dataesr/scientific-paragraphs-categorization huggingface dataset:
Burkholderia pseudomallei (Bp), the causative agent of the often-deadly infectious disease melioidosis, contains one of the largest prokaryotic genomes sequenced to date, at 7. 2 Mb with two large circular chromosomes (1 and 2). To comprehensively delineate the Bp transcriptome, we integrated whole-genome tiling array expression data of Bp exposed to. 80 diverse physical, chemical, and biological conditions. Our results provide direct experimental support for the strandspecific expression of 5,467 Sanger protein-coding genes, 1,041 operons, and 766 non-coding RNAs. A large proportion of these transcripts displayed condition-dependent expression, consistent with them playing functional roles. The two Bp chromosomes exhibited dramatically different transcriptional landscapes -Chr 1 genes were highly and constitutively expressed, while Chr 2 genes exhibited mosaic expression where distinct subsets were expressed in a strongly conditiondependent manner.
This versatility suggests that Bp could prove useful as a model to study how pathogens adapt to extreme environments and different hosts. Indeed, it has been proposed that Bp is an example of ''accidental virulence'', where genetic pathways used by the bacterium to survive in environmental niches may have indirectly contributed to its ability to cause clinical disease  [12]. In this study, we sought to obtain a global overview of how the environment might influence the Bp transcriptional landscape, by integrating expression data from whole-genome tiling microarrays covering. 80 diverse environmental and genetic conditions. Our aims in this study were threefold: First, we generated a comprehensive strand-specific catalog of condition-dependent transcripts in Bp, including genes, operons, and non-coding RNAs.
Genomic Landscape of the Bp Condition-Dependent Transcriptome Whole-genome tiling microarrays containing strand-specific probes overlapping at 35-base resolution were used to profile Bp transcriptional responses under 82 different conditions (Figure  S1A-C ). Conditions were selected to mimic natural exposures Bp might encounter in the environment or in infected hosts. Many of these conditions were selected based on prior scientific reports where Bp responses were explored at the phenotypic level. Experimental conditions and their scientific rationales are provided in Table  S1. The transcription profiles were found to be robust and reproducible through technical and biological replicates  [13]  (Figure  S1D ). We integrated the array data to generate a comprehensive catalog of condition-dependent transcripts in Bp.
Abundance of Condition-Dependent Non-coding RNAs in Bp Non-coding RNAs (ncRNAs) are emerging as an important class of regulatory molecules in several prokaryotes  [18]. Using stringent filtering criteria and manual curation (see Materials and Methods), we identified a ''high-confidence'' set of 766 ncRNA transcripts ranging in size from 111 to 750 bp exhibiting high expression levels in the Bp compendium (Figure  2A , Table  S2 , Text S1). All 81 ncRNAs computationally predicted by the ncRNA database Rfam to in the BpK96243 genome were detectibly expressed  [18]. Of the 766 ncRNAs, 532 and 150 ncRNAs were conserved in B. mallei (Bm) and B. thailandensis (Bt) respectively, at both the levels of sequence identity and chromosomal synteny (Figure  S5A, B ). On average, 168 ncRNAs were expressed in any single condition (Table  S5 ).
Transcript boundaries correspond to predicted start and stop coordinates of Sanger annotated genes and FGENESB novel genes. (C) Differential expression of a Bp operon. Expression of a predicted flagella operon (BPSL0026 -BPSL0032) in a specific condition (taurine exposure). (D) Antisense transcription. BPSL0095, a gene coding for hypothetical protein exhibits antisense transcription upon exposure to human serum. doi:10. 1371/journal. pgen. 1003795. g001 ARACNe, an information theoretic algorithm for biological network construction, to identify significantly co-expressed pairs of Bp genes  [21]. ,91% of Sanger genes exhibited significant coexpression relationships to at least one other gene (Figure  4A ). Second, we used Markov Clustering  [22]  to group these linked genes into larger clusters, approximating a scale-free topology commonly associated with transcriptional networks (Text S1)  [23].
Among the upregulated genes, we identified five that might function as potential effector proteins -BPSS1498 (tssD-5), BPSL3319 (fliC), BPSS1525 (bopE), BPSS1529 (bipD) and BPSS1532 (bipB). These effectors were identified using the program PSORTb 3. 0  [43]  -a subcellular localization prediction tool. Notably, several of these genes have been previously validated as secreted effector proteins  [44, 45] , and are thus likely to be secreted into lung cells to hijack host cellular pathways. Other clusters upregulated during lung infection (C080, C446, C087) contained genes involved in ferric ion acquisition, including BPSL1775 (C446), an iron uptake receptor, and the pyochelin genes (pch) and fptA in cluster C087. The murine lung infection profile was also significantly similar to in vitro profiles related to nutrient starvation (pv0:01, Text S1).
Discussion In this study, we integrated strand-specific whole-genome transcriptional data over 80 environmental, chemical and genetic perturbations to generate a transcriptional condition compendium of Bp. Previous molecular studies on Bp have largely focused on protein-coding genes defined by the original genome annotation study  [8]. However, our data suggests that additional functional elements are also likely to reside in the Bp genome. For example, of ,500 putative novel genes identified by an alternative gene prediction algorithm (FGENESB  [15] ), 59% of these novel genes were associated with expressed transcripts indicating that they are transcribed. Notably, previous analysis of these novel genes has also shown that 46% are associated with other proteins in the COG, KEGG, STRING and NR databases, and high-confidence ribosome binding sites have also been identified in 60% of these novel genes  [13].
pylori and L. monocytogenes, antisense RNAs are involved in regulating metabolic enzymes and virulence factors  [5, 55]. Taken collectively, these results strongly suggest that several features of Bp biology are likely to be modulated by other molecular entities beyond protein-coding genes, specifically ncRNAs and antisense RNAs. Our data demonstrates that the two Bp chromosomes exhibit very different transcriptional landscapes. Specifically, Chr 1 genes were often constitutively and highly expressed, while Chr 2 genes exhibited ''mosaic'' expression, where distinct subsets of Chr 2 genes were expressed in a strongly condition-dependent manner. Previous genome analysis has also suggested that the two Bp chromosomes are distinct in composition and function, and Chr 1 has been proposed as a ''housekeeping'' chromosome.
Interestingly, when compared against other prokaryotic transcriptome studies, the transcriptional landscape of Bp Chr 1 bears high resemblance to other single chromosomal microbes E. coli, L. monocytogenes and B. subtilis  [4, 5, 56] , while the consistently lower expression levels of Bp Chr 2 and its condition response profiles more closely resemble profiles previously observed in plasmid pXO 1 in B. anthracis and pSymB in S. meliloti, respectively  [57, 58]. Comparison of our gene expression data to previously published proteomic studies also revealed that there is a positive but modest correlation between transcript and protein data, as has been reported for other prokaryotes  [46, [59] [60] [61]  (Figure  S8 ). However, to our best knowledge, this is the first formal report demonstrating the distinct transcriptional landscapes of multi-chromosomal bacteria, and suggests very different evolutionary origins for the two Bp chromosomes.
In the Bp network, examples of co-expressed genes included clusters related to motility, aerobic respiration, detoxification, and ribosomal function (Figure  4A ). Besides known genes, such ''guilt-by-association'' approaches can also often shed light on genes with poorly-understood or unknown functions. Despite ongoing genome annotation efforts, many hypothetical and putatively assigned genes still exist in the BpK96243 genome, and less than 50% of Bp genes are currently annotated in the KEGG (Kyoto Encyclopedia of Genes and Genomes) PATHWAY database (www. genome. jp/kegg/ pathway. html). Linking these genes to other co-expressed genes of known function may thus prove useful in inferring potential functions. For example, we highlighted a set of ''hypothetical'' protein-coding genes (BPSL2828, BPSL2829) which strongly coexpressed with genes associated with heat-shock and protein unfolding.
BpK96243 Tiling Microarrays and Expression Profiling High-density tiling arrays were fabricated by Roche NimbleGen (Roche NimbleGen, USA) based on the BpK96243 reference genome  [8]. Bacterial RNAs were extracted and processed for microarray hybridization as described in  [13]. In total, 166 samples were profiled; however one sample (K9BALBcLungs 1) had overwhelmingly high background and was excluded. The final Bp condition-specific compendium comprises 165 array profiles. Microarray images were analyzed by Roche NimbleScan software (Roche NimbleGen, USA), and LOWESS normalized (Locally Weighted Scatter Plot Smoother) by GeneSpring GX software (Agilent, USA). All arrays were median-normalized. Normalized signals from biological replicates were averaged to obtain a single, normalized, probe signal for each condition.
Identification and Annotation of Transcriptionally Active Regions (TARs) A moving window binomial approach was performed for de novo TAR identification  [14]  (Text S1). TARs were visualized using Artemis (Sanger, UK) or SignalMap (Roche NimbleGen, USA), and annotated against Sanger coding genes [8], ncRNAs (Rfam,  [18] ), FGENESB predicted genes  [13, 15] , and predicted operons. Genes passing a pv0:05 (Binomial test) cut-off were classified as expressed. Polycistronic operons were classified as expressed only if all gene members within the operon were classified as expressed in the same condition. Antisense transcripts were defined as expressed TARs mapping to the complementary strand of a Sanger or FGENESB gene, either spanning the entire gene or partially. Differential expression between conditions was determined by comparing the log-transformed median probe expression levels of probes corresponding to genic units (e.
Gene Co-expression Networks and Co-expression Clusters Co-expression associations between genes were defined by the ARACNe algorithm  [21]. Each gene pair was assigned to a mutual information score (MIS) greater than zero, and we retained the top 2% of gene pairs (MIS §0:3168). The MISs were also used to form a weighted adjacency matrix, and indirect interactions between gene pairs were identified and removed by ARACNe using a Data Processing Inequality strategy. The final network covers 5,387 genes connected by 60,024 direct interactions. Distances between adjacent genes were computed by subtracting the power transformed weight by its maximum, forming a distance matrix. The iGraph R package was used to compute the shortest distance between any two genes based on the distance matrix. To define co-expression clusters, we identified groups of highly co-expressed genes using Markov Clustering (MCL)  [22].
To identify the optimal level of cluster granularity, the clustering analysis was performed using different inflation parameters (1. 0 -3. 5) and at each value the clustering results were evaluated for structural efficiency and functional coherence, measured by the fraction of gene pairs within the cluster sharing identical or similar Riley functional categories. We also confirmed the robustness of the cluster compositions by a leave-one-out validation approach where the network construction and clustering was repeated on a reduced data set with one sample removed in an iterative fashion. An observed cluster was deemed robust if at least 75% of the cluster composition was also observed in at least 95% of the reduced data sets. Stable clusters were compared to Riley's classifications, and functional annotations were assigned to clusters exhibiting a statistical over-representation of the same or similar annotations (pv0:05, after Benjamini & Hochberg multiple testing correction).
Clusters connected by Z §17were deemed significant. The condition-dependent co-expression network was visualized using Cytoscape 2. 8. 1. heat stress. 158 genes were commonly down-regulated by at least 2-fold in the presence of chloramphenicol (K9Chlamp), ceftazidime (K9Ceft), 2M of sorbitol (K9Sorb) or under 42uC for 16 hours (K942C16hrs). Genes that are significantly enriched Figure  S8  Comparison between transcript levels from our study to protein levels from published literature. (A) The scatter plot shows the ratio of 28 transcripts to corresponding proteins in rpoE mutants compared to wild type  [61]. Significance of the Pearson correlation coefficient (R) was tested using a two-tailed t-test. (B) Approximately 80% of Bp proteins detectibly expressed at early stationary phase  [46]  were also associated with detectible transcripts (pv0:004, Text S1).
Transcriptomic analysis In order to compare gene expression patterns between ASCmo and ASCn, a comprehensive transcriptomic analysis was performed using Affymetrix whole-transcript expression array. To visualize gene expression data we performed Principal Component Analysis (PCA) that demonstrated the independence of transcriptomic signature in ASCmo and ASCn (Additional file 2: Figure  S1 ). To further explore genes differentially expressed between ASCmo and ASCn, we used modified analysis of variance (ANOVA) analysis. With a p-value < 0. 2, a 60% False Discovery Rate (FDR) and a fold change > 1. 5, 637 differentially expressed transcripts were detected while with a p-value < 0. 1, a 60% FDR and a fold change > 1. 5, 457 gene transcripts were detected (Additional file 3: Figure  S2 ). Among the 637 detected genes, 319 exhibited an increased expression in ASCn (fold change range: 1.
Finally, we have observed a significantly reduced expression of DAPK1 in ASCmo. The death-associated protein kinase-1 (DAPK1) is the prototypic member of a family of death-related kinases. However, DAPKs can also act as a survival factors and block apoptosis in response to certain cytokine signals modulating the balance between pro-apoptotic and pro-survival pathways  [48]. To address the signaling pathways differentially activated in ASCmo, the validated gene expression data was analyzed in silico with the IPA software to obtain the most probable networks and biological pathways. Candidate proteins identified by IPA were validated by western blot. As such, a lower activation of Smad3 was found in the stem cells of the subcutaneous WAT of obese patients. Interestingly, the transcription factor Smad3 plays a key role in the inhibition of adipocyte differentiation and in the development of insulin resistance  [49, 50].
Flow cytometry characterization For further characterization, cell surface antigen phenotype was performed on ASCmo and ASCn at passage 0 and passage 3. The following cell-surface epitopes were marked with anti-human antibodies: CD105-fluorescein isothiocyanate (GeneTex), CD90-fluorescein isothiocyanate (FITC), CD29-phycoerythrin (PE), CD44-FITC, CD34-PE-Cy™5 and CD45-PE (BD Biosciences; San Diego, CA, USA). Mouse isotype antibodies served as control (BD Biosciences). Cellular events (30000) were acquired and analyzed by Fluorescence-activated cell sorting using Coulter EPICS XL flow cytometer (Beckman Coulter) running Expo32 software (Beckman Coulter).
Single strand DNA was obtained from 10 ug of cRNA. Then 5. 5 ug of single strand DNA were fragmented and labelled with biotin using the WT Terminal Labeling Kit (Affymetrix). Hybridization controls from the Hybridization, Wash and Stain Kit (Affymetrix) were added to the sample. Every sample was hybridized to GeneChip Human Gene 1. 0 ST array during 16 h at 45°C and 60 rpm, according to manufacturer's instruccions. Hybridization, washing, staining and scanning of microarrays were performed according to Affymetrix instructions using the Affymetrix GeneChip 3000 7G System (645 Hybridization Oven, 450 Fluidic Station and GeneChip 3000 7G Scanner). Raw data were pre-processed with Robust Multiarray Average (RMA) method  [53] : background correction, quantile normalization and median polish summarization of the probes.
Bioinformatics was used to analyze the extracted log2 expression data. The raw data from the array experiments has been deposited in NCBI's Gene Expression Omnibus  [54]  and is accessible through GEO Series accession number (GSE48964; http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE48964). Microarray quality control and statistical analyses were performed using the Partek Genomics Suite software (Partek Inc. , St Louis, Miossouri, USA). PCA procedure was used to represent samples variability, which defines a group of Principal Components in order to explain the variability existing between the samples. ANOVA, modified by Partek Inc, was used for statistical comparisons between groups. Derived p-values were adjusted for multiple testing with the method of Benjamini and Hochberg  [55].
Adjusted p-value, FDR <0. 6 and fold change ≥ ±1. 5 were used to obtain differential expressed genes. Some of the modified genes were validated using real-time PCR in ASC from additional non-obese individuals (n = 8) and morbidly obese patients (n = 16). Differential expressed genes were associated with biological functions and/or diseases based on literature and the Ingenuity Knowledge Base. Differential expressed genes were analyzed through the use of IPA (Ingenuity® Systems, www. ingenuity. com). Right-tailed Fisher's exact test was used to calculate a p-value determining the probability that each biological function and/or disease assigned to that data set is due to chance alone. The data set from our study was used to construct hypothetical protein interaction based on a regularly updated Ingenuity Pathways Knowledge Base.
Secondary antibodies consisted of horseradish peroxidase-conjugated antibodies (Dako; Barcelona, Spain) and were detected using SuperSignal chemiluminescence system (Pierce; Rockford, IL, USA). β-tubulin and Histone H1 were used as a membranecytoplasmic and nuclear fraction marker, respectively. Protein expression was determined using Image LabTM Software (BIO RAD). Nuclear translocation was calculated as a relation between nuclear protein vs total protein (nuclear + membrane/cytoplasmic protein) quantification.
Acknowledgments This work was supported by Grants  SAF 2010/16549 ,  Retic-RIC  ( RD12/0042/ 0027 ) and Retic-TERCEL  RD/06/0010/0017  from  Instituto Salud Carlos III, Madrid , Spain; and  Fundación Jesús Serra ,  Barcelona, Spain. L. B. is the  Cardiovascular Research Chair, UAB, Barcelona, Spain. B. O. is the recipient of a  Predoctoral Fellowship  from  Instituto Salud Carlos III, Madrid, Spain. G. V. is a recipient of a contract from the  Innovation and Science Spanish Ministry  ( RyC-2009-5495 ),  MICINN, Spain. The authors thank  Marta Sánchez  for her technical support.
b. Glider We use data from the Spray glider mission executed in 2006 under the Marine Environment and Security for the European Area (MERSEA) project. The glider path is shown in yellow in Fig. 1. The Spray glider is an autonomous underwater vehicle that uses changes in buoyancy to propel itself through the water column while taking profiles of temperature, pressure, and salinity. While at the surface, the Spray glider transmits its GPS position fix and dive data through the Iridium satellite system. The GPS fixes are used to calculate an absolute depth-averaged velocity using dead reckoning; from here on, when the phrase ''glider velocity'' is used, a velocity averaged over the depth of the dive is implied. A detailed description of the Spray glider can be found in other publications (e. g.
c. Satellite altimetry For satellite altimetry data, the gridded merged Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO) product (described in  Picot et al. 2003 ) is used in this study. The sea surface height anomaly is an objectively mapped estimate calculated relative to a mean sea surface averaged over 1992-2005. This product has a time resolution of 7 days and comes corrected for various effects [wet and dry troposphere, inverse barometer, electromagnetic bias, and ocean tides; see  Picot et al. (2003)  for more details]. The merged product is so called because it is a combination of the Ocean Topography Experiment (TOPEX)/ Poseidon, Jason-1, EnviSat, and Geosat satellites. Both latitude and longitude coordinates are mapped onto a 1 /38 Mercator projection while accounting for longwavelength errors  (Ducet et al.
2000). A dataset derived from the satellite altimetry-merged product results from  Chelton et al. (2007) ; it identifies and tracks coherent mesoscale eddies globally from 1992 to 2008. Details of the eddy-tracking algorithm can be found from  Chelton et al. (2011). The method that produced the dataset in this study will be referred to as the ''Chelton'' algorithm. It should be noted that the gridded satellite altimetry product dampens eddy signals smaller than 40 km in size  (Chelton et al. 2011 ) and appears to have large uncertainties near the EGIC region  (Gourcuff et al. 2011).
d. Argo Argo floats within the Irminger Sea provide profiles of temperature and salinity from the surface to 2000-m depth approximately every 10 days. These floats drift at 1000 m for about 10 days, then descend to 2000 m and rise to the surface over 6 h, collecting measurements on ascent. At the surface, data are transmitted and the float's location is determined. The float then dives back to 1000 m to repeat its 10-day cycle. The data from these floats are available through the International Argo Project and can be found through the Global Ocean Data Assimilation Experiment (GODAE) project server (http://www. usgodae. org/argo/argo. html). Only delayed-time quality-controlled data were used in this study.
e. ''Ovide'' ship sections The Ovide cruises were carried out as described in detail by  Lherminier et al. (2007 Lherminier et al. ( , 2010)). They were performed on the R/V Thalassa between June and July of  2002, 2004, and 2006. Part of their cruise track, shown with white circles in Fig. 1 , crosses the Irminger Basin from the coast of Greenland to beyond the Reykjanes Ridge. At each of the approximately 30 stations in the Irminger Sea, measurements included conductivity, temperature, and pressure via a Neil Brown Mark III CTD probe. Salinity was calibrated with seawater samples analyzed on board. f. 15-m-drogued drifters Two drifter tracks are used to illustrate different flow situations in this study. They were obtained from the public archives of the Global Drifter Data Assembly Center at the National Oceanographic and Atmospheric Administration's (NOAA's) Atlantic Oceanographic and Meteorological Laboratory (AOML).
a. Methods Figure  2  shows the mooring salinity time series with potential density contours superimposed. Here, both salinity and density are smoothed with a 3-day running mean at each pressure level. One first notices a change in overall color from cooler to warmer tones from 2002 to 2009, representing an overall increase in salinity over the dataset. The surface to 1000-m mean salinity increases 5. 7 3 10 23 yr 21 over the time series. This is within the 1. 15-7. 3 3 10 23 yr 21 rate of salinity increase described by others in the Irminger Sea between 2003 and 2007  (Sarafanov et al. 2007; De Jong 2010). The trend is also the same magnitude as the general salinification observed in the Labrador Sea and Nordic Seas beginning in the 2000s (see  Falina et al. 2007; Sarafanov et al. 2007; Holliday et al.