dataesr/scientific-paragraphs-categorization huggingface dataset:
Septic plasma elicits transcriptional responses that can be measured on a systems scale in a cell reporter assay We first set out to determine which cell reporter system would be most amenable to detect meaningful changes in response to septic plasma in vitro. PMNs, PBMCs and monocytes were isolated from two healthy donors (Table  1 ). Monocytes were cultured with interleukin 4 (IL4) and granulocyte-macrophage colony-stimulating factor (GM-CSF) to generate MoDCs. Plasma samples were obtained from a first set of patients with cultureconfirmed sepsis (n = 12) and from uninfected healthy controls (n = 6). Cells were cultured for 6 hours in medium alone (unstimulated) or in the presence of plasma (stimulated) using a final concentration of 20% (Figure  1 ). Microarray data were generated to assess transcriptional responses on a genome-wide scale.
In contrast, there were fewer changes recorded in PBMCs and MoDCs and for the majority of those genes, transcript abundance decreased upon exposure to septic plasma. In an effort to determine potential molecules present in septic plasma inducing these patterns of gene expression, we selected those genes over-expressed in each of the 3 reporter cell types and predicted the upstream transcription factors using GeneGo MetaCore. One hundred and sixty-one transcription factors were identified in total (Additional file 8). There was significant overlap among the 3 lists of transcription factors with 27 predicted for all 3 cell types, including several well established in core immune pathways such as STAT3, STAT4, STAT5A, STAT5B, and CREB1. These findings are consistent with the idea that a relatively small number of molecules are driving the observed transcriptional responses and could suggest future studies to determine the relevant immunomodulatory molecules in septic plasma.
To better understand the observed gene expression patterns and the functions of those genes, a heatmap was generated with the 1,366 transcripts differentially expressed in at least one cell type (Figure  3 ). We performed hierarchical clustering and further characterized the function of transcripts in each of the resulting clusters by using the GeneGo MetaCore pathway analysis tool. Networks with statistically significant enrichment of functional annotations were found in 5 out of 12 clusters. Most of the enriched functional networks are relevant to immune processes. Cluster 1 (n = 267), the largest cluster, is formed by transcripts over-expressed in PMNs. This cluster contains many genes involved in neutrophil function (e. g. CD177, IL1R2, NLRP3, TNFSF8, FCGR1A, FCGR1B, and FCGR1C). Cluster 3 (n = 84) contains transcripts over-expressed in PBMCs, MoDCs and to a lesser extent PMNs.
To assess variation of the cell donor source, we tested the same set of plasma samples with PMNs from an additional donor not used in either of the previous two experiments. PCA plots and Euclidean distance comparisons demonstrate similar findings as with the first donor suggesting this sort of PMN reporter system can be reproducible independent of the cell donor (Additional file 11). We then used a RKNN-FS algorithm to identify markers for differentiating severe and not severe sepsis (Table  3 ). Data from the third experiment were used as the training set because it was the most balanced and largest dataset (severe sepsis n = 20; not severe sepsis n = 15). RKNN-FS identified a set of 30 transcripts as being able to provide the highest accuracy (82. 02%) in predicting sample class in a leave one out cross-validation scheme.
82 (95% confidence interval, 0. 78 -1. 00) and 0. 90 (95% Table  3  Sepsis severity markers identified by the PMN reporter system No. confidence interval, 0. 66 -0. 99) for SVM and RF, respectively (Figure  5 ). Meaningful validation could not be done using data from experiment I because almost all of these plasma samples (11/12) were obtained from patients with severe sepsis, 3 of which were collected at >48 h after admission. Functional annotation of the candidate gene signature is a further indication of its relevance as a severity biomarker Enrichment analyses were performed to characterize the functional relevance of our severity signature panel in GeneGo MetaCore. Collectively, these genes are involved in immune system response as their major biologic function (Figure  6A ). Analyses for significant cellular process (Pathway Maps) also suggested their roles in cell cycle regulation (Figure  6B ).
It should be noted that the arrays of receptors expressed by different immune cell types vary widely, and that while our report indicate that neutrophils are especially well equipped to respond to plasma from septic patients it may not be the most appropriate cell type in other settings. The PMN cell reporter system coupled with whole transcriptome readout allowed identification of a severity signature for sepsis that was highly accurate in two independent datasets. Current guidelines for diagnosing sepsis and grading severity are based on multiple clinical parameters, which can be inaccurate and do not predict prognosis well  [15]. Although many potential biomarkers including cytokines, coagulation factors, and several others have been investigated for sepsis diagnosis and prognosis, none have proved reliable enough to enter routine clinical practice and so there is a need for better markers for risk stratification of septic patients to guide treatment and prognosis  [17] [18] [19].
Design of primers and probes Target gene sequences of each pathogen (Table  S1  in Text S1) were first obtained from Genbank database. Sequence alignments were performed using the ClustalW algorithm  [91]  in MegAlign (DNAStar, Inc. , Madison, WI). A consensus sequence representing clinically relevant strains (Table  S1  in Text S1) was created for each pathogen. Each target oligonucleotide sequence was designed through multiple, successive steps of evaluation of candidate sequences, based on user-defined criteria, followed by analysis with Basic Local Alignment Tool (BLAST)  [92]  against the nucleotide sequence database (nr/nt)  [93]  for non-target genomes potentially present in the specimen that could cause interference. Genusspecific PCR primers were designed for all chosen target genes sequences as previously described  [94, 95].
Acknowledgments We acknowledge the study participants and healthy volunteers for their participation in the study; the clinical and research staff from the  Communicable Disease Centre/Tan Tock Seng Hospital and Shoklo Malaria Research Unit  for patient enrollment, study coordination, and data entry. We are grateful to  Prof. Kevin Tan  ( Department of Microbiology, Yong Loo Lin School of Medicine, National University of Singapore ) for his help in validation testing of the system. We are also grateful to  Kai Er Eng  from the  Singapore Immunology Network, A*STAR  for her help in manuscript preparation.
The diverse effects on different targets may lead to a synergistic effect relying on pharmaceutical product ''albitiazolium'' is currently under clinical development operated by the funder. All the authors confirm that this does not alter their adherence to PLOS ONE policies on sharing data and materials. diverse biochemical activities (choline transport, membrane biogenesis, food vacule function). This multiple mechanism of action is a substantial advantage by preventing the emergence of drug resistance events. With the aim of identifying all potential targets of albitiazolium, we designed a chemical proteomics approach for in situ capture of proteins targeted by the drug during their native interaction inside living parasites. Choline analogs of the bisthiazolium series are not metabolized by malaria-infected erythrocytes and they interact in a noncovalent manner with their parasite targets.
25 and 1. 5, respectively, which represents 32-and 5. 3-fold enrichment for the detection of this protein in samples treated with UA1936 as compared with the two respective controls. In the same way, the multidrug resistance-associated protein 1 (PFA0590w) was preferentially detected in UA1936 treated parasites, with enrichment ratios of 26. 6 and 6. 6, respectively. Interestingly the rab specific GDP dissociation inhibitor (PFL2060c) and glycerol-3-phosphate acyltransferase (PFL0620c) were consistently detected with roughly 10-and 3-fold more peptides in parasites treated with UA1936 than in the respective controls. Similarly, a putative cation transporting ATPase (PF07_0115) was found with a 10-fold PSM increase in UA1936 treated samples versus untreated samples and was not detected in samples treated with UA2050.
The peptides were analyzed by mass spectrometry in a LTQ-Orbitrap VELOS mass spectrometer. After spectral data analysis, the identified parasite proteins were clustered based on gene ontology annotations using different bioinformatics packages. doi:10. 1371/journal. pone. 0113918. g004 PFL0765w) were also enriched in UA1936 treated samples, as shown in Table  1. Finally, a protein annotated as a putative mRNA-decapping enzyme (PF10_0314) was enriched 6. 6-fold and a serine/threonine protein kinase from the FIKK family (PF10_0160) presented a 4. 6 enrichment ratio versus both controls. The exact peptide matches for each protein identified under the different experimental conditions are provided in Table  S1. In addition, the differences between the PSM found in UA1936 treated samples and the PSM found in the two controls were analyzed for each protein using a paired t-test, which revealed no significant differences (Table  S3 ).
Proteins targeted by UA1936 compound are involved in membrane dynamics of P. falciparum A bioinformatic search and interactome analysis of proteins found in interaction with UA1936 was carried out based on existing gene ontology annotations and available protein-protein interaction data  [23, 24]. Interestingly, several proteins Parasites were incubated in the presence or absence of the UA1936 bifunctional choline analog and in the presence of the UA2050 inactive bifunctional compound. UA1936-interactiing proteins were subsequently photocrosslinked and clicked to an alkyne resin to be identified by mass spectrometry. Means of peptide spectrum matches (PSM) of three independent experiments are indicated for each experimental condition together with the standard deviaton (SD). *Fold enrichment was calculated by dividing the mean spectral counts of samples incubated with UA1936 by the mean spectral counts of samples incubated either without UA1936 or with UA2050.
Interestingly, plasmoMap showed two interactions for this gene with proteins involved in phospholipid metabolism: PF11_0257 (ethanolamine kinase) and PF14_0681 (putative diacylglycerol kinase), with likelihood scores of 18. 38 and 13. 74, respectively. A substantial number of UA1936-interacting proteins were found to be involved in transport processes; importantly, the GO Term Finder tool found significant enrichment of transport ontology terms among potential albitiazolium targets, with a p value of 0. 00035, as shown in Figure  5. Among these genes the multidrug resistance-associated protein 1 (PFA0590w), which is involved in the extrusion of glutathione and several antimalarial drugs from chloroquine-resistant parasites  [25] , a putative Cu 2+ -transporting ATPase protein containing an E1-E2 domain (PFI0240c) and another cation transporting P-ATPase (PF07_0115).
A fourth cluster of unknown or unrelated functions was created and named ''miscellaneous'', as depicted in orange. For each protein (node), the PlasmoDB ID accession number is indicated in a colored rectangle followed by the putative name of the gene. Proteins in dark-colored rectangles were found in the three replicated experiments and proteins in light-colored rectangles were found in two replicates. The proteins depicted in ellipses are interacting proteins identified in the PlamsoMap interactome analysis with their respective likelihood scores. The colors on the edges in the network represent: Codes for (black), Interacts with (blue), and Participates in (orange). The p values for the gene ontology terms found to be significantly enriched are presented for genes having those annotations in the biological process.
In agreement with this, we observed high accumulation of the UA1936 compound in the endoplasmic reticulum and cis-Golgi network, i. e. important places for phospholipid biosynthesis and vesicular burgeoning. Another cluster of UA1936-interacting proteins was involved in transport functions. Two of them were found to be P-ATPases containing E1-E2 domains, with respective putative cation and copper transporter annotations (PF07_0115 and PFI0240c). Proteins containing E1-E2 domains, also known as P-ATPases, can transport different substrates, including ions and phospholipids. Interestingly, a common feature of P-ATPases interacting with UA1936 was their direct or indirect relationship with lipid transport and/or lipid metabolic activities of the parasite via protein associations deduced from the interactome data.
Mass spectrometry analysis The samples were analyzed on line using nanoflow HPLC (Ultimate 3000, Dionex) coupled to a mass spectrometer containing a nanoelectrospray source (LTQ-Orbitrap VELOS, Thermo Fisher Scientific). The peptides were separated in a reverse-phase capillary column (C18, Pepmap, Dionex) over a 0-40% gradient of acetonitrile containing 0. 1% formic acid for 150 min at a 300 nL/min flow rate. Spectra were saved using the Xcalibur 2. 1 program (Thermo Fisher Scientific) and spectral data were analysed using Proteome Discoverer 1. 4 (Thermo Fisher Scientific) and Mascot (Matrix Science) version 2. 4 programs. Spectral data were interrogated using CPS human 2013_10 (88266 entries) andPlasmoDB (relase 9. 3 including 5538 entries). Spectra were also searched against databases comprising common protein contaminants, using the contaminants_20120713 database (including 247 entries).
All databases included carbamidomethylation (C) and oxydation (M) modifications. The mass tolerance was set at 10 ppm for precursor ions and at 0. 8 ppm for fragment ions. A maximum of two miscleavages were allowed. Proteins were identified using a target FDR of 0. 05. The proteomics experiments were performed using the Functional Proteomics Platform facilities (FPP: http://www. fpp. cnrs. fr/) at the Institut de Genomique Fonctionnelle (Montpellier, France).
Bioinformatic data analysis Parasite proteins identified by mass spectrometry were further analyzed and filtered based on two criteria. The first criterion was the reproducibility of the detection of consistent results in the three independent experiments, and the second was the discrimination between nonspecific detected proteins and specific UA1936-bound proteins. All proteins consistently found in samples from parasites incubated without compound were thus considered as background proteins. In the same way, proteins that were consistently found in samples treated with the UA2050 control compound were considered as nonspecific binders not related to the pharmacophore. Finally, proteins that were abundantly detected in UA1936 treated parasites as compared with the two experimental controls were retained for subsequent bioinformatic examination.
Selected proteins were then clustered based on gene ontology annotations using PlasmoDB  [41, 42]  (release 7. 2 and 8. 0), Ontology Based pattern Identification  [43]  UniProt-GOA  [44]  PlasmoDraft  [45]  and MPMP (Malaria Parasite Metabolic Pathways)  [46]  databases. Amino acid sequences of conserved Plasmodium proteins of unknown function were also analyzed in OrthoMCL-DB  [47]. Clusters and protein interaction networks were built in Cytoscape 2. 5. 1  [48]  using the PlasmoID database  [49] , which integrates data from different experimental and computational resources, including LaCount DJ  [23]  and PlasmoMAP  [24]  datasets. The GO Term Finder tool  [50]  was used at the web interface http://go. princeton. edu/cgi-bin/GOTermFinder to identify ontology terms significantly enriched among the putative albitiazolium targets.
Statistical analysis The data were analyzed with Microsoft Excel 1 and Graph-Pad Prism 1 software. All results are presented as median values and 5th and 95th percentiles. Reference values were established for seven age groups. Before determining the reference intervals, the data were examined to identify outliers. The outlier exclusion was performed according to the Dixon outlier method recommended by the CLSI (Clinical Laboratory and Standards Institute) for non-Gaussian populations  [30]. In Dixon's test, if the calculated absolute difference between the suspected outlier and the next closest observation is more than one-third of the absolute difference between the outlier and the furthest observation from the outlier, then the outlier can be rejected. If two or more outliers are suspected, Dixon's rule is applied to the least extreme outlier and the entire group of outliers may be excluded.
It is of note that Slit2 had no detectable expression in cells collected from control mice; expression was only seen in Rxra/b mel2/2 cells (Figure  S7C ). Altogether, results suggest that melanocytic RXRa and RXRb regulate expression of genes encoding chemokines and cytokines involved in chemoattraction/chemorepulsion of immune cells such as macrophage; as well as genes involved in melanocyte apoptosis/survival. We then hypothesized that RXRs, and in particular RXRa, may directly regulate expression of a subset of genes whose expression was dysregulated in melanocytes lacking Rxra/Rxrb expression described above. To test that, a region of ,5 kilobases upstream of the transcriptional start site (TSS) for each gene was interrogated for possible binding motifs of RXRa/NR heterodimers (RXREs) as reported in the TRANSFAC database.
UVR treatment of mice Neonatal mice (P4 for data in Figure  S7 , P2 for all other experiments) were exposed to a single dose of 800 mJ/cm 2 of UV-B light from a bank of four Philips FS-40 UV sunlamps as described  [6]. The irradiance of the sunlamps was measured with an IL-1400A radiometer with an SEE240 UVB detector (International Light). Mice were euthanized 24, 48, 72 and 96 h after UVR and skin samples retrieved. 0 h samples were taken from P2 mice not exposed to UVR.
Imaging and quantitation of histological and IHC experiments Brightfield images were captured with a Leica DME light microscope using the Leica Application Suite software, version 3. 3. 1. Fluorescent images were captured using a Zeiss AXIO Imager. Z1 with a digital AxioCam HRm and processed using AxioVision 4. 8 and Adobe Photoshop. Epidermal thickness was measured using the Leica Application Suite, taking random measurements across 20 image fields per animal; which were then averaged to calculate an average value per animal. Quantifications of cell labeling were performed by randomly choosing multiple fields imaged from several replicate animals in each group and counting cells using ImageJ software (NIH). All slides were analyzed independently in a double-blinded manner by two investigators and significance was determined using a Student's two-tailed t-test as calculated by GraphPad Prism software.
ELISA assays Protein lysates were prepared as described above. Assays were performed using the eBioscience 'Mouse IFNg 'Femto-HS' High Sensitivity ELISA Ready-Set-Go!' kit according to manufacturer's protocol. 25 mg of protein was loaded per well. Lysates from three mice per group were used and assays performed in triplicate wells. Student's two-tailed t-test was performed using GraphPad Prism software.
Reverse transcription-quantitative PCR (RT-qPCR) analyses of transcriptional changes Total RNA was extracted from cell monolayers using Trizol (Invitrogen), followed by purification using RNEasy spin columns (Qiagen). cDNA was synthesized using SuperScript III RT (Invitrogen). Amplification was performed on an ABI Real Time PCR machine using a QuantiTect SYBR Green PCR kit (Qiagen), and all targets were normalized to the housekeeping gene Hprt. All reactions were performed in quadruplicates or sextuplicates. Melting curve analyses were performed to ensure specificity of amplification. Student's two-tailed t-test was performed using GraphPad Prism software.
Acknowledgments We thank members of the  Indra lab  and the  OSU College of Pharmacy , specifically  Dr. Mark Zabriskie  and  Dr. Gary Delander  for their continuous support and encouragement. We would also like thank the  OSU Environmental Health Sciences Center (EHSC). We are grateful for services provided by the Cell and Tissue Analysis Facilities and Services Core of the EHSC at  OSU ; specifically  C. Samuel Bradford  for help with flow cytometry. We would also like to thank the  OHSU Knight Cancer Institute.
We are grateful for services provided by the Cell and Tissue Analysis Facilities and Services Core of the EHSC at  OSU , grant number  P30 ES00210 ,  National Institute of Environmental Health Sciences, National Institutes of Health ; specifically  C. Samuel Bradford  for help with flow cytometry. We also thank the  OHSU Knight Cancer Institute , funded by the  National Cancer Institute ,  P30CA069533. Research reported in this publication was supported by the  National Institute of Environmental Health Sciences  of the  National Institutes of Health  under award numbers  ES016629-01A1  and  ES00210 , as well as the  National Cancer Institute of the National Institutes of Health  under award number  T32CA106195. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The low amounts of lung-associated flagellin represented either transepithelial-translocated flagellin or flagellin attached to the epithelium. Flagellin was degraded within hours in BAL, whereas the total BAL IgG used as a surrogate indicator of quality of samples were stable (Figures  4c  and d ). First signs of proteolysis were observed as soon as 1 h post administration and at 12 h most protein was fully degraded. In addition, the degradation in BAL was found to correlate with the concomitant decrease of capacity of flagellin to induce innate signaling, as defined on the Caco-2 cells that are potent responders to TLR5-dependent signaling (Figure  4e ). These data are consistent with the susceptibility of native flagellin to protease activity. 34, 35 Finally, we determined that the susceptibility of flagellin to degradation was similar in Tlr5 À / À animals and the TLR5-sufficient controls (Figures  4f-g ), suggesting that absence of TLR5 does not impact on flagellin degradation in airways.
This set of genes may limit lung damage caused by cellular infiltration and promote a rapid feedback to homeostasis. Microarray data were validated by quantitative PCR gene expression analysis and the correlation was highly significant (Supplementary Figures  4A-C ). This signature was not specific of C3H/HeJ mice as similar patterns of activation were defined in C57BL/6, BALB/c, and the outbred NMRI mice (Supplementary Figure  4E ). Next, the kinetics of the lung response to flagellin was investigated. We determined the gene expression and protein levels in lung (Figures  5a-b ) and BAL (Figure  5c ) of chemokines CXCL2 and CCL20. A swift and transient transcription and secretion was observed with a peak at 2 and 6 h, respectively. Interestingly, the steady-state expression was mostly reinstated at 24 h.