dataesr/scientific-paragraphs-categorization huggingface dataset:
An adjustment for hypertension, lipid level, and diabetes, which reflect possible mechanisms by which obesity increases mortality, was made in separate, subsequent steps. For these analyses, hypertension was defined as systolic BP. 140 mmHg, diastolic BP. 90 mmHg, or ongoing antihypertensive treatment, and diabetes was defined as a fasting serum glucose. 126 mg/dL or ongoing treatment. Current smokers were compared with the pooled subgroups of nonsmokers and former smokers. Previous CV events represented previous myocardial infarction or stroke, by self-report. For obesity markers, different models were used, and BMI and WC were further adjusted for each other (WC adjusted for BMI and BMI adjusted for WC). All statistical analyses, including interactions, were performed using SAS 8. 2 statistical software (SAS Institute Inc, Cary, NC, USA).
Sequence assembly Reads were assembled using Trinity  [33]. The longest assembled sequences were referred to as contigs. The reads were then mapped back to contigs with paired-end reads to detect contigs from the same transcript and the distances between these contigs. Finally, sequences were obtained that lacked Ns and could not be extended on either end. Such sequences were defined as unigenes. The predicted amino acid sequences encoded by unigenes were aligned with sequences in protein databases such as NCBI Nr database, the Swiss-Prot Protein database, the KEGG pathway database and the Cluster of Orthologous Groups (COG) database using BLASTx (E-value,0. 00001). Sequence orientations were determined according to the best match in the database. If the results from different databases conflicted with each other, a priority order of Nr, Swiss-Prot, KEGG and COG was followed when deciding the sequence direction of the unigenes.
Sequence annotation Unigene annotations provide functional annotations for all unigenes, along with their expression levels. Functional annotations of unigenes were analyzed using protein sequence similarity, KEGG Pathway, COG and GO analysis. All unigene sequences were against the protein databases (Nr, SwissProt, KEGG, COG) using BLASTx (E-value,0. 0001). Protein function information could be predicted from annotations of the most similar proteins in the databases. The KEGG pathway database records networks of molecular interactions in the cells, and variants of these pathways are specific to particular organisms. COG is a database where orthologous gene products are classified. The assumption is that every protein has evolved from an ancestor protein, and the entire database is built on coding proteins with complete genomes as well as system evolutionary relationships between bacteria, algae and eukaryotes.
All unigenes were aligned to the COG database to predict and classify their possible functions. GO functional annotation was obtained from Nr annotation. GO annotation comprises three ontologies, i. e. , a molecular function, a cellular component and a biological process. The basic GO unit is a GOterm, and every GO-term belongs to a type of ontology. Using Nr annotation, the Blast2GO program was used to obtain GO annotations of all of the unigenes  [35, 36]. Blast2GO has been cited more than 150 times in other reports and is a widely recognized GO annotation program. After obtaining GO annotation for every unigene, Web Gene Ontology Annotation Plot (WEGO) software was used to carry out GO functional classification for all unigenes and to characterize the distribution of gene functions in the species gene functions at the macro level  [37].
Annotation of predicted proteins For annotation, distinct gene sequences were first searched using BLASTx against the non-redundant (Nr) NCBI nucleotide database, with a cut-off E-value of 10 25. Using this approach, 25,239 genes (87. 39% of all distinct sequences) returned an above cut-off BLAST result (Table  S1 ). Table  3  indicates that the proportion of shorter assembled sequences with matches in the Nr database was greater than that in the other databases. A total of 59. 47% of the matches were observed for sequences ranging from 100 to 500 bp, whereas the match efficiency decreased to 21. 00% for those ranging from 500 to 1,000 bp and 6. 51% for sequences longer than 2,000 bp (Table  3 ). GO assignments were used to classify the functions of the predicted C. kiinensis genes. Based on sequence homology, 6,032 sequences were categorized into 51 functional groups (Fig.
The percentages of infected cells and viability were assessed by GFP expression and propidium iodide, respectively, using a FACSCalibur flow cytometer (BD Biosciences). The CELLQuest TM software (BD Biosciences) was used for data acquisition and analysis. For rescue experiments, PANC-1 cells stably expressing HA-tagged, shRNA-resistant PRP4 wild type and PRP4 kinase-dead were established with blasticidin selection (6 g/ml) using the pLENTI9-IRES_BLASTR lentiviral expression system. Stable cell lines were then transduced with pSI-LENT9 shRNA D, and the cells were monitored for viability as described above.
Each individual fraction was desalted and concentrated prior to phosphopeptide enrichment by solid phase extraction using Waters Oasis reversed-phase HLB cartridges (Waters). The eluted samples were lyophilized and reconstituted in a solution containing 80% acetonitrile with 2. 5% TFA. The phosphopeptides were enriched using a TiO 2 affinity column (GL Sciences) following the manufacturer's procedure. The peptide mixture was incubated in the TiO 2 column for 30 min and washed twice with 0. 3 M oxalic acid in 80% acetonitrile with 0. 1% TFA. Finally, the phosphopeptides were eluted with 5% NH 4 OH, and the eluents were dried in a SpeedVac (Thermo Fisher). Mass Spectrometry Analysis and Data Interpretation-LCtandem mass spectrometry (MS/MS) analysis was carried out on an LTQ OrbitrapVelos mass spectrometer (Thermo Scientific) equipped with a Thermo nanospray source (Thermo, San Jose, CA) and coupled with an Eksigent NanoLC Ultra 2D system (AB SCIEX).
Singly charged ions with unassigned charge state were also excluded from MS/MS. The mass spectrometry data were processed using Proteome Discoverer 1. 3 (Thermo Scientific). The MS/MS peak lists were extracted and searched by Mascot against a concatenated forward and reversed version of the Swiss-Prot human database. The precursor and fragment mass tolerances were set to 10 ppm and 0. 5 Da, respectively. Cysteine carbamidomethylation was searched as a fixed modification, whereas N-acetyl protein; N-pyroglutamine; oxidized methionine; phosphorylation of serine, threonine, and tyrosine; and SILAC labels on arginine and lysine were searched as variable modifications. The raw MS files were loaded to Proteome Discoverer 1. 3, and peptides were quantified with the MSQuant software embedded in Proteome Discoverer.
MSQuant calculated the ratio average over the peptide elution time, and the assignments used for quantification were visually displayed and validated. To minimize the false discovery rate, all phosphopeptide identifications were filtered by Mascot score. We accepted peptides based on the criterion that the number of forward hits in the database was at least 200-fold higher than the number of reversed database hits, which gives an estimated false discovery rate of less than 1% (p Ͻ 0. 01). The final filtering criteria at which p was less than 0. 01 were a peptide with Mascot score Ն20 and the peptide should be identified in the correct SILAC form (heavy or light) and contain the correct number of lysine and arginine residues specified by the mass difference observed in the full scan between the SILAC partners.
Water containing 0. 1% (v/v) formic acid was used as eluent A, and acetonitrile containing 0. 1% (v/v) acid formic was used as eluent B with a linear gradient from 5 to 60% B in 25 min and from 50 to 100% B in 3 min. MS/MS analysis was performed in positive ionization mode using an ion spray voltage of 5500 V. Analyst 1. 4. 2 software was used for the instrument control, data acquisition, and data processing. Data were acquired with both Q1 and Q3 at the unit resolution. The Turbo ion spray source was operated with the following parameters: 5. 0-kV capillary voltage, 500 °C source temperature, 20-p. s. i. curtain gas, 35-p. s. i. GS1, and 40-p. s. i. GS2. For the final validation of PRP4 substrate candidates, peptide RDSPPPPAR from PAK4 (MRM transitions 538. 5 3 865. 3 and 538. 5 3 213. 2) was selected for the quantitative measurement as the assay readout.
5 nM to 1 M was preincubated with PRP4 for 30 min at room temperature before the reaction was initiated with PAK4 protein substrate. Compound dilution (3-fold) was prepared in 100% DMSO and diluted 1:100 into the final reaction. After a 60-min incubation at room temperature, the reaction was terminated with formic acid (1% final). Phosphorylation of Ser 104 in PAK4 was quantified by mass spectrometry as described above. IC 50 was calculated by fitting data to a four-parameter logistic equation.
2 M ammonium sulfate, 20 -30% PEG 3350, and 0. 1 M HEPES, pH 7. 0 at 18 °C in the presence or absence of ligand. Crystals were cryoprotected by addition of glycerol or PEG 400. For the reported complexes of PRP4 with ADP, AMPPNP, and Compound A, apo crystals were soaked for longer times in cryoprotectant solution in the presence of 5 mM ligand (final concentration). Data sets were collected at 100 K using at Advanced Photon Source LS-CAT beamlines 21-ID-D and 21-ID-F (equipped with MAR300 CCD MARMosaic 225 detectors, respectively) and processed using HKL2000 by Shamrock Structures, LLC  (17). For all data sets, Matthews coefficient calculation (18) using matthews_coef  (19)  in CCP4i  (20, 21)  established the presence of two molecules per asymmetric unit. Phasing of the apo data set was performed by molecular replacement in Phaser (  22 ) using CLK2 (Protein Data Bank code 3NR9) as molecular replacement attempts using the closest analog, apo-DYRK2 (Protein Data Bank code 3K2L), failed to give a reasonable solution using both Phaser and AMoRe  (23).
Subsequent structures were solved using apo-PRP4 as the starting point. Model building was done using Coot  (24). Refinement was carried out using CNX  (25)  with NCS constraints released in the last refinement cycles (apo) or BUSTER (all complexes)  (26). The final coordinates for all discussed structures have been deposited in the Brookhaven Protein Data Bank with accession codes 4IAN, 4IFC, 4IIR, and 4IJP.
In parallel, we initiated a SILAC-based quantitative global phosphoproteomics profiling experiment and confirmed the resulting candidate list using an AIMS approach. PRP4 binders revealed from the interactome studies were then cross-referenced with the data obtained from the phosphoproteomics profiling effort, resulting in the identification of high confidence novel substrates of PRP4 kinase. These results enabled us to develop both cell-based and biochemical assays to reliably monitor PRP4 enzymatic activity (Fig. 2A ). To carry out the PRP4 global interactome experiment, we first generated a doxycycline-inducible construct expressing HA-tagged hPRP4 and stably integrated it into the HEK293 Flp-In TM cell line (Invitrogen) to generate a HEK293-FlpIn-HA-PRP4 cell line. Upon addition of doxycycline, the expression of HA-PRP4 was robustly induced (Fig.
3C  and supplemental Table 4). A representative example of the identified spectrum for PAK4 is shown in Fig. 3D. To examine the existence of a PRP4 consensus motif, we analyzed a 13-amino acid sequence surrounding the central phosphorylated residue with Motif-X software for the phosphopeptides that were down-regulated more than 2-fold  (29). We found that the consensus sequence for PRP4 kinase was significantly overrepresented by the following motifs: XXS*PXX and XXS*XXE*XX (where X is any amino acid and S* and E* are phosphoacceptors). According to the NetworKIN algorithm  (30) , the Ser-Pro could also represent the consensus motif for MARK1 or CDK1 kinase (Fig. 3E , panel i). However, the -Ser-X-X-Glu sequence was identified as a novel motif and could be unique to PRP4 kinase (Fig. 3E , panel ii).
To further validate candidates obtained from SILAC-based phosphoproteomics profiling, we performed AIMS in the Orbitrap MS system. Data for m/z and charge state were collected based on a user-defined inclusion list, which consists of 153 phosphopeptides selected based on the -fold change and the reliability of the peptides identified from the prior global phosphoproteomics profiling experiment. We performed multiple AIMS replicates on the enriched phosphopeptides and summed the total ion chromatogram for each peptide across replicates. As a result, 29 phosphopeptides that were consistently down-regulated in both AIMS (ϩdoxycycline/Ϫdoxycycline ratio Յ0. 6) and SILAC methods were identified; albeit the extent of down-regulation for each phosphopeptide varied between the two methods (Table  1 ).
The apo data set resolution is 2. 44 Å, and its space group is C2 with cell parameters a ϭ 73. 27 Å, b ϭ 75. 29 Å, c ϭ 148. 00 Å, ␣ ϭ ␥ ϭ 90°, and ␤ ϭ 97°. ADP, AMPPNP, and Compound A complex data sets were processed in P1 space group with cell parameters around a ϭ b ϭ 52 Å, c ϭ 79 Å, ␣ ϭ 103°, ␤ ϭ 105°, and ␥ ϭ 93°and set to 2. 13-, 2. 00-, and 2. 25-Å resolution, respectively. Table  2  summarizes the data collection and refinement statistics. A few regions on the PRP4 construct could not be built in any of our models due to the lack of electron density. These include the initial 12-14 amino acids at the N terminus, loops FIGURE 3. SILAC-based phosphoproteomics profiling for PRP4 substrate identification. A, SILAC experiment flowchart. PANC-1 cells with a doxycycline (DOX)-inducible PRP4 shRNA construct stably integrated were divided into two populations and grown in light or heavy medium.
Cells cultured in heavy medium were treated with doxycycline for 72 h to induce knockdown of endogenous PRP4, whereas those cultured in light medium were left untreated. Both batches of cells were combined in a ratio of 1:1 followed by nuclear protein extraction, protein digestion, affinity enrichment of phosphopeptides by strong cation exchange chromatography and a TiO 2 tip, and LC-MS/MS analysis. Data generated were processed by Proteome Discovery software. B, depletion of endogenous PRP4 by inducible PRP4 shRNA 72 h after doxycycline treatment. C, PRP4 depletion resulted in significant down-regulation of certain phosphopeptides. Most of the identified phosphopeptides maintained an intensity ratio of heavy/light close to 1, indicating minimal changes in phosphorylation. However, some were significantly down-regulated after induced knockdown of PRP4.
Apo To investigate the possibility of generating PRP4-specific inhibitors, we dissected structural differences between PRP4 and its closely related kinases, namely DYRK2 and CLK2, for which structural information is currently available. To this end, we superimposed the structures of PRP4 with those of apo-DYRK2 (Protein Data Bank code 3K2L) and small molecular mass-bound CLK2 (compound, (5Z)-5-(quinolin-6-ylmethylidene)-2-[(thiophen-2-ylmethyl)amino]-1,3-thiazol-4(5H)-one; Protein Data Bank code 3NR9). Subsequently, we calculated the overall root mean square deviation values for all main chains atoms in Molecular Operating Environment (Chemical Computing Group, Montreal, Canada) as 1. 65 Å between apo-PRP4 chain A and DYRK2 chain A and 1. 37 Å between apo-PRP4 chain A and CLK2 chain A  (37).
Pro 769 in PRP4 at this position is highly unusual. The residue preceding the DFG is shown in green. Cys 833 in PRP4 at this position is unusual as well. F, structural alignment between apo-PRP4 (yellow), DYRK2 (red), and CLK2 (orange). Detail of the differences at the G-rich loop is shown. Note that although the entire protein backbone could be built, Gln 695 and Val 697 were modeled as alanine due to the lack of clear electron density for the side chains. G, inhibition of PAK4 phosphorylation by Compound A. The effect of Compound A on PRP4 kinase activity was evaluated in a PAK4 biochemical assay. The assay was performed as described under "Experimental Procedures. " The PAK4 peptide harboring Ser 104 was identified and quantified by Orbitrap mass spectrometry. IC 50 was obtained by fitting data to a four-parameter logistic equation.
5F ). To further explore the feasibility of generating more potent and selective PRP4 kinase inhibitors, a complex between PRP4 and Compound A was examined by co-crystallization. Compound A is a small molecule identified from a high throughput compound screen and shown to be active in MS-based biochemical assay using PAK4 as substrate (IC 50 ϭ 0. 016 M) (Fig. 5G  and supplemental Table  5  for kinase selectivity study) but exhibits low cellular permeability (data not shown). As illustrated in Fig. 5H , this co-crystal structure at 2. 25-Å resolution shows the PRP4 kinase domain in its active conformation with an intact salt bridge between conserved Lys 717 and Glu 732. In addition, the DFG motif adopts an "in" conformation with Asp 834 pointing to the active site. Here, the tip of the Gly-rich loop is well defined as the ligand does not push the Phe 698 side chain away as in the AMPPNP complex.
PRP4 kinase is a large protein that possesses an RS domain at its N terminus. This domain serves as an essential platform for interaction with other RS domain-containing proteins. The potential significance of the PRP4 kinase adaptor function therefore cannot be overlooked. However, it is apparent from the data presented herein that the kinase activity of PRP4 is essential for the regulation of cancer cell growth and survival, indicating the potential interest of PRP4 as a therapeutic target in oncology. In an attempt to rapidly identify physiological substrates of PRP4 kinase, we utilized a SILAC-based methodology followed by AIMS validation to filter through the candidate list. As a result of this effort, we identified 29 potential substrates with robust changes following PRP4 knockdown.
Acknowledgments We acknowledge financial support from the  European Research Council Advanced Grant DYNamo  ( ERC-2010-AdG-267374 ),  Spanish Grants  ( 2010-21282-C02-01  and  PIB2010US-00652 ),  Grupos Consolidados UPV/EHU del Gobierno Vasco  ( IT578-13 ) and  European Commission projects CRONOS  (Grant number  280879-2 CRONOS CP-FP7 ). Computational time was granted by  BSC Red Espanola de Supercomputacion  and  GENCI  (Project number  544 ).
e. , they both reentered the cell cycle and formed microcolonies) and because CATMA microarrays are based on the Col-0 genome, we performed our transcript analyses using Col-0 material. Massive (8984) transcriptomic changes took place during the first week of protoplast culture, corresponding to 5276 different genes being differentially expressed (Table  1 ). There was a remarkable balance between the number of genes that were up-and downregulated in each comparison, except that there was a bias toward downregulation in the Pliv/Pls comparison (Table  1 ). These data are presented in a searchable format to facilitate analysis and sorting of profiles and information (see Supplemental Data Set 1 online). Supplemental Data Set 1 online includes a user-friendly spreadsheet (gene selector) that was designed with advanced Excel functions to extract expression profiles using lists of genes sorted by AGI annotations.
27 and 3. 23 normed frequencies, respectively) and in genes encoding plastid components (4. 23 normed frequency). The Pliv/Pls transcript profile comparison reflected adaptations of the photosynthetic apparatus, cell walls, and chromatin composition in plantlets cultured in vitro. Among the downregulated genes, we identified genes encoding two CELLULOSE SYNTHASE genes (CESA1 and CESA3), one chitinase (AT2G43620), the fasciclinlike protein FLA9, a pectinacetylesterase (AT2G46930), and the HTR12 centromeric histone. Among the 30 upregulated genes, we noticed an enrichment in genes involved in other biological processes and in the response to stress (3. 52 and 2. 99 normed BAR frequencies, respectively) and in several transcription regulators (i. e. , LATE EMBRYOGENESIS ABUNDANT3 and MULTIPROTEIN BRIDGING FACTOR1C), which may be interesting candidates for genes involved in the adaptation to in vitro culture conditions (see Supplemental Data Set 1 online, column AM).
The GLK2 TF, which coordinates photosynthesis; HEMA1, the glutamyl-tRNA reductase gene involved in tetrapyrrole synthesis; and genes that regulate the stability of chloroplastencoded transcripts (HCF152 and 173) were downregulated in protoplasts (see Supplemental Data Set 1 online). However, chloroplast degradation was not complete in protoplasts, and some of their essential cellular roles were preserved. Although six senescence-associated genes (SAG1, 3, 13, 20, 24, and 29) were strongly upregulated in P0, SAG12, which encodes a Cys protease active in chloroplast degradation during late leaf senescence, was not activated in protoplasts. The upregulation of SPERMINE SYNTHASE in P0 could be related to the response to oxidative stress, as the homolog in mouse (Mus musculus) was found to be involved in this process  (Eisenberg et al.
Further investigation of the expression of ribosome transacting factors, such as SWA1  (Shi et al. , 2005)  (absent from the CATMA microarray), that regulate division would facilitate the characterization of RP regulation in our system. Regulation of protein translation also modulates the protein content of a cell during various adaptive responses, with the initiation of translation being the main target of such regulatory mechanisms. In our data set, four translation initiation factor genes were upregulated (C1 and C3; see Supplemental Data Set 1 online). Furthermore, RACK1A (for Receptor for Activated C Protein Kinase1), a component of the plant 40S ribosome subunit that regulates translation  (Chang et al. , 2002; Giavalisco et al. , 2005; Guo et al. , 2011) , clustered in C6, and accordingly was also upregulated at 24 h.
Cell Wall Reestablishment during the First Week of Culture Cell wall reorganization is essential for protoplast survival and adaptation to the external medium. This complex process is regulated by numerous enzymes and relies on interactions with the cytoskeleton, which determines microfibril orientation  (Szymanski and Cosgrove, 2009)  and facilitates the extracellular delivery of polysaccharide precursors via Golgi-derived vesicle trafficking  (Parsons et al. , 2012). As the complete set of genes involved in the elaboration of the cell wall has not yet been established, we selected 285 genes involved in cell wall synthesis based on GO annotations (GO:0071554 and GO:0009664) and the literature (see Supplemental Data Set 1 online, columns BA to BD). Briefly, clustering analysis of 217 cell wall-tagged genes, such as those encoding pectin lyases and plant invertases, revealed that 42% of the genes were excluded from the eight main clusters identified in this study and showed complex profiles (see Supplemental Data Set 1 online).
Progression of Transcriptional Control Based on knowledge of the role of TFs in animal stem cells  (Takahashi and Yamanaka, 2006) , we analyzed TF expression in the protoplast cultures. We extracted lists of TFs and their family annotations from previous reviews  (Mitsuda and Ohme-Takagi, 2009; Lu et al. , 2012). Of the 5276 DEGs in protoplast culture, 500 TFs were deregulated and assigned to different clusters (see Supplemental Data Set 1 online, columns C to J), a high number (193) of which were deregulated in freshly isolated protoplasts (P0). Our data provide an interesting catalog of putative crucial regulators to be tested for functional roles in cell reprogramming. The 61 TFs stably deregulated in C1 and the 41 TFs in C3 may be essential regulators of cell reprogramming. Most TF families were represented.