dataesr/scientific-paragraphs-categorization huggingface dataset:
Pyrosequencing, read assembly and ORF detection Fosmid pyrosequencing was performed using a 454 GS FLX System (454 Life Science, CT) by the INRA Genomic Platform (Auzeville, France). Read assembly was performed using CAP3 software  [72]. Contigs showing a sequence length ,1, 000 bp and a sequencing depth ,8 were removed. Remaining contigs were cleaned from the vector pCC1FOS sequence using Crossmatch (http://bozeman. mbt. washington. edu/phredphrapconsed. html). Open reading frames (ORF) of at least 20 amino acids were predicted using MetaGene  [73]. Annotated contig sequences were deposited in European Nucleotide Archive under accession numbers: HE663537 and HE717006 to HE717020 (Table  S2 ).
ORF analysis CAZyme encoding genes were identified by BLAST analysis of the predicted ORFs against the functional modules of glycoside hydrolases, polysaccharide lyases, carbohydrate esterases, carbohydrate-binding modules and glycosyltransferases included in the CAZy database (http://www. cazy. org) using a cut-off E-value of 7N10 26 followed by visual inspection and alignment with known CAZy families. Functions of other ORFs were inferred and manually annotated, based on BLASTX analysis against the NCBI non redundant and environmental database (E-value ,10 28 , identity. 35%, query length coverage $50%). ORFs were assigned to clusters of orthologous groups of proteins (COGs) using RPS-BLAST analysis against the COG database (E-values #10 28 ). Finally, gene occurrence in the human gut microbiota was determined by TBLASTN comparisons of the predicted ORFs with the metagenomic data sets available, from fecal sampling of 162 subjects of the MetaHIT cohort (http://www.
bork. embl. de/ ˜arumugam/Qin_et_al_2010/;http://www. bork. embl. de/Docu/Arumugam_et_al_2011/data/genes/; [5, 69] ); of 139 U. S. subjects from the NIH-HMP cohort (http://www. hmpdacc. org/HMASM/; [70]  and of the 110 subjects from different cultures, ages and families (http://metagenomics. anl. gov/projects/98;  [43]  (E-value = 0, identity $90% and = 100%). Contig taxonomic assignment was based on ORF sequence similarity with sequenced genomes, using BLASTX analysis against the non-redundant NCBI database (E-value #10 28 , identity $90%, query length coverage $50%). Contigs were assigned to a class, genus or species only if at least 50% of the ORFs were assigned to the same organism. Contigs containing ORFs assigned to different classes were not assigned. The most probable common ancestor of the organism from which the nonassignable contigs came from was retrieved using MEGAN v3.
This work was carried out with the financial support of  Institut National de la Recherche Agronomique (INRA) , and of the  French National Agency for Research , in the frame of the  ANR/DEDD/PNRA/PROJ/200206-01-01  project. ICEO is supported by grants from the  Region Midi-Pyrenees, France , the  European Regional Development Fund , and  INRA, France. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Therefore, we proposed 29 sets of reference genes that differed depending on the tissue and/or species. As examples of the 29 sets, EIF3K, TOP2B and UXT were proposed as the most stable reference genes in bovine AT; UXT, EIF3K and RPLP0 were the most stable reference genes in bovine and caprine AT. The optimal number of reference genes for data normalisation was 3 for 27 of the proposed 29 sets. In two of the 29 sets, four to five reference genes were necessary for data normalisation when the number of studied tissues was increased. For example, UXT, EIF3K, TBP, TOP2B and CLN3 were required for data normalisation in bovine mammary gland, AT, muscle and liver. We have evaluated some of our proposed sets of reference genes for the normalisation of CD36 gene expression. Normalisation using the three most stable reference genes has revealed downregulation of CD36 gene expression in bovine mammary gland by a concentrate-based diet that is supplemented with sunflower oil and upregulation of CD36 gene expression in caprine liver by including a rapidly degradable starch in the diet.
We verified the lack of DNA contamination by a PCR amplification using b-actin primers flanking an intron (F 5 0 -GAC TAC CTC ATG AAG ATC CTC-3 0 and R 5 0 -CGG ATG TCC ACG TCA CAC TTC-3 0 ). For previously published primers (Table  1 ) and primers that were designed for this study (i. e. CLN3 and TOP2B; Table  1 ), the specificity against the gene of interest was analysed by a dissociation curve (Supplementary Figure  2 ), which is indicative of a single amplicon in bovine and caprine, after the RT-qPCR experiment. In addition, the designed primers for this study were submitted to a BLAST search against the human genomic plus transcript, mouse genomic plus transcript, non-human non-mouse expressed sequence tags and reference RNA sequence databases that are deposited at the National Center for Biotechnology (http:// www.
Analytical protocol for reference gene selection To determine the most stable genes and the optimal number of genes that should be used for normalisation, relative mRNA amounts were entered directly into the Excel Macro geNorm analysed following the procedures of  Vandesompele et al. (2002). The application geNorm is now part of qBASE plus (Biogazelle, Ghent, Belgium). The gene expression stability (M) value for each gene and the number of reference genes that should be used to calculate the NF were then calculated using geNorm. Briefly, M was determined as the average, pair-wise variation of each gene with all other reference genes. Genes with the lowest M values have the most stable abundance regardless of the experimental conditions or cell type. Using the software, the optimal number of reference genes that should be used for normalisation was calculated as the pair-wise variation (V) between the NF that was obtained with n genes (NF n ) and the NF that was obtained using n 1 1 genes (NF n 1 1 ).
Discussion No consensus on the identity of suitable reference genes for normalising gene expression abundances in any ruminant species and/or ruminant tissues exists. The present study proposed 29 sets of reference genes that could be used for gene expression data normalisation depending on the ruminant tissues (e. g. AT, muscle, mammary gland and liver) and/or the ruminant species (bovine or caprine) under investigation. The relevance of the 29 sets of reference genes that we proposed could be checked by comparing some of our sets to those that were previously published for ruminant tissues. However, such comparisons were difficult because of the differences between the studies in the reference genes that have been tested. Nevertheless, some of the most stable reference genes that we proposed in our sets were also previously proposed as reference genes in ruminant tissues.
Indeed, we proposed EIF3K, UXT and RPLP0, and  Kadegowda et al. (2009)  proposed EIF3K, UXT and GPR175 as the most stable reference genes in mammary gland of lactating cows. The stability of GPR175 and RPLP0 were not tested in either study. EIF3K and UXT could be proposed as suitable reference genes in bovine mammary gland because they were ranked among the most stable genes in these two independent studies. In the liver of bulls that differed by age and breed,  Lisowski et al. (2008)  proposed TBP as the most stable gene among the six tested genes, which agreed with the ranking of TBP among the three most stable genes in our study. Wang et al. (2005)  proposed RPLP0 as the most stable reference by comparing it with GAPDH, 18S RNA, SDHA in bovine muscle. Interestingly, RPLP0 (with PPIA and CLN3) was one of the genes that we proposed to use for data normalisation in bovine muscle.
Recently,  Saremi et al. (2012)  proposed EIF3K among the most stable gene for data normalisation in bovine AT, and mammary gland, as in our study. Some agreement between a subset of our results with those from other studies suggests that our 29 sets of reference genes are reliable for data normalisation in AT, muscle, liver and mammary gland from bovine and/or caprine. Figure  2  CD36 relative expression in bovine mammary gland and caprine liver that was normalised to the most or least stable reference genes. The abundance of CD36 was assayed in mammary gland from bovine that were iso-energetically fed with hay (white bars) or concentrate plus sunflower oil (dark bars) and normalised with the geometric means of the abundance of EIF3K, UXT2, RPLP0 (which were the most stable) or TBP, TOBP2, CLN3 (which were the least stable).
e. 36 combinations of two genes) for co-expression or co-citation. We checked co-expressions and co-abundances of the pair-wise reference genes by using all transcriptomic data sets that were stored in the Gene Expression Omnibus (GEO)-curated repository (http:// www. ncbi. nlm. nih. gov/gds). No co-identification and, thus, no co-expression for the pair-wise reference genes have been currently reported for any of the data sets. Subsequently, we checked co-citations of the pair-wise reference genes in PubMed (http://www. ncbi. nlm. nih. gov/pubmed) to determine if any co-regulations had been reported. We did not find co-citations for 25 of the 36 combinations of pairwise reference genes. For the remaining 11 combinations, we found 1 to 29 co-citations of two reference genes in previous publications.
01 125 98 Kadegowda et al. transcript R1: gTgTCTgggACCACTgTgTCAA (2009) F2:TgTggCCCTTggATATggTT 24. 12 101 100 Bionaz and Loor R2:ggTTgTCgCTgAgCTCTgTg (2007) CD36 Platelet glycoprotein 4 (fatty Lipid transport NM_174010 F:ACAgATgTggCTTgAgCgTg 20. 13 186 89 Bernard et al. acid translocase) R:ACTgggTCTgTgTTTTgCAgg (2012) 1 Biological processes were provided by the Gene Ontology Annotation (UniProtKB-GOA) Database http://www. ebi. ac. uk/GOA/ and the David database http://david. abcc. ncifcrf. gov according to Gene Ontology (search for GO done in January 2012). 2.
aegypti CYP6Z8 substrate selectivity and its ability to metabolize insecticides. For this purpose, a yeast expression system allowing the co-expression of any mosquito microsomal P450 along with its associated CPR was developed. Using this system, we obtained a functional microsomal membrane complex of CYP6Z8 and Ae. aegypti CPR, which was used for in vitro metabolism assays. Our data indicate that although CYP6Z8 metabolizes various substrates, it is not capable of metabolizing most insecticides. However, our data reveal that CYP6Z8 and CYP6Z2, an anopheline orthologue of CYP6Z8, are likely to play a pivotal role in the clearance of pyrethroid insecticides via further catabolism of pyrethroid derivatives obtained by the action of carboxylesterases. This is significant from an operational vector control perspective as it is the first direct evidence that secondary metabolism of insecticides pyrethroids by P450s is linked to resistance.
Cloning and heterologous expression of Ae. aegypti Cyt b5 The full cDNA sequence of Ae. aegypti Cyt b5 (VectorBase accession number AAEL010017) was amplified by PCR using high-fidelity Taq Expand TM (Roche Applied Science) and the following primers: forward, 5 -ATGTCGGAAGTGAAAACCT-TCTCGC-3 , and reverse, 5 -GTTAGCAGGTTACTGAGTAA-AGTAGAAC-3. The amplification product was purified, cloned and sequenced. BamHI and EcoRI restriction sites were inserted by PCR using the following primers: forward, 5 -AAGGATCC-AAAATGGCTGAAGTGAAAACC-3 (start codon underlined), and reverse, 5 -GCCGAATTCTTTACTGAGTAAAGTAGAAC-C-3. The fragment was purified, cloned into the pET22b expression vector (Novagen) and resequenced. Protein expression was performed in Escherichia coli Rosetta 2 (DE3) cells as described previously  [30].
Cloning of CYP6Z8 and Ae. aegypti CPR for expression in yeast The full cDNA sequence encoding Ae. aegypti CYP6Z8 (VectorBase accession number AAEL009131-RA) was amplified by PCR using high-fidelity Taq Expand TM (Roche Applied Science) and the following primers: forward, 5 -GCAAAGAGTT-CAAAATGGTCAT-3 , and reverse, 5 -GCACAAGTTCTCTA-TTCAGC-3 (start and stop codons underlined). A similar procedure was used to amplify AeCPR (Ae. aegypti CPR; VectorBase accession number AAEL003349-RA) using the following primers: forward, 5 -ATGGACGCACAGACGGAAC-3 , and reverse, 5 -TTTCAGCGGGAGATGGATTAAC-3 (start and stop codons underlined). Both amplicons were fully sequenced and the corresponding synthetic gene was synthesized by GeneCust with optimization for yeast expression (codon usage, mRNA structure, GC content, and insertion of BamH1 and EcoRI sites at 5 and 3 ends respectively).
Induction of CYP6Z8 by hydrolysed pyrethroid metabolites The induction of CYP6Z8 by PBAlc, PBAld (3phenoxybenzaldehyde) and PBA (3-phenoxybenzoic acid) was investigated by exposing third stage larvae for 24 h to 25, 250 and 2500 nM of each compound and measuring the CYP6Z8 transcription level by reverse transcription followed by quantitative real-time PCR (RT-qPCR). Experimental procedures used for RNA extractions, reverse transcription, real-time PCR and primers used for qPCR are described in Poupardin et al. [27]. Data analysis was performed according to the C T method taking into account PCR efficiency  [37]  and using the housekeeping gene encoding the ribosomal protein L8 (AeRPL8, GenBank ® accession no DQ440262). Three independent biological replicates using different egg batches were performed and results were expressed as the mean transcription ratio relative to controls (unexposed larvae).
In silico substrate docking 3D modelling The molecular models of CYP6Z8 and other CYP6Zs were created based on the crystal structure of CYP3A4 (  [38] ; PDB code 1TQN), currently the most homologous protein with a known structure, with 28 % identity. Docking studies were carried out using GOLD v3. 1 with the ChemScore scoring function  [39]  and an active site radius of 20 Å (1 Å = 0. 1 nm). Ligand structures were obtained from ChemIDPlus (http://chem. sis. nlm. nih. gov/chemidplus/). In total 50 binding modes were obtained for deltamethrin, PBAlc, PBAld and PBA. Figures were prepared using PyMOL (http://www. pymol. org).
CYP6Z8 sequence analysis Ae. aegypti CYP6Z8 was cloned from cDNA (Bora-Bora strain) and fully sequenced (CYP6Z8v1, GenBank ® accession number JQ970488). Sequence analysis revealed 49 nucleotide variations compared with the genome sequence (gene AAEL009131, Liverpool strain, AaegL1. 2 genset) (Supplementary Figure  S1  at http://www. biochemj. org/bj/455/bj4550075add. htm). Of these, 13 were non-synonymous, leading to 97. 14 % identity with AAEL009131-PA (Supplementary Figure  S2  at http://www. biochemj. org/bj/455/bj4550075add. htm). Only one nonsynonymous variation was located within SRS (substrate recognition site) regions. This variation (C855A), leading to the replacement of a phenylalanine by a leucine at position 285 is not likely to affect substrate-binding properties. Indeed, structural models showed that this amino acid does not project into the active site and does not affect its dimensions.
FUNDING This work and A. C. -P. were supported by the  French National Research Agency Project  [ ANR  project number  07SEST14 MOSQUITO-ENV]. A. C. -P. was also funded by a  Grenoble University Research Fellowship. The Grenoble Mass Spectrometry facility (PSM/ICMG) used in the present study was funded by the  French NanoSciences Foundation , the  Rhone-Alpes Region , the  CNRS and Grenoble University. We also acknowledge additional funding from the  EU Seventh Framework programme  [grant number  265660  AvecNet].
Twenty-four Del-Mar 14K Chicken Integrated Systems microarrays (NCBI GEO Platform # GLP1731) were hybridized with 48 labeled samples using a balanced block design, where half of the birds from each genotype and age were labeled with Alexa Fluor® 647 (red dye) and the other half with Alexa Fluor® 555 (green dye; see Additional file 1 for details of the hybridization design). Hybridized slides were scanned using a GenePix 4000B scanner with GenePix Pro 4. 1 software (Molecular Devices, Union City, CA) at wavelengths of 635 nm (Alexa Fluor® 647labeling) and 532 nm (Alexa Fluor® 555-labeling) generating a combined TIFF image file for each slide. The laser power was set at 100% with the photomultiplier tube (PMT) setting being adjusted for each scan to produce a PMT count near unity. All slides were manually checked for quality and all spots with inadequacies in signal, background or morphology were eliminated from further analysis.
The image analysis results were merged with Excel files in GenePix Report (GPR) format, which contains clone identification, spot location on slide, and most current gene name/function (based on BLASTX/ BLASTN score). The microarray GPR files were analyzed using the linear models for statistical analysis of microarray data (LIMMA, version 3. 4. 5) software  [36]  package in R (version 2. 11. 1)  [37]. Median intensities for each dye were Loess normalized (without background subtraction) within array and between array ("Aquantile" method) to correct for dye and slide biases. A two-way analysis of variation (ANOVA) was used on Loess normalized intensity values from this factorial design experiment to determine the main effect of genotype (G), main effect of age (A), and the interaction of age and genotype (A × G).
Quantitative RT-PCR analysis Several DE and prior candidate genes were selected for verification of expression by quantitative RT-PCR (qRT-PCR) analysis. First-strand cDNA synthesis was performed by incubation of a 13 μl reaction volume (containing 1 μg of total DNase-treated RNA, 1 μl of 100 μM oligo dT 20 , 1 μl of 10 mM dNTP mix, and water to 13 μl total volume) for 5 min at 70 C and then placed on ice for 2 min. A master mix containing 5 μl of 5× first-strand synthesis buffer, 1 μl of 0. 1 M dithiothreitol (DTT), 1 μl of RN aseOUT, and 200 U of SuperScript® III reverse transcriptase (Invitrogen, Carlsbad, CA) was added to the RNA in a final reaction volume of 20 μl. The cDNA was diluted to achieve a concentration of 50 ng/μl. Primers were designed for qRT-PCR using Primer Express® v2. 0 software (Applied Biosystems, Foster City, CA).
Detailed information for each primer pair including gene name, gene symbol, primer sequences (forward and reverse), Gen-Bank accession number and amplicon size are provided in Additional file 2. The qRT-PCR assay was performed in an ABI Prism Sequence Detection System 7900HT, using Power SYBR® green PCR master mix (Applied Biosystems, Foster City, CA) and 400 nM of each primer (forward and reverse; Sigma-Aldrich, St. Louis, MO) in duplicate wells. Disassociation curves of each sample were analyzed to validate specific amplification and verify absence of primer dimers. PCR products were analyzed using agarose gel electrophoresis to compare approximate product size to expected amplicon size. The Ct for each sample was normalized to the corresponding sample geometric mean of three housekeeping genes [protein kinase, AMP-activated, beta 2 non-catalytic subunit (PRKAB2), protein kinase, AMP-activated, gamma 1 non-catalytic subunit (PRKAG1), and serpin peptidase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), member 2 (PAI-2 or SERPINE2)].
These housekeeping genes were selected using the Ref Finder website  [39]  as the most stably expressed genes (i. e. , genes with the lowest M-value) in the experiment. The 2 -(ΔΔCt) formula was used to calculate relative transcript abundance  [40]. The statistical analysis was performed using a general linear model procedure in SAS v9. 3. The data (log2 transformed normalized expression values) was analyzed using a two-factor analysis of variance to determine significant effects of genotype (G), age (A), and the interaction of age x genotype (A × G). Pearson's correlation coefficient (r) was used to compare log2 FL/LL expression ratios between the microarray and qRT-PCR analyses of select genes.
Ingenuity® Pathway Analysis (IPA®) of DE gene sets Significant genes (cDNA clone IDs) from the microarray analysis were annotated using the GeneBase tool on our website  [41] , which provides protein IDs (from GenBank or Swiss-Prot databases) of microarray cDNA probes derived from BLASTX analysis. Lists of DE genes containing the protein ID and log2 ratio for each gene were then submitted to the Ingenuity® Knowledge Base  [42]  for functional annotation and mapping to canonical metabolic and regulatory pathways. "Analysis ready" genes were mapped by IPA for the genotype (100 DE genes), age (2,301 DE genes), and age x genotype interaction (212 DE genes) lists. The IPA® Upstream Regulator Analysis was used to identify transcription factor (TF) interaction networks, predicted activation or inhibition of TF, and their direct targets from DE gene sets.
For example, the expression of ANXA5, F2, adiponectin (ADIPOQ), adiponectin receptor 1 (ADIPOR1) and attractin (ATRN) were highest in abdominal fat of the LL at 9 wk. Similarly, expression profiles of A2M, retinol binding protein 4 (RBP4) and angiopoietin-like 4 (ANGPTL4) were greatest in the LL at 7 wk. The adipokine visfatin [or nicotinamide phosphoribosyltransferase (NAMPT)] was not differentially expressed in adipose tissue of juvenile FL Figure  3  Verification of differential expression of hemostatic genes by qRT-PCR analysis. The abundance of six genes associated with blood coagulation was determined by quantitative reverse transcription PCR (qRT-PCR) analysis. Data points represent Least Squares Means (LSMEANS; n = 4 birds/genotype) of normalized expression values generated by the general linear models (GLM) procedure in Statistical Analysis System (SAS) software.
The Ingenuity Upstream Regulator Analysis identified several additional targets of PPARD (Figure  7B ), which were expressed at higher levels in either the FL (FASN, FLT1, SCD and TLR5) or the LL (ACAA, APOA4, BCL2, GPD2, SLC27A1, SLC27A2, UCP3 and VLDLR) chickens. The IPA software predicts that PPARD is inhibited (blue color) based on prior knowledge of PPARD action in mammals and the observed higher expression of PPARD-activated targets in the LL (green symbols). IPA predicts that SIRT2 should be activated (orange color) and has a direct positive action on five target genes (DHCR7, HMGCR, LSS, MVD and SC5DL), which were up regulated in adipose tissue of the FL chickens. However, the yellow-colored arrows indicate that IPA expected three target genes (AACS, FDFT1 and PMVK) to be up regulated in the FL, rather than the LL as we observed.
The present study has identified a large number of lipogenic genes that are up regulated in abdominal fat of FL chickens (Table  3 ). A prime example of this lipogenic group is our clone for GH1 (GenBank accession BI3904 57) that corresponds to the short form of chicken GH (scGH), which lacks a signal peptide and is highly expressed in ocular tissue  [90, 91] , pituitary gland and heart of chick embryos  [91]. The short alternativelyspliced (16. 5 kDa) isoform of full length GH (20 kDa) appears to function as an "intracrine" factor within the cell  [91]. Our discovery of higher expression of scGH in abdominal fat of the FL chicken supports the idea of a local lipogenic action of GH on adipose tissue, rather than the lipolytic response usually observed in mammals  [92]. In fact, our earlier work clearly established the lipogenic action of exogenous GH in juvenile chickens  [93] [94] [95] [96].
Acknowledgements This work was supported by a grant from the  United States Department of Agriculture, Initiative for Future Agricultural and Food Systems, Animal Genome Program (USDA-IFAFS ; Award #  00-52100-9614 ) to LAC, TEP, SEA, and  JS. The longitudinal study and tissue sampling of the divergent lines required the contribution of a large number of scientists and technicians from the laboratories of  INRA UR83  and breeding facilities at  INRA UE1295 Pôle d'Expérimentation Avicole de Tours , F-37380 Nouzilly, France. And although not named, each individual's essential contribution toward completion of this project is gratefully acknowledged. Portions of this work were also supported by a grant from the  USDA Cooperative State Research, Education, and Extension Service (CSREES)  (Grant #  2009-34562-20008 ) and the  Avian Biosciences Center, University of Delaware.