dataesr/scientific-paragraphs-categorization huggingface dataset:
Rhythmic binding of paraspeckle proteins on Neat1 RNA We used RIP experiments to examine whether binding of NONO, SFPQ, RBM14 and PSPC1 on Neat1 RNA in GH4C1 cells was rhythmic. As shown in Figure  3C , binding of the four paraspeckleassociated proteins on Neat1 RNA displayed a rhythmic pattern (cosinor fit values given in Figure 3-source data 2). Maximum binding on Neat1 RNA was reached between 6 and 10 hr after the medium change for the four proteins (Figure  3C ). The following source data is available for figure  2:  Source data 1. Sequences of qPCR primers and oligonucleotides. DOI: 10. 7554/eLife. 14837. 005 Figure  3. Rhythmic expression and association of paraspeckle components. (A) Rhythmic expression of two paraspeckle proteins in pituitary GH4C1 cells The expression of PSPC1 and RBM14 is determined by Western Blot analysis over a 30 hr time period.
Each data point (mean ± SD of three independent samples) represents the ratio of the depicted proteins to ATF2 and is expressed relative to the value obtained at ZT 30. Experimental values can be adequately fitted (R 2 >0. 55) with a non-linear cosinor equation in which the period value is set to 24 hr (see also Figure  3 -source data 1). (B) Rhythmic expression of the long noncoding Neat1 RNA. The expression of the lncRNA Neat1 is determined by RT-qPCR over a 40 hr time period. Primers used to allow the detection of both Neat1-1 and Neat1-2. Experimental values (n=4) expressed as a percent of the initial value obtained at ZT 0 can be adequately fitted (R 2 >0. 55) with a non-linear cosinor equation in which the period value is set to 24 hr (see also Figure  3 -source data 1). (C) Rhythmic association of paraspeckle proteins with Neat1 RNA RNA Immuno-Precipitation (RIP) experiments (n=4 for each antibody) are performed over a 30h time period.
IRAlu element induced EGFP protein circadian expression After cell synchronization, EGFP fluorescence was recorded with real-time video microscopy. Individual cells included in the analysis are those that can be monitored during at least 48 consecutive hours and whose EGFP fluorescence intensity is 10% higher than the background. Values obtained in these selected cells were fitted by a cosinor equation. Example of data from two cells that could (right part) or could not (left part) be fitted by cosinor equation were given in Figure  5D. An example of a rhythmic fluorescence pattern over 48 consecutive hours in an individual cell was also given in Video 1. The percent of cells that could be fitted by cosinor equation with a R 2 >0. 5 was then calculated for each cell line. This percent was significantly higher in IRAlu-egfp cell line compared to the Alu-egfp and egfp cell lines (Figure  5D ) allowing to show that insertion of an IRAlu element in the 3'-UTR of egfp mRNA promoted EGFP protein circadian expression.
Association of rhythmic transcripts with paraspeckles Using publicly available datasets, we then tried to evaluate whether known cycling transcripts can be shown to be associated with paraspeckles. To allow comparison between gene lists generated from different species, namely rat and mouse, we used only official gene symbols (3928 genes in our 4268 gene list if we consider only official gene symbols). We first compared the mouse pituitary circadian transcriptome previously published  (Hughes et al. , 2007)  with our data set of Neat1 RNA targets. It appeared that 68 genes out of 362 from the list of pituitary circadian transcripts (18. 8% ) were found to be included in our data set (Figure  6A ). We then tried to assess whether genes known to be post-transcriptional cyclers are associated with paraspeckles.
Contribution of paraspeckle nuclear retention to circadian gene expression Nuclear retention of egfp mRNA in IRAlu-egfp cells is no more cycling when paraspeckles were disrupted assuming that the nuclear retention of the reporter gene by paraspeckles contributes to cycling. To determine whether this paraspeckle impact on rhythmic gene expression occurs 'in vivo' on a much larger scale, we characterized in our pituitary cells the list of the putative Neat1 RNA target genes and we analyzed in publicly available datasets the known cycling transcripts that are included in this Neat1 RNA target list. Whereas it is well known that rhythmic circadian genes are tissue specific and that it is unfavorable to compare datasets obtained in two different species we found a robust overlap of our gene list with circadian genes in the mouse pituitary  (Hughes et al.
Western-blot analysis Total or nuclear protein extracts prepared as previously described  (Becquet et al. , 2001)  from confluent GH4C1 cells grown in 10 cm dishes, were submitted to Western-blot analysis as previously described  (Guillaumond et al. , 2011)  with polyclonal primary antibodies raised against PSPC1 (1:1000, sc-84576, Santa Cruz Biotechnology, Heidelberg, Germany), RBM14 (1 mg/ml, Abcam, Paris, France), ATF2 (1/1000, sc-187, Santa Cruz Biotechnology), GFP (1/1000, A6455, Molecular Probes, Paisley, UK) and monoclonal primary antibody raised against a-Tubulin (1/1000, T 6199, Sigma-Aldrich, Saint-Louis, USA). The ratio of PSPC1 and RBM14 to ATF2, a constitutive transcription factor, and the ratio of GFP to tubulin were determined by densitometry using ImageJ software (National Institutes of Health, USA).
Neat1 RNA pull-down Neat1 RNA pull-down is a hybridization-based strategy that uses complementary oligonucleotides to purify Neat1 RNA together with its targets from reversibly cross-linked extracts. In cross-linked extracts, it is expected that some regions of the RNA will be more accessible for hybridization than others due in particular to secondary structure. To design oligonucleotides that target these regions and then can hybridize specifically to Neat1 RNA, we modeled the secondary structure of Neat1 RNA using bioinformatics (MaxExpect software  [Lu et al. , 2009] ). Two antisense DNA oligonucleotide probes that target accessible regions of the lncRNA Neat1were designed and used for Neat1 RNA specific pull-down (Figure  4 -figure supplement 1). One biotynylated irrelevant probe was used for Neat1 RNA non-specific pull-down.
, 2011; Hart et al. , 2013) , the log2 (FPKM) distribution shows a primary peak of high expression genes, with a long left shoulder of low-expression transcripts. Therefore, only transcripts with FPKM values higher than 1 were taken into account. In order to assess the specificity of the NEAT1 RNA pull-down we crossed the results obtain with the two oligonucleotides (see Neat1 RNA pull-down) to generate the list of transcripts associated to the paraspeckles. All the RNA sequencing data are available at Gene Expression Omnibus (GEO) (accession no. GSE81972).
Multiple-label fluorescence immunocytochemistry GH4C1 cells were grown on glass coverslips coated with poly-ornithine. After incubation in 30% normal blocking serum containing 0. 2% Triton X-100 (30 min), coverslips were transferred to primary antibody or a mixture of primary antibodies (overnight, 4 ˚C) and, after rinsing, to fluorescent secondary antibody or a mixture of fluorescent secondary antibodies (2 hr, 22 ˚C). Nuclei were stained with Hoechst dye (1 mmol/ml, 5 min). All dilutions and rinsing between each step of the procedures were made in Tris buffer added with 1% normal serum. NONO, SFPQ, PSPC1, RBM14 were detected using the respective polyclonal rabbit antibodies: ab45359 (1: 1000) Abcam, ab38148 (1: 1000) Abcam, HPA038904 (1: 1000) Sigma-Aldrich, ab70636 (1: 1000) Abcam. After mounting with PBS containing 50% glycerol, the reacted sections were kept in darkness at 4 ˚C until examination.
An EMCCD camera (Rolera EM-C2, Q-Imaging) was used to collect emitted fluorescence at 530/50 nm. Timelapse recordings of multiple regions in each well were realized over 96 hr with 1 picture every 30 min (Metamorph, Roper Scientific), using a 40X objective. The data were processed for tracking and measuring fluorescence fluctuations of individual cells with the CGE (Circadian Gene Expression) Plugging of ImageJ  (Sage et al. , 2010). Cells retained for analysis fulfilled two criteria: they were detected for at least 48 hr and their mean fluorescent level was 10% higher than mean level of the background.
Figure3. Rhythmic expression and association of paraspeckle components. (A) Rhythmic expression of two paraspeckle proteins in pituitary GH4C1 cells The expression of PSPC1 and RBM14 is determined by Western Blot analysis over a 30 hr time period. Each data point (mean ± SD of three independent samples) represents the ratio of the depicted proteins to ATF2 and is expressed relative to the value obtained at ZT 30. Experimental values can be adequately fitted (R 2 >0. 55) with a non-linear cosinor equation in which the period value is set to 24 hr (see also Figure3-source data 1). (B) Rhythmic expression of the long noncoding Neat1 RNA. The expression of the lncRNA Neat1 is determined by RT-qPCR over a 40 hr time period. Primers used to allow the detection of both Neat1-1 and Neat1-2. Experimental values (n=4) expressed as a percent of the initial value obtained at ZT 0 can be adequately fitted (R 2 >0.
55) with a non-linear cosinor equation in which the period value is set to 24 hr (see also Figure3-source data 1). (C) Rhythmic association of paraspeckle proteins with Neat1 RNA RNA Immuno-Precipitation (RIP) experiments (n=4 for each antibody) are performed over a 30h time period. At each time point, the levels of Neat1 RNA determined after immuno-precipitation by the antibodies directed against NONO, SFPQ, RBM14 and PSPC1 were normalized relative to Neat1 RNA input levels and expressed as a percent of the value obtained at T0. Experimental values can be adequately fitted (R 2 >0. 55) with a non-linear cosinor equation in which the period value is set to 24 hr (see also Figure 3-source data 2). (D-E) Rhythmic fluctuations of paraspeckle number Cells were arrested at four different times after the medium change and processed for FISH of Neat1 RNA.
Acknowledgements We thank  Gustavo Ramos ,  Luis Cauich  and  Jocelyn Champagnon  for statistical advice. We thank  Dr. Emmanuel Faure ,  Dr. Mariana Benitez  and  Dr. Christophe Godin  for their comments and discussion. We also gratefully acknowledge support form PASPA-DGAPA-UNAM and Conacyt grants  221341  and  261225. LM acknowledges the sabbatical scholarships from  PASPA-DGAPA-UNAM  and  CONACYT 251420.
Later, according to phylogenetic studies and for its ability to reduce GShydroquinones, ECM4 was moved to the GHR family  [12]. Resolution of the first GHR structure (Phanerochaete chrysosporium PcGHR1, pdb code 3PPU) revealed unique structural properties  [2]. Its canonical GST fold showed a new dimerization mode by the helical C-terminal domain and a new active site including a triad of tyrosines. These features led to establish a new structural Xi class (GSTX) for these GHRs. Up to now, structures of GHRs from Poplar (PtGHR1)  [13]  and from the bacteria Escherichia coli (EcYqjG) and Sphingobium chlorophenolicum (ScPcpF)  [14]  have been described. In addition, the Xi-class also contained proteins for which GHR activity was not established (PDB entries 3M1G and 4PTS). In this study, we described the X-ray structures of ScECM4 and glutathionylated ScECM4 (ScECM4-SG).
Data collection, processing and refinement ScECM4 and ScECM4-SG crystals were flash-frozen after a quick soaking in the mother liquor containing 20% (v/v) glycerol as a cryoprotectant. X-ray diffraction experiments were carried out at 100 K on the ESRF beamline FIP BM30A (Grenoble, France). ScECM4 and ScECM4-SG crystals diffracted respectively to 1. 45 Å and 1. 68 Å according to the CC 1/2 correlation coefficient  [15]. Data sets were indexed and integrated with XDS  [16]  and scaled with XSCALE  [16]. The data sets were complete to 1. 5 Å and 1. 8 Å resolution for ScECM4 and ScECM4-SG, respectively. The structure of ScECM4 was solved by the molecular replacement method using MOLREP of the CCP4 package  [17]  with the coordinates of PcGHR1 (PDB code 3PPU)  [2]  as the search model. Structures were refined with PHENIX  [18]  and built with COOT  [19].
After the final refinement/building cycle, atoms that were unobserved in the electron density notably for side chains of surface residues were set with a null occupation and with a B-factor fixed to the respective mean B-factor of the structure. Validation of both structures was performed using MolProbity  [20]  and the wwPDB Validation server  [21, 22]. Coordinates and structure factors of ScECM4 and ScECM4-SG have been deposited in the Protein Data Bank under accession codes 5LKB and 5LKD, respectively. Their PDB Validation reports are provided as supplementary materials (S1 and S2 Files, respectively).
Bioinformatics For the structural alignment, atomic coordinates of five distinct Xi GSTs were selected (PDB codes 3PPU, 4USS, 4G0K, 4FQU, 4PTS). The structural alignment was performed with the module Multiseq  [23]  integrated in VMD  [24]  and manually annotated. For the phylogenetic analysis of yeast ECM4-like proteins, the sequence of ScECM4 (accession number NP_013002) was used in BLASTP on the Joint Genome Institute (JGI) Website to query the fungal genome database  [25] , or in BLASTP on the National Center for Biotechnology Information (NCBI) website. Multiple sequence alignment was made using Promals3D  [26]  with the structure of ScECM4 as input. The sequence alignment was manually annotated to highlight the secondary structure element and the putative residues of the active site.
Crystals with the same aspect were obtained by the cocrystallisation method. Three Xray diffraction data sets were collected (ScECM4, ScECM4 soaked with GS-menadione and ScECM4 cocrystallized with GS-menadione) and analyzed for each experiment. Structures of ScECM4, ScECM4 soaked with GS-menadione and ScECM4 cocrystallized with GS-menadione were solved at 1. 45 Å, 1. 68 Å and 1. 72 Å, respectively (Fig  1 ). All crystals were isomorphous and belonged to space group P2 1. The asymmetric unit contained two monomers related by a 2-fold axis. ScECM4 exhibited no GSH in the active site but rather glycerol molecules. Surprisingly, both soaked and cocrystallizedforms with GS-menadione resulted in a clear electron density that was unambiguously interpreted as glutathione covalently bound to C46 of ScECM4 (ScECM4-SG), while no GS-menadione was detected in the electron density (Fig  1 ).
e. disappearance of the yellowish color due to GS-menadione). Assays with decreasing concentrations of GSH (1 to 0. 05 mM) revealed a concomitant decrease of activity (S1 Fig). The need for an excess of glutathione could be explained by the high K m value of ScECM4 for GSH (350 μM previously determined with 600 μM of GS-methylhydroquinone  [1] ). Altogether, these ScECM4 activity measurements stated for reduction of GS-menadione to menadione in presence of glutathione. Earlier, Lam et al. [1]  observed a good activity of ECM4 with the reduced form of GS-menadione (i. e. GS-menadiol). Thus, ScECM4 resembled YqjG from E. coli, GHR1 from poplar and GHR1 from P. chrysosporium in their ability to catalyze the reduction of glutathionyl-(hydro)quinones  [13]. PDB entry 5LKB 5LKD R merge = ∑ hkl ∑ i |I i (hkl) -hI(hkl)i|/∑ hkl ∑ i I i (hkl).
These observations were consistent with the identification of a triad of tyrosine residues proposed to be involved in the catalytic mechanism of GHRs according to the bacterial model EcYqjG  [14]. The structures of ScECM4 and ScECM4-SG raised questions about binding of GS-(hydro) quinones in the active site, and about the entailed catalytic mechanism. Unexpected absence of the menadione moiety in the active site did not allow precise description of the H site. Consequently, our analysis was based on the sole available structure of a complex between GHR and a glutathionylated compound. Overall superimposition of one monomer of EcYqjG in complex with GS-menadione (PDB code 4g0k,  [14] ) onto one monomer of ScECM4-SG resulted in Besides local rearrangements at the side chains of residues F14 and R15 to adapt to the presence of the menadione moiety of the substrate, most residues appeared in a suitable position to receive the substrate (Fig  6 ).
2 is the correlation coefficient of the mean intensities between two random half-sets of data. R work = ∑ hkl ||F obs | -|F calc ||/∑ hkl |F obs |. 5% of reflections were selected for R free calculation. Ligands PDB codes GOL: glycerol, GSH: glutathione. R. m. s. z. : root mean square Z-score. R. s. r. z. : real space refinement Z-score (indicate the fit between residue and electron density derived from experimental data, according to the wwPDB validation server). The molprobity clashscore is the number of serious clashes per 1000 atoms. The molprobity score is a log-weighted combination of the clashscore, percentage Ramachandran not favored and percentage bad sidechain rotamers. Values in parentheses are for highest resolution shell.
Acknowledgments Access to the  'Plateforme de mesures de diffraction X' of the Université de Lorraine  is appreciated. We are grateful to the staff members of the BM30A beamline at  ESRF (Grenoble, France). Financial support was received from  CNRS/University of Lorraine  ( PEPS Mirabelle 2016 ) and the  Laboratory of Excellence ARBRE  ( ANR-11-LABX-0002-01 ).
2 is the correlation coefficient of the mean intensities between two random half-sets of data. R work = ∑ hkl ||F obs | -|F calc ||/∑ hkl |F obs |. 5% of reflections were selected for R free calculation. Ligands PDB codes GOL: glycerol, GSH: glutathione. R. m. s. z. : root mean square Z-score. R. s. r. z. : real space refinement Z-score (indicate the fit between residue and electron density derived from experimental data, according to the wwPDB validation server). The molprobity clashscore is the number of serious clashes per 1000 atoms. The molprobity score is a log-weighted combination of the clashscore, percentage Ramachandran not favored and percentage bad sidechain rotamers. Values in parentheses are for highest resolution shell. doi:10. 1371/journal. pone. 0164678. t001.
Animals Data obtained for 13 Shepherd dogs (German: 1 female, 8 males; Belgian: 1 female, 3 males) over a period of 10 and a half years were used in this study. All of the dogs came from breeding establishments in Hungary (Dunakeszy School of Dog Handlers, Dunakeszy, Hungary) and France (Dogs Company, Mounsempe, France; and La Petite Renardière, Saint-Romain de Jallonias, France), where they were raised and selected for their olfactory abilities. Each dog had its own living box (2m wide × 1. 9m high × 4m long) with an elevated straw-bottomed wooden crate (0. 9m wide × 0. 9m high × 1. 5m long). Dogs had access to two recreation areas (63. 5m 2 ), with water basins and various games, for about 3 hours every day, alone or in pairs. The dogs were trained 5 days per week when possible (from monday to friday), and rested during the weekend.
The present data confirm this attitude, inasmuch as sensitivity was critically dependent on the type of odour presentation during the task and specificity dependent on the dog's breeding (FAs made exclusively by Belgian Shepherds). The fact that the dogs' ability to perform human odour line-ups was significantly higher when the type of odour in the sample and in the line-up was the same (BS/BS and TS/TS) clearly suggests that comparison between two odour samples of the same kind is much easier for dogs than comparison between two samples of different kinds. These two observations were also confirmed by the fact that all 8 False Alarms observed in the 18,127 trials in continuous training were obtained with the TS/BS combination and in Belgian Shepherds. BS and TS likely consist of mixtures of various odorant compounds (body molecules + distractors) present in different proportions.
Acknowledgments The authors gratefully acknowledge the contribution of  Olivier Thiery ,  Vincent Bellido ,  Cynthia Pillard  and  Benjamin Henry  for animal care and collection of data and all trainers, handlers and canines that took part in the dog trial. Authors also want to thank  Daniel Grignon  who has founded the group of odorologie at the  DTSP  and  Elvire Arrighi  for helpful comments on the manuscript.
NB: non-stinging ants are thought to contain mostly non-peptidic venom components, and are therefore not included. iii The toxin name is then followed by an uppercase letter that indicates the genus of the ant and a lowercase letter which identifies the species of the ant from which it was isolated. An additional one or two lowercase letters may be required to distinguish species with the same first letters. Due to several taxonomic revisions concerning ants, their species names are often subject to modifications; therefore, all ant venom studies should follow the world 1 s largest online ant database AntCat  [6]  when defining the most current species name. iv Finally, an alpha-numerical code will be used to separate different structural classes of peptides based on their molecular scaffold and amino acid sequences.
Identified Proteins with Unknown Functions Due to the lack of proteomic data, a recurring issue with ant venom proteomic studies is the abundance of unassigned and unannotated predicted proteins in database searches. According to published venom gland transcriptomes  [69, 85, 91] , there are thousands of unique hypothetical proteins which could not be assigned to any biological function or previously described protein through sequence searches. Such proteins could include unique venom toxins that could be investigated as potential insecticidal or antimicrobial drugs.
5 for 5 min at 30 °C. The active pH ranges of these enzymes overlap in the pH 6 7 region, and so pH 6. 5 was selected for all of these experiments. Measured responses were oil extraction yield, protein dissolution, and non lipid material dissolution. Trials for the full factorial design experiment were not replicated, while all other tri als reported were made in triplicate. Error estimation for analysis of variance (carried out using JMP 7 software from SAS Institute, Inc. Cary, NC) was based on the assumption of interactions of an order higher than two, being nonsignificant.
Extruded meal Suntlower kernels were extruded at bench scale with a single screw press extruder. The composition of the extruded meal used in ait experiments was determined as 9. 0% (±0. 1) moisture, 20. 6% (±0. 1) oil (dry basis), and 30. 1% (±0. 4) protein (dry basis). Based on the mass of oil expelled during extrusion, the oil content of the entire seed (kemel plus huit) prior to extrusion was 44% and oil extraction yield was 68%. The mass weighted particle size distribution profile of the extruded meal has been determined (data not shown). The only fraction where hulls were not clearly visible was the smallest one, <0. 25 mm. The largest fraction appeared to be mostly hulls, with the other fractions containing a mix of seed particles and huit. The high oil content of the extruded meal caused considerable dumping, making sieve separation ineffective for the smaller par ticle size ranges.
Acknowledgements The authors would like to thank the  USDA  CREES Grants # 2005 34432 1406  and  2006 34432 17128 , the  Iowa State University Plant Science Institute , and the  US Department of Education Renewable Resources and Clean Technology  grant for funding this research. We would also like to thank the  Centre de Microscopie Electronique Appliquée in Toulouse, France , as well as Tracey Pep per and  Randall DenAdel  at the  NanoImaging Facility at Iowa State University  for the microscopy.