dataesr/scientific-paragraphs-categorization huggingface dataset:
The FA composition of AT reflects balance between exogenous FAs from food, triglyceride hydrolysis/synthesis and FA synthesis from glucosederived acetylCoA, so-called de novo lipogenesis (DNL). Studies on FA composition of AT during weight control trials are scarce  [9, 10]. Low 16:1(cis-9) (palmitoleic acid) and 14:1(cis-9) (myristoleic acid) may predict favorable weight control outcome  [11]. Omics, especially transcriptome studies, have proved great potential in clarifying the role of AT biology with respect to response in weight controlling trials  [12]. However, analyses based on single omics often do not provide enough information to understand biology. The integration of multiple omics may give a better understanding of a biological system as a whole. Global network-based approaches authorize multiple datasets analyses and carry the advantage of highlighting functionally related pathways and biological entities of potential relevance as hubs  [13].
Networks are valuable models to dissect complex traits  [14]. However, integrative analysis of datasets of different data types raises the issue of different scales of the multiple datasets. In gene expression networks, clusters are more robust than individual interactions  [15]. Multivariate statistical approaches were recently developed to jointly analyze omics datasets, dealing with high dimension and using variable selection  [16]. The present study aimed at revealing the characteristics of AT biological networks relevant to clinical traits during a long-term dietary intervention (DI) including calorie restriction and ad libitum follow-up after weight loss. Studies on human AT gene expression or lipidomic profiles from a systems biology point of view have only been reported at baseline  [17, 18]  but not during DI.
Source data Ethics statement. The samples investigated in this paper were collected from 2006 to 2007 during the DiOGenes study, a pan-European randomized DI trial which was approved by the ethics committees of each of the 8 European centres participating to the program (registration no. NCT00390637). Written informed consent was obtained from each patient according to the local ethics committee of the participating countries: 1, Medical Ethics Committee of the University Hospital Maastricht and Maastricht University, The Netherlands; 2, The Committees on Biomedical Research Ethics for the Capital region of Denmark, Denmark; 3, Suffolk Local Research Ethics Committee, UK; 4, University of Crete Ethics Committee, Greece; 5, the Ethics Commission of the University of Potsdam; 6, Research Ethics Committee at the University of Navarra, Spain; 7, Ethical Committee of the Institute of Endocrinology, Czech Republic; 8, Ethical Committee to the National Transport Multiprofile Hospital in Sofia, Bulgaria.
Study design. The data presented in this paper are part of those collected during the DiOGenes study (contact information at www. diogenes-eu. org) The DiOGenes project investigated the effects of diets with different content of protein and glycemic index on weight-loss maintenance and metabolic and cardiovascular risk factors after a phase of calorie restriction, in obese/overweight individuals. The trial protocol and supporting CONSORT checklist are available as supporting information; see S1 Protocol and S1 Checklist. Healthy overweight (body mass index (BMI) 27 kg/m2) individuals, aged <65 years were eligible for the study. Exclusion criteria were BMI 45 kg/m2, liver or kidney diseases, cardiovascular diseases, diabetes mellitus (type 1 or type 2), special diets/eating disorders, systemic infections/chronic diseases, cancer within the last 10 years, weight change >3 kg within the previous 3 months, and other clinical disorders or use of prescription medication that might interfere with the outcome of the study.
A third step consisted in merging the 3 networks obtained in the first step with the 3 networks obtained in the second step. As the number of variables in the 3 datasets was very different (from 15 bio-clinical variables up to 221 gene expressions), a naive strategy consisting in estimating the selected edges in each set of variables (or in each pair of two sets) in a same manner would have led to give too much importance on the largest set of variables, i. e. , to the gene expression dataset. The number of selected edges was thus adjusted to be equal to the number of nodes in each set (or pair of sets) of variables, leading to smaller densities for the largest networks. The first step of the analysis was performed using the R package glasso (cran. r-project. org/ web/packages/glasso) and the second step using the R package mixOmics (http://perso.
The nodes with an observed betweenness in the top 5% were declared significant. The network analysis (node clustering and betweenness calculation) was performed with the R package igraph (igraph. org; [37] ). Finally, clusters with identical central nodes in two different networks were tested for the significance of the number of common nodes using a Fisher exact test with the set of all variables as reference: pairs of clusters with a p-value smaller than 5% in the Fisher exact test are those that have a larger number of common nodes than what was expected by random chance only. Sub-graphs (clusters) were laid out using force-based algorithms in Gephi 0. 8. 2 software (gephi. org, [38, 39] ). Nodes' sizes indicate degree, i. e. , the number of edges adjacent to the node. Nodes with the largest degrees, called hubs, were systematically extracted.
All FAs were n-6 with Figure  6. Adipose tissue networks during maintenance phase in women with continued weight loss. A sparse Graphical Gaussian Model (GGM) was used to estimate partial correlations in each set of variables and regularized canonical correlation analysis (CCA) was used to assess links between paired sets of variable. Clustering was performed using a spin glass model and simulated annealing. This analysis displays the variables that are connected independently from other variables. Graphs were laid out using force-based algorithms in Gephi 0. 8. 2 software. Nodes' colors and font size indicate betweenness centrality. The red nodes have the highest betweenness and the green nodes the lowest one. Edge thickness is proportional to the strength of the correlation (CCA) or of the partial correlation (GGM).
g. , the strength of the relation between two transcripts or the strength of the relation between a transcript and a FA level) have very different scales. This caveat is controlled using a non-global inference approach, in order to have a global model of the interactions between all sets of variables. The regularized CCA has previously been used in combination with sparse partial least squares regression to investigate AT transcriptionally coordinated paths correlated with PUFA intake during the LIPGENE study  [17]. Here, we used a 3-step inference method to infer a global model using 3 datasets: first, inferring a network in each dataset using a sparse GGM; second, inferring a network between each pairs of two different sets of variables using regularized CCA; third, merging the 3 networks obtained during the first step with the 3 networks resulting from the second step.
As the numbers of variables in the 3 datasets were very different (from 15 bio-clinical variables up to 221 mRNA levels), a simple strategy consisting in estimating the selected edges in each set of variables (or in each pair of two sets) in a same manner would have led to give too much importance on the largest set of variables, i. e. , to the gene expression dataset. The number of selected edges was thus adjusted to be equal to the number of nodes in each set (or pair of sets) of variables, leading to smaller densities for the largest networks. To improve the significance of our findings, systematic statistical tests were performed to test the significance of the betweenness centrality of the nodes compared to their degrees. Significance of nodes indicates that, given their degrees, they have a betweenness larger than expected and are thus significantly central in their clusters.
Acknowledgments We acknowledge the  GenoToul GeT PlaGe  and TQ core facilities for expertise in high throughput gene profiling. We thank  Marion Combes  and  Jean-José Maoret  for outstanding assistance in gene expression and  Pavla Kalouskova ,  Barbora Stankova  for adipose tissue lipids treatment. We are grateful to  Dr. Magali San Cristobal  ( INRA, UMR 1388, Auzeville ) for expert advices in network analyses and to  Dr. Jean-Sébastien Saulnier-Blache  ( Inserm UMR1048, Toulouse ) for critical reading of the manuscript. DL is a member of  Institut Universitaire de France.
3A ) or 50 g/ml (Fig. 3B ) cycloheximide (or volume equivalent of ethanol) for 0, 4, 6, 9, or 13. 5 h. Cells were lysed in Cytobuster TM protein extraction reagent (EMD Millipore 71009) supplemented with 25 units of Benzonase nuclease (EMD Millipore 101654). The protein extract concentration was determined with Bradford reagent (Bio-Rad 500-0006) and a standard curve generated by known quantities of bovine serum albumin. Ten g of lysate was run on SDS-PAGE and subjected to Western blotting. Western Blotting and Densitometry-Western blotting was performed using a Typhoon imager (GE Healthcare) as described elsewhere  (31)  or using a LI-COR Odyssey as described elsewhere  (32). Densitometry was performed using ImageJ software or LI-COR Image Studio software. Chromatin Immunoprecipitation-Chromatin immunoprecipitation was performed using the ChIP-IT High Sensitivity kit (Active Motif 53040) according to the manufacturer's instructions, except that the input samples were purified by phenol: chloroform extraction.
The following antibodies were used: anti-H3K9me2 (Abcam ab1220), anti-H3K9me3 (Abcam ab8898), anti-G9a (Abcam ab40542), and Rabbit IgG (Jackson ImmunoResearch 011-000-003). ChIP samples were quantitated using the Qubit 2. 0 fluorometer (Invitrogen). A portion of each sample was confirmed by qPCR as described above. The fold change over background was calculated using the ⌬⌬Ct method  (33) , where immunoprecipitation (IP)/input for the ChIP antibody signal was compared with the IP/input value for IgG (background). After a confirmation of the ChIP signal over background, the remainder of the sample was submitted for high-throughput sequencing. Oligo sequences are available upon request. High-throughput Sequencing and Data Analysis-Libraries were prepared for high-throughput using the Illumina TruSeq kit as per the manufacturer's recommendations.
Single-end 50-bp reads were sequenced on an Illumina HiSeq. Reads were checked to be of sufficient quality, filtered to remove those with a significant adapter contribution using TagDust  (34) , and aligned to the reference human genome (hg19) using Bowtie  (35). Regions of significant ChIP enrichment relative to input control ("peaks") were identified using MACS2, assuming fragments were 250 bp in length. Heat maps were generated in MATLAB by plotting the normalized ChIP-seq signal around the midpoints (Ϯ2 kb) of the union set of G9a peaks. Changes in the ChIP signal were illustrated by taking the arithmetic difference between treated and untreated samples. ChIP-seq data are available from NCBI Gene Expression Omnibus (accession number GSE67317). Genomic distributions were computed using CEAS  (36).
The CCCTC-binding factor (CTCF) and H3K4me3 ChIP-seq data from 293T cells were mined from BAM files provided by ENCODE  (37). To find regions with significantly altered ChIP-seq enrichment, the number of overlapping aligned reads for all 500-bp non-overlapping windows across the genome was first tabulated for all samples. Only those windows with an average number of aligned reads exceeding 10 were retained. A binomial test was then applied to compare the G9a ChIP-seq signal between control siRNA-treated and WIZ siRNA-treated cells at each of these windows; p values were corrected for multiple testing using the q value. Windows with q Ͻ 1 ϫ 10 Ϫ5 were retained and used for subsequent analyses. Gene Expression Microarrays-RNA was extracted using the RNeasy Plus mini kit (Qiagen) and quantified on the Qubit fluorometer (Invitrogen).
RNA quality was verified on an Agilent bioanalyzer before hybridization to human 8 ϫ 60 K arrays (Agilent Technologies 28004). After scanning, microarray images were processed using Agilent Feature Extraction software according to the manufacturer-supplied protocols. The resulting background-subtracted and local regression (LOESS)normalized log ratio data were collected and filtered to remove data generated from poor quality or low intensity spots. Probes with Ͼ30% missing data were removed from further evaluation. Dye swap replicates were inverted to match the log ratio direction of non-dye swap replicates. The remaining missing data were then imputed using weighted K-nearest neighbors (KNNimpute)  (38). Testing for differential expression was performed using the statistical analysis of microarray (SAM)  (39)  one-class option for a comparison with DMSO or scrambled siRNA control and the two-class unpaired option when comparing UNC0638 with WIZ siRNA knockdown.
1). Expression data are available from NCBI Gene Expression Omnibus (accession number GSE70914). G9a Purification for Proteomics-G9a was purified from the HeLa FLAG-HA-G9a cell line (9) grown in a volume of 5 liters (ϳ8 ml cell pellet), treated with either 1 M UNC0638 or volume equivalent of DMSO for 48 h. Cells were centrifuged, washed once in PBS, and then frozen at Ϫ80 °C prior to purification. Purification was performed as follows. All buffers were supplemented with 1ϫ protease inhibitor mixture (Roche 05056489001). Cell pellets were thawed on ice, resuspended in 40 ml of nuclei prep buffer (50% glycerol, 0. 5% Nonidet P-40, 1 mM PMSF, 10 mM Tris-HCl, pH 7. 4, 10 mM NaCl, and 3 mM MgCl 2 ), and incubated on ice for 10 min or until the nuclei were visible with trypan blue stain. Nuclei were pelleted, washed once with reticulocyte standard buffer (RSB: 10 mM Tris-HCl, pH 7.
The reaction was quenched with formic acid and then pressure-loaded onto a microcapillary column pack with both strong cation exchange resin and reverse phase resin as described previously  (42). A nine-step MudPIT run was performed for each sample on an LTQ-Velos Pro that was in-line with a Proxeon Easy nanoLC  (41). The resulting RAW data files were processed by protein database matching using SEQUEST within Proteome Discoverer 1. 4 (Thermo Fisher Scientific). Database matching was performed using a Homo sapiens FASTA database downloaded from UniProt on  03/16/2014. The database also contained potential sample contaminant proteins including human keratins, IgGs, and proteolytic enzymes. The database search was performed with ϩ57 daltons on cysteine as a static modification and the following modifications as dynamic: ϩ16 daltons on methionine (oxidation), ϩ14 daltons on lysine (mono-methylation), ϩ28 daltons on lysine (di-methylation), and ϩ42 daltons on lysine (tri-methylation).
RESULTS SHP/RovS forms a cell-to-cell communication system in S. agalactiae strain NEM316. In a previous study, we identified a short gene that encodes a short hydrophobic peptide (SHP); this gene is located upstream of the rovS gene in GBS  (11). Thus, it seemed highly probable that SHP and RovS act together as part of a cellto-cell communication system, as has already been seen in other streptococci  (12, 13). Notably, the shp/rovS locus is present in all 285 sequenced genomes of GBS deposited in GenBank, and both shp and rovS display more than 99% identity at the DNA level among the strains. As the shp/rovS locus sequence is highly conserved across all GBS strains, we chose to use strain NEM316 as a representative with which to decipher the role of the SHP/RovS system, as this strain was responsible for a fatal case of septicemia in an infected infant  (22).
3B ). This observation confirms that shp and gbs1556 form an operon that is controlled by a promoter region located upstream of the shp gene. Analysis of the sequence of Gbs1556 revealed the presence of a peptide signal signature in its N-terminal domain (predicted by SignalP software v. 4. 1), suggesting that Gbs1556 is a secreted protein. We therefore used a label-free proteomic approach that combined SDS-PAGE and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis to compare the secretomes of NEM316 and the ⌬rovS mutant grown in DMEMg. As expected, Gbs1556 was much more abundant in the supernatant of the NEM316 strain (an average of 93 mass spectra were detected) than in that of the ⌬rovS mutant strain (an average of 1 mass spectrum was detected) (P Ͻ 0. 01). No significant differences between the abundances of the 70 other identified proteins in the supernatants of the two strains were observed (data not shown).
To determine the role of SHP/RovS in GBS virulence, we investigated the persistence of the NEM316, ⌬rovS, and ⌬shp strains in different organs of mice that had been infected with a sublethal dose of bacteria (10 6 CFU). The brain, liver, spleen, and lungs were analyzed. Two days postinfection, bacteria were found in these organs, but no differences were observed in the amounts of bacteria present in the lungs and brain (data not shown). However, bacterial counts were significantly lower in the spleens and livers of mice infected with the ⌬rovS mutant (1. 4 and 1. 5 log) (Fig. 6A  and B ) and the ⌬shp mutant (1. 3 and 1 log) (Fig. 6C  and D ) than in those infected with the WT strain NEM316. In conclusion, SHP/RovS seems to be a determining factor in the ability of GBS to persist in infected liver and spleen tissues.
8). The adhesion assay was performed in microtiter wells previously coated with a fixed amount of human fibrinogen. A total of 10 6 CFU of culture grown in DMEMg (C) or BHI medium (D) was placed in each well, and the plates were then incubated for 30 min at 37°C. After the wells were washed to eliminate unbound bacteria, adhered cells were recovered and quantified in terms of CFU/ml. The data presented are the means Ϯ SD of results from three independent experiments. Differences were considered to be highly significant if P values were Ͻ0. 01 (**) and extremely significant if P values were Ͻ0. 001 (***). volved in the production of these pheromones. It will be challenging to understand how and where PptAB and Eep work together to produce and secrete these signaling peptides in streptococci.
A combination of genetic and proteomic approaches allowed us to identify gbs1556 as a new target gene positively regulated by the SHP/RovS system. The gbs1556 gene occurs, with 100% identity, in the 285 GBS sequences available in the NCBI database. It encodes a 530-amino-acid secreted protein of unknown function that has a conserved domain (amino acids 380 to 488) characteristic of a transglutaminase/protease enzyme. Transcriptome analyses revealed that the gbs1556 gene was significantly overexpressed when bacteria were grown in human amniotic fluid or blood  (34, 35) , suggesting that Gbs1556 plays a role in GBS infection. A recent study has shown that the gbs1556 gene is also upregulated during GBS infection of macrophages, which raises the question of whether it interacts with the host immune system  (36).
The quantitative real-time PCR was carried out using the SYBR Green PCR master mix (Eurogentec) as recommended. PCRs were performed in triplicate and run on a Mastercycler Ep realplex detector (Eppendorf). The critical threshold cycle was defined for each sample. The expression levels of the studied genes were normalized using the NEM316 rpoB gene, whose transcription level is constant under our experimental conditions  (45). For each gene, an analysis of variance was performed on the cycle threshold (C T ) corrected by the C T of rpoB in order to determine whether the difference in expression levels of the strains was significant (P Ͻ 0. 05). Data were computed using the comparative critical-threshold method (2 Ϫ⌬⌬CT )  (46). RT-PCR. RT-PCR experiments were performed with cDNA synthesized with total RNA extracted from strain NEM316 grown in DMEMg and harvested at an OD 600 of 0.
ACKNOWLEDGMENTS This study was supported by the  Marie Curie Intra European Fellowship for Career Development (IEF) , grant no. 327146 , and a  DIM-ASTREA grant  from the  Region Île-de-France  (grant  ast110095 ). We thank the  Plateforme d'Analyse Protéomique Paris Sud-Ouest (PAPPSO)  for its assistance with the proteomic analyses;  L. Guedeville  and  M. Bauducel  of  IERP  for their help with the animal assays;  V. Rochet  for her assistance with the cell biology procedure;  H. Guillou  from  INRA Toxalim  for HepG2 cell support; and  E. Chambellon  for her technical assistance and advice with regard to the EMSAs. Finally, we are grateful to  V. Juillard ,  F. Rul , and  D. Lechardeur  for their critical reading of the manuscript.
, 2012; Colette et al. , 2011). Whereas the vast majority of the scientific literature on ozone trends relies on rural supersites dedicated to the assessment of long-term atmospheric change, the records of regulatory monitoring networks are becoming progressively long enough to complete such assessments. In the 1990s European air quality legislation began to include some targets with regards to the ozone monitoring network, so that the network now includes several thousand sites, a few hundred of which have records longer than 10 years. The size and diversity of the data gathered in the Airbase repository (maintained by EEA) offers an unprecedented view into air quality trends in Europe, provided that (1) 10-year records can be considered long enough to assess trends, and (2) the station representativeness of thousands of urban and rural background sites can be compared to the handful of supersites used elsewhere.
, 2010)  emissions reported by the European countries. In North America, the US Environmental Protection Agency (EPA) has provided emissions since 1980 for the USA (available at http://www. epa. gov/ttnchie1/), and Environment Canada has provided non-gridded emissions for Canada since 1985 (available at http://www. ec. gc. ca). Inventories are provided for Asia by the REAS-v1 (0. 5 × 0. 5 • spatial resolution;  Ohara et al. , 2007)  and REAS-v2 (0. 25×0. 25 • spatial resolution;  Kurokawa et al. , 2013). The MEIC data set (available at meicmodel. org) also provides emissions for China, at a 0. 25 × 0. 25 • resolution;  Sahu et al. (2012)  provide emissions at a 1 × 1 • resolution for India, with  Garg et al. (2006)  providing non-gridded emissions for the same country. Recently, an inventory was also developed for Africa by  Liousse et al.
An example of this variability is shown in Fig. 16 , from the FINN data sets  (Wiedinmyer et al. , 2011) , which also highlights the large number of high fire episodes in each hemisphere. Several studies have evaluated the differences between estimates of fire inventories, as well as highlighting the large differences remaining between available data sets in different regions. Figure  17  displays a comparison of several of the data sets previously detailed, for carbon monoxide in Africa and South America (from  Granier et al. , 2011). Differences in the emissions for each year, as well as in the interannual variability in the emissions, are very large. It should be noted that the AMMA inventory for Africa, which includes the use of satellite observations as well as local information on the fires, provides significantly larger emissions than the other inventories, which are based either on satellite data only or on modelling of fires.