dataesr/scientific-paragraphs-categorization huggingface dataset:
Peptide derivation with iTRAQ adds an isobaric tag (ϩ304. 2054 Da) to amine groups at pH Ͼ8 (i. e. N-terminal amine and -amine of lysine) and the labeled peptides coming from eight different samples are differentiated in MS/MS spectra by eight reporter ions ranging from 113 to 121. As a consequence, iTRAQ 8-plex is designed to study a maximum of eight samples. In order to increase the number of samples analyzed up to 24 in our case, we split the samples in four iTRAQ series, each containing an internal standard for integration purposes. Peptides estimation with Lowry assay allowed to adjust the peptides/iTRAQ ratio as recommended by the manufacturer (here one unit per 50 g peptide) and derivation efficiency was verified using database query and iTRAQ settled as variable modification. Then it appeared that only full iTRAQ peptides were identified and no tyrosine/ arginine were labeled.
2B ). As a direct consequence, duty cycle for each precursor was dramatically FIG. 2. Proteomic worflow and data acquisition. A, 24 tissue samples were digested with trypsin and purified with TiO 2 chromatography. Unpurified fraction (Total fraction) and purified fraction (TiO 2 fraction) were labeled with iTRAQ -8Plex. B, iTRAQ reporter ions were detected with HCD fragmentation (right) and peptides were identified with CID fragmentation. This example corresponds to the phosphorylated peptide GELGAPEVWGL*SPK (m/z: 747. 74261, z ϭ 3). increased and the number of identified peptides inversely decreased. Within a 2 h LC-MS analysis, an average of 1083 and 360 peptides were identified in iTRAQ series for the total and TiO 2 fractions, respectively. The mixture complexity was obviously unresolved with a simple LC-MS analysis, thereby inducing a low redundancy of identified peptides between iTRAQ series.
Considering the total fractions, 2384 peptides were identified in the 18 tumors, corresponding to 1476 nonredundant peptides, but only 32% (472 peptides) were identified in all the iTRAQ series and 387 were quantified with iTRAQ reporter ions (Fig. 3A , left panel). In the TiO 2 fractions, 806 peptides were identified in the tumors, corresponding to 497 nonredundant peptides whereas 139 peptides (26%) were identified in all iTRAQ series and 104 were quantified with iTRAQ reporter ions (Fig. 3B , left panel). Anyway the 387 and 104 peptides in the total and TiO 2 fractions respectively were quantitatively compared between tumors and nontumors (supplemental Table  S1 ). As iTRAQ results are expressed as ratio, data were processed using the MeV software, a dedicated suite of tools developed for transcriptomic data.
e. post-translational modification, maturated/degraded form), Interestingly, no mixture of peptides from the same protein was observed between up-and down-regulated set. For instance, five peptides from histones were only observed as enriched in nfHCC. In the same manner, 14 peptides from alcohol dehydrogenases were exclusively found as decreased in nfHCC. These observations provided therefore another comprehensive but nonstatistical criterion for peptides validation. A functional protein interaction network was built using the String database, which demonstrated a high connectivity between the proteins overrepresented in nfHCC (Fig. 4B , red nodes). Interestingly when a functional annotation was performed using the Gene Ontology database  (16) , two major functional groups were found highly modified in nfHCC, namely proteins belonging to metabolic pathways (Fig.
5C ). The comparison of both enrichment plots also indicated that the S/P site enrichment in nfHCC was not caused by a bias in the analysis (e. g. dephosphorylation of this site in nontumor samples). To follow up on this observation, we analyzed the enriched phosphopeptide set using the Networkin program that predicts kinase-substrate pairs based on site prediction and experimental databases. Interestingly, five peptides were found to be predicted substrates for Prolinedirected kinases, namely SRRM1, CANX, YBX1, substrates for MAPK1 and LMNA, and NUCKS1, substrates for CDK1 (Fig. 5D ). These results indicated that both MAPK1 and CDK1 FIG. 4. Data analysis of total fraction. A, Hierarchical clustering of peptides significantly over-(red) and under-expressed (green) in tumors compared with nontumors.
B, Functional interaction network based on the String database and over/underrepresented peptides. The red nodes correspond to the overrepresented peptides and the green nodes to those underrepresented in the proteomics analysis. C, Functional interaction network based on the String database and Gene Ontology classification. The blue nodes correspond to proteins involved in metabolic pathways. The purple nodes correspond to proteins involved in the control of protein homeostasis. The yellow nodes correspond to cytoskeletal proteins. The white nodes correspond to histones and the green nodes correspond to proteins involved in translation. might be overactivated in nfHCC, an observation already made for all HCC (not discriminating the etiology)  (18). To test this hypothesis in our nfHCC samples, we evaluated the phosphorylation status of MAPK1/ERK1 (relative to its activa-tion status) in 77 nfHCC (comprising the 18 of the proteomics analysis) using the Alphascreen® technology (Fig.
6D ). Identification of a Kinase-regulated Proteostasis Network in nfHCC-Our proteomics data unveiled the existence of a perturbed proteostasis network comprising the cytosolic proteins HSPA1B, HSP90AA1, HSPA9, UBC, CNDP2, TXN, and VCP, the Endoplasmic Reticulum (ER) luminal proteins, HSP90B1, HSPA5, P4HB, PDIA6, and the ER resident transmembrane lectin Calnexin (CANX), whose function is regulated by phosphorylation  (9, 19, 20)  (Fig. 7A ). The connectivity of this network was high (12 nodes, 35 edges versus 4. 94 expected, Pval ϭ 2. 2e-10), thus suggesting an integrated adaptive mechanism set in place to promote tumor development  (21). To confirm the deregulation of this proteostasis network in nfHCC compared with nontumoral liver, we evaluated the expression of HSP90AA1/GRP94, HSPA5/GRP78 using anti-KDEL antibodies, CANX, and VCP in 18 nfHCC compared with four nontumoral livers using immunoblot.
Collectively, our data unravel the perturbation of the proteostasis network in nfHCC and lead us to propose that its pharmacological targeting, for instance through the inhibition of the proteasome, HSP90 or VCP might represent an interesting strategy either to avoid surgery or to reduce tumor volume to allow surgery on cases that would not have been resected before. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository with the data set identifier PXD001253.
* This work was supported by the  Institut National du Cancer (INCa)  with the  ICGC  project, the  PAIR-CHC  project  NoFLIC  (funded by  INCa and Association pour la recherche contre le Cancer, ARC) , the  French CRB Liver tumors and Tumorotheque CHU Bordeaux. □ S This article contains supplemental Figs. S1 and S2 and Tables  S1  to S5.
Taken together these data suggest that the Perkinsea phylum is a diverse group of parasites infecting a wide range of species such as: molluscs, amphibians and dinoflagellates  [14]. Numerous clone library surveys of eukaryotic diversity in marine waters have now been published (e. g. [17] [18] [19] [20] [21] [22] ) yet only a few Perkinsea sequences have been identified. Specifically, only nine sequences belonging to Perkinsea that are distinct from either Perkinsus or Parvilucifera cluster groups are currently available in GenBank (May-2013). To our knowledge, most of the environmental surveys of marine environments have, however, focused on subsurface or deep chlorophyll maximum (DCM) water column samples, with only a few studies sampling sediments (e. g. [18, 23, 24] ). As such, marine sediments are often thought of as a 'black box' in terms of microbial diversity and function  [25] , lacking in eukaryotic-specific molecular surveys (e.
Processing of sequence data Using 454 pyro-sequencing, we investigated the diversity of Perkinsea in a selection of European marine samples, sequencing the V4 region of the SSU rDNA  [27]  using both rDNA and rRNA as template. A similar DNA-based approach has been used to investigate freshwater Perkinsea  [14]. We obtained sequence data from samples collected in four European coastal sites (Figure  1A ), including sediment and multiple size filtrates from the sub-surface and the DCM water column samples (using a plankton net for the 2000-20 μm fraction and sequential filtration for 3-20 μm and 0. 8-3 μm fractions). All V4 sequence reads (~380 bp) were assigned to eukaryotic taxonomic groups using a custom-built pipeline developed by the BioMarKs consortium (see  [28] ). This analysis identified 271 sequences preliminarily classified as Perkinsea.
The taxonomic affiliation of these sequences was checked using phylogenetic analyses of a SSU rDNA dataset comprising representative alveolate groups (Additional file 1: Figure  S1  and Additional file 2: Table  S1 ) and environmental sequences retrieved from GenBank (Additional file 3: Table  S2 ). Of the 271 sequences initially identified as Perkinsea, alignment based analyses and phylogeny confirmed that 265 individual 454 'tag' sequences were not chimeras  [29]  and branched with known Perkinsea sequences. Moreover, a large number of the 265 sequences were highly similar and so were clustered at 99% identity resulting in 150 unique V4 sequences branching within, or close to, Perkinsea taxonomic groups (Additional file 1: Figure  S1 ). Table  1  summarises the provenance of the sequences sampled and provides information regarding the total % of Perkinsea sequences within each V4 sequence dataset, which ranges from 0.
Analysis of 454 reads of the V4 area SSU and phylogenetic analysis Only reads with exact forward and reverse primer sequences and an estimated sequence error of ≤ 0. 1% were retained for further analysis. Reads were assigned to taxonomic groups by co-clustering of sample sequences with those from a custom-built reference SSU rDNA database PR 2  [38]  truncated to the V4 region. Reads were assigned to the Perkinsea when they were more similar to a reference Perkinsea sequence than to any other sequence in the PR 2 database, in terms of global alignment identity. This process identified 271 sequences tentatively classified as Perkinsea (each sequence has been labelled with a sequence number followed by the sequencing ID, see Additional file 5: Table  S4  for details). All existing Perkinsea SSU rDNA sequences (both environmental and from cultured organisms) plus a selection of 31 published sequences that encompass all the other major Alveolata lineages were recovered from the NCBI non-redundant nucleotide database and assembled into a reference dataset of 67 sequences (Additional file 2: Table  S1  and Additional file 3: Table  S2 ).
The Perkinsea 454 V4 sequences were aligned to the previous reference dataset using Muscle  [39] , as implemented through the multiple alignment-editing program Seaview  [39, 40]. The alignment was then improved manually, with particular attention to the V4 region. Ambiguously aligned characters were masked and excluded from the alignment prior to phylogenetic analysis. A preliminary tree was used to identify long-branch or highly novel sequences that could potentially represent chimerical sequences. Candidate chimerical sequences were investigated further by visual inspection of the alignment according to methods described by Berney and co-authors  [29]. Of the 271 putative Perkinsea 454 tags identified from the Biomarks dataset, 265 marine V4 454 sequences branched with the Perkinsea and were retained for the final analyses (Table  1  and Additional file 5: Table  S4 ).
All 271 sequences are available in the European nucleotide archive (https:// www. ebi. ac. uk/ena) under accession numbers PRJEB5698. We have included the six putative chimeras in the submission so these can be checked historically, these six sequences have the reference numbers 37-0005, 54-0139, 134-0005, 138-0140, 206-0147 and 268-0287. Two datasets were then created, with different alignment masks: 1) a dataset encompassing the complete SSU rDNA sequence alignment and including a wide selection of Alveolata lineages (Alveolata SSU dataset composed of 1,437 positions and 377 sequences) and 2) a second dataset restricted to an alignment mask of the V4 region and focusing only on the Perkinsea phylotypes including sequences from Bråte et al. [14]  (Perkinsea V4 dataset; 330 positions and 351 sequences).
As the 454 sequences only encompassed the V4 region, for the first alignment, all missing positions in the Alveolata SSU dataset were encoded as gaps (consistent with the approach used in  [14] ). Prior to phylogenetic analyses we used the program Modelgenerator v0. 85  [41]  to determine the best model parameters for the two datasets. For the Alveolata SSU dataset a general time reversible model was selected with a discrete gamma distribution of the substitution rates (8 categories) and a proportion of invariable sites of 0. 14 (GTR + Γ + I; gamma distribution shape parameter of 0. 32). For the Perkinsea V4 dataset a GTR + Γ model was selected, with a gamma distribution shape parameter of 0. 32 and 8 rate categories. We then conducted Bayesian analyses using MrBayes v3. 2. 1  [42]. For both datasets we used the covarion parameter and a Γ rate correction with nst = 6 (equivalent to the GTR substitution model).
The chains were run for 5,000,000 generations with two replicate tree searches both with 4 chains with a heat parameter of 2. Trees were sampled every 250 generations. In both analyses the MrBayes runs reached a stationary phase by 500 generation samples, and so the first 500 samples were discarded (as the burnin), and a consensus topology calculated from the remaining trees. For both analyses, the covarion model was compared to the non-covarion via Bayesian model comparison. This should be done using Bayes factors (the ratio of the respective marginal likelihoods for the two models)  [43]. Unfortunately, the high dimensionality of parameter space makes the marginal likelihood term computationally intractable to evaluate directly. Therefore, the simplest, if somewhat imperfect, method of estimating the marginal likelihood is that of the modified  [44]  harmonic mean estimator  [45]  as implemented in the Trace package v1.
4  [46]  using 1,000 bootstrap pseudo-replicates. These analyses demonstrate that the use of covarion parameters produced an improved tree search (Additional file 6: Table  S5 ). For both datasets, support for the tree topology was evaluated by the bootstrap method and using the Bayesian posterior probabilities (PP) from the MrBayes runs  [42]. Bootstrap support values (BV) were estimated using RAxML v7. 0. 3  [47] , with 1,000 pseudo-replicates. For the Perkinsea V4 dataset, we also conducted a LogDet distance analysis  [48]  with 1,000 pseudo-replicates, as implemented in the Seaview  [40]  tree calculation module. This extra analysis was included to account for the possibility of compositional biases in the sequences  [49]. We did not conduct LogDet analysis for the Alveolata SSU dataset because the large number of missing characters resulted in poor bootstrap results (which was not an issue for the likelihood and Bayesian analyses).
Acknowledgements AC is supported by a  Marie Curie Intra-European Fellowship grant  ( FP7-PEOPLE-2011-IEF -299815 PARAFROGS ) and an  EMBO   Long-Term fellowship  ( ATL-1069-2011 ). The work is part of  EU ERA-Net program BiodivERsA , under the project  BioMarKs (Biodiversity of Marine euKaryotes ). TAR is an EMBO Young Investigator and is supported by research grants from the  Gordon and Betty Moore Foundation  (Grant  GBMF3307 ),  NERC  and the  BBSRC. CB thanks  NERC for a Standard Research Grant  ( NE/H009426/1 ). We are grateful to  Laure Guillou  for providing the Parvilucifera rostrata (RCC2800) SSU sequence prior to publication.
Flow Cytometry A primary antibody against CD31 (FITC-conjugated) was purchased from eBioscience (San Diego, California, USA), and unconjugated antibodies against Melan-A/MART-1 and gp100 (clone HMB45) were purchased from DAKO (Glostrup, Denmark). Anti-gp100 and anti-Melan-A/MART-1 antibodies were detected with FITC-conjugated goat anti-mouse secondary antibody (BD Pharmingen, San Jose, California, USA). Dead cells were excluded from the analysis by 7-aminoactinomycin D (7-AAD) staining (eBiosciences). Isotype controls were included in each experiment. For intracellular staining, cells were fixed with 4% paraformaldehyde and permeabilized with 0. 1% Triton X-100 in PBS for 20 minutes. Typically, 30,000 cells were analyzed. The flow cytometric data were acquired with a FACSCalibur or FACSVersa (BD Biosciences) and analyzed using BD FACSuite software.
RNA Extraction and Microarrays Melanospheres were grown in SCM for several weeks until they reached an average size of 300 mm. Cells from the dissociated melanospheres were propagated as monolayers in serum-containing medium for at least 2 weeks prior to harvesting at 90% confluence. Cells were stored in RNAlater solution. Total RNA was extracted using Trizol (Invitrogen Life Technologies, Carlsbad, California, USA) according to the manufacturer's protocol. The RNAs from the melanospheres and their adherent monolayer counterparts were analyzed using the dual-color Human CGH Array G4450A (Agilent Technologies, Santa Clara, California, USA). The data were checked for quality and normalized with no background subtraction. The microarray data related to this paper have been submitted to the Array Express data repository at the European Bioinformatics Institute (http:// www.
C. The classification of differentially expressed genes between monolayers and melanospheres based on their functions (Cellular Components, Molecular Functions and Biological Processes) using PANTHER Classification System database. The percentages were calculated as the numbers of gene hits found for melanospheres or monolayers against total number of hits in each category. doi:10. 1371/journal. pone. 0095157. g001 and the concentrations of DKK1 in the samples were obtained by reducing the data using a four-parameter logistic curve fit.
Microarray Data and Statistical Analysis The microarray data were log 2 transformed for a two-class analysis, and P values were computed (with P values ,0. 05 used as a cutoff threshold) using Rosetta revolver software. Unsupervised hierarchical clustering accurately separated melanospheres from the adherent monolayers. Fold change (FC) analysis was used to identify genes with expression-level differences between the two classes. The volcanoplot was created using Limma software (version 3. 16. 4) with a threshold of FC = 2 and P values ,0. 05. The genes were analyzed with regard to their GO terms by using PANTHER classification system (version 8. 1) (http://www. pantherdb. org). The Significance Analysis of Microarray (SAM) was performed using a two-class t-test in the R program (version 3.
0. 1). The Gene Set Enrichment Analysis (GSEA) of the differentially expressed genes was performed using software downloaded from the Broad Institute (http://www. broadinstitute. org/gsea/index. jsp). Specific biological pathways were defined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (www. genome. jp. /kegg/). A false discovery rate (FDR) correction not exceeding 0. 15 was used as a measure of relevance. In other experiments, the data represent the means 6 SD. Student's t-test was used to determine significant differences between the mean values of the tested parameters. The difference was considered significant if P,0. 05.
2 and P values ,0. 05. A hierarchical clustering of the cells from melanospheres and monolayers (Fig. 1B ), indicated that the microenvironment consistently had a strong effect on gene expression across all of the cell lines. The PANTHER Classification System database  [21]  was used to review the genes with at least twofold differential expression according to their functions (Fig. 1C ). With regard to the Cellular Components category, the monolayers showed an enrichment of genes allocated into the intracellular cluster. In contrast, the melanospheres were mostly enriched with gene transcripts coding for 'protein complexes' and proteins residing in the 'extracellular region', which might partially explain the strong cell-cell interactions in multicellular melanospheres. Interestingly, a cellular component termed 'ribonucleoprotein complex' was only allocated to the monolayers.
According to the PANTHER classification, the Wnt (Wingless-type) pathway was the most overrepresented pathway in the melanospheres, with 11 genes being allocated to this pathway corresponding to 4. 4% of the total gene hits; 18 genes of the Wnt pathway comprised 4. 2% of the gene hits in the monolayers. When a Significance Analysis of Microarray (SAM) was applied to the genes with FC values. 8 and P values ,0. 05, 58 genes appeared to be highly up-regulated (FC from 8. 2 to 48. 3) in the monolayers, and 79 genes were highly up-regulated (FC from 8. 0 to 376. 7) in the melanospheres (Fig. 2A ). The full lists of the affected genes are shown in Tables  S2  and S3  in File S1. The KEGG PATHWAY database and Gene Set Enrichment Analysis (GSEA) were employed to detect biological processes distributed across an entire network of differentially expressed genes.
4A ). The higher expression levels of anti-apoptotic genes as well as RAB27A and GRP143 (Table  S5  in File S1) in melanospheres indicate that the response to many anticancer drugs might be artificially enhanced in monolayers. Genes encoding acid phosphatase 5 (ACP5), a transport protein recently recognized as a prognostic biomarker in primary melanomas  [22] , and T-box 2 (TBX2), a transcription factor that is crucial in embryonic development and in suppressing senescence in melanoma  [23] , were also up-regulated in melano-database and the GSEA tool, 25 genes from the microarray analysis were linked to the Wnt pathway (FDR: 0. 09, P value: 0. 007), and 22 genes were linked to melanogenesis (FDR: 0. 14, P value: 0. 03). The heatmaps compare the expression of the indicated genes in monolayers (a; left panels) or melanospheres (s; right panels).
In monolayers, DKK1 inhibited Wnt signaling by sequestering LRP6. Certain target genes, including DKK1 and CTGF, were up-regulated in monolayers, indicating that other signaling pathways, such as TGFb/TCF4, might contribute to Wnt pathway activity in this microenvironment. B. Activation of MAPK/ERK pathway in melanospheres and monolayers was assessed by monitoring ERK1/2 phosphorylation via western blot analysis. C. Expression of selected Wnt pathway genes was validated using qRT-PCR. The expression level of each gene was normalized to the expression of a reference gene, RPS17. Data are presented as fold change in monolayers versus melanospheres, in which the expression levels of the genes were set as 1. The mean values and SD were calculated from at least 2 experiments performed in triplicates.
D. ELISA was used to measure DKK1 concentrations in the culture media of melanoma cells grown either as monolayers or as melanospheres. Statistical differences were confirmed using the unpaired t-test; P,0. 05 (*), P,0. 01 (**). doi:10. 1371/journal. pone. 0095157. g003 obtained in melanospheres but was substantially reduced in monolayers (Fig. 5A ). The expression of Wnt pathway components was similar in tumor tissue/initial cultures, melanospheres and monolayers, except for DKK1 (Fig. 5B ). DKK1 was up-regulated in monolayers compared with the original tumor or melanospheres, suggesting that alterations in the gene expression profiles generated by serum might be, at least in part, due to suppression of the Wnt pathway. These data suggest that melanospheres closely resemble the original tumors, whereas several properties are modified in monolayers.