dataesr/scientific-paragraphs-categorization huggingface dataset:
The correlations among these 3,815 OGs constituted a coexpression network from which nine coexpression subnetworks were extracted by removing connections between OGs based on absolute PCC cutoffs in the range of 0. 1 to 0. 9, which were denoted N1 to N9, respectively (Fig. S6 ). For each coexpression subnetwork, we identified highly connected gene modules using the ClusterONE algorithm  (25) , which allows for identification of overlapping modules, in agreement with the biological context where one gene product can have activities in multiple pathways. For larger networks, fewer but larger modules were found (Fig. S7 ), in agreement with previous observations  (26) , and a total of 59 significant modules were detected (P Ͻ 0. 1). To assess the overlap between these modules, the Jaccard index was computed based on module nodes (OGs), and this highlighted that many OGs were shared among multiple modules and, hence, represented different levels of resolution (Fig.
All strains used in this study are available from the culture collection (IBT) at the Technical University of Denmark. Transcriptome analysis. Cells from frozen biomass samples were disrupted using a TissueLyser LT disrupter (Qiagen), and total RNA was extracted using an RNeasy minikit (catalog no. 74104; Qiagen). DNA libraries for sequencing were prepared from the total RNA using Illumina's TruSeq protocol and sequenced using an Illumina 2500 machine, yielding 99 nucleotide paired-end reads with an average insert size of 600 nucleotides. Raw RNA-seq reads were mapped to the individual Penicillium genomes using a documented work flow  (37)  based on the TopHat2 (version 2. 0. 9) (38)  and HTSeq (39) programs. Both programs were run with default parameters. Gene-level statistics for CM versus DM were calculated using the DESeq2 program with default parameters, and differentially expressed genes were identified based on an adjusted P value cutoff of 0.
05. For all downstream analyses, log-transformed expression levels were used. Determination of orthology and phylogeny. The genome sequences of the six species were downloaded from NCBI, orthologous protein groups were identified using the orthoMCL algorithm  (40)  with default parameters, and these orthologous groups were used to define the core genome and the pangenome. Based on single-copy core genes, a concatenated maximum likelihood phylogenetic tree was reconstructed by using the RAxML program  (41)  and by applying a work flow previously described  (7). Gene set enrichment analysis. A gene set analysis was conducted based on a MetaCyc annotation (17) that was retrieved from previously published genome-scale metabolic models of the Penicillium species (  16 ), and GO terms were annotated using the InterProScan (version 5.
6 for the overall score was selected on the basis of having similarity to previous clustering of Penicillium BGCs  (7). Within GCFs, orthologous genes were identified using the MultiGeneBlast program  (45) , based on having 25% coverage and 30% identity. Coexpression network analysis. Gene expression was correlated using the Pearson correlation coefficient (PCC). In addition, the single-copy orthologous genes were correlated with backbone genes (PKSs, NRPSs, NRPS-like, or PKS-NRPSs) present in at least two different species, as identified in the BGC clustering. The pairwise correlations of the expression of all 3,815 single-copy core genes constituted a weighted coexpression network with orthologous groups as nodes and PCC values as edges. This coexpression network was divided into nine subnetworks by applying different cutoffs for the PCC values (0.
1 to 0. 9), and the corresponding networks were denoted N1 to N9, respectively. For each of these subnetworks, correlation coefficients were converted to absolute values and normalized to distribute between 0 and 1 (minimum/maximum normalization). Highly connected clusters of genes, referred to as modules, were detected in the subnetworks using the ClusterONE algorithm  (25). A total of 56 significant modules were identified using a P value cutoff of 0. 1 for a t test assessing the connectivity within a module versus outside a module.
RNA-Seq HPASMCs and EVs from supernatants were collected and RNA was extracted with miRNeasy kit (Qiagen), quantified using Qubit technology (Thermo Fisher Scientific) and samples' quality was checked in a 2100 Bioanalyzer (Agilent, Additional file 1: Fig. S1B ). The total amount of RNA obtained from EVs was 9 ng. Thus, library was prepared with SMARTer Low-Input Strand-Specific Total RNA-Seq for Illumina (Takara, Clontech). NGS was performed by BGI Tech Solutions on a HiSeq 4000 (Illumina). The sequencing quality was assessed using FastQC. Number of paired-end reads were between 5. 5 × 10 7 -6 × 10 7 for all samples (Additional file 1: Fig. S1 C ). Read mapping to the human genome was carried out with STAR  [22]  using GENCODE GRCh38 genome sequence and GEN-CODE v26 transcriptome annotation.
To study differential expression at the cell level, we quantified gene expression using HTSeq-count  [23] , based on the mapped reads. As expected with low-input library sequencing  [24] , we noted a high level of duplication, in particular for EV samples. To study the transcriptome of EVs, we decided to add a step to remove duplicated reads before the HTSeq-count quantification analysis. The removal of duplicates was done using the markdup option from SAMtools  [25]. RNA-Seq data has been submitted to NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE131998. The gene ontology analyses was done using topGO on differential expressed genes over a background of expressed genes  [26]. Interaction analyses were performed with String Database v10. 5. The interactions calculated by the software include direct (physical) and indirect (functional) associations and are based on genomic context predictions, highthroughput lab experiments, automated text mining and database searching.
Immunocytochemistry (ICC) Cells were fixed, permeabilized and incubated with the following primary antibodies: mouse monoclonal to CD9, sc-59,140; mouse monoclonal to CD81, sc-166,029; mouse monoclonal to CD63, ab59479, rabbit polyclonal to BMP11 (=GDF11), ab220951, rabbit polyclonal to TGF-β3, ab15537, from Abcam; and rabbit polyclonal to Zeb1, HPA027524, from Sigma-Aldrich. Phalloidin-iFluor 488 (Abcam) was used to counterstain the cell's actin cytoskeleton. The secondary antibodies used were: goat anti-rabbit Alexa Fluor 594 (Abcam) and goat antimouse Alexa Fluor 647 (Invitrogen). Nuclei were stained with DAPI. Cells were visualized in a TCS SP8 confocal microscope (Leica).
) Fig. 2  HPASMC-EVs show a specific transcriptome and are enriched in Zeb1, GDF11 and TGF-β3. Data from the low-input RNA-Seq was analysed to examine the cargoes sorted into HPASMC-EVs. Enrichment analysis was performed consisting in the quantification of cargoes with higher expression in EVs compared to their donor HPASMCs (pvalue < 0. 05 and FC > 2). a, Proportion of coding/non-coding RNAs detected in HPASMC and their EVs. b, Volcano plot showing the transcripts depleted or enriched in EVs compared to donor HPASMCs (pvalue < 0. 05 and FC < -2 or FC > 2). Among the enriched we found: Zeb1, GDF11 and TGF-β3. c, Validation by qRT-PCR of the enrichment of GDF11, TGF-β3 and Zeb1 in HPASMC-EVs (n = 3 biological replicates, data are presented as mean ± SEM. *P ≤ 0. 05, **P ≤ 0. 01, ***P ≤ 0. 001 (Student's t-test).
d, Immunocytochemistry of Zeb1 and CD63 proteins showing co-localisation within the cytoplasm of HPASMCs. e, Western Blot showing a specific expression of Zeb1 protein in EVs. Scale bars = 10 μm. Protein interaction analyses were performed with STRING and GO enrichment using DAVID software. PCA = principal component analysis, GO = Gene Ontology Fig. 3  Stimulation of TGF-β signalling triggers fibrotic phenotype and downregulates inflammatory pathways on HPASMCs. a, Control, TGF-β1treated and BMP4-treated HPASMCs were analysed by RNA-Seq. PCA analysis of HPASMCs showed a greater effect of TGF-β1 than BMP4, as these HPASMCs were separated from the rest by the first principal component, accounting for 69% of the variance. b, Heatmap showing the activation of specific downstream markers of TGF-β1 and BMP4 pathways.
c, GO analysis for biological process of the differential transcripts in TGF-β1treated HPASMCs provided significant terms relevant to PAH: cell migration (yellow), proliferation (brown) and inflammation (grey), as well as with cell communication (pink). d, Interactome analysis of the top differential RNAs in TGF-β1-treated HPASMCs. Most relevant clusters are highlighted: Inflammation, ECM remodelling/cell adhesion, serotonin signalling and signal transduction. e, Heatmap showing the inflammatory markers downregulated in response to activation of TGF-β signalling in HPASMCs. Treatment of HPASMC with TGF-β1 or BMP4 (both 10 ng/ml) was performed for 48 h and renewed every 24 h. Protein interaction analyses were performed with STRING and GO enrichment using DAVID software. PCA = principal component analysis, GO = Gene Ontology cells (Fig.
b, Representation of fold change in TGF-β1 treated HAPSMCs and their EVs showing correlation of the alterations found on differential transcripts. c, GO analysis for biological processes performed with the differential transcripts found in EVs from TGF-β1 treated HAPSMCs. d, Protein interaction analysis of the RNAs differentially expressed in EVs from TGF-β1-treated HPASMCs compared to control EVs. A node involved in actin and ECM remodelling has been underlined. Position of palladin in this node is highlighted. e, Validation by qRT-PCR of the upregulation of palladin and bHLH40 in HPASMCs and their EVs after 48 h TGF-β1 stimulation (n = 3 biological replicates). Data are presented as mean ± SEM. *P ≤ 0. 05, **P ≤ 0. 01, ***P ≤ 0. 001 (Student's t-test). Treatment of HPASMC with TGF-β1 (10 ng/ml) was performed for 48 h and renewed every 24 h.
Protein interaction analyses were performed with STRING and GO enrichment using DAVID software. PCA = principal component analysis, GO = Gene Ontology HPAEC and translation into protein in the recipient HPAECs. Two markers related to EndoMT and vascular remodelling were analysed on HPAECs Rep + after 7 days co-culture with HPASMCs Cre + , SMA and collagen III. We observed increase of SMA + cells (13-fold, pvalue = 0. 016) on HPAECs that took up HPASMC-EVs after coculture (eGFP + ), compared to those that did not (DSRed + eGFP -), showing induction of the expression of this EndoMT marker by HPASMC-EVs. Collagen III showed a moderate close-to-significance (1. 06-fold, pvalue = 0. 086) increase on positive HPAECs. EV-mediated transfer of mRNA from HPASMC to HPAEC in vitro is enhanced by TGF-β1 and IL-1β Using this methodology, we next investigated whether EV-mediated communication from HPASMC-to-HPAEC was altered by excessive TGF-β signalling, as well as by other cytokines involved in PAH including IL-1β, TNF-α and VEGFA, which have been found increased in the serum of PAH patients  [30, 31].
e, Quantification of Cre-induced recombination by FACS showed significant increase in co-cultures as opposed to control (HPAEC Rep + only) and HPASMC Null + : HPAECs Rep + cells co-cultures. f, The markers SMA and collagen III were quantified by flow cytometry at day 7 after HPASMC Cre + : HPAECs Rep + co-culture. Differential analysis of HPAECs that take up HPASMC-EVs (eGFP + ) vs. HPAECs that do not (DsRed + eGFP -) showed a significant increase of SMA (13-fold, pvalue = 0. 016) in eGFP + cells and a mild close-to-significant increase of collagen III (1. 06-fold, pvalue = 0. 087). Data are presented as mean ± SEM. *P ≤ 0. 05, **P ≤ 0. 01, ***P ≤ 0. 001 (Student's t-test). All procedures were carried out with n = 3 biological replicates. Scale bars 4x = 300 μm; 10x = 120 μm; 20x = 60 μm. SMA: smooth-muscle actin.
Then we compared the cargoes altered in HPASMCs-EVs in response to TGF-β stimulation. Upregulation of transcripts involved in actin and ECM remodelling by TGF-β1 was found in EVs according to the GO enrichment analysis. Thus, these EVs show a potential to activate and induce phenotypic changes on HPAECs during PAH development if uptake was to be proven. Among these differential RNAs from TGF-β1-EVs treated HPASMCs, palladin and bHLHE40 (also called Dec1), which have been shown to promote a malignant proproliferative phenotype in cancer cells by promoting epithelial-to-mesenchymal transition  [36] [37] [38] [39] , were further validated. The gene bHLHE40 is besides a downstream target of TGF-β signalling  [40]. Since our transcriptomic analyses have suggested that HPASMC-EVs could have the potential to affect HPAECs in physiological and pathological conditions, we aimed to study the potential ability of these EVs to transfer mRNA cargo to neighbouring HPAEC.
Acknowledgements The authors thank  Gregor Aitchison ,  Yvonne Harcus  and  Kathryn Newton  for technical assistance, and  Dr. Angela Bradshaw  and  Dr. Andrea Caporali  for their useful comments. Flow cytometry and sorting was performed with support from the  QMRI Flow Cytometry and Cell Sorting Facility, University of Edinburgh. Confocal images were obtained with support from the  QMRI Confocal and Advanced Light Microscopy Facility, University of Edinburgh.
Funding This work was supported by  H2020-MSCA-IF 2016  ( European Commission MSCA-IF  grant  EV-LNC  (Proposal number:  747287 ) to F. C. ). European Research Council  [ Advanced Grant VASCMIR  ( RE7644 ) to A. H. B. ], and  British Heart Foundation  [ BHF CVR grant  ( RM/17/3/33381 ) and  BHF Chair of Translational Sciences  ( CH/11/2/28733 ) to A. H. B. ].
The expeditions have provided new insights into the Arctic's Cenozoic paleoenvironmental and climatic evolution, new data on the paleoceanographic evolution of the tropics as recorded in coral reefs, new knowledge of the role of serpentinization in driving hydrothermal systems that sustain microbiological communities, and new information on the mechanics and effects of large meteorite impacts. The platforms and drilling services used in MSP expeditions are typically contracted from the commercial sector. In addition, the majority of International Ocean Discovery Program (IODP) standard measurements are not collected at sea, but instead during onshore science gatherings hosted by the IODP Bremen Core Repository. In this respect, MSPs give the community access to leading-edge as well as alternative technologies without the need to maintain a permanent drilling infrastructure.
Results were obtained from RNAseq profiling of three independent biological replicates. Significance of differential expression was based on FDR < 0. 05, numerical data are presented in Table  S1. (b) qRT-PCR validation of the P. brassicae-induced changes in expression of selected core cell cycle genes identified from RNA-seq profiling. Means of the relative expression, normalised to the 18S rRNA housekeeping gene, are plotted with the standard error. Three independent biological replicates were used, each with 30 plants per treatment. Statistically significant differences between uninfected and infected plants determined by Student's t-test are indicated as follows: *P < 0. 3 ). This pattern is altered upon P. brassicae infection and, at 16 DAI, MYB3R1 promoter activity is present in regions marking meristematic areas (Fig  3a ) typically expanded with P.
Transcriptome data analysis RNA-seq was performed at the Applied Genomics and Analytical Technologies Dept. , National Research Council of Canada using the Illumina Hi-Seq 2500 system. This data has been previously published by  Malinowski et al. (2016)  and raw data from this experiment can be obtained from the European Nucleotide Archive (https://www. ebi. ac. uk/ena) under the identifier PRJEB12261. Significantly differentially expressed genes data were identified using the DESeq2 package in the R statistical environment with False Discovery Rates (FDR) calculated using the qvalue package  (Love et al. , 2014). Heatmaps were generated using the ggplot2 package in R.
Ploidy analysis Ploidy changes were estimated using a previously described flow cytometry method  (Sliwinska et al. , 2015). For this, hypocotyls from appropriate experimental samples were excised and nuclei were isolated using Galbraith's buffer  (Galbraith et al. , 1983) , supplemented with propidium iodide (PI; 50 lg ml À1 ) and RNase A (50 lg ml À1 ). Nuclear DNA content was established in 5000-10 000 nuclei per sample using a CyFlow SL Green (Partec GmbH, M€ unster, Germany) flow cytometer. Analyses were performed on 12 replicates, using a logarithmic amplification. Histograms were evaluated using the FloMax program (Partec GmbH, M€ unster, Germany), and the percentage of nuclei with particular DNA contents and the mean C-value  (Lemontey et al. , 2000)  were calculated. Only nuclei having a DNA content of at least 8C were considered to be endopolyploid, as it is not possible to distinguish by FCM © 2018 The Authors The Plant Journal published by John Wiley & Sons Ltd and Society for Experimental Biology.
Anatomy and histology Histochemical b-glucuronidase activity assays and microscopic observations were performed as described in  Malinowski et al. (2012). For anatomical observation and measurements hypocotyls were fixed in Carnoy's solution (absolute ethanol:glacial acetic acid, 3:1) and Technovit embedded and cut as described previously. The obtained 5-lm sections were stained with 0. 05% (w/v) toluidine blue (Sigma-Aldrich) solution. All sections were mounted in 50% w/v glycerol solution and photographed using the Carl Zeiss AXIO Imager. M2 microscope system. For each sample 6À15 hypocotyls from three independent experiments were sectioned and measured. Hypertrophied cell size area was measured using Image J software  (Schneider et al. , 2012)  on toluidine blue stained sections for cells fitting into five randomly chosen squares from a grid dividing the whole section into nine squares.
Figure1. Changes in core cell cycle gene expression triggered by P. brassicae infection correlate with cellular DNA content and reflect cellular patterns during gall formation. (a) Heat maps showing changes in expression of core cell cycle genes with significant differences between levels in mock and P. brassicae-infected hypocotyls at either 16 or 26 days after inoculation (DAI) defining, respectively the proliferative and expansive stages of gall development. Yellow indicates upregulation in the infected tissue relative to controls while blue represents downregulation, the colour bar corresponds to log (base 2) ratios. Results were obtained from RNAseq profiling of three independent biological replicates. Significance of differential expression was based on FDR < 0. 05, numerical data are presented in TableS1.
ACKNOWLEDGEMENTS We thank the following individuals for providing materials that were used in this work:  Ian Moore  ( University of Oxford, UK ) for the pOpON2. 1 vector,  Lieven De Veylder  ( VIB, Gent, Belgium ) for the anti-E2Fa antibody,  Jim Murray  ( Cardiff University, UK ) for the anti-CYCD3;1 antibody and  Willhelm Gruissem  ( ETH Zurich, Switzerland ) for the anti-RBR1 antibody. Research was supported by the  National Science Centre Poland   SONATA BIS2  grant no. 2012/07/E/NZ3/00510  'Integrated approach for deciphering the mechanism leading to shift in cell proliferation/differentiation balance accompanying clubroot infection'. RM and WT received support from the  EU   FP7 programme for research, technological development and demonstration  under grant agreement no. 621321  and Polish financial sources for education in the years 2015À2019 allocated to international co-financed projects.
Filters were transferred onto Cytoclear ® slides and frozen (-20 • C) until microscopy analysis. For the analysis, 30 images for each filter were captured under 200× magnification using a light microscope (Zeiss Axio Scope. A1) connected to a color camera (AxioCam MRc). Particle abundance and area were measured semiautomatically using an image analysis system including the WCIF ImageJ software. The RGB was split into three channels (red, blue and green), and the red was used to quantify the amount of TEPs and CSPs. Additionally, TEPs and CSPs in water samples from the stations where we deployed sediment traps were analyzed spectrophotometrically (with higher vertical resolution than microscopy) according to  Passow and Alldredge (1995)  and  Cisternas-Novoa et al. (2014) , respectively. Concentrations of TEPs are reported relative to a xanthan gum standard and expressed in micrograms of xanthan gum equivalent per liter (µg XG eq.
, 2014). The abundance and size of MnOx-like particles were determined using particle recognition on filters and image processing similar to the method used by  Neretin et al. (2003)  but without the chemical composition analysis of the particles. For the image analysis, we used the same images as for TEP and CSP analysis and modified the image analysis procedure described above as follows: 30 images per filter (200×) were analyzed semiautomatically using ImageJ software. After RGB split, the blue channel pictures were used to quantify MnOx-like particles in the water column and sediment traps. In this manner, the MnOx-like particles were clearly visible with a negligible disruption from TEPs or CSPs stained blue. Total hydrolyzable amino acids (TAAs) were analyzed in unfiltered seawater and trapped material.
Phytoplankton, <20 µm, cell abundance was quantified using a flow cytometer (FACSCalibur, Becton Dickinson, Oxford, UK). 2 mL samples were fixed with formaldehyde (1 % final concentration) and stored frozen (-80 • C) until analysis (2 weeks later). Red and orange autofluorescence was used to identify chlorophyll and phycoerythrin cells. Cell counts were determined with CellQuest software (Becton Dickinson); picoplankton and nanoplankton populations of naturally containing chlorophyll and/or phycoerythrin (i. e. , Synechococcus) were identified and enumerated.
Acknowledgements. This research was supported by the  DFG Collaborative Research Center 754  " Climate-Biogeochemistry Interactions in the Tropical Ocean " (to Anja Engel,  Carolina Cisternas-Novoa  and  Frédéric A. C. Le Moigne ), by a Fellowship of the Excellence Cluster "The  Future Ocean " ( CP1403  to  Frédéric A. C. Le Moigne ), and by a DAAD short-term grant ( 57130097  to  Carolina Cisternas-Novoa ). We thank  Jon Roa ,  Tania Klüver ,  Scarlett Sett ,  Angela Stippkugel ,  Carola Wagner ,  Clarissa Karthäuser ,  Moritz Ehrlich ,  Sonja Endres ,  Hannes Wagner ,  Ruth Flerus ,  Sven Sturm  and  Christian Begler  for support during trap preparation and deployments, as well as help with experiment or analyzed samples. We thank  Judith Piontek  for her contribution to the  design of the scientific program  at sea,  Jaime Soto-Neira  for valuable discussion and help with figure preparation,  Annegret Stuhr  for useful discussion about phytoplankton data, and  Cindy Lee  for helpful advice.
The cryo-EM imaging was carried out at a final object pixel of 3. 6 Å, with a total dose of ∼90 e -/Å 2 in order to limit specimen damage. Computation of the tomogram was carried out with the IMOD software package using a WBP-based algorithm. 56 egmentation and 3D visualization were carried out using the Amira package (Thermo Fisher Scientific, USA). " Magnetic Accumulation Experiment. In three disposable cuvettes, the required amounts of single NCs, the 2D-MNBs, and the 3D-MNBs were dispersed in 500 μL of deionized water in order to produce suspensions containing the same amount of the magnetic material (40 μg of iron). Each sample was simultaneously exposed to a permanent magnet (0. 3 T). The three vials were photographed with a digital camera after 80 s for 2D-MNBs, after 120 s for 3D-MNBs, and after 24 h of magnetic exposure for the single NC.