dataesr/scientific-paragraphs-categorization huggingface dataset:
It plays an important role in the glycolytic phenotype of tumors  [10]. Forkhead transcription   [1] [2] [3]. Gene networks were inferred using the Reactome FI Cytoscape Plugin  [7]. A total of 182 genes with more than 7 mutations in 602 breast cancer samples were used. Among them, 78 (43%) were mapped in the subnetwork and subsequently clustered into 12 modules, six of which were greater than 4. Finally, modules functions were assessed with pathway enrichment analysis (FDR < 0. 05). Three main pathways can be recognized: the P53 pathway and DNA repair (14 genes/18%) in blue, the PI3K/AKT signaling pathway (13 genes/18%) in pink and the MAPK pathway (8 genes/10%) in green. factors FOXO are downstream of the PI3K/AKT and JUN/MAPK cascades. FOXOs play a major role in the regulation of both the cell cycle and metabolism  [11].
Figure2Subnetwork representation of protein interactions based on 78 mutated genes in 602 breast cancers [1] [2] [3]. Gene networks were inferred using the Reactome FI Cytoscape Plugin [7]. A total of 182 genes with more than 7 mutations in 602 breast cancer samples were used. Among them, 78 (43%) were mapped in the subnetwork and subsequently clustered into 12 modules, six of which were greater than 4. Finally, modules functions were assessed with pathway enrichment analysis (FDR < 0. 05). Three main pathways can be recognized: the P53 pathway and DNA repair (14 genes/18%) in blue, the PI3K/AKT signaling pathway (13 genes/18%) in pink and the MAPK pathway (8 genes/10%) in green.
Acknowledgements Studies on breast cancer in our laboratory are supported by  Inserm ,  Institut Paoli-Calmettes ,  Ligue Nationale Contre le Cancer (Label DB)  and  Institut National du Cancer  ( ACI2007 ,  AO2008 , IVOIRES, and  INCa-DGOS-Inserm   6038 ) Received: 14 May 2013 Accepted: 20 June 2013 Published: 27 June 2013.
Data collection Information on menopausal hormone therapy and other covariates was collected at inclusion in January 1992 and updated at the time of each subsequent questionnaire. For each episode of menopausal hormone therapy, the start date, duration and brand names of the products used were recorded, as described previously. 17 Data on other covariates were also updated during follow-up, except parity, breastfeeding, age at menarche and education level, which were recorded in the baseline questionnaire. Dietary data were collected in June 1993 with use of a validated questionnaire on diet history. 18, 19 holecystectomy and first diagnosis of gallstones, along with their respective dates, were self-reported by the participants. A validation study was performed on 3 random samples: 100 women with self-reported cholecystectomy, 100 who reported having untreated gallstones and 50 with no declaration of gallstone disease or cholecystectomy.
For the 61 026 women with available dietary data, models were also adjusted for intake of polyunsaturated fatty acids, simple sugars, dietary fibre, alcohol and coffee (in quartiles). Covariates were included in the final adjusted model if they were significantly associated with risk of cholecystectomy or if they changed the estimate associated with ever use of menopausal hormone therapy by more than 10%. Missing data for adjustment variables were imputed by means of a multiple imputation procedure. 20 We explored potential interactions between use of menopausal hormone therapy and covariates by including an interaction term in the models. We calculated incident rates of cholecystectomy according to the various regimens of menopausal hormone therapy. Absolute risks were calculated among women who never used menopausal hormone therapy (reference) and in the treatment groups, considering the multivariable models.
Results Overall, 70 928 women were included; the mean follow-up period was 11. 5 years, for a total of 819 889 person-years of follow-up. During the study period, 45 984 (64. 8%) of the women reported ever using menopausal hormone therapy. The baseline characteristics of the participants are shown in Table  1  according to their exposure menopausal hormone therapy. Overall, 78 435 courses of treatment with menopausal hormone therapy were recorded (Table  2 ). The main route of estrogen administration was transdermal. Most of the estrogens were in combined formulations with progestagens. Oral equine estrogens use was infrequent. During follow-up, 2819 incident cholecystectomies were recorded. There was a positive association between risk of cholecystectomy and increasing BMI, higher parity, hypercholesterolemia, diabetes and education level (data not Note: BMI = body mass index, E3N = Étude Épidémiologique de femmes de la Mutuelle Générale de l'Éducation Nationale, IQR = interquartile range, MET-h/wk = metabolic equivalent hours per week, SD = standard deviation.
001), leaner (p < 0. 001) and less likely to have a history of gallstones at baseline (p < 0. 001) than women with no exposure to menopausal hormone therapy (n = 824). They were also somewhat less likely to be nulliparous (p = 0. 09) (data not shown). The absolute risk of cholecystectomy was 49 per 10 000 person-years among women who reported ever using menopausal hormone therapy and 35 per 10 000 person-years among those who reported no exposure. From these rates, over 5 years, about 1 cholecystectomy in excess would be expected in every 150 women using oral estrogen therapy without a progestagen, compared with women not exposed to menopausal hormone therapy.
31 Unlike most reports from cohort studies in which exposure to menopausal hormone therapy is assessed only at baseline, data on the use of menopausal hormone therapy and covariates in the E3N cohort were updated every 2 years during the follow-up period. These updates allowed us to determine the use of menopausal hormone therapy more precisely, reduced misclassification bias and allowed a better adjustment of models. In our study, less than 5% of women had missing data on covariates, and loss to follow-up was uncommon. The size of the cohort, the large number of events and the long follow-up period conferred a high statistical power; however, power was reduced in subgroup analyses, especially when we restricted exposure to a small proportion of women. Thus, the lack of interaction between exposure and other covariates may have been due to reduced statistical power for those analyses.
Limitations Our study has limitations. Exposure to meno pausal hormone therapy and the occurrence of cholecystectomy were self-reported. However, the study population was composed mostly of highly adherent and educated women. To limit record bias regarding use of menopausal hormone therapy, we used a booklet with colour photographs and names of all of the relevant products marketed in France. Regarding the outcome, the validation study showed excellent concordance between self-reports and validated reports of cholecystectomy. Thus, missing data and case misclassification are unlikely to explain our findings. Nevertheless, because the medical indication for cholecystectomy was available only for the small sample of women in the validation study, it is difficult to conclude whether menopausal hormone therapy promotes gallstones per se or its complications.
Funding: The E3N cohort is being studied with the financial support of the Mutuelle générale de l'éducation nationale, the European Community, the Ligue nationale contre le cancer, the Institut Gustave-Roussy, INSERM, and a grant from the World Cancer Research Fund (grant no. 2007/14). The current study was funded by a grant from Assistance publique hôpitaux de Paris. The study sponsors had no role in the design of the study, the collection, analysis or interpretation of data, the writing of the report or the decision to submit the article for publication.
5 U of Taq DNA polymerase (Promega) and 50 pmol of each degenerate primer in a final volume of 25 mL. PCR amplification products were separated on 1% agarose gels. Bands with the expected molecular weight were excised, purified with the Nucleospin Extract kit (Macherey_Nagel) and cloned in pGEM-T vector according to the manufacturer recommendations (Promega, Madison, WI, USA). One to three clones were sequenced (http://www. beckmangenomics. com) and the nucleotide sequences were submitted to the GenBank database (see Table  S1  for accession numbers).
Chimeric consensus elements were finally determined by joining the different PCR fragments using the Cap contig assembly program included in BioEdit  [49]. We developed an efficient strategy that allows characterizing all parts of a full-length LTR-retrotransposon with the fewest possible PCR steps (Figure  S1 ). (1) Detection of fragment of the RT domain using degenerate primers that can be used as an anchor sequence for PCR walking. This anchor sequence is compared with closely related retrotransposons to extrapolate the putative Primer Binding Site (PBS) sequence of the element. (2) Then the 5' edge of the element is obtained using a peculiar 'TE walking' step, we call 'PBS walking', which associates two specific primers designed within the anchor fragment and on the PBS sequence, respectively.
Data Mining To identify Copia and Gypsy elements in various crustacean species, we screened several genomic or transcriptomic databases. Gypsy and Copia sequences from the sequenced genome of D. pulex were obtained either from National Center for Biotechnology Information (http://www. ncbi. nlm. nih. gov) or RepBase (http:// www. girinst. org/server/RepBase/index. php). Transcriptomic sequences from Antarctic krill Euphausia superba  [50]  and those of Euphausia crystallorophias were kindly provided by JY Toullec (Station biologique de Roscoff); those from the amphipod Parhyale hawaiensis were obtained from DOE Joint Genome Institute (http://genome. jgi-psf. org/parha/parha. info. html) and those from the porcelain crab Petrolisthes cinctipes from Tagmount  [51]  (http://sequoia. ucmerced.
edu/PetrolESTes/index. php). We also investigated nucleotide collection (nr/nt), expressed sequence tags (est) and whole-genome shotgun (wgs) databases from the NCBI, the Marine Genomics Project database (http://www. marinegenomics. org) and the Penaeus Genome Database (http://sysbio. iis. sinica. edu. tw/page/). Similarity searches were performed using the TBLASTX program  [52]. To avoid any bias that would favor the detection of GalEa clade elements  [35] , two different Copia elements were used as queries: the Drosophila melanogaster transposable element Copia (X02599. 1) and the chimeric sequence of CoRex2 (herein described). Only the pol sequence of GyRex2 (herein described) was used as query to detect Gypsy elements. To investigate the distribution of GalEa-like elements in all eukaryotes, we performed TBLASTX searches on all NCBI databases using GalEa1 (DQ913005.
1) and Zeco1 (DQ91300) pol sequence as queries. When possible, chimeric sequences of the newly identified GalEa-like elements were designed. In few cases, the sequences from one species do not overlap themselves, we were thus unable to check whether they belong to the same element or not. Subsequently, we tested the GalEa clade affiliation of the newly identified elements using two different approaches: sequences covering the RT/RH domains were included into phylogenic analyses whereas the remaining sequences were classified using similarity searches using BLAST on the Gypsy Database 2. 0  [19]. In the latter case, an element was assigned to the GalEa clade under the two conditions: (i) the five best hits must correspond to the five GalEa1-like elements referenced in the database; and (ii) the difference between the best E-values obtained with GalEa-like and other reference elements must be greater than 1e-10.
Copia and Gypsy Retrotransposons in Crustaceans To estimate the diversity of Copia and Gypsy elements within crustaceans, we PCR-screened 25 decapods and crustacean species using degenerate primers. We additionally looked for retrotransposons in the crustacean genomic and transcriptomic databases using similarity searches. These two complementary approaches led to the identification of 35 Copia and 46 Gypsy elements distributed among 15 and 18 species, respectively (Figure  2 ). Sixteen and twenty-nine of these Copia and Gypsy elements were included in phylogenetic analyses based on the RT/ RH domain and the remaining sequences were classified using a BLAST-based approach (see Materials and Methods and Table  S2 ). Gypsy retrotransposons from crustaceans are divided in several clades (Figure  3 ).
Crustaceans: a Suitable Study System for Transposable Element Dynamics Given their abundance, high level of phylogenetic diversification, huge diversity of environment and life styles, and extended range in C-values with particularly large genomes (460-fold variation from 0. 14 to 64. 62 pg  [22] ), crustaceans appear a worthy focus for comparative study of metazoan genomes at an intermediate scale (i. e. within a subphylum or a class). Crustaceans also appear as one suitable system for a comparative genome evolution study with Hexapoda, one of the most studied group in biology. Indeed, crustaceans are, for example, the second most studied group of ''invertebrates'', after hexapods, for genome size reports (318 species reported in the Genome Size Database, Gregory 2008). However, crustaceans remain greatly underrepresented in genomics.
Indeed, even if CoDpu elements seem relatively abundant in D. pulex  [32] , the absence or rarity of Copia elements could be a genomic feature frequent in crustacean species. For example, none of these retrotransposons have been reported in repetitive element families of P. monodon  [71]. Likewise, we could not identify any Copia elements in the well-sequenced transcriptome of L. vannamei (141030 contigs available in the Penaeus Genome Database: http://sysbio. iis. sinica. edu. tw/page/). This feature is however not restricted to crustacean species since LTR-retrotransposons are known to be less abundant in animals  [10]. Compared to their close relatives, the crustaceans do not differ from the other species. Indeed, de la Chaux and Wagner  [21]  recently reported that Copia elements have a small relative abundance in hexapods, Copia elements being usually much rarer than the Gypsy or Pao/Bel retrotransposons.
Acknowledgments We are grateful to  Stephane Hourdez  and  Nicolas Rabet  for generously providing samples. We thank  Jean Yves Toullec  for freely providing transcriptomic sequences from Antarctic krill and  Nelly Le  ´ger for rimicaris RNA sample. We kindly acknowledge  Angela Atwood-Moore  for English revisions of the manuscript. The authors wish also to thank chief scientists, captains and crews of the oceanographic cruises ( Norfolk1 2001 ,  Norfolk2 2003 ,  BIOSPEEDO 2005 , MoMARETO 2006, MoMARDREAM-Naut 2007 and MESCAL 2010) and the crew of the submersibles (Nautile and  ROV Victor6000 ). We would like to thank two anonymous referees for useful comments on this manuscript.
Strongylocentrotus purpuratus SpDIRS1, biocadmin. otago. ac. nz/fmi/xsl/retrobase/home. xsl. accession numbers of the Copia elements used in phylogenetic analyses are: Drosophila melanogaster 1731, X07656. 1; Xanthias, FJ238509. 1; Arabidopsis thaliana Araco, AC079131. 4; Endovir1-1, AY016208. 1; Drosophila simulans Copia, D10880. 1; Phaeodactylum tricornutum CoDi4. 4, EU432484. 1; CoDi5. 1, EU432486. 1; CoDi6. 4, EU432495. 1; CoDi6. 6, EU432497. 1; CoDi7. 1, EU432499. 1; Thalassiosira pseudonana CoDi5. 5, EU432490. 1; CoDi6. 1, EU432492. 1; CoDi6. 2, EU432493. 1; Zea mays Hopscotch, AC084320. 10; Opie-2, AC104473. 2; Sto-4, AF082133. 1; Nicotiana tabacum Tnt-1, X13777. 1; Tto1, D83003. 1; Volvox carteri Osser, X69552. 1; Oryza longistaminata Retrofit, AH005614. 1; Saccharomyces exiguus Tse1, AJ439547.
1; Saccharomyces cerevisiae Ty4, M94164. 1; Vitis vinifera Vitico1-1, AM465428. 1; Bombyx mori Yokozuna, AB014676. 1. The accession numbers of the Gypsy elements are: D. melanogaster 17. 6, X01472. 1; 297, X03431. 1; Gypsy, M12927. 1; Idefix, AJ009736. 1; Springer, AF364549. 1; Tripneustes gratilla SURL, M75723. 1; Beta vulgaris Beetle1, AJ539424. 1; Schistosoma mansoni Boudicca, AY662653. 1; Colletotrichum gloeospor- ioides Cgret, AF264032. 1 and AF264028. 1; Z. mays Cinful-1, AF049110. 1; CRM, AY129008. 1; Lycopersicon esculentum Galadriel, AF119040. 1; A. thaliana Gimli, AL049655. 2; Magnaporthe grisea Grasshopper, M77661. 1; MGLR3, AF314096. 1; Hydra magnipa- pillata Hydra2-1, NW_002123104. 1; Pinus radiata Ifg7, AJ004945. 1; B. mori Kabuki, AB032718. 1; Mag, X17219. 1; Musa acuminata Monkey, AF143332.
Labeled cRNA was hybridized on Affymetrix GeneChip Human Genome U133A 2. 0 Arrays containing probes for over 18,400 transcripts. Hybridized arrays were stained and washed on a GeneChip Fluidics Station 450 and then scanned on a GeneChip Scanner 3000 7G (Affymetrix). Cell intensity values and probe detection calls were computed from the raw array data using the Affymetrix GeneChip Operating Software (GCOS). Further data processing was performed in the R computing environment using specific packages from the BioConductor software project  [18]. Robust Multi-Array Average (RMA) normalization was applied. Normalized data were filtered based on the Affymetrix detection call, so that only probes that had a Present call in at least one of the arrays were retained. Probes with low intensity values (less than 100) in all arrays were also excluded from statistical analysis.
Data were imported into the MultiExperiment Viewer (MeV) software  [19] , and statistical analysis was performed to detect significantly differentially expressed genes in SCA28 patients versus healthy controls, using two different tests as implemented within the MeV: Rank Product (RP)  [20]  and Significance Analysis of Microarrays (SAM)  [21]. RP was run using 100 permutations and a False Discovery Rate (FDR) of 0. 005%. The SAM test was run using 1000 permutations and a FDR of 3%. Differentially expressed genes were then specifically examined based on their Gene Ontology annotations  [22]  and through the use of the Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources  [23] [24] [25].
Validation of data by real-time RT-PCR Genes to be validated were selected on the basis of three criteria: (i) an expression level higher than 7, (ii) fold changes higher than 1. 5 or lower than 0. 7 in patients vs. controls, and (iii) the potential interest of the gene in SCA28 pathogenesis as inferred by its function, the pathway in which it was involved and/or the functional similarity with known ataxia-related genes. Quantitative real time PCR was used to validate array findings for 11 genes in the same LCLs batches used for microarray expression profiling (see above). The validated genes were then tested using a different RNA extraction from the same subjects (4 patients vs. 6 controls) to obtain a technical replicate. In addition, a third real-time PCR experiment on the same genes was performed on LCLs generated from four new different SCA28 patients obtained later [mutations p.
A negative control was included in each experiment to exclude microbial contamination. After 1 hour incubation at 37°C in the dark the MTT solution was carefully removed, and cells were dissolved in 100 μL of DMSO for 10 minutes at 37°C. Proliferation rate of cells was determined by measuring the optical density (OD) of each sample at 570 nm with an ELISA Microplate Reader Model 680 (Biorad). To confirm growth curves with a second method, independent of mitochondrial function, we used a cytoplasmic membrane-dye (Neuro-DiO, Biotium) whose fluorescence intensity decreases as a consequence of cell divisions. The design of the experiment was the same as that of the MTT assays, and fluorescence was analyzed by a FACS Calibur flow cytometer using CellQuest software (Becton Dickinson, USA).
5 ml of RPMI 10% FCS at the final concentration of 5 × 10 5 / well. At the designed time-points (0, 3 and 24 h), each well was washed with PBS and resuspended in 1 ml of PBS with 0. 1% BSA. One ml of 70% Ethanol was added to each sample for 45 minutes at 4°C, samples were then rinsed with PBS, suspended in 0. 5 ml of PBS with 0. 1% BSA and 5 μl of RNaseA [10 mg/ml], and incubated 30 minutes at 37°C. One μl of PI was added to each sample for 10 minutes at 4°C in the dark, then cells populations were analyzed on a FACS Calibur flow cytometer using CellQuest software (Becton Dickinson).
Analysis of apoptosis Apoptosis was evaluated by fluorescence-activated cell sorting (FACS) after Annexin V/propidium iodide (PI) double staining (Biovision, San Francisco, CA, USA). LCLs from six healthy controls and six patients carrying the p. Thr654Ile (n = 1), p. Met666Arg (n = 1), p. Met666Thr (n = 1), p. Met666Val (n = 2), and p. Gly671Arg (n = 1) mutations were used  [29]. Cells were washed twice with Annexin-Binding Buffer (ABB)  [30] , and incubated for 5 minutes in the dark with: 1) ABB alone; 2) Annexin V-FITC; 3) PI; 4) Annexin V-FITC and PI. Early apoptotic cells were only stained by Annexin V-FITC, whereas late apoptotic or necrotic cells were double-stained by Annexin V-FITC and PI. Cells were analyzed on a FACS Calibur flow cytometer using CellQuest software (Becton Dickinson).
Reactive Oxygen Species evaluation Intracellular Reactive Oxygen Species (ROS) levels were determined by using the fluorescent dye, DCFH-DA, 2,7-dichlorodihydrofluorescein diacetate (5 μM). DCFH reacts with ROS to generate a new highly fluorescent compound, dichlorofluorescein, which can be analyzed by flow cytometry. Cells were incubated with DCFH-DA (5 μM) at 37°C for 30 min, washed twice with PBS, and then measured on a FACS Calibur flow cytometer using CellQuest software (Becton Dickinson). Cells treated with H 2 O 2 (10 μM) were used as the positive control. Three independent experiments were performed.
Whole genome expression analysis in SCA28 lymphoblastoid cells To identify pathways associated with SCA28, we analyzed whole genome expression profiles in four patients' LCLs carrying different missense mutations [p. Thr654Ile (n = 1); p. Met666Thr (n = 1); p. Met666Val (n = 1); p. Gly671Arg (n = 1)], compared to six LCLs from healthy individuals matched for sex, ethnic origin and age. We used the Affymetrix GeneChip Human Genome U133A 2. 0 Arrays, which allowed to screen for 18,400 transcripts including 14,500 wellcharacterized genes. With Rank Product analysis we obtained a list of 76 statistically significant differentially expressed probes in patients vs. controls, 41 of which were up-regulated (Fold Change -FC = 2. 5-10) and 35 down-regulated (FC = 0. 1-0. 3). These probes corresponded to 66 differentially expressed genes (35 up-regulated and 31 downregulated: one gene may be tested with more than one probe) (Figure  1A ) (GEO accession number GSE42406 and Additional file 3).
Microarray analysis did not show any statistically significant difference in the mean expression of AFG3L2. The ratio between the expression of AFG3L2 and his paralog SPG7 was ~1. 2 (9. 3 vs. 7. 7) both in patients and controls, in line with data previously obtained in mice nervous tissues, where AFG3L2 is always more expressed than SPG7  [37]. Hierarchical clustering showed that patients' whole genome-expression profiles gathered together, with the exception of patient P3M (mutation p. Met666Val) whose profile was slightly different (Figure  1A ). We used eleven genes to validate microarray data by real-time RT-PCR (ADCY1, GPX7, FOXO3, TES, CCDC92, BLK, FYN, ATF5, NOTCH2, CYB5R2, and DPYD) in a total of eight SCA28 patients lines. Expression levels were comparable to those obtained by array profiling (Figure  1B ).