dataesr/scientific-paragraphs-categorization huggingface dataset:
OsGS1-2 is the only gene whose response to M. oryzae is amplified by nitrogen treatment (Figure  3D ). This expression pattern is not correlated with the detected level of glutamine in the leaves at 2 dpi (Supplementary Figure  2F ). When comparing plants grown under 0N and 1N regimes, an increase in glutamine level was observed in mock inoculated samples but not in M. oryzae inoculated samples. In the same experiment used for RNAseq analysis, OsGS1-2 expression increased at a higher lever under 1N regime than under 0N regime. OsNADH-GOGAT2 is nitrogen specific induced (Figure  3A ) and a GDC (Os06g45670) is induced by M. oryzae at a level that is not significantly different in both N treatments. We identified 21 genes associated to several metabolism process that possibly link the cell death induction to GS/GOGAT pathway in our dataset: 17 are not differentially expressed in any of the conditions.
OsNOA1 is repressed and OsPAO4 is induced during infection in both nitrogen regimes. OsOXO4 is the only gene whose transcriptional response to M. oryzae is amplified by high nitrogen. However, the role of OsOX4 in rice blast resistance is not clear  (Zhang et al. , 2013; Li et al. , 2015). Finally, we identified in our datasets 11 genes coding for enzymes potentially involved in the TCA cycle replenishment or GABA shunt, 9 of which are not differentially expressed. OsGDH3 and OsGAD3 have an amplified response to M. oryzae in presence of nitrogen. OsGAD3 and OsGDH3 may be controlled by the pathogen in order to boost TCA cycle and compensate the drain of nutrient away of the infected tissues. In N and P deprived environments, OsGDH3 is induced in rice shoots probably to boost nitrogen recycling  (Qiu et al.
This tendency was not observed for other pathogenicity genes, vesicle trafficking components, transcription factors or CAZY enzymes (Supplementary Table  5 ). In order to validate this tendency, we tested the expression of a set of 19 genes coding for SSPs and other pathogenicity factors by Q-RT PCR in a biological replicate of the RNA-seq experiment (Kasalath plants showing NIS). Seven of these genes were indeed differentially expressed as expected from RNA-seq data (Figure  6 ): Pmk1  (Thines et al. , 2000) , Bas2  (Mosquera et al. , 2009) , AvrPi9  (Wu et al. , 2015) , and three putative effectors (MGG_08715, MGG_15774 and MGG_08428) were more expressed whereas VPS39  (Ramanujam et al. , 2013)  was less expressed in 1N conditions. Interestingly, all these genes were less expressed in 1N condition in Nipponbare (where NIS was not observed).
The 12 other genes tested were not differentially expressed in either variety (Supplementary Figure  8 ). This data suggests that M. oryzae can sense the physiological status of the plant and potentially adapt its pathogenicity strategy by delivering a higher amount of effectors in high nitrogen conditions. 6 ). Symptoms caused by M. oryzae (isolate Guy11) were estimated by counting the number of susceptible lesions 5 days after inoculation on eight different replicates. Different doses of nitrogen fertilization (0N, 1N; see Section Materials and Methods) were supplied to rice 1 day before inoculation. This experiment was repeated three times and 12 independent replicates were obtained. Data were normalized between the two replicates using the symptoms obtained for the most susceptible plants in each replicates.
Nitrogen Fertilization Has a Complex Impact on Defense Expression Although a link between amino acids metabolism and defense response has been demonstrated several times, a remaining question is how nitrogen fertilization affects defense. Nitrogen fertilization and subsequent modifications of amino acid content could play a role in fueling the defense response  (Masclaux-Daubresse et al. , 2006; Bolton, 2009; Nussbaumer et al. , 2015; Park et al. , 2015)  or in regulating key defense regulators  (Rojas et al. , 2014). Similarly to previous results, we observed in our dataset that defense-related genes (e. g. , PR genes and genes involved in the biosynthesis of the antimicrobial secondary metabolites) are more expressed during infection under high nitrogen fertilization (Figure  3 ). However, our results also show that there could be a complex regulation of defense in nitrogen fed plants.
RNA extraction and real-time PCR Total RNAs were extracted using TriPure kit (Roche). 1μg of RNA was converted to first strand cDNA using IScript cDNA synthesis kit (Biorad). Real-time PCR were performed in a 96-well plate using the IQ SYBR Green Supermix (Biorad). Data were quantified by the ΔΔ-Ct method and normalized to 36b4 expression (Table  1 ).
Bio-informatical analysis RNA-seq (Illumina HiSeq) data obtained in human breast samples were collected from The Cancer Genome Atlas (TCGA) data portal in 2016/05 (https://gdc-portal. nci. nih. gov/). Data were available as raw counts or TPM (transcript per million) for a total of 20531 genes. Paired samples from 112 patients, made on one breast tumor and one normal sample from the same patient, were used to study gene expression.
LSD1/ERRα mRNA expression in breast tumors Both LSD1 and ERRα proteins have been shown to display increased expression in cancer lesions as compared to the corresponding normal tissues including in breast tumors  [11] [12] [13] [14] [23] [24] [25] [26] [27] [28] [29] ]. However, it is unclear whether these elevations are due to increased expressions of the corresponding mRNAs. To examine this hypothesis, we analyzed data from a publicly available database (The Cancer Genome Atlas [TCGA]) for LSD1 (KDM1A) and ERRα (ESRRA) mRNA expression. We focused on data obtained by RNA-sequencing of pair-wise comparisons of breast cancer vs normal tissues within individuals, ending up in examination of 112-paired sequencing results. As expected, the expressions of ERα (ESRA) as well as those of its target genes pS2 (TFF1) and cathepsin D (CTSD) were more elevated in tumors than in normal tissues (Fig  1 ).
However, this mechanism does not appear active in all cells. Indeed, in MCF7 cells, induction of ERRα protein degradation by its inverse agonist XCT790 does not impact on ERRα-corresponding mRNA, which is expected in an auto-regulatory loop scheme  [40]. Regulations of ERRα expression at the post-transcriptional level have also been documented. For instance, several microRNAs (miRs) induce the degradation of ERRα-corresponding mRNA leading to reduced protein levels  [32] [33] 41]. Interestingly, at least some of these miRs (miR-135a, miR-497) display a reduced expression in cancer (i. e. opposite to ERRα)  [33, 42] , together suggesting that this reduction may account for the increase in ERRα expression at the mRNA level. Nevertheless, our analysis of publicly available database suggests that ERRα mRNA is equally expressed in breast cancer vs normal tissues.
Genome project history Overall, 140 pure bacterial isolates were obtained from a single activated sludge sample, and screened for lipid inclusions using the Nile Red fluorescent dye. The genomes of 85 Nile Red-positive isolates were sequenced, of which isolate LCSB065 has already been published  [5]. In particular, the genome of Zoogloea sp. LCSB751 was analyzed to obtain information about the functional potential of this genus, which has no publically available representative genome sequence, but also based on its particular phylogenetic position and to acquire knowledge on the genes related to lipid accumulation. The permanent draft genome sequence of this strain is available on NCBI with the GenBank accession number MWUM00000000 (BioSample: SAMN06480675). Table  3  summarizes the project information according to the MIGS compliance  [18].
Genome annotation Gene (i. e. open reading frame) prediction and annotation was carried out on the assembled contigs using Prokka ver. 1. 11  [26]  and the RAST server  [27] , both executed using default parameters and databases. Briefly, Prokka predicted a total of 5200 features including 5118 CDS, 3 rRNA, 76 tRNA genes and one tmRNA genes as well as two repeat regions. Similarly, the RAST server predicted a total of 5202 features, of which 5125 represent coding sequences (CDS), 6 rRNA and 71 tRNA genes. The annotation derived from the RAST server was used for most of the genome descriptions and downstream analyses, unless explicitly mentioned. CDS on the forward and reverse strands within contigs above 1 kb are represented in Fig. 3. In addition, the proteins predicted by the RAST server were submitted to i) the WebMGA server  [28] , ii) the SignalP server v.
Genome properties The draft genome assembly of Zoogloea sp. LCSB751 consists of 5,817,831 bp with a G + C content of 64. 2%, distributed over 776 contigs (773 scaffolds) with an N50 value of 180,423 bp (Table  4 ), GC-skew and -deviation of contigs above 1 kb are represented in Fig. 3. The raw reads are available via the GenBank nucleotide database under the accession number MWUM00000000, while the assembly and the annotation (IDs 6666666. 102999) can be accessed through the RAST server guest account. The rRNA operon region is assumed to be occurring in multiple copies, because all reads from this region Table  1  Classification and general features of Zoogloea sp. strain LCSB751 according to the MIGS recommendation  [18]  MIGS ID Property Term Evidence code a Classification Domain Bacteria TAS  [34]  Phylum Proteobacterium TAS  [35]  Class Betaproteobacterium TAS  [36]  Order Rhodocyclales TAS  [13]  Family Rhodocyclaceae TAS  [13]  Genus Zoogloea IDA Species Unknown IDA Strain: LCSB751 Gram stain Negative TAS  [1]  Cell shape Rod TAS  [1]  Motility Motile TAS  [1]  Sporulation Not reported NAS Temperature range 5-40 °C TAS  [11, 13, 14]  Optimum temperature 25-30 °C TAS  [11, 13]  pH range; Optimum 6.
2  Phylogenetic tree based on 16S rRNA gene sequences. The type species strains of every species of the Rhodocyclaceae family were used (in bold) as well as all the type strains of the genus Zoogloea, according to the List of Prokaryotic names with Standing in Nomenclature (LPSN; http://www. bacterio. net). Whole genome GenBank IDs are provided in brackets. The 16S rRNA sequences were aligned using ClustalW, the alignment was curated using Gblocks conserving 81% of the initial positions and the phylogeny was computed with BioNJ using 100 bootstraps and the default (K2P) substitution model, using the pipeline Phylogeny. fr [38]   Additionally, the catechol 2,3-dioxygenase that has been studied in Z. oleivorans, was found to be encoded by the genome of Zoogloea sp. LCSB751  [11].
3  and Additional file 1: Table  S1 ). For the vast majority, (4732 genes), no transcripts were detected, which can be explained by the low population size of Zoogloea sp. in situ. This was estimated by phylogenetic marker gene (16S rRNA) amplicon sequencing on the sample collected on 25 January 2011 (data from  [6] ), for which the Zoogloea sp. population size was estimated at 0. 1%. Similarly, metagenomic data from all the samples further support the low abundance of this strain in situ (Additional file 1: Table  S2 ). Nitrate reductase encoding genes (specifically the periplasmic nitrate reductase; NapA) were found to be expressed in all the four time points, while nitrite reductase or nitrogen fixation genes were sporadically expressed in those four time points. Interestingly, at least one copy of the acetoacetyl-CoA reductase and of the polyhydroxyalkanoic acid synthase were found to be expressed at each time point, possibly suggesting PHA accumulation by the population of Zoogloea sp.
CRISPR-Cas system and putative bacteriophages A total of three CRISPR loci were detected with metaCRT, accompanied by six CRISPR-associated (cas) genes. Five of the predicted cas genes occur consecutively, within the same contig and all of the predicted cas genes occur adjacent to a CRISPR locus  [7]. Two of CRISPR repeats types were 37 bp in length (sequence: GTTTCAATCCACGTCCGTTAT TGCTAACGGACGAATC; GTGGCACTCGCTCCGA AGGGAGCGACTTCGTTGAAGC) while one of them is 32 bp (sequence: CACTCGCTCCGGAGGGAGC GACTTCGTTGAAG). These CRISPRs contain 175, 51 and 11 spacers, respectively, ranging from lengths of 33 to 46 bp. A total of 77 matches were found when searching the spacers against the ACLAME phage/viral/plasmid gene database, NCBI phage and NCBI virus databases using the CRISPRtarget tool  [32]. 51 of the spacers match to bacteriophages, 6 to viruses, 11 to genes within plasmids and six to genes within prophages (Additional file 1: Table  S3 ).
Acknowledgements The authors thank  Mr. Bissen  and  Mr. Di Pentima  from the  Syndicat Intercommunal à Vocation Ecologique (SIVEC)  for their permission to collect samples and gain access to the monitoring platform of the Schifflange wastewater treatment plant;  Dr. Nicolás Pinel  for assisting with the large data transfers and initial analysis of the isolates. Dr. Aidos Baumuratov  for his support for microscopic analyses of the isolates; and the high performance computing facilities of the  University of Luxembourg. This work was supported by an  ATTRACT program  grant ( ATTRACT/A09/03 ) and a  European Union Joint Programming in Neurodegenerative Diseases grant  ( INTER/ JPND/12/01 ) both to PW, and  Aide à la Formation Recherche (AFR)  grants to both EELM (PRD-2011-1/SR) and  SN  ( PHD-2014-1/7934898 ) as well as a  CORE Junior grant  to EELM ( C15/SR/10404839 ), all funded by the  Luxembourg National Research Fund (FNR).
Data collection The medical records of all patients were reviewed and abstracted by using a standardized data collection tool for demographic information and data related to risk factors (age, gender, delay between onset of symptoms and admission, pallor, splenomegaly, size of splenomegaly, hepatomegaly, nutritional status, bleeding, co-infections, leucopenia <4000 cells/mm 3 , haemoglobin <6g/dL), platelet count <50000/mm 3 , cytolysis and hemophagocytic syndrome). Hypotrophy or failure to thrive is often defined as weight per age that falls below the 5th percentile on multiple occasions or weight deceleration that crosses two major percentile lines on a growth chart  [24]. Laboratory variables were categorized according to the following cut-off points: (i) leucopenia (<4,000/mm 3 ); (ii) deep anaemia (haemoglobin <6 g/dL); (iii) deep thrombocytopenia (platelet count <50,000/mm 3 ) and cytolysis (ALT > 80 UI/L and/or AST > 70 UI/L).
Statistical analysis Data analysis was performed using GNU PSPP version 0. 10. 4 and the R software for statistical computing version 3. 2. 4 for Windows. Continuous variables were described as means ± standard deviations. Categorical variables were summarized with absolute and relative frequencies. To compare percentages, we used Chi square test. Means were compared using Student t-test. Potential risk factors with p-values of 0. 20 or less in the initial univariate logistic regression were included in the multivariate binary logistic regression models. The model was reduced by means of manual backward elimination. Statistical significance was set at p-value 0. 05. Multiple corresponding analysis (MCA) was performed using the R package FactoMiner to detect potential multivariate association between categorical or categorized clinical variables and the mortality by ZVL.
Human explanted hearts Since 2010, 34 patients have received a heart transplant for ischemic heart disease in Xavier Bichat hospital in Paris. Among these patients, 10 had at least 1 coronary stent implanted before the transplantation and were included in the present analysis. The Institutional Review Board, IRB 0006477 of Hôpitaux Universitaires Paris-Nord Val de Seine, Paris 7 University, and Assistance Publique-Hôpitaux de Paris approved the use of explanted hearts for research. Following heart explantation, stented segments were removed, fixed in 4% PFA for 48 h, and then embedded in a methyl methacrylate resin. We collected 13 BMSs and 7 DESs (2 paclitaxel-, 2 zotarolimus-, and 2 everolimus-eluting stents and 1 unknown). Clinical data from transplanted patients are presented in Supplementary data and Supplementary Table  1.
Statistical analysis Quantitative variables are expressed as means ± standard deviation and were compared using a Mann-Whitney nonparametric test. Qualitative variables are expressed as percentages and were compared with the Fischer's Exact Test. All P values are two-sided. A P value of. 05 was considered significant. All analyses were performed using the MedCalc 14. 10 software (MedCalc Software bvba, Ostend, Belgium).
L γ ) for the fixed whisker experiment (resp. rotating whisker experiment). In Equation (  6 ), Ū is defined by the relationship Üqs (s, t) = δ(t) A Ū(s) where Üqs is the second time-derivative of the quasi-static profile, δ(t) is the Dirac function, and G(t) is the Green's function for the resonant system given by G(t) = e -ζ ω i t sin( 1 -ζ 2 ω i t) 1 -ζ 2 ω i (7) where ζ is a damping factor assumed to be constant. The integral in Equation (  6 ) was computed numerically using Matlab 2015 (Mathworks Inc. , USA).
This indicates that there may be signaling pathways governing antiviral gene expression in C. elegans (Fig. S1 ). To gain insights into the nature of the signaling events following viral infection, we extracted the promoters from the set of genes upregulated in strain N2 after OrV infection (Data Set S1) and searched for associated motifs (E value of Ͻ0. 1 against a background model drawn from the nucleotide composition of the input sequences) using the motif prediction software MEME  (17). We then compared these motifs to known transcription factor DNA binding motifs in the JASPAR core database using Tom-tom  (18, 19). Remarkably, we identified an enriched motif with similarity to the one of STAT transcription factors (Fig. 1B ), well-known to have a conserved role in antiviral defense in mammals.
Importantly, we recovered an enriched motif that was nearly identical to the motif predicted from our gene expression analysis (Fig. 1B ) and matching the mammalian IRF and STAT motifs in the JASPAR core database and the consensus core interferon-sensitive response element (ISRE) TTCNNTTT (Fig. 3E  and F )  (24). Intriguingly, we additionally identified a separate highly enriched motif with strong similarity to the consensus sequence for GATA-like transcription factors (Fig. S3 ). The predicted GATA and STAT motifs were not found at the same set of genes, suggesting that GATA-like transcription factors may be able to recruit STA-1 to DNA independently of STA-1 DNA binding, consistent with previous studies of mammalian cells  (25). This may serve to expand the number of sites bound by STA-1 beyond those containing a core STAT binding element.
To identify potential kinases upstream of STA-1, we performed an RNAi screen, testing genes with the serine/threonine/tyrosine-protein kinase catalytic domain IPR001245  (33). The C. elegans genome encodes 176 proteins with this domain, 116 of which were available as clones as part of C. elegans genome-wide RNAi libraries  (34, 35). Following RNAi by feeding, we infected animals with the Orsay virus and then quantified the viral load for each of the 116 candidates and additional controls (Fig. 5B  and C  and Data Set S4). Using a stringent cutoff (|Z-score| Ͼ 3), we identified only a single regulator of viral load, sid-3. RNAi of sid-3 resulted in ~100-fold reduction of viral RNA accumulation compared to the control (Fig. 5C ). We confirmed that sid-3 is required for permissivity to viral infection by testing two independent deletion mutants of sid-3 (Fig.
Microarray data were processed using rma, and annotations were obtained through the Bioconductor pipeline in the R programming environment. Differentially regulated genes in any condition were identified by using t test, P Ͻ 0. 01, and a twofold difference upon infection. Hierarchical clustering on the drh-1 array was carried out on the set of differentially regulated genes using the hclust function in R and Ward's method. Motif analysis. To specifically identify potential viral response genes, we normalized the array by cul-6, as this removed variation in the final level of infection. Qualitatively, this identified trends similar to those previously published  (16) , such as putative antibacterial response genes upregulated specifically in strain JU1580 and drh-1 upon infection. We identified genes that were Ͼ40% induced relative to cul-6 in strain N2 upon infection and used BiomaRt to download the upstream sequences (Data Set S1).
These sequences were used as input for MEME, attempting to find 0 or 1 site per gene. We screened these manually to avoid spurious hits and then compared potential motifs identified to the JASPAR core database. To test enrichment of STAT motifs in STA-1 chromatin immunoprecipitation-DNA sequencing (ChIP-seq) peaks, we used FIMO to search for the STAT-1 core motif within the 500-bp sequences upstream of genes containing a STA-1 binding site expressed at an increased level (DESeq q Ͻ 0. 1) in sta-1 mutants relative to strain N2, applying a false-discovery rate cutoff of 0. 2. We compared this to a random set of genes chosen from the transcriptome sequencing (RNA-seq) data (see below). RNA-seq. Animals were grown on 50-mm NGM plates, infected or not, and harvested as described in "Viral infection" above.
Total RNA was extracted using TRIsure (Bioline, UK). RNA library preparation was performed with the NEBNext Ultra RNA Library Prep kit for Illumina with total RNA purified with the NEBNext poly(A) mRNA magnetic isolation module, according to the manufacturer's instructions (NEB, USA). RNA-seq analysis. RNA-seq data were aligned to the C. elegans transcriptome WS190 using bowtie2. Counts were obtained from resulting BAM files using BEDTools  (45) , and these were used to generate normalized data tables using DESeq (46) (Data Set S2). Significance between intersecting data sets was calculated by a Fisher's exact test (Fig. S4  and Fig. S5 ). A cutoff of mean 25 normalized reads (normalized according to DESeq's negative binomial distribution) for at least one condition was used, and significantly altered genes were selected (DESeq Benjamini-Hochberg multiple-test correction q Ͻ 0.
ACKNOWLEDGMENTS We thank  David Jordan  for scientific discussions and help with data visualization,  Jérémie Le Pen  for scientific discussions and providing reagents,  Alyson Ashe  for providing reagents,  Alex Appert  for technical support with ChIP sequencing experiment,  Kay Harnish  and  Sylviane Moss  for high-throughput sequencing. We thank the  Caenorhabditis Genetics Center (CGC) , which is funded by  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ) for providing some strains used in this study. This work was supported by  Cancer Research UK  grant  C13474/A18583 ,  Wellcome Trust  grant  104640/Z/14/Z , and  European Research Council (ERC)  grant  260688  to  E. A. Miska ,  M. Tanguy , and  L. Véron , by  Wellcome Trust  grant  101863  to  J. Ahringer  and  P. Stempor , and  Medical Research Council  grant  MC-A652-5PZ80  to  Peter Sarkies.
Dna sequencing Amplicons from serum samples found to be MCPyV LT-positive were DNA sequenced  (13)  in order to verify MCPyV LT region specificity. MCPyV DNA sequencing data were compared with reference sequences from the National Center for Biotechnology Information (NCBI) Entrez Nucleotide database using the NCBI Blast program. To verify the presence of MCPyV DNA sequences, DNA from serum samples were analyzed by qualitative PCR with MCPyLT1709. F and MCPyLT1846. R primer sets, which generate an amplicon of 138 bp  (17). The forward and reverse primers are located at nucleotide position 1,709-1,846, based on GenBank sequence EU375803. pUC57MC1 recombinant plasmid  (20)  carrying the MCC 350 strain DNA (1) was used as a positive control. Amplified MCPyV PCR products were purified using the QIAquick PCR Purification Kit (Qiagen, Milan, Italy) according to the manufacturer's instructions.