dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, 7 genes in our study underwent parallel kinetics in a gene expression study of the choroid plexus from P2 to Adult, (Abcc4, Slc22a8, Slco1a5, Slco1c1, Slco2a1). In another study, convergent data were noted on the antioxidant protein coding genes (Sod3, Maob, Ddc and Cat)  [40]. Using a different database (Ingenuity pathway analysis) convergent lists of enriched pathways were obtained, related to Glycolysis/neoglucogenesis, phenylalanine metabolism Wnt, IL1, or nuclear receptor signalling  [17]. Other reports using different approaches to extract the core of endothelial secreted proteins from 4 independent studies identified a "vasculome" of 99 proteins from adult mouse brain microvessels  [13]. Near 60% of these proteins were identified in forebrain enriched microvessels as proteins (n = 22) or exhibiting mRNA expression modulation (n = 42).
Acknowledgments Mass spectrometry proteomic data were formatted using PRIDE convertor [50] and deposited in the ProteomeXchange Consortium [51] via the PRIDE partner repository with the dataset identifier PXD001718 and  10. 6019/PXD0017188 ". We are grateful to  Nikki Sabourin-Gibbs ,  Rouen University Hospital , for writing assistance, and boldfaceeditors. comfor reviewing the manuscript in English.
Coefficients of matrix A were calculated between pairs of individuals as the proportion of shared parental alleles on the segregating sites from the remaining chromosomes. At each tested position k; the linear mixed effect model was estimated by REML using the R-ASReml package for R, and p-values for the QTL fixed effect obtained with the Wald test. Vectors of 99,900 p-values were then adjusted for the false discovery rate (FDR) using R function p. adjust implementing the method from  Benjamini and Hochberg (1995)  and significant associations were retained at adjusted q-values = 0. 5. Initially, we used this high threshold as a mean to obtain a greater number of putative QTL to incorporate a larger number of candidate genes into our screen. QTL mapping results are shown in Table  S3.
RNA-seq data analysis A total of 16 individuals with extreme phenotypes for ammonium consumption (eight segregants with high and eight with low consumption levels, Table  S2 ) were fermented. Fermentations were carried out as previously described in duplicate for each individual for 24 hr. Cultures were harvested by centrifugation and cells were treated with 2 U of Zymolyase for 30 min at 37°. RNA was individually extracted for each segregant utilizing the E. Z. N. A. Total RNA Kit I (OMEGA) according to the supplier's instructions. RNA samples were then treated with DNase I (Promega) to remove genomic DNA traces and total RNA was recovered using the GeneJET RNA Cleanup and Concentration Micro Kit (Thermo Scientific). RNA integrity was confirmed using a Fragment Analyzer. Later, four RNA pools were generated utilizing equal amounts (1 mg) of RNA per individual: two high levels of ammonium consumption (HLA) and two low levels of ammonium consumption (LLA).
Each pool was generated from independent replicates. The RNA-seq libraries were constructed using the TruSeq RNA Sample Prep Kit v2 (Illumina). Briefly, mRNA from 1 mg of total RNA was enriched by mRNA purification magnetic beads. Enriched mRNA was eluted and fragmented at 94°for 5 min. The double-stranded cDNA was acquired by RT-PCR using the above fragmented mRNA, followed by end repair, single A base addition, and adapter index ligation. The ligation product was amplified by PCR. The size of the end product was $260 bp. The sequencing was conducted on a HiSequation 2500 (Illumina) in a single lane for the four samples. Reads are available in the Biosample Database Project #PRJNA379146 with accession codes SAMN06602320, SAMN06602321, SAMN06602322, and SAMN06602323 for HLA-1, HLA-2, LLA-1, and LLA-2 samples respectively.
Raw reads were first assessed for their quality using the FASTQC tool kit (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Low quality reads were discarded using the Trimmomatic tool using default score settings and a phred score cut-off of 30 (http://www. usadellab. org/cms/?page=trimmomatic). RNA-seq reads were then aligned to the S288c reference [S. cerevisiae genome obtained on 03/03/2016, from the Saccharomyces Genome Database (SGD), FTP SITE: http://downloads. yeastgenome. org/sequence/S288C_reference/genome_releases/ corresponding to a stable release from January 2015] utilizing Tophat with default score settings  (Trapnell et al. 2009). BAM files were sorted and indexed using Samtools with default score settings  (Li et al. 2009). Alignments were then processed and gene counts were attained utilizing HTSeq  (Anders et al.
2015)  with the no-stranded and -gene counts configuration from the S288c gff file. Differential expression was assessed with DESeq utilizing the nbinomTest function  (Anders and Huber 2010). The results obtained from the DESeq analysis provided differentially expressed genes between pools with FDR , 0. 05. FDR was estimated utilizing the default Benjamin-Hochberg correction  (Benjamini and Hochberg 1995). SNP frequencies were estimated by initially extracting SNP data from the four pools (two HLA and two LLA) utilizing the mpileup tool from Samtools with -u and -f options together with bcftools view -vcg options to export and obtain a. vcf file. Only high quality SNPs (at least 10 reads for the reference and alternative nucleotides) present in all four samples were considered. For this, we utilized the intersectbed tool from Bedtools filtering out SNPs absent in at least one of the pools  (Quinlan and Hall 2010).
Data availability As stated above, genotype data can be obtained from Table  S1. These genetic marker data were already published before in  Cubillos et al. (2013). Phenotype data are available in Table  S2. Gene transcript data can be obtained from the Biosample Database Project #PRJNA379146 with accession codes SAMN06602320, SAMN06602321, SAMN06602322, and SAMN06602323 for HLA-1, HLA-2, LLA-1, and LLA-2 samples, respectively.
05 FDR (Table  S6 ), with 491 genes expressed significantly higher in HLA compared to LLA and 234 vice versa. Subsequently, we utilized the KEGG database to determine what pathways could be enriched within this subset of genes. We found several implicated fermentation-related pathways associated with the differences found between pools, representing interesting candidates for gene validation (Figure  4B  and Table  S7A ). Among others, we found carbon metabolism-related traits such as "glycolysis" and "citrate cycle (TCA cycle)" together with nitrogen metabolism traits such a: "phenylalanine, Figure  2  Reciprocal hemizygosity analysis on ALP1, LYP1, ASI2, and CPS1 mapped through a QTL approach. The hybrid hemizygote strains consumption levels (mg/L) are shown. Ã , ÃÃ , and ÃÃÃ represent a significant statistical difference between the hemizygote strains for the same gene using a t-test; Ã p-value , 0.
Within each pathway, several genes emerged as causative candidates for the consumption differences between individuals. To help us elucidate causative genes and increase the power of our study, we initially focused the analysis on differentially expressed genes within nitrogen-related pathways such as: "phenylalanine, tyrosine, and tryptophan biosynthesis" together with the "alanine, aspartate, and glutamate metabolism" pathways and cross-referenced this data with the QTL mapping results. Moreover, to augment the likelihood of finding common regions, a p-value , 10 26 (2logPvalue. 6) was used for all phenotypes. Eight genes present in both datasets were found (Table  S7B ). Interestingly, ARO1, which encodes for a protein involved in chorismate biosynthesis (a precursor of aromatic amino acids), represents a good candidate since it emerged with a p-value , 10 27 in the QTL mapping approach for ammonium and is upregulated in the HLA pool with respect to the LLA pool (0.
ACKNOWLEDGMENTS This work was supported by grants from the  Comisión Nacional de Investigación Científica y Tecnológica (CONICYT) Fondo Nacional de Desarollo Científico y Tecnológico (FONDECYT)  (grants  11140097  to F. A. C. , 1150522 to C. M. , 3150159 to C. B. , and  11130148  to V. G. ;  Millennium Nucleus in Fungal Integrative and Systems Biology (MN-FISB)  grant  120043  to F. A. C. ,  Programa de Cooperación Científica ECOS/CONICYT  ( C13B02 ) to F. A. C. , C. M. , and G. L. ; and  Proyecto Basal USA   1555 -Vridei O21771CR_CONT   Universidad de Santiago de Chile.
Glioblastoma multiforme is the most frequent primary malignancy of the central nervous system. Despite remarkable progress towards an understanding of tumor biology, there is no efficient treatment and patient outcome remains poor. Here, we present a unique anti-proteomic approach for selection of nanobodies specific for overexpressed glioblastoma proteins. A phage-displayed nanobody library was enriched in protein extracts from NCH644 and NCH421K glioblastoma cell lines. Differential ELISA screenings revealed seven nanobodies that target the following antigens: the ACTB/NUCL complex, VIM, NAP1L1, TUFM, DPYSL2, CRMP1, and ALYREF. Western blots showed highest protein up-regulation for ALYREF, CRMP1, and VIM. Moreover, bioinformatic analysis with the OncoFinder software against the complete "Cancer Genome Atlas" brain tumor gene expression dataset suggests the involvement of different proteins in the WNT and ATM pathways, and in Aurora B, Sem3A, and E-cadherin signaling.
In-silico analysis and antigen validation for class prediction The molecular interactions database OncoFinder was used to retrieve and explore the roles of these proteins in cells, and their possible contributions to tumor formation and/or progression  [34]. An interaction network was created that showed the functional links among these proteins (Supplementary Figure  1 ). The pathways in which these proteins are involved are given in Table  1. GBMc, cytosolic protein extract from GBM tissues; LGGc, cytosolic protein extract from lower grade gliomas; REFc, cytosolic protein extract from reference samples; GBMm, membrane protein extract from GBM tissues; LGGm, membrane protein extract from lower grade gliomas; REFm, membrane protein extract from reference samples. (B) Quantification of ACTB and NUCL protein expression in cytosolic and membrane protein fractions of pooled GBM, LGG, and reference samples showed 1.
27-fold higher NUCL expression in the membrane protein extract from GBM tissues compared to the membrane protein extract from reference tissues, and almost 3-fold higher ACTB expression in the membrane protein extract from GBM versus the membrane protein extract from reference tissues. Band intensity of each protein was plotted after normalization to GAPDH signal of the same lane. To determine whether the target genes have any value as class predictors, the mRNA expression data of these genes as retrieved from The Cancer Genome Atlas was used to calculate the area-under-the-curve (AUC) values for all of these genes in various ways. For DPYSL2, the "glioma versus reference" samples (Figure  5A ) defined an AUC score of 0. 985, indicating that it is a promising biomarker. Moreover, NUCL, TRIM28, VIM, and NAP1L1 were indicated as good markers to distinguish glioma tissues from reference tissues (Figure  5A ).
CONCLUSIONS With combined use of nanobodies, proteomics and bioinformatics, we identified a set of proteins with altered expression in GBM samples. The analysis of the genes of interest using RNA sequencing data from TCGA suggested the possible use of some of them as biomarkers for glioma class differentiation, with DPYSL2 demonstrated as a glioma-specific biomarker. The use of CRMP1, NAP1L1, NUCL, ACTB, and VIM as potential class predictors also needs to be validated in biological fluids. Our bioinformatic results were verified using a small scale confirmatory study. Validation of our findings on a dataset that contains a greater number of glioma samples and reference samples should now be initiated. Further studying the significance of these biomarkers for     glioma formation, progression and prognosis would help development of new treatment strategies, which may be beneficial for GBM patients.
Samples were then washed five times with PBS, and the protein was digested with 10 μL 5 ng/μL trypsin solution (0. 5 μg porcine trypsin in 100 μL PBS), overnight at room temperature. The next day, supernatant was removed, samples were washed twice with www. impactjournals. com/oncotarget PBS, and the antigens were separated from the nanobodies with 150 μL 6 M urea. Urea elutions and overnight digests were cleaned up using stop-and-go-extraction tips (Stage Tips)  [57] , and analyzed using liquid chromatographytandem mass spectrometry with the Easy-nLC system connected to an electron-transfer dissociation ion trap (Amazon; Bruker). The chromatogaphy was developed using a 75-min discontinuous gradient from 0% to 80% methanol in 0. 1% formic acid. The tandem mass spectrometry spectra were searched against the human database using the X!tandem and MASCOT search engines, allowing a 5% false-recovery rate.
BlueStar Prestained Protein Ladder (Nippon Genetics) was used as the protein marker. Residual protein binding sites on the membranes were blocked with 5% PBSmilk for 1 h at room temperature. A mouse monoclonal anti-GAPDH antibody (Sigma Aldrich) was used for the loading control. The incubations with the primary antibodies were at 4 °C overnight, with shaking at 60 rpm. The incubations with the secondary anti-mouse IgG (whole molecule)-peroxidase antibody produced in goat (Sigma Aldrich) were for 1 h at 4 °C, with shaking at 60 rpm. The bands were revealed with SuperSignal West Pico Chemiluminescent Substrate (Thermo Scientific), visualized with Fujifilm LAS-4000 (Tokyo), and analyzed with the Multi Gauge version 3. 2 software. The numerical values of the antigen intensities were calculated as the ratio between the arbitrary units (AU) of the antigen and the loading control, as GAPDH ( AU antigen AU GAPDH ( ) ( ) ).
Bioinformatics Possible interactions between the identified proteins were analyzed using the OncoFinder software  [59]  and the Reactome  [60] , Kyoto Encyclopedia of Genes and Genomes  [61] , and HumanCyc [(www. humancyc. org)] molecular interactions databases. The results from our previous study for TRIM28 and ACTB  [35]  were also included in the present analysis. Furthermore, the genes of interest were analyzed against TCGA RNA sequencing dataset which consists of 171 GBM, 530 LGG and five reference samples. This contains information about 19801 genes, including the genes of interest in the present study (i. e. , CRMP1, DPYSL2, NAP1L1, NUCL, TRIM28, VIM, and ACTB), with the sole exception of ALYREF. The genes were checked for specific overexpression or underexpression among the data available from TCGA, using "cancer-to-normal" ratios calculated with OncoFinder.
e. TBP, HPRT1, RPL13A, GAPDH, CYC1) chosen from the literature  [62] [63] [64] , RPL13A and CYC1 were selected for gene normalization using the NormFinder algorithm  [65]. The primers for the reference genes (Table  4 ) were chosen from the literature  [64] , while the primers for the genes of interest (Table  5 ) were obtained from the PrimerBank PCR primer database for quantitative gene expression analysis (https://pga. mgh. harvard. edu/primerbank/) [66]. Relative quantification was performed as previously described  [67]. For samples following Gaussian distribution differential expression analyzed as "GBM versus LGG versus reference" was calculated using one-way ANOVA and Holm-Sidak's corrections for multiple comparisons. For samples not following Gaussian distribution differential expression was calculated using Kruskal-Wallis test.
Statistical analysis To confirm the bioinformatic findings, relative mRNA expression levels of the genes of interest were analyzed as "glioma versus reference" and "GBM versus LGG" in GraphPad Prism 6 (GraphPad Software Inc. , La Jolla, CA, USA). For samples following Gaussian distribution analysis was performed using unpaired twotailed Student's t-test. Mann-Whitney test was used where samples did not follow Gaussian distribution. In both cases P ≤0. 05 was considered statistically significant (*, P ≤0. 05; **, P <0. 01; ***, P <0. 001; ****, P <0. 0001).
ACKNOWLEDGMENTS AND FUNDING The authors would like to thank the  Nanobody Service Facility  ( Vrije Universiteit Brussel, Brussels, Belgium ) for the library construction, and  Helena Klavžar  ( Centre for Functional Genomics and Bio-Chips, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia ) for technical support. This study was supported by the  P1-0104  and  P1-0390   Research Programme Grants  from the  Slovenian Scientific Agency (ARRS)  to RK. SM acknowledges  Strategic Research Programme  financing from the  Vrije Universiteit Brussel. IP and AB were supported by a  EuroTransBio 'Omicsglioma' grant  from the  Foundation for Assistance to Small Innovative Enterprises, Russia. The funders had no roles in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Moreover, the species complex has been divided into three species supported by genome analysis  (Prior et al. , 2016). Because of its highly diverse geographical distribution, host range, and genetic diversity, control of the pathogen is difficult, resulting in large economic losses  (Hayward, 1991; Genin and Boucher, 2002; Genin and Denny, 2012). To better understand the functions of pathogenicity determinants and the traits of aggressiveness under different ecological environments, the whole genome of R. solanacearum was sequenced. Phylotype I strain GMI1000 was the first strain subject to whole genome analysis  (Salanoubat et al. , 2002). Currently, there are 67 genomes in the National Center for Biotechnology Information (NCBI) database, of which more than ten genomes are complete  (Salanoubat et al.
Sequencing and Assembly The whole genome was sequenced using the PacBio RS II platform with a 20-kb library. Reads were assembled using HGAP (version 2. 3. 0, Pacific Biosciences)  (Chin et al. , 2013). Assembly data for the complete genome have been deposited in GenBank with accession numbers CP016914 and CP016915 (chromosome and megaplasmid, respectively).
Genome Components and Genome Annotation CDS were predicted using Prodigal  (Hyatt et al. , 2010). A circular map of the genome was drawn by CIRCOS  (Krzywinski et al. , 2009). Genomic Islands (GIs) were predicted by using the GI prediction method IslandPath-DIOMB  (Dhillon et al. , 2015). Clustered regularly interspaced short palindromic repeat sequences (CRISPRs) were found using CRISPRFinder  (Grissa et al. , 2007b)  and PILER-CR  (Edgar, 2007). Functional annotation was based on BLASTp searches against the NCBI non-redundant (NR) database and the KEGG, Pfam, Swissprot, and TrEMBL databases. Cluster of Orthologous Group of proteins (COG) analysis was performed to generate functional annotations for coding sequences (reference to orthologous groups 1  )  (Tatusov et al. , 2001).
The Virulence Dataset Virulence factors were predicted based on the virulence factors database (VFDB 2  ). The virulence factors of the R. solanacearum strain CQPS-1 analyzed in this study were selected according to  Remenant et al. (2010). T3Es were annotated using the IANT "Ralstonia T3E" database  (Peeters et al. , 2013). Every gene annotation was then manually validated to ensure homogeneity of the start codon positions and to detect frameshifts and pseudogenization.
Genomic Comparisons The genome sequences of GMI1000, Y45, YC45, FQY_4, PO82, CFBP2957, CMR15, and PSI07 were downloaded from the NCBI and EMBL databases. Sequences were aligned using Clustal x  (Jeanmougin et al. , 1998). Phylogenetic analysis was performed The distribution of the circle from outer to inner indicates genome size, forward CDS, reverse CDS, repeat sequences, tRNA (blue) and rRNA (purple), GC ratio (yellow means GC ratio of the region is higher than average GC ratio, blue means GC ratio of the region is lower than average GC ratio), and GC skew (gray represents a region with G content greater than C, red represents a region with C content greater than G). using neighbor-joining (NJ) and the algorithm of  Jukes and Cantor (1969)  with 1,000 bootstrap resamplings in MEGA version 5  (Tamura et al.
Genomic Islands and CRISPR Prediction Genomic Islands are evidence of horizontal acquisition  (Langille et al. , 2010; Remenant et al. , 2010). The GIs predicted in CQPS-1 are listed in Supplementary Table  S1 : a total of 21 GIs were predicted in the chromosome (13 GIs) and megaplasmid (8 GIs). CRISPRs can confer resistance to exogenous genetic elements such as phages and plasmids  (Barrangou et al. , 2007). To predict the CRISPRs of CQPS-1, the methods PILER-CR and CRISPRFinder were used. From the result predicted by the program PILER-CR, seven CRISPRs were found in the genome of CQPS-1; three were located in the chromosome, and four were in megaplasmid (Supplementary Table  S2 ). Whereas two different questionable CRISPRs were predicted by using CRISPRFinder, one in the chromosome and another in the megaplasmid (Supplementary Table  S2 ).
Virulence Factors Potential virulence factors in the strain CQPS-1 were identified using the BLAST search in the VFDB database. A total of 622 putative virulence factors were aligned (the chromosome and megaplasmid had 363 and 259 genes, respectively). We also compared the virulence factors reported by  Remenant et al. (2010)  with strain GMI1000, including exopolysaccharide (EPS) biosynthetic genes, cell wall-degrading enzyme (CWDE) genes, response genes to the host defense and key virulence regulators. The results showed that these genes were highly identical with strain GMI1000 (Supplementary Table  S8 ); the identities were more than 97%, except twitching motility gene pilA, whose identity was 91%.
, 2003; Guidot et al. , 2009). Bacteria could get genes from other different species such as archaea, bacteriophage, and eukaryotes  (Koonin et al. , 2001). R. solanacearum can transfer genes to adapt to novel ecological niches  (Guidot et al. , 2009). GIs, which known as pathogenicity islands, were thought to be the result of HGT  (Langille et al. , 2010). There were 21 GIs found in the genome of CQPS-1. The details of these GIs need to be further analyzed. Our   complete genome can supply the resource to explore the species evolution interacted with different host plants and study the HGT of R. solanacearum strains occurring in nature. There were nine CRISPRs predicted in CQPS-1 genome by using two methods, PILER-CR and CRISPRFinder. PILER-CR is a fast and accurate program based on an elegant algorithm to identify the CRISPR properties  (Edgar, 2007) , and CRISPRFinder is chosen because it can find very small CRISPRs (contained less than three, three or seven spacers)  (Grissa et al.
INTRODUCTION Urinary bladder cancer accounts for approximately 2% of all reported malignancies in dogs affecting tens of thousands of dogs worldwide every year  [1]. The prevalence of canine bladder cancer in North America, Europe and South America continues to increase despite ongoing research in the prevention and treatment of TCC  [2]. According to the Veterinary Medical Database (VMDB), dogs with bladder cancer comprised 0. 7% of dogs presenting to university based teaching hospitals  [2]. Multiple risk factors contribute to the development of TCC in dogs including exposure to older types of fl ea control products, lawn chemicals, obesity, sex, and breed  [1, [3] [4] [5]. Invasive TCC is the most common form of canine urinary bladder cancer and the majority of TCCs are categorized as intermediate to high grade papillary infi ltrative tumors  [6, 7].