dataesr/scientific-paragraphs-categorization huggingface dataset:
In our data, opportunistic Proteobacteria reached their highest abundance in corals, where Actinobacteria were below B2. 5% relative abundance (Supplementary Fig. 4 ), suggesting that antibiotic-secreting Actinobacteria may help to protect corals from opportunists. Actinobacteria, in turn, decreased with increasing algal cover or elevated temperatures, suggesting that these stressors remove an important biotic barrier to potential pathogens (Supplementary Fig. 3 ; Supplementary Data 3 sheets c,d). Increasing algal cover or elevated temperatures also shifted the predicted functional profiles of corals towards microbial pathogenesis. We used the PICRUSt software package  40  to estimate the functional consequences of changes in microbial community composition (Methods). According to these estimates, microbiomes subject to algal competition or above-average temperatures became enriched in pathways involved in opportunism (for example, cell motility and secretion systems) and depleted in pathways for antibiotic production that may help healthy coral microbiomes resist invasion (Supplementary Fig.
We did not detect any differences in algal abundance or community composition between the open unmanipulated areas and exclosure controls (Supplementary Data 1). Coral tissue growth or loss analyses. At the beginning of the experiment, we mapped each coral colony in the experimental plots that were 42 cm in diameter and took close-up photographs of these corals in situ. Subsequently, we photographed each of these corals every B16 weeks throughout the experiment for a photographic record of changes in coral colony health. In each picture a ruler or object of known size was placed next to the coral to provide scale. In total, we tracked the fate of 226 individual corals spread across each of the treatments for over 3 years. The most common corals were Porites porites (41. 1 % of corals), Agaricia spp.
The primary coral disease observed was DSS (see ref. 52 for additional discussion). Statistical analysis of benthic cover and coral health. Algal cover was analysed using mixed effects models to determine whether the response variable differed among enrichment treatment, herbivore treatment and seasons, as well as whether there was an enrichment Â herbivore Â season interaction (Supplementary Data 1 sheet c). The nested, split-plot design of the experiment was incorporated into the model by nesting replicates of the exclosures and exclosure controls within ambient or nutrient-enriched plots. We analysed cover for important species or functional groups, as well as for overall upright algal cover, which is a proxy for the competitive environment of corals. Upright algal cover included all macroalgae and tall filamentous turf, but excluded crustose coralline algae and short filamentous turf as these two functional groups are relatively benign for corals  10.
Therefore, we analysed coral growth both excluding the corals that died, which satisfied normality requirements for the analyses, and including the corals that died. Both analyses produced relatively consistent results (Supplementary Data 5), with the exception that only the interaction of herbivory Â nutrient loading was significant in Porites corals (rather than each factor also being individually significant) when total colony mortality was excluded. We used a w 2 -test to determine if coral mortality was higher or lower than expected across different seasons given the null hypothesis that coral mortality would be distributed evenly across seasons (25% of total mortality per season). To assess the impact of algal competition and parrotfish predation on coral mortality for Porites spp. , (the corals with the highest mortality rates), we used Fisher's exact test to test for differences in the proportion alive versus dead corals across enrichment and/or herbivore exclusion treatments.
We also used Fisher's exact tests to determine how factors such as competition with different types of algae (that is, corals with or without algal contact) led to loss/gain of coral tissue or coral death/survival. Mixed effects models were run using the 'nlme' package and post hoc comparisons conducted using the 'multcomp' package in R v3. 0. 0. Fisher's exact tests on contingency tables were run using JMP software (SAS). PCoA of Bray-Curtis divergences and PERMANOVA of benthic community data were conducted in QIIME 1. 8. Field sampling of coral mucus for microbial analysis. Coral mucus microbial communities were studied in depth for three coral genera common to the study region and most abundant within the plots: Siderastrea (Siderastrea siderea only), Porites and Agaricia. We used 16S rRNA gene surveys to study the microbial changes in the coral mucus across the course of treatment, focusing especially on whether detectable microbial changes accompanied specific routes to mortality or tissue loss in corals.
(For metadata and sequencing statistics, see Supplementary Data 2; for OTU table in BIOM format, see Supplementary Data 7). We focused on mucus communities because these are thought to provide a barrier against invasion by opportunistic pathogens, and can be sampled non-destructively from the same individuals over time. We deemed other methods, such as collecting live coral tissue, too harmful and invasive to the coral for our goal of monitoring the coral microbiome over the long term. Coral-associated bacteria and archaea were collected using sterile syringe removal of the coral surface mucus layer on SCUBA. A sterile syringe was used to first agitate the coral and then remove 10 ml of mucus using negative pressure. Once on the surface, the sample was placed in a sterile 15 ml conical tube, frozen on dry ice for the return trip and kept frozen until processing.
5% agarose gel, and successful triplicate reactions were pooled and cleaned using AMPure magnetic beads from Agencourt. Before sequencing, libraries were quantified using a Qubit dsDNA HS kit from Invitrogen and then pooled into equimolar ratios. The pooled library was checked for amplicon length and purity on an Agilent Bioanalyzer 2100 and then sequenced on a 454 Roche pyrosequencer (GSJunior platform) at the Oregon State University's Center for Genome Research and Biocomputing Core Laboratories. Microbial community data quality control. The QIIME (v. 1. 8) software pipeline  65  was used for quality control, selection of operational taxonomic units and analyses of community diversity (Supplementary Data 2). Sequence libraries were demultiplexed, and sequences with quality scores less than a mean of 35 were removed.
Error-correcting barcodes were used to detect and recover sequences whose barcode sequence had exactly one sequencing error. Barcode sequences with two or more errors were removed. Sequences were clustered into operational taxonomic units (OTUs), at a 97% 16S rRNA gene identity threshold using USEARCH 6. 1. 544, and the subsampled open-reference OTU-picking protocol in QIIME v. 1. 8 (ref. 65, using greengenes 13_8 as the reference  66. This OTU-picking protocol clusters all reads, but assigns reference ids to OTUs in greengenes, which can be useful in comparisons across studies. Chimeric sequences were removed with UCHIME. OTUs represented in the overall analysis by only a single count (singletons) account for a large proportion of noisy reads. Because our emphasis was overall community trends (rather than exploration of the rare biosphere of corals), singleton sequences were removed.
Representative sequences for each OTU were classified taxonomically according to the greengenes taxonomy version 13_8 66 using the Ribosomal Database Project (RDP) Classifier software v. 2. 2. Sequence alignment and phylogenetic inference for the representative sequence of these OTU is described below in the context of b-diversity analysis. We took additional steps to account for aspects of the data set unique to host-associated samples. Because coral mucus can contain some amounts of sloughed tissue, we tested whether coral mitochondria were present in any mucus samples. Similarly, because Symbiodinium and other photosynthetic microbial eukaryotes frequently inhabit coral mucus, chloroplast sequences are frequently observed in microbial diversity surveys of corals. As our interest was primarily in cellular bacteria and archaea rather than organelles, coral mitochondrial sequences and 16S sequences classified as chloroplasts with at least 70% confidence by the RDP classifier were removed before analysis.
Because we observed that many mitochondrial sequences were not efficiently identified by RDP, we also removed sequences with very high (1 À e À 50 ) sequence similarity and at least 90% sequence similarity to a reference coral mitochondrial sequence. A table of the coral mitochondrial sequences used is available as Supplementary Data 2 sheet b. These thresholds were selected with care to avoid indiscriminate removal of a-proteobacteria sharing evolutionary ancestry and 16S rRNA gene sequence similarity with mitochondria. The e-value for mitochondrial removal was selected by testing several e-values (1 À e À 10 ,1 À e À 30 ,1 À e À 50 and 1 À e À 100 ). For each e-value the best BLAST (Basic Local Alignment Search Tool) similarity against NCBI's nr database was examined. We selected the highest (most lenient) e-value that removed mitochondria, but not related bacteria.
At a 1 À e À 50 BLAST e-value threshold, the best BLAST hit of all removed 16S rRNA sequences was coral mitochondria, and this threshold was therefore selected for the screen. The final OTU table after quality control (QC) used for our analysis described in this manuscript can be found in Supplementary Data 7. Analysis of microbial b-diversity. Microbial community b-diversity was calculated based on the weighted UniFrac distance matrix  67. This is a phylogenetic measure of community similarity that takes into account organismal abundance and phylogeny  67. Phylogenetic trees used to calculate this metric were constructed in QIIME 1. 8 (ref. 65) through alignment of representative sequences of each OTU with PyNAST against the greengenes core set alignment  66  , and approximate maximum likelihood phylogenetic inference with FastTree.
We considered the pool of distances between samples within each metadata category of interest (for example, algal competition or categories of temperature) using QIIME's make_distance_boxplots. py. Significance was assessed by non-parametric t-tests, each with 1,000 Monte Carlo permutations (permutation is important in this instance to account for the non-independence of distances). The effect of this procedure is to ask whether different factors increase the dispersion of communities. PCoA plots of b-diversity were visualized in the Emperor software. When multiple categories (for example, different algal types) were tested for effects on b-diversity, the false discovery rate (FDR) for multiple comparisons was controlled at a threshold of q ¼ 0. 05 using the Bejamini-Hochberg method. Comparison of microbial and environmental distance matrices.
For example, seasonal variation in algal cover might potentially confound the effects of temperature on microbial communities-partial Mantel tests were used to test for such effects. All Mantel and partial Mantel tests were performed in QIIME 1. 8 (ref. 65) using the script compare_distance_matrices. py Analysis of microbial community richness and evenness. Microbial community richness, evenness and b-diversity were calculated in QIIME v. 1. 8. Richness and evenness were calculated using the chao1 and equitability statistics, respectively, in QIIME's alpha_diversity. py, collate_alpha. py and compare_alpha. ph scripts. In each case, the data were repeatedly (10 Â ) rarified to 500 reads per sample. Values for chao1 and equitability were calculated for each rarified table, and averaged into a single value and compared across categories.
Significance of results was assessed by FDR-corrected non-parametric t-tests with 1000 Monte Carlo permutations, using qo0. 05 as the significance threshold. Equitability was calculated as defined in the QIIME software package. Specifically, the Shannon entropy (using a base 2 logarithm) was divided by the base 2 logarithm of the number of observed OTUs. Thus, for a sample with n OTUs considered in turn (n 1 , n 2 , n 3 , y, n i ), the equitability/evenness was calculated as: À P n i ni n À Á log 2 ni n À Á À Á log 2 n The effects of treatment, temperature, coral genus and individual coral head on richness and evenness were analysed using linear models in R (3. 1. 1). Seawater temperatures were binned into 'low temperature', 'moderate temperature' and 'high temperature' categories, with thresholds ato24 °C for 'low temperature' and 430 °C for 'high temperature'.
The high temperature threshold represents the MMM from the NOAA Pathfinder v5. 2 SST data set  45  (see above). The specific form of these linear models in the R language was: lm(alpha_diversity B treatment * seawater_ temperature_category * coral_genus þ individual_coral_head). The significance of each effect was compared with ANOVA (Supplementary Data 3 sheet f). Random forest analysis. Random forest analysis is a machine-learning method for supervised classification. We used random forest analysis (Supplementary Data 3 sheets i-j) to (i) determine the conditions under which Synechococcales would be displaced as the most abundant order in the coral microbiome, and (ii) generate a model predicting whether specific coral colonies would lose tissue using ecological (for example, long-term contact with algae from photo series) and microbiological data (the abundance of different bacterial orders).
For both analyses, random forest was conducted in QIIME v. 1. 8 with the script supervised_learning. py. In both models, the random forest was constructed with 1,000 decision trees and validated by 10-fold cross-validation. This analysis resulted in an overall prediction accuracy, a per-category prediction accuracy and a ranking of feature importance, defined as the amount of accuracy gained or lost when a specific feature was not included in the model. It is worth noting that we employed random forest analysis primarily to provide a summary of the overall predictive power of the data; the internal complexity of the algorithm does not allow inferences to be made about the relationships between variables, beyond their empirical influence on accuracy. For mechanistic insight we rely on other analyses presented here.
In the first model, we sought to predict the dominant (most abundant) order of bacteria in samples given only non-microbial data about the coral colony from which the sample was taken at that point in time. A total of 127 features were used in the prediction including: herbivore exclusion, nutrient addition, coral species (species were converted to binary variables), per cent cover of different macroalgae, turf algae and cyanobacterial mats from quadrat surveys, singly and grouped by functional type (for example, total macroalgae), plot and subplot number, measures of instantaneous and weekly average temperatures and salinities, season (quarter), and the simultaneous presence of parrotfish bites and nutrient enrichment. Given these features, we asked the model to predict the dominant order of microorganism in each DNA sample, with our main purpose being to determine whether the conditions under which Synechococcus was dominant were predictable.
2  45  ). The effects of upright algal cover or increasing temperature were tested by Spearman regression against KEGG functional category abundance. The FDR across KEGG categories was controlled in all cases at qo0. 05. Comparing PICRUSt results with previous experiments. To compare whether the functional categories predicted to change in this field study were broadly consistent with previous laboratory experiments, we compared PICRUSt data with a previous study that exposed corals to several stressors in aquaria and sequenced their metagenomes. KEGG pathways correlated with extremes of temperature (squared deviation from 28 °C, see 'Predicting coral microbiome function', above) in this study were compared with KEGG categories that increased or decreased in a previous experiment, in which Thurber et al.
Acknowledgements We thank numerous volunteers for assistance with the experiment and the  Florida Keys National Marine Sanctuary  for permits ( FKNMS-2009-047  and  FKNMS-2011-090 ). This research was supported by  National Science Foundation  grant  OCE-1130786  to D. E. B and  R. V. T. ,  Florida International University and Oregon State University. R. van  Hooidonk of NOAA Atlantic oceanographic & meteorologic laboratory (AOML)  provided the Pathfinder SST time series data.
miR-182 targets Bcat2, Foxo3, and Adcy6 to potentially regulate the hypertrophic response. To better understand the regulation of the Akt/mTORC1 pathway and to identify the miR-182 targeted genes, which might be linked to the hypertrophic response subsequent to angiogenesis, we conducted comparative mRNA expression profiling in PlGF mice (angiogenesis with hypertrophic response) and PlGF/RGS4 mice (angiogenesis with inhibition of hypertrophic response). The mRNA transcripts that were selectively downregulated on microarrays of PlGF mouse hearts, but not of PlGF/RGS4 mouse hearts, were further analyzed using the miRWalk database of predicted and validated microRNA targets  15. Using this approach, we identified 9 potential miR-182 targets associated with the hypertrophic response in PlGF mice (Supplementary Table  S1  on line).
The data were analyzed and normalized using Expression Console Software with Affymetrix analysis settings. We used the Affymetrix mouse gene 1. 0 ST arrays (~29000 transcripts) for mRNA expression profiling. The arrays were processed and analyzed by W. M. Keck Biotechnology Resource Laboratory at Yale. High quality total RNA (250 ng) samples were processed to biotin-labeled cDNA using an Ambion WT expression kit and Affymetrix GeneChip WT Terminal Labeling kit according to the manufacturer's instructions. Biotin-labeled samples were hybridized for 16 hours at 45 °C and 60 rpm on GeneChip Mouse Gene 1. 0 ST Array. GeneChips were washed and stained in an Affymetrix Fluidics Station 450, then scanned using a GeneChip Scanner 3000 7G. The data were analyzed with a Microarray Suite version 5. 0 (MAS 5.
Capillary/myocyte ratio and cardiomyocyte cross-sectional area were determined in captured images using a Nikon Eclipse 80i microscope and analyzed with the Image J analysis software. Cell culture experiments. Neonatal rat cardiomyocytes (NRCs) were isolated from 2-day-old Sprague-Dawley rats using a Neonatal Cardiomyocyte Isolation System (Worthington Biochemical Corp. ) and plated on fibronectin-coated plates in DMEM supplemented with 10% FBS  7, 11. NRCs were treated with NO donor DETA-NONOate (Cayman Chemical Co), PlGF-2 (R&D System) or Ang II (Sigma-Aldrich) in serum-free medium (DMEM supplemented with 1 mg/ml BSA, 10 μ g/ml transferrin and 10 μ g/ml insulin) for 24 hours, as we previously described  11. Transfection of NRCs with anti-rno-miR-182 5p (Ambion), rno-miR-182 mimic 5p (Ambion), control miR scramble (Ambion), Bcat2 siRNA (Ambion), and AllStars Negative Control siRNA (Qiagen) were carried out using Lipofectamine RNAiMAX (Invitrogen).
After transfection, NRCs were kept overnight in complete medium, then were placed in serum-free medium and used for experiment. NRCs surface area. For cell area measurements, NRCs were plated on fibronectin-coated glass bottom plates and fixed at the end of experiments with 4% ice-cold PFA. After permeabilization with 2% PFA, 0. 1% Triton X-100 and 0. 1% NP-40, the cells were stained with an anti-α -actinin sarcomeric Ab (Sigma-Aldrich) and DAPI (Invitrogen) as previously described  11. NRCs were visualized with a Nikon Ti E Eclipse inverted spinning disk confocal microscope. Cell surface area was determined in 12 random fields (20 × 1. 5 magnification) captured with a Volocity 3D imaging software (PerkinElmer) and then measured using an Image J analysis software. 3′-UTR luciferase reporter assay.
Statistical analysis. Data are presented as mean ± SEM. Differences between multiple groups were assessed, as required by the experimental design, using one-way ANOVA, two-way ANOVA or repeat measures two-way ANOVA followed by Tukey's post-hoc test for multiple comparisons. Comparisons between two independent groups were performed using a two-sample t test. All p values were calculated using two-tailed statistical tests. Differences were considered significant when p < 0. 05. Data were analyzed using GraphPad Prism Version 6. 03.
A DNA fragment including the lipase gene was amplified by PCR, with primers designed from the genomic sequence of an Arabidopsis thaliana homologue (GeneBank accession number NM112294. 4) (forward primer sequence: 5'-ATGCACAAAGACAACGATTCGGGTT-3', reverse primer sequence: 5'-ATTATCGGGTCCAAGCCTAAC-3'). The PCR reaction mixture contained, in a total volume of 50 µl, 2 µl of B. napus genomic DNA, the four dNTPs (200 µM each), 2. 4 mM MgCl2, 5 µl of each primer (1 µM final concentration), 1 unit of GoTaq Flexi DNA polymerase, and reaction buffer (Promega). The PCR program included an initial denaturing step at 94 ºC for 2 min, 35 cycles of amplification (denaturing for 30 s at 94 ºC, primer annealing for 30 s at 55 ºC, and 2 min extension at 72 ºC), and a final extension at 72 ºC for 10 min. The PCR amplified products were cloned into pGEM T-easy vector and sequenced using M13 forward and reverse primers.
Sequence analysis and bioinformatics Programs accessed at the US National Center for Biotechnology Information (NCBI) were used for nucleotide sequence analysis, deduction of the amino acid sequences and database searches. Genes encoding several plant lipases were retrieved from the GenBank database by BLAST searches and used for sequence comparison. Protein sequences were aligned using the Clustal X 1. 8 programme. The phylogenetic tree was produced by neighbour-joining (NJ) methods (Kimura 2-parameter model, 10,000 replicates, bootstrap phylogeny test) based on lipase amino acid sequences, using MEGA software version 4. 0.
2 ). Protein BLAST searches detected several sequences in the databases with significant homology to the newly described Brassica napus lipase, most of them putative or not yet characterized proteins; some of these sequences have been aligned and are shown in Fig. 3. One of the proteins with highest homology (about 54% identical amino acids, and 60% similarity) is a putative GxSxG-motif lipase from a closely related species of the same genus, Brassica rapa (GeneBank accession nº XM 009148129. 1); it should be mentioned that a similar degree of sequence homology has been found with some other B. napus protein sequences (not included in the alignment of Fig. 3 ). Other putative lipases from species belonging to the Brassicaceae (Eutrema salsugineum, Camelina sativa, Capsella rubella, Arabidopsis thaliana) or the related family Cleomaceae (Tarenaya hassleriana), showed somewhat For some of the boundaries between exons and introns, the flanking sequences are shown, with the conserved splice sites (GT, AG) underlined lower homology to BnLIP (45-50% identity, 55-60% similarity) or shorter overlapping regions (Fig.
2. The cDNA nucleotide sequence (deduced from the genomic sequence by elimination of intron sequences), and the corresponding amino acid sequence of BnLIP from Brassica napus (GenBank accession nº KR998334). The translation initiation codon (ATG) and the stop codon (TGA) are shown in boldface. The GxSxG-motif is underlined (GHSLG), and the catalytic triad amino acids (S, D, H) are typed in bold letters.