dataesr/scientific-paragraphs-categorization huggingface dataset:
To our knowledge, such is the scarce repertoire of structures available for bitopic membrane proteins. Specific difficulties in both purification and ordered crystallization can explain this fact. The structural characterization of full-length NccX, the membrane-anchored nickel, cobalt, and cadmium sensor protein of the NccYXH transmembrane signaling complex, also contributes to fill this void in the Protein Data Bank. NccX was efficiently extracted regardless of the detergent used (i. e. DPC, n-dodecyl ␤-D-maltopyranoside, n-octyl ␤-D-glucopyranoside, or tetraethylene glycol monooc-tyl ether (C 8 E 4 )), but only DPC allowed achieving both purification and crystallization. Although widely used in membrane protein investigation  (41, 42) , DPC has been seldom used for crystallization, although DPC micelles can be considered a membrane mimetic of choice for NMR studies because of the number of structures observed  (41).
Acknowledgments-We thank  Eve de Rosny ,  Juliette Trepreau , and  Adeline Veyret  for help during the first steps of purification of NccX. We thank beamline Proxima-1 ( Synchrotron SOLEIL ,  Gif Sur Yvette ) for provision of beamtime and  Pierre Legrand  for data collection, structure determination, and refinement of the reported structure of NccX. We also thank  Dr. P. Fuchs  ( Paris Diderot University ) for providing useful Python scripts for trajectory analyses with Gromacs. Molecular graphics and analyses were performed with the  UCSF Chimera package. Chimera was developed by the  Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco  (supported by  NIGMS ,  National Institutes of Health  Grant  P41-GM103311 ).
Through their pleiotropic actions, statins, fibrates, thiazolidinediones and resveratrol can target multiple mechanisms involved in sepsis. Their actions on mitochondrial function are of interest in a pathological state where bioenergetic failure may play a key role in the development of organ dysfunction. We review these four drug groups as potential adjunctive therapies in sepsis with a particular focus upon mitochondria. Systematic review of clinical and experimental trials was done with a literature search using the PubMed database. Search terms included statins, fibrates, thiazolidinediones, resveratrol, mitochondria, sepsis, peroxisome proliferator-activated receptors, inflammation, oxidative stress and organ dysfunction. With the exception of statins, most of the compelling evidence for the use of these agents in sepsis comes from the experimental literature.
Data collection In the 6-yr-old and 9-yr-old groups, children answered questions during a personal interview with an investigator. For the 12-yr-old children, the questionnaire was self-administrated. Children of all groups were examined while seated facing the examiner either in the school dispensary or in a classroom. For the oral examination, investigators used disposable gloves and mouth mirrors and a portable light. Probing was not permitted but debris could be removed with cotton rolls, if necessary. To count functional units (PFU), examiners used blue articulating paper of 200 mm thickness (Bausch)  [15]. The children were asked to bite on the paper and the number of posterior mandibular teeth that had at least one coloured mark was counted.
Data analysis Data entry was duplicated and errors were corrected on Microsoft ACCESS software before analysis. The statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS, Inc. Chicago, IL, USA, version 16. 0) and Stata software (version 13, Stat/SE, StataCorp, College Station, US). To describe the dental status of Caledonian children (national epidemiological survey), all statistical analyses were conducted separately for each age group as participants were stratified by age. Results were expressed with 95% confidence intervals (CI). According to literature  [16] [17] [18] [19] , mixed models were used, including the school and examiner parameters as random effects  [20]. Indeed, due to the correlation of observations within a school, the usual tests were invalid and inter-cluster variances were inflated.
Statistical analysis Time patterns of gas production were calculated with the data obtained over the 48 h incubations from the first trial and corrected with the actual quantity of samples introduced and actual initial volume of inoculum in order to have the volume of gas produced by 200 mg DM of sample with 30 mL of inoculum. These patterns were fitted according to a Gompertz function  (Huhtanen et al. 2008) , Y ¼ a Â exp ðÀ expð1ÀcÂðtÀlÞÞÞ with Y as the cumulated gas production at time t (mL 200 mg À1 DM), a the potential total gas production, c the fractional rate of gas production (h À1 ), and l the assumed discrete lag phase before the onset of degradation. This model assumed that there was no gas production until t reached the value l. The adjusted values of parameters a, c, and l were obtained with a non linear (NLIN) procedure of  SAS (2002).
The gas production after 16 h and 40 min (GP16) was calculated, because this duration corresponds to the mean duration feed remains in the gastrointestinal tract assuming a fractional outflow rate kp = 0. 06 h À1 , as for in situ data  (Michalet-Doreau et al. 1987). The CP degradation was calculated using the DM degradation for each replicate and the mean CP content in the residue. The least square mean (LSMEANS) values of DM and CP degradation were fitted either with the monomolecular model used by  Ørskov and McDonald (1979) , Y ¼ a þ bð1 À e Àct Þ; model 1 or by the modification of this model proposed by  McDonald (1981) , Y ¼ a þ bð1 À e ðÀcðtÀLÞÞ Þ; model 2 where Y is the degradation at time t in %, a the immediately soluble fraction (%), b the potentially degradable fraction (%), and c the relative rate of degradation of fraction b (h À1 ).
The adjusted values of parameters a, b, and c were obtained with the NLIN procedure of SAS  (2002). The effective degradability (ED) was calculated assuming that the outflow rate of particles (kp) was equal to 0. 06 h À1 (V erit e et al. 1987) according either to the following formula for the first model  (Ørskov and McDonald 1979) , ED ¼ a þ b Â c ðc þ kpÞ or for the second model  (McDonald 1981)  to: ED ¼ a þ bÂcÂexpðÀkpLÞ ðcþkpÞ. Statistical analysis was carried out using PROC GLM (General Linear Model) of SAS 9. 1. (SAS 2002). The model Y ij = l + a i + b j + a i 9 b j + e ij where Y ij is the dependent variable, l is the least square mean, a i the feed effect, b j the grinding effect and a i 9 b j the interaction between the first and second grinding. This was applied to initial pH, buffering capacity, the parameters of in vitro fermentation of the first trial, and the in situ degradation.
Although the pH of PCF medium was higher than that of the other samples, it decreased between 8 and 48 h of incubation contrary to the pH of the media containing fine flours, which remained fairly stable. The ammonia concentration increased during all the experiments regardless of the sample considered and was quite high at 48 h of incubation. The concentration of the soluble carbohydrates was very variable: it was quite high for GPFF after 2 h of incubation, then Data are presented as LSMEANS AE SEM. PCF, pea coarse flour, obtained with a crushing roller with a 2. 5 mm space; GPCF, PCF reground with a screen of 1 mm aperture; PFF, pea fine flour, obtained with hammer mill with a 2 mm screen; GPFF, PFF reground with a screen of 1 mm aperture; F, feed effect (coarse vs. fine); G, regrinding effect; F 9 G, interaction between feed and regrinding effects.
Buffering capacity was determined with the method described by  Giger-Reverdin et al. (2002). Mean values in the same row without a common superscript are significantly different at P < 0. 05. Data are presented as LSMEANS AE SEM. PCF, pea coarse flour, obtained with a crushing roller with a 2. 5 mm space; GPCF, PCF reground with a screen of 1 mm aperture; PFF, pea fine flour, obtained with hammer mill with a 2 mm screen; GPFF, PFF reground with a screen of 1 mm aperture; F, feed effect (coarse vs. fine); G, regrinding effect; F 9 G, interaction between feed and regrinding effects; RSE, residual standard error of the model; GP16, gas production after 16 h and 40 min. Mean values in the same row without a common superscript are significantly different at P < 0. 05. decreased at 8 h, and increased a second time after 48 h.
During this phase of intense fermentation, NH 3 might have been recaptured by microorganisms for their growth  (Chen et al. 1987). With the same in vitro method, a transient decrease in ammonia nitrogen concentration was also observed by  Guzzon et al. (1997)  during the period of active fermentation of cellulose and maize starch. This role for ammonia of intermediate substrate between degradation and assimilation of nitrogen by microorganisms was also emphasized by  Al-Rabbat et al. (1971)  and  Tamminga (1979). It could be also modeled, as shown by the mechanistic model proposed on pea seed fractions data by  Serment and Sauvant (2010). The rate of NH 3 production was more constant and lower for the PCF flour, which degraded more slowly. Ammonia production measured by in vitro gas method and in situ crude protein (CP) degradation.
This study confirmed that a decrease in particle size induced an increase in the in situ degradation of pea. For CP, Michalet-Doreau and Cerneau (1991) indicated that the ED decreased from 94. 7% to 82. 4% as mean particle size increased from 186 to 1032 lm. When various data in the literature about protein effective degradation of protein rich feed were pooled  (Freer and Dove 1984; Michalet-Doreau and Cerneau 1991; Maaroufi et al. 2009) , it appeared that an increase of 1000 lm of mean particle size induced a mean decrease of 14. 1 AE 1. 00% of the protein ED, lower than the 18. 3% found in this study. The in situ results of the fine flours were very close to those of the pea seed in the INRA-AFZ feed table  (Sauvant et al. 2004 ) for the a and b fractions and for the ED. This is because the in situ parameters given in the table were obtained on fine pea flours.
Acknowledgments The authors thank the representatives of the  GIE Euretec II (ARRIVE, CCPA, CETIOM, GUYOMARC'H, ITCF , ONIDOL, SANDERS, SOFIPROTEOL, UCANOR,  UNI-COPA, UNIP)  for advice and support of this study within the  Eureka project Europroteins EU-623. They gratefully acknowledge  J. P. Melcion ,  F. de Monredon ,  B. Giboulot ,  D. Guibert ,  E. Drouet ,  G. Lorand  ( INRA-Nantes, France ), and TECALIMAN for advice, sample preparation, and technical collaboration. They are also thankful to  D. Augerat ,  V. Bontems ,  F. Bourgoin ,  A. Crispin ,  O. Dhumez ,  J-B. Kloster ,  N. Lemaire ,  J-L. Le Pierres ,  F. Mayer ,  P. Poissonnet ,  F. Ternois , and  J. Tessier  ( INRA-AgroParisTech-Paris, France ) for technical assistance and to  N. C. Friggens  for English revision.
We performed two quantitative proteomic studies in parallel using SILAC coupled to mass spectrometry in order to compare the effects of different doses of the two lactoferrin isoforms. The proteome of untreated, delta-lactoferrin expressing and human lactoferrin treated MDA-MB-231 cells were compared. Overall, around 5300 proteins were identified and quantified using the in-house developed MFPaQ software. Among these, expression was increased by 1. 5-fold or more for around 300 proteins in delta-lactoferrin expressing cells and 190 proteins in lactoferrin treated cells. At the same time, about 200 and 40 proteins were found to be downregulated (0-0. 7-fold) in response to delta-lactoferrin and lactoferrin, respectively. Conclusions/Significance: Re-introduction of delta-lactoferrin and lactoferrin expression in MDA-MB-231 mainly leads to modifications of protein profiles involved in processes such as proliferation, apoptosis, oxidative stress, the ubiquitin pathway, translation and mRNA quality control.
LC-MS/MS analysis Each fraction was analyzed by nanoLC-MS/MS using an Ultimate3000 system (Dionex, Amsterdam, The Netherlands) coupled to an LTQ-Orbitrap Velos ETD mass spectrometer (Thermo Fisher Scientific, Bremen, Germany). Five microliters of the sample were loaded on a C18 precolumn (300 mm ID65 mm, Dionex) at 20 mL/min in 5% acetonitrile, 0. 05% trifluoroacetic acid. After 5 min desalting, the precolumn was switched online with in-house packed column (15 cm reversed-phase capillary emitter column: inner diameter 75 mm, ReproSil-Pur C18-AQ, 3 mm resin), equilibrated in 95% solvent A (5% acetonitrile, 0. 2% formic acid) and 5% solvent B (80% acetonitrile, 0. 2% formic acid). Peptides were eluted using a 0 to 50% gradient of solvent B during 105 min at a flow rate of 300 nL/min. The LTQ-Orbitrap was operated in data dependent acquisition mode with the XCalibur software.
Database search and quantitative data analysis Mascot (version 2. 3. 01) was used to automatically extract peak lists from raw files. The following parameters were set for creation of the peak lists: parent ions in the mass range 300-4500, no grouping of MS/MS scans. MS/MS data were searched against human sequences in the public database UniProt which consists of Swiss-Prot and TrEMBL. Carbamidomethylation of cysteine residues was set as a fixed modification and oxidation of methionine residues, protein amino terminal acetylation and 13 C 6 and/or 15 N 4 label on lysine and arginine were set as variable modifications. Specificity of trypsin digestion was set for cleavage after Lys or Arg except before Pro, and two missed trypsin cleavage sites were allowed. The mass tolerances in MS and MS/ MS were set to 5 ppm and 0.
8 Da, respectively, and the instrument setting was specified as ElectroSpray Ionization (ESI)-Trap. Mascot results were parsed with the in-house developed software Mascot File Parsing and Quantification (MFPaQ) version 4. 0  [54] , and protein hits were automatically validated if they satisfied one of the following criteria: identification with at least one top ranking peptide with a Mascot score of more than 50 (p value ,0. 001) or at least two top ranking peptides each with a Mascot score of more than 33 (p value ,0. 05).
Bioinformatics resource All identified proteins were converted into gene names with the database for annotation, visualization and integrated discovery (DAVID) bioinformatics resource  [55, 56]. The PANTHER (protein annotation through evolutionary relationship) classification system  [57]  was also used in our large-scale proteomics experiments. Up-and downregulated proteins were classified into families and subfamilies of shared functions, which were then categorized by molecular function and biological process ontology terms.
Equal amounts of cells from each condition were combined, creating a single sample that was then subjected to two fractionations. First, subcellular fractionation into cytosolic and nuclear fractions was carried out. In a second fractionation proteins in each subcellular fraction were separated by 1D PAGE. The gel was cut into 20 slices, proteins were digested in the gel slices with trypsin and the resulting peptides extracted from each gel slice were analyzed by reversed-phase nanoscale liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS). The peptides were electrosprayed into the source of a linear ion trap-orbitrap mass spectrometer (LTQ-orbitrap velos). Bioinformatic analyses were assessed using MFPAQ software that processes the results of the Mascot search engine and performs protein quantification.
A conservative ratio threshold of 1. 5-fold increase (50% higher than the protein mixing error) or 0. 7-fold decrease (30% lower than the protein mixing error) in protein abundance above basal level in MDA-MB-231 dox-upon DLf or hLf stimulation was considered significant. Our analysis revealed a total of 5030 identified proteins when low doses of hLf or DLf were used (Tables  S2-S3 ). After manual validation taking into account protein identification, the quality of the MS (for quantification) and MS2 spectra (for peptide sequencing) analysis by MFPAQ software, 3 proteins up-regulated in the presence of DLf and 8 proteins up-regulated in presence of hLf with a very high confidence were distinguished (Table  1 ). Moreover, 12 proteins were down-regulated in the presence of DLf and 11 were down-regulated in presence of hLf (Table  S4 ).
S1 ). Further work has to be done to clarify whether hLf directly or indirectly upregulates GGH, CTSZ and heparanase expressions. The inhibition by hLf of the growth of cancer cells contradicts the notion that hLf may favor malignization and increases synthesis of protumoral proteins. Therefore, we performed an invasiveness assay in order to evaluate whether hLf alters the migration potential of MDA-MB-231 cells. Our preliminary results showed that the penetration of MDA-MB-231 cells into reconstituted basement membrane gel using Matrigel was increased by 2. 5-fold in the presence of 50 mg/mL of hLf and by a 3-fold with 500 mg/mL of hLf (data not shown) and even if only 0. 2-0. 3% of the cells are concerned by this process, this could be quite important in terms of invasiveness and formation of S1 G G C A C T -T A/G C [43] DLfRE Skp1 G G C A C T G T A C [13] DLfRE DcpS A G C A C T -T G G [47] DLfRE Bax G G C A C T -T A T [42] SelH G G C A C T G T G G 22815 ENSG00000211450 ALDH18A1 A G C A C T -T A G 2741 ENSG00000059573 RPS9 A G C A C T -T G G 2478 ENSG00000170889 CTSZ G G C A C T -T G G 23392 R** ENSG00000101160 GGH A G C A C T T TG G 23243 ENSG00000137563 MAN2B1 A G C A C T T T G G 21890 ENSG00000104774 HPSE A G C A C T T T G G 23071 ENSG00000173083 GTF2F2 A G C A C T -T G G 24361 ENSG00000188342 GTF2F1 G G C A C T -T G G 22873 ENSG00000125651 UBE2E1 A G C A C T T T G G 23256 R**, 22787 R** ENSG00000170142 CKAP4 G G C A C T G T G T 23569 ENSG00000136026 CKAP4 A G C A C T -T A T 22277 ENSG00000136026 EIF3E G A C A C T -T A T 2210 ENSG00000104408 ANP32B A G C A C T -T G G 23479 ENSG00000136938 ZNF265 G G C A C T G T A T 22578 ENSG00000132485 RTF1 AG C A C T T T G G 24333, 2390 ENSG00000137815 PSMD7 A G C A C T T T G G 22789, 22040 ENSG00000103035 IFIT2 G G C A C T G T G C 23995R** ENSG00000119922 IFIT2 A G C A C T -T A G 23219 ENSG00000119922 IFIT3 A G C A C T T T G G 2593 ENSG00000119917 IFIT1 A T C A C T -T G G 2 24519 R** ENSG00000185745 IFIT1 A G C A C T G T G G 2 24182 R** ENSG00000185745 ISG15 A G C A C T T T G G 23991 ENSG00000187608 IFITM1 G G C A C T -T G G 24435R** ENSG00000185885 IFITM1 A G C A C T -T G C 23279R** ENSG00000185885 *: Location is relative to the transcription start site.
Antiproliferative activity has also been ascribed to IFIT3 expression, which leads to upregulation of P27 and P21 and cell accumulation at the G 1 phase  [74]. Interestingly IFIT1 function and stability are modulated by conjugation to the ubiquitin-like protein ISG15  [75] , which is itself upregulated in DLf expressing cells by 12-fold (Table  S8 ). There is strong evidence that in some cancers immunosurveillance plays an integral role in tumor initiation and growth. Therefore by upregulating IFIT members, DLf may contribute directly or indirectly to an increase in the immune response and host cell protection against tumorigenesis. Further work will be needed to correlate DLf and IFITs expressions with immunoprotection against breast cancer. Since this second SILAC experiment showed very large proteomic changes we carried out analyses using the Database for Annotation, Visualization and Integrated Discovery (DAVID).
The Uniprot accession numbers were uploaded to the DAVID tools and upregulated nuclear and cytosolic proteins were classified by molecular functions and biological process. The classification of the proteins upregulated in the presence of hLf (Fig. 2A ) showed that approximately 60% were involved in the regulation of cellular processes among which 40% of this protein pool was involved in the maintenance of cellular homeostasis with proteins involved in cell signaling, the cell cycle and apoptosis. These data are in accordance with what is known of Lf function  [22]. Interestingly, some of the identified proteins are involved in protein turnover: synthesis and degradation. The classification of the proteins upregulated in the presence of DLf (Fig. 2B ) showed that approximately 90% were involved in the regulation of cellular processes.
First, subcellular fractionation into cytosolic and nuclear fractions was carried out. In a second fractionation proteins in each subcellular fraction were separated by 1D PAGE. The gel was cut into 20 slices, proteins were digested in the gel slices with trypsin and the resulting peptides extracted from each gel slice were analyzed by reversed-phase nanoscale liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS). The peptides were electrosprayed into the source of a linear ion trap-orbitrap mass spectrometer (LTQ-orbitrap velos). Bioinformatic analyses were assessed using MFPAQ software that processes the results of the Mascot search engine and performs protein quantification. SILAC light/medium/heavy ratios were assessed by MFPAQ for protein quantification. (B) The subcellular fractionation for the SILAC-screen was assessed using marker proteins of known localization.
Acknowledgments We would like to thank  Dr. C. Tastet  ( INSERM U908 ,  USTL ,  IFR 147 ,  59655   Villeneuve d'Ascq, France ) for helpful discussions on SILAC labeling of the MDA-MB-231 cells and  Dr. R. J. Pierce  ( CIIL, Institut Pasteur de Lille, France ) for critical reading of this manuscript.
After sample application, extraction plate was washed with 10% MeOH (2 ml). After drying under aspiration, lipid mediators were eluted with 2 ml of MeOH. Prior to LC-MS/MS analysis, samples were evaporated under nitrogen gas and reconstituted in 10 mL of MeOH. LC-MS/MS analyses of prostanoids were performed as previously described in Ref. [35]. Briefly, lipid mediators were separated on a ZorBAX SB-C18 column (2. 1 mm, 50 mm, 1. 8 mm) using Agilent 1290 Infinity HPLC system coupled to an ESI-triple quadruple G6460 mass spectrometer (Agilent Technologies). Data were acquired in Multiple Reaction Monitoring (MRM) mode with optimized conditions (ion optics and collision energy). Peak detection, integration and quantitative analysis were done using Mass Hunter Quantitative analysis software (Agilent Technologies) based on calibration lines built with commercially available prostanoid standards (Cayman Chemicals).
Differential effect of u6 PUFAs on white and brite adipocyte differentiation hMADS cells were differentiated after 17 days into white (rosiglitazone treatment between days 3 and 9, R3e9) or brite (rosiglitazone treatment between days 3 and 9 and then between days 14 and 17, R3e9/14e 17) adipocytes according to our published protocol  [13, 14]  and treated or not with ARA between days 14 and 17. At the end of this treatment, UCP1 mRNA and protein levels were assessed. As shown in Figure  1A , ARA strongly inhibited UCP1 mRNA expression in cells acquiring the brite phenotype. This decrease in UCP1 expression was confirmed at the protein level (Figure  1B ). Of note, using the same protocol, linoleic acid did not modulate UCP1 mRNA expression (data not shown). In contrast to the effect on UCP1 gene expression, ARA treatment did not affect adipogenesis per se.
ACKNOWLEDGMENTS The authors greatly acknowledge of  IRCAN Animal core facility. This work was supported by  CNRS ,  EU   FP7  project  DIABAT  ( HEALTH-F2-2011-278373 ),  French Agence Nationale de la Recherche  ( ANR-10-BLAN-1105  miRBAT),  Nutricia Research Foundation  ("2011e25") and  Société Française de Nutrition  ( 2012 Research Prize ).