U. S. Department of Energy  under contract  DE-AC05-76RLO-1830.
Data Dissemination Raw microarray data have been deposited in NCBI's Gene Expression Omnibus  [63]  and are accessible through Gene Expression Omnibus (GEO) SuperSeries accession GSE47963 (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc = GSE47963), as well as SubSeries accessions GSE47960 (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc = GSE47960), GSE47961 (http://www. ncbi. nlm. nih. gov/ geo/query/acc. cgi?acc = GSE47961), and GSE47962 (http:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc = GSE47962). Raw proteomics data corresponding to peptide identifications used to populate the AMT tag database are available at the PRoteomics IDEntification (PRIDE) database (http://www. ebi. ac. uk/pride/) under the project name A Systems Biology Approach to Emerging Respiratory Viral Diseases in the PRIDE Public Projects folder and corresponding to PRIDE Accession numbers 19878 (H5N1) 19877-19890.
The raw quantitative proteomics data can be accessed at the PNNL Biological MS Data and Software Distribution Center (http://omics. pnl. gov/) in the Systems Virology Contract Data folder within the Browse Available Data folder. All data sets and associated metadata have been submitted to Virus Pathogen Resource (ViPR, http://www. viprbrc. org). Additional details from this study and similar studies can be accessed through the Systems Virology website (http://www. systemsvirology. org). If these data are used in additional publications please acknowledge the Systems Virology Center, NIAID Contract No. HHSN272200800060C.
TG received funding from the  FungiSex   ANR-09-0064-01  grant (http://urgi. versailles. inra. fr/Projects/FungiSex). DDV received a postdoctoral grant from the  Chaire Modelisation Mathematique et Biodiversite(MMB) of Veolia Environnement-Ecole Polytechnique-Museum national  d'Histoire naturelle-Fondation X (http://www. cmap. polytechnique. fr/chaire-mmb/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Comparative CAZome analysis The translated protein sequences of R. gnavus ATCC 29149 and R. gnavus E1 were compared to the full length sequences derived from the Carbohydrate-Active enZymes (CAZy) database (www. cazy. org; [39] ) using BLAST  [40]. The sequences that had an evalue. 0. 1 were assigned to GH, GT, PL, CE and CBM families using a parallel procedure involving a BLAST search against partial sequences corresponding to individual GH, GT, PL, CE and CBM modules and a HMMer search  [41]  using hidden Markov models built for each CAZy module family  [39]. The counts for each CAZy family of each strain were then compared and the putative function of the proteins of interest was evaluated by alignment with the sequences of biochemically characterized enzymes  [39]. Total RNA extraction from R.
Transcriptional profiling by microarray A total of 1499 60-mer probes were designed for microarray experiments based on R. gnavus ATCC 29149 genome information using Array Designer 3. 0 software (PREMIER Biosoft International, Palo Alto, CA) and printed on Agilent Custom Oligonucleotide Microarrays 8615 k. For sample preparation, the Sau3AI-digested ATCC 29149 genomic DNA (gDNA) and each cDNA were fluorescently labelled using the BioPrimeH Array CGH Genomic Labeling System (Life Technologies Ltd) according to supplier's instructions, and Cy3-dUTP or Cy5-dUTP respectively (GE Healthcare UK Ltd, Little Chalfont, UK). The microarrays were then hybridized overnight at 63uC with Cy5-cDNA/Cy3-gDNA mixtures prepared according to supplier's advice. The slides were scanned on GenePixH 4000B scanner (Molecular Devices, Inc.
, Sunnyvale, CA). Image processing was done with GenePix Pro 6. 0 software (Molecular Devices, Inc. ). Data analysis was performed using GeneSpringGX version 7. 3 software (Agilent Technologies). A per spot and per chip intensitydependent normalization (also called LOWESS normalization) was applied using corrected signal obtained for Cy3-gDNA at 532 nm as a control signal (see Protocol S1 for detailed information).
DNAse-treated RNA was also treated the same way but without addition of the reverse-transcriptase (RT2). Each qPCR reaction (10 mL) was then carried out in triplicates with 1 mL of a 20-fold diluted sample (cDNA or RT2) and 0. 2 mM of each primer, using the QuantiFast SYBR Green PCR kit (Qiagen) according to supplier's advice (except that the combined annealing/extension step was extended to 35 s instead of 30 s). Data obtained with cDNA were analyzed only when C T values above 36 were obtained for the corresponding RT2. For each cDNA sample, the 3 C T values obtained for each gene were averaged. The data were then analyzed using the 2 2DDCT method using housekeeping gyrB (RUMGNA_00867) gene as a reference gene and glucose as a reference condition. For each gene in each condition, the final value of the relative level of transcription (expressed as a fold change in gene transcription compared to glucose) is an average of 2 biological replicates.
H nuclear magnetic resonance analysis 1 H NMR was used to identify the presence, absence, and concentration of several metabolites in R. gnavus growth medium. Supernatant samples were thawed at room temperature and prepared for 1 H NMR spectroscopy by mixing 400 mL of spent medium with 200 mL of phosphate buffer (0. 2MNa2HPO4, 0. 038 M NaH2PO4 [pH 7. 4]) made up in 100% D 2 O and containing 0. 06% sodium azide, and 1. 5 mM DSS (sodium 2,2dimethyl-2-silapentane-5-sulfonate) as a chemical shift reference. The sample was mixed, and 500 mL was transferred into a 5-mm NMR tube for spectral acquisition. The 1 H NMR spectra were recorded at 600 MHz on a Bruker Avance spectrometer (Bruker BioSpin GmbH, Rheinstetten, Germany) running Topspin 2. 0 software and fitted with a cryoprobe and a 60-slot autosampler.
The rate of MU release/min was calculated using data from the linear portion (,20-40 min) of the reaction using Prism 6 (GraphPad Software CA, USA), and corrected by subtracting the ''No enzyme'' control rates. This rate was then divided by the OD600 for the cell culture at this time. 1 H NMR was used to analyze the reaction products. For this, an appropriate amount of R. gnavus ATCC 29149 supernatant (1/5 to 1/10 reaction volume) was incubated with the following substrates in PBS pH 7. 4 at 37uC: 39SL (1. 5 mM); 4MU-Neu5Ac (0. 5 mM) for 2 h to 24 h. The reaction was stopped by denaturing the enzyme by boiling for 20 min, the denatured enzyme and any particulate material was removed by centrifugation at 17 000 g, 4uC for 10 min, and the supernatant was analyzed by 1.
Nucleotide and protein sequence analyses Protein sequence homologies were searched using the blastp software (http://blast. ncbi. nlm. nih. gov/Blast. cgi). SignalP 4. 1 server (http://www. cbs. dtu. dk/services/SignalP/) and CW-PRED software (http://bioinformatics. biol. uoa. gr/CW-PRED/) were used to predict signal peptides and cell-wall anchored proteins, respectively. These analyses were completed by a prediction of the cellular localisation of the protein using PSORTb version 3. 0. 2 (http://www. psort. org/psortb/). Putative transcriptional terminators were predicted in silico using the RNAfold program (http://rna. tbi. univie. ac. at/cgi-bin/RNAfold. cgi). Prediction of the promoters was performed using the BPROM program (http://linux1. softberry. com/berry. phtml?topic = bprom& group = programs&subgroup = gfindb).
R. gnavus ATCC 29149 transcriptomics reveal the importance of a functional nan gene cluster in mucin utilisation To examine the molecular basis underlying host glycan utilisation of R. gnavus ATCC 29149, we then compared the CAZome transcriptome of R. gnavus ATCC 29149 grown on mucin, mucin glycans and HMOs. We used Custom Oligonucleotide Microarrays representing all predicted ORFs encoding CAZymes. Four probes per gene were designed for 96 of 98 CAZyme genes (see Protocol S1 for details) and were printed in duplicate on the array. The specific transcriptional response to growth on a particular glycan was determined after normalization using the signal obtained with genomic DNA hybridization. The level of expression was then compared to a reference dataset of the strain grown in minimal medium with Glc as the sole carbon source (Fig.
Other mucin-specific upregulated genes include a predicted GH2 b-galactosidase (RUMGNA_01638) and a putative GH36 a-galactosidase (RUMGNA_03611), although both seem to be intracellular enzymes because of the lack of an N-terminal signal sequence. qRT-PCR analysis was performed on RNA extracted from R. gnavus ATCC 29149 grown on different sugars. The data were normalized using gyrB (RUMGNA_00867) as a reference gene and expressed as a fold change in gene expression compared to Glc. These experiments revealed the physiological significance of the nan cluster in mucin metabolism (Fig. 5 ). This gene cluster contains 11 open reading frames (ORFs) (Fig. 6 ). The first gene of the cluster encodes a protein of unknown function. The second gene (RUMGNA_02700) encodes a putative sugar isomerase involved in sialic acid catabolism.
To confirm this bioinformatics analysis, RT-PCR analysis using primer sets encompassing the neighboring ORFs (RUMGNA_02696 to RUMGNA_02691) was performed on total RNA extracted from a mid-logarithmic phase culture of R. gnavus ATCC 29149 grown with mucins or 39SL as sole carbon source. The data showed that genes encoding the potential SAT2 transporter RUMGNA_02696gp, GH33 sialidase (RUMG-NA_02694gp), NanE (RUMGNA_02693gp) and NanA (RUMG-NA_02692gp) were co-transcribed. Interestingly, nanK (RUMGNA_02691) also seemed to be co-transcribed with nanA while a transcriptional terminator was predicted between the two genes (Fig. 6B ). Taking together, our data suggest that the 11 genes of the cluster are organized in an operon, which is transcribed from the promoter upstream of the RUMGNA_02701 gene.
Acknowledgments We would like to thank the  Genoscope (Evry, France)  for R. gnavus E1 genome sequencing ( AP05/06  Project# 27 ),  Ange Pujol  ( University of Aix Marseille, France ) for assistance with the bioinformatics analysis,  Jack Dainty  and  Aline Metris  ( IFR ) for help with statistical and DMFit analysis, and  Glycom A/S (Lyngby, Denmark)  for providing some of the HMOs.
The authors gratefully acknowledge the support of the  Biotechnology and Biological Sciences Research Council (BBSRC). EC was a recipient of a Newton International Fellowships-2009. This research was partly funded by the  Royal Society  and the  BBSRC   Institute Strategic Programme   BB/J004529/1 : The  Gut Health and Food Safety ISP. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Fluorescence at the plasmalemma accounted for 66-70%, and cytoplasm for 30-34% of total labeling for Cntn4, -5 and -6 proteins (Fig. 1C ). Co-cultures of transfected HEK293 and MAP2-stained neurons are illustrated in Fig. 1D. Based on our observations, most of the neurons were not in direct contact with other cells. We can therefore assume that neurons elongated randomly without favoring contacts with surrounding cells. To determine whether the amounts of secreted forms of Cntns were proportional to the number of HEK293 cells, we performed Western blot analysis on HEK293 cell extracts and aliquots of culture supernatants. Two cell densities were used (Fig. 1E ). We observed a single band for Cntn4, -5 and -6, at 130 kDa, corresponding to the expected size of these proteins. Our data clearly indicate that the amounts of secreted forms of Cntn4, -5 and -6 were directly related to cell densities, and therefore to the amount of anchored Cntn proteins at the level of cell membranes (Fig.