dataesr/scientific-paragraphs-categorization huggingface dataset:
Code and data availability. The BFM biogeochemical model and its documentation can be downloaded at the following address: http://bfm-community. eu/ (last access: 22 April 2019). The qualitycontrolled databases used in the present paper are publicly available from the SEANOE (SEA scieNtific Open data Edition) publisher at https://doi. org/10. 17882/49388 (Barbieux et al. , 2017)  and https://doi. org/10. 17882/47142 (Organelli et al. , 2016b)  for vertical profiles and products within the first optical depth respectively. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-16-2527-2019-supplement. Author contributions. ET and PL have designed the paper. ET performed the BGC-Argo data analysis, and PL performed the simulations. ET, PL, EO, SS, CS, FD, and PC contributed to the paper writing.
The main aims of the IOBC GCWG are to share, design, discuss, and promote best management practices of cacti in their introduced ranges (https://www. iobc-global. org/global_wg_cactus. html). A symposium on the management of cactus invasions was held in 2015 in Waikoloa Village, Hawaii, as part of the 13th International Conference on Ecology and Management of Alien Plant Invasions (EMAPi  [25, 26] ). As a result of discussions during this symposium, and aiming to collect available information on cactus management worldwide, members of the IOBC GCWG developed a web-based questionnaire in English, French, Italian, Portuguese, and Spanish, and distributed it to all parts of the world known to have invasive cacti (Supplementary Material). Additionally, information was collated from scientific and grey literature, online databases, and scientific reports.
Policy and Legislation Once species that pose a risk following introduction are identified, it is important to regulate their importation, use and management  [33]. According to our survey, the introduction and use of cactus species is regulated in a number of countries (Table  2 ): 87% of respondents reported regulations concerning the introduction of new alien cacti from other regions and 38% reported regulations concerning the movement of cacti within their region. We crosschecked this information with the Food and Agriculture Organization Legislative and Policy Database (FAOLEX; http://www. fao. org/faolex/). Some of the policies regulate the introduction, use and management of cacti at a national level (e. g. , the South African National Environmental Management: Biodiversity Act 2004 (Act No.
Detection of New Incursions For the successful eradication of any invasive species, new incursions must be promptly detected and delimited. Actions to detect alien cacti are in place in several regions of the world, and 80% of the respondents of the questionnaire mentioned some. For example, the Southern African Plant Invaders Atlas has recorded the presence of alien plants (including cacti) growing outside of cultivation since 1994 and has recently focused efforts on finding populations of potentially eradicable species  [58]. In 2008, the government of Valencia (Spain) established an alert network of 242 forest wardens and other trained staff to detect Cylindropuntia rosea (DC. ) Backeb. [59]. Within two years, the network detected 38 new invasions. In Kruger National Park (≈1. 9 million ha), rangers record alien plant observations in the course of their daily patrols using customised CyberTracker software  [60, 61].
1b  and Supplementary Data 1). We also tested a second chemical inhibitor of transcription elongation, mycophenolic acid (MPA)  24. Similar to the results with 6AU, MPA had a slight negative effect on root growth in wild type (Fig. 1c  and Supplementary Data 1); however, bdr1,2,3 root length more than doubled when grown on MPA. These results suggest that the short-root phenotype may be due to increased transcriptional elongation in the bdr1,2,3 background. BDR proteins are enriched at gene borders. We performed chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) to determine the localization of BDR1, BDR2, and BDR3 using MYC-tagged constructs driven by their respective endogenous promoters in the bdr1,2,3 background. All three constructs rescued the short-root phenotype of bdr1,2,3 (Supplementary Fig.
4 ). These data show that some correlations are stronger in particular regions. For example, the correlation between BDR3, which shows relatively little binding near the TSS, with BDR1, BDR2, and Pol II is higher near the TES (Supplementary Fig. 4 ). We also examined sequence conservation around BDR peaks. PhastCons  25  examines sequence conservation between Arabidopsis and the genomes of 20 other angiosperms. In order to focus on the conservation of intergenic regions, sequences corresponding to annotated genes were removed. Because BDR peaks are preferentially found in nucleosome-depleted regions, we included other nucleosome-free regions, as well as random intergenic sequences, as controls. We found that sequence conservation was significantly higher at BDR peaks compared to surrounding intergenic sequences (Fig.
All high-throughput sequencing data and corresponding experimental details are available in GEO SuperSeries GSE112443. Chromatin immunoprecipitation followed by next-generation sequencing. Nuclei were isolated from cross-linked samples as described previously  50  and resuspended in nuclei lysis buffer (50 mM Tris-HCl pH8, 10 mM EDTA, 1% sodium dodecyl sulfate (SDS), 1 mM phenylmethanesulfonylfluoride (PMSF), 1% Plant Protease Inhibitors from Sigma). After fragmentation using a Covaris S200, the chromatin samples were diluted with ChIP dilution buffer (final concentration: 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl pH8. 0, 150 mM NaCl, 1 mM PMSF, 0. 1% SDS, 1% Plant Protease Inhibitors, Sigma). The diluted chromatin samples were subjected to immunoprecipitation with antibodies (anti-MYC tag, clone 4A6, Millipore 05-724 (30 μg); Anti-RNA polymerase II CTD repeat YSPTSPS antibody [8WG16] Abacm ab817 (20 μg); Anti-RNA polymerase II CTD repeat YSPTSPS (phospho S2) antibody, Abcam ab5095 (30 μg); Anti-RNA polymerase II CTD repeat YSPTSPS (phospho S5) antibody Abcam ab5131 (30 μg); and control IgG Abcam ab18413 (20 μg)).
PGK, GAPDH, and FBA are rapidly induced by light and are located a short distance from an upstream neighbor on the same strand. In bdr1,2,3, the upregulation by light is reduced at the Center of Genomics and Bioinformatics, Indiana University. All highthroughput sequencing data and corresponding experimental details are available in GEO SuperSeries GSE112443. RNA-seq computational analysis. Two independent RNA-seq studies each with biological triplicates were performed in wild type and bdr1,2,3 triple mutant seedlings that were grown under standard conditions (GSE112440 and GSE112441). The second study (GSE112441) also included bdr1, bdr2, and bdr3 single mutant seedlings. Except when otherwise stated, study GSE112441 was used to compute the figures presented in this manuscript. We systematically verified that consistent results were obtained with both the RNAseq studies.
For GSE112440 (49 bp single-end reads sequenced on Illumina HiSeq 2000 instrument) and GSE112441 (2 × 43 bp paired-end reads sequenced on Illumina NextSeq 500 instrument), read alignments (For GSE112440: topHat 2  52  ; for GSE112441: STAR  53  ), filtering to keep uniquely aligned reads (samtools and grep commands  54  ), and gene-level read counting (featureCounts  55  and differential expression analysis with DESeq2 56 ) were performed as described in the corresponding GEO records. Genes with Benjamini-Hochberg (BH)-adjusted p values <0. 05 were considered differentially expressed. Using GSE112441 RNA-seq data, we defined a list of 1408 control, non-differentially expressed genes ("Not DE") by selecting genes with high p values (p > 0. 45) and low absolute log2(foldchange) (<0. 25) for all comparisons (single bdr1, bdr2, and bdr3 mutants and the bdr1,2,3 triple mutant vs wild type) and removing genes with extreme read counts (DESeq2 basemean >3 and <1e5).
Enrichments for Gene Ontology (GO) biological processes among the gene sets (upregulated or downregulated) were evaluated using the goseq package  57. ChIP-seq computational analysis. All ChIP-seq samples were sequenced in paired-end mode on an Illumina NextSeq500 instrument (read length of 40, 43, or 155 bp, as specified in the corresponding GEO entries). Sequencing adapters were removed using Trimmomatic 0. 33 in paired-end mode  58  and reads were aligned to the Arabidopsis genome using Bowtie2 59 using the -dovetail parameter and a maximum insert size of 1 Kb. Duplicate fragments were removed with Picard 2. 2. 4 MarkDuplicates (http://broadinstitute. github. io/picard/). Samtools v 1. 3 was used to keep only reads mapped in proper pairs with mapping quality (MapQ) >2. For MNase-seq and ChIP-seq on histone modifications, we found that fragments <70 bp were enriched for background signal and fragments >250 bp for signal corresponding to dinucleosomes.
Thus we only kept the reads corresponding to fragment sizes between 70 and 250 bp. Aligned reads were imported in R (v. 3. 3. 2) to obtain coverages using Bioconductor v3. 4  60. Coverages were normalized as fragments per 10 million fragments (FP10M) and exported to bigWig files with the rtracklayer package  61. ChIP-seq peaks were detected using MACS2 2. 1. 0 62 in paired-end mode. Peaks located in blacklisted regions were removed. Annotation of peaks relative to genomic features were obtained using the ChIPpeakanno package  63. Average profiles and metagene plots. Coverages (e. g. , FP10M for ChIP-seq data, phatsCons score, or annotation coverages) or normalized coverages (e. g. , log2(BDR ChIP/WT control ChIP)) were directly used, without binning or smoothing, to produce average profiles centered on genomic features of interest (e.
g. , peak centers, TSS, or TES). After selecting a gene set of interest, the most extreme 0. 01% coverage values were replaced by the upper 99. 99th percentile value before calculating the average value ("metagene", solid line) and the associated 95% confidence interval (CI) (shade). For the latter, we used a normal approximation, which on several examples gave results nearly identical to bootstrap estimates of the CIs as implemented in the ChIPseeker package  64. For metagene plots centered on gene bodies, we first averaged the signal in 100 bins covering the gene body (i. e. , bin size of 1% of gene length). Multigene heatmaps. Multigene heatmaps were produced with the Enriched-Heatmap package  65  from coverages (in FP10M) or normalized coverages that were averaged in 20 bp bins before/after genomic features of interest (TSS, TES, or peak center) or in bins covering every 1% of gene length along gene bodies.
Definition of a blacklist for the Arabidopsis genome. We used 20 control samples (input DNA or IgG ChIP) obtained in our laboratory from different ChIPseq experiments (both sonicated and MNase-fragmented chromatin) to define a blacklist of genomic regions with systematically high signal in control samples. We used the Bioconductor package GreyListChIP 66 to generate a list of regions with high signal (95th percentile of negative binomial distribution estimated from 100 random samples of size 30,000) in >50% of the control samples and refined these regions manually on a genome browser using independent ChIP and control samples. A bed file of blacklisted regions is provided as a supplementary file (BlackList_TAIR10. bed). Bioinformatic methods for each figure. Figure  2a. Coverages from BDR1::MYC (GSE113059), BDR2::MYC (GSE113059), BDR3::MYC (GSE131772), and Pol II (GSE113078) ChIP-seq fragments (units: FP10M) and average coverage from RNA-seq (GSE112441) fragments obtained from 3 wild-type samples (units: RPM, sign indicating on which strand the reads align) were plotted for ~32 Kbp region of chromosome 5 using the Gviz package  67.
After removing peaks located in blacklisted regions, we obtained 21,334 peaks for BDR1, 11,997 peaks for BDR2, and 12,178 peaks for BDR3 (p < 0. 01, peak coordinates available in GSE113059 and GSE131772). Overlaps between the peaks were evaluated with the ChIPpeakAnno package  63  and plotted as a Venn diagram using the Vennerable R package  68. Figure  2d. The summits of BDR1, BDR2, and BDR3 peaks were annotated relative to genomic features with the ChIPpeakanno package  63  using the following order of precedence (e. g. , once a peak is annotated as promoter, it cannot be attributed to another category): Promoters (<300 bp upstream of TSS), immediate downstream (<300 bp downstream of TES), 5′-UTR, 3′-UTR, exons, introns, intergenic regions (>300 bp from any gene annotation). The percentage of peaks annotated in each category is shown.
DNase-hypersensitivity signal was obtained from GSE34318  69. Figure  3b. Sequence conservation (phastCons) scores from the alignment of 20 angiosperm plant genomes were obtained from ref. 25  and values overlapping with TAIR10 gene annotations were masked in order to focus on intergenic sequence conservation only. We selected: 1. 19,317 (BDR1), 10,020 (BDR2), or 9,204 (BDR3) ChIP-seq peaks that overlapped to some extent with intergenic regions; 2. 12,431 random regions overlapping with intergenic regions but not overlapping with BDR1 peaks or with blacklisted regions; and 3. 18,255 nucleosome-free regions defined as regions with >500 bp of consecutive MNase-seq and H3 ChIP-seq coverage below the first quartile of the respective datasets (GSE113076) and not overlapping with BDR1 peaks or blacklisted regions.
For each of these groups, we plotted the average phastCons scores around the respective BDR peak summits (±500 bp) or the respective region centers. Shades around the lines show the associated 95% CI using a Gaussian approximation. Figure  3c. For the two motifs that were found to be enriched under BDR1 and BDR2 peaks (see Fig. 3d ), the TCP-like motif and the E-box motif, we plotted their distribution around BDR1 or BDR2 peak summits and around the TSS or TES of protein-coding genes. At each base, the plot represents the percentage of bases that overlap with a TCP-like (blue) or an E-box motif (green), along with the 95% CI based on a Gaussian assumption (shade). Figure  3d. We performed a de novo search for motifs enriched under BDR1 and BDR2 peaks using the peak-motif program from Regulatory Sequence Analysis Tools: RSAT 70.
ChIP-seq data are from GSE113078. Figure  4b. For each expressed gene >1 Kb, we calculated the Pol II 3′ pausing index as the ratio of read density just after the TES to read density in the gene body, as illustrated. Read density is the ratio of the number of reads aligned on the region to the region length in bp. The 3′ pausing indexes were calculated for wild type and bdr1,2,3 using Pol II, S2P or S5P ChIP-seq data from GSE113078. Figure  4c. We split genes into 9 groups according to their mRNA expression levels as in Fig. 2b  and plotted as boxplots the distribution of log2 (3′ pausing indexes) for wild-type and bdr1,2,3 plants using Pol II (upper panel), S2P (middle panel), or S5P (lower panel) ChIP-seq data from GSE113078. Significance of the difference in mean log2 (3′ pausing index) values between wild type and bdr1,2,3 was evaluated using paired Student's t test with BH p value adjustment.
01 are shown. Figure  5c. From the gene groups defined for Fig. 5a , we selected the genes with an upstream gene on the same strand and a non-null read count in our RNA-seq study GSE112441. Then we plotted as boxplot the distribution of expression values (average log2(RPKM) from triplicate wild-type samples, GSE112441) for the upstream tandem genes and evaluated the significance of the differences between distributions using Mann-Withney U test. Enrichment for GO processes among the upstream tandem neighbors of BDR-protected genes was evaluated with the goseq R package  57. Genes located upstream on the same strand of all expressed genes were used as the gene universe. Only categories with a p value <0. 001 are shown. Percentages of genes annotated with each GO category in the universe (gray) and in BDR-protected genes (green) are shown.
BH-adjusted p values <0. 05 are represented. Figure  6f. Using the same data as for Fig. 6e , we assessed by Fisher exact test the enrichment of genes forming gene loops in genes regulated in the bdr1,2,3 mutant (upregulated or downregulated) and for their upstream or downstream neighbors located on the same or opposite strand. For each group of genes, the percentage of genes forming gene loops is indicated. p values from Fisher exact test were adjusted by the BH method. Figure  7a. Experimental set-up of the photomorphogenesis experiment. The corresponding RNA-seq data are available in GEO series GSE112442. Figure  7b. Principal component analysis was performed on the top 500 genes with the highest variance across samples using the DESeq2 plotPCA function  56. Figure  7c. Intersections of the genes significantly regulated (DESeq2, FDR < 5%) in wild type and in the bdr1,2,3 triple mutant after 2 or 4 h light, compared to dark, were plotted as Venn diagrams using the Vennerable R package  68.
We used Wilcoxon signed-rank test to evaluate the change in log2(bdr1,2,3/wt) between the dark and light conditions. All p values <0. 05 are reported. Figure  7f. Using RNA-seq data GSE112442, we selected all genes that were upregulated by light at 2 or 4 h in wild-type plants (DESeq2; fold-change >1. 5, BHadjusted p value <0. 05) and separated them by both orientation of their upstream gene (O: upstream gene is on the opposite strand, and S: upstream gene is on the same strand) and intergenic distance between their TSS and the upstream gene border (<600 bp; between 600 and 1200 bp or >1200 bp). For each group of genes and under each condition (dark, light 2 h, and light 4 h), we plotted the distribution of the log2(bdr1,2,3/wt) for the downstream genes and compared genes with an upstream neighbor on the same strand (S) to those having an upstream neighbor on the opposite strand (O) with Mann-Whitney U test with a BH p value adjustment.
All adjusted p values <0. 05 are shown. Figure  7g. The Gviz Bioconductor package  67  was used to plot the coverage tracks for BDR1::MYC and BDR2::MYC (FP10M, GSE113059), Pol II ChIP-seq (FP10M, GSE113078), and RNA-seq data (average RPM from triplicates in each group, GSE112442) for PCK1 (AT3G12780), GAPDH B subunit (AT1G42970), and FBA7 (AT4G26520) and their respective upstream tandem gene neighbor. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Data availability Raw and processed ChIP-seq and RNA-seq data, along with detailed experimental and bioinformatic procedures, are provided in GEO Series GSE112443 and its subseries. TAIR10 annotations were used for all analyses. All other relevant data supporting the key findings of this study are available within the article and its Supplementary Information files or from the corresponding author upon reasonable request. A reporting summary for this article is available as a Supplementary Information file.
Acknowledgements Next-generation sequencing was performed at the  Indiana University Center for Genomics and Bioinformatics. We thank  Mathilde Gorieu  for technical help during the photomorphogenesis experiment. We are grateful to the genotoul bioinformatics platform  Toulouse Midi-Pyrenees  ( Bioinfo Genotoul ) for providing computing and storage resources. This work was supported by a grant to S. D. M. from the  National Institutes of Health  ( GM075060 ). P. G. P. M. received the support of the  EU  in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills+ fellowship  (under grant agreement no. 609398 ).
Crystal Structure of Human NUDT12 in Complex with m 7 GTP To understand the structural basis for the requirement of homodimerization for catalytic activity, we crystallized the active (Figure  1G ) catalytic core of human NUDT12 (111-462 aa) in complex with m 7 GTP (PDB: 6SCX; Table  1 ) and determined the structure by X-ray crystallography (STAR Methods). The 2. 9-A ˚structural model of human NUDT12 presents a homodimer. Each monomer is folded into three distinct regions, two tandem Nudix-fold domains (the N-terminal domain [NTD] and the C-terminal domain  [CTD] ), which are separated by a zincbinding motif (Figure  2A ). The overall structure is very similar  (Figure 2B)  to that recently described for the mouse NUDT12 complexed with the bound product AMP and three Mg 2+ ions (G) Decapping assay with homodimeric human NUDT12 carrying different N-terminal truncations, and a monomeric version (5A-mut).
The monomeric version of the protein is inactive. See also Figures  S1D  and S1E. (H) Western analysis of HA-tagged proteins expressed in HeLa cells. The FL NUDT12 protein or one lacking the ANK repeats are expressed, and either has homodimeric or monomeric (carrying 5A-mutations) versions. See STAR Methods and Figures  S1D  and S1E. Note the lower abundance of the monomeric versions. (PDB: 6O3P; root-mean-square deviation [RMSD] of 1. 0 A ˚over 308 aa)  (Grudzien-Nogalska et al. , 2019)  and the E. coli NudC homodimer (PDB: 5IW5)  (Ho ¨fer et al. , 2016). The only other major differences between the structures, apart from some alternate loop conformations, are an additional 8 aa observed at the N terminus of the human structure and replacement of Mg 2+ and Zn 2+ (of the zinc-binding motif) ions in the mouse structure (Figure  2B ) with Cd 2+ ions in the human structure (Figure  2A ).
, 2009). In this context, we find that transcript levels of CLOCK downstream targets Per 1, 2, and 3 and Cry 2 are upregulated in the RNA-seq datasets (F) Electron micrographs of negative-stained NUDT12-BLMH complexes. The magnification used and scale are indicated. The different views of the complex are shown, likely indicating a collapse of the complex during the preparation of the grid. The complex shows a central barrel of BLMH (double layer of trimers) with three spokes composed of NUDT12 dimers. Scale bar in nanometer (nm) is indicated. (G) Decapping assay with m 7 G-RNA and indicated NUDT12 proteins, carried out in presence of increasing amounts of BLMH. (H) Cleavage assays with labeled NAD analog and FL NUDT12, carried out in presence of increasing amounts of BLMH or a control protein (BSA).
Constructs for mammalian cell expression Coding sequences for full-length (FL) human NUDT12 (462 aa; Accession number NP_113626) and human BLMH (455 aa; Accession number NP_000377) were amplified from human HeLa cell total RNA by reverse transcription-PCR (RT-PCR). Mammalian expression vectors (pCI-neo vector backbone) allowing production of fusion proteins with an N-terminal 3xFLAG-HA or N-terminal N-HA-tag or C-terminal HA-tag from a cytomegalovirus (CMV) promoter were used. We also used a vector allowing production of proteins with an N-terminal 3xMYC tag (pcDNA3. 1 vector backbone) from a cytomegalovirus (CMV) promoter. Sequence of the N-terminal 3xFLAG-HA tag is as following: on m 7 G-capped RNA (Figure  1G ). However, note that NUDT12 protein (103-462 aa) carrying only the Y384A mutation (one of the five mutations needed for making the protein monomeric) lacks decapping activity on the m 7 G-capped RNA, although it retains hydrolysis activity with 32 P-NAD  (Figures 2G, S2B, and S2C).
Mass spectrometry Mass spectrometry to confirm purified recombinant proteins was carried out at the Proteomics Core Facility, EMBL, Heidelberg. Identification of components within an immunoprecipitated complex was carried out at the Functional Genomics Center Zurich (ETH Zurich) using the shotgun mass spectrometry analysis. Database searches were performed using the Mascot (SwissProt, all species) search program. Applied settings, if not stated differently, are 1% protein false detection rate (FDR), min. 2 peptides per protein, 0. 1% peptide FDR.