dataesr/scientific-paragraphs-categorization huggingface dataset:
5 • C/s). Negative controls and standard curves (constructed with ten-fold serial dilutions) were included in each qPCR to validate reaction specificity and determine the PCR efficiency. The average expression stability (M-value) of the reference genes (Supplementary Table  S3 )  [62]  and gene relative quantification were assessed based in the geNorm algorithm  [63]  and the Pfaff method  [64] , respectively, included in the CFX Manager™ Software (Bio-Rad, CA, USA).
Tick Biological Parameters Recovery rate (RR, as the percentage of the ratio between live ticks and the total number of female ticks), drop-off (DO, as the percentage of the ratio between ticks that dropped-off and the total number of female ticks), and engorged female weight (EFW) were determined in order to elucidate about tick fitness, while reproductive parameters such as egg mass weight (EMW), egg production efficiency (EPE, as percentage of the ratio between EMW and EFW), and egg hatching rate (EHR, as the mean value of visual evaluation performed by five technicians separately) were analyzed after dropped-off females laid the eggs. The data are expressed as mean ± standard deviation, and statistical significance was determined using SPSS v24. 0  [67]  (*p < 0. 05, **p < 0. 01, ***p < 0.
In uninfected ticks, blood feeding resulted in a higher number of over-represented proteins but in infected ticks blood feeding reduced the levels of a larger number of proteins (Figure  2B ). These results suggested that the response to Babesia infection and blood feeding leads to an increase in tick vector protein levels when acting independently, but the combination of both stimuli overcomes this effect by reducing protein levels in response to feeding and infection. Gene ontology (GO) annotation was assessed for each UniProt ID obtained from the R. bursa proteome using Blat2GO and UniProt-related databases. Of the 1586 identified-proteins, only 97 proteins were classified as "unknown" due to the absence of GO and domain function. The remaining 1489 annotated proteins were classified according to the GO terms molecular function (MF), biological process (BP), and cellular component (CC) at level 3.
Overall, both studies clearly demonstrate that blood ingestion requires a high production of cellular molecules, which is reflected by the high expression of genes and its subsequent translation. In this set of proteins, multiple GO terms are exclusively over-represented. Moreover several proteins were exclusively found in this dataset, being associated to amide and amine binding, extracellular matrix structural constituent, modified amino acid binding, neurotransmitter transporter activity, structural constituent of nuclear pore, and sulphur compound binding (MF), as well as anatomical structural development, cell adhesion, interspecies interaction between organisms, localization of cells, response to biotic and external stimulus, and finally, single-organism development process (BP). Besides that, the GO terms related to "structural constituent of cuticle," "cell-cell junction," and "anatomical structural development" confirms the investment of blood feeding in tick engorgement and development  [72].
Effect of Feeding in the Sialoproteome of B. ovis Infected R. bursa By comparing the infected fed and infected unfed sialoproteomes, a feeding process is analyzed when ticks are subjected to infection (Figures  2B  and 4B  and Supplementary Figure  S3B ). In this context, from a total of 65 proteins, 96. 9% proteins were under-represented (Figure  2B ). The most represented GO terms are represented in Figure  4A  and Supplementary Figure  S3A , with more proteins under-represented than in the uninfected dataset. The majority of GO terms includes exclusive under-represented proteins, except DNA packing and protein-DNA complex, outer and whole membrane (CC), and finally substrate-specific transporter activity (MF) that are exclusively over-represented. Interestingly, when ticks are exposed to both Babesia infection and blood meal (infection in fed ticks and feeding in infected ticks), over-represented proteins with DNA packaging and protein-DNA complex properties are commonly presented.
R. bursa Cellular Machinery in Response to Feeding and Infection To build knowledge about the response of R. bursa SG, the datasets were analyzed to identify proteins commonly represented at the different conditions (Supplementary Table  S1 ). Figure  5  summarizes the proteins involved in infection, feeding, or both. Vaccines 2019, 7, x 13 of 22 virion part and whole membrane). "Response to endogenous stimulus" was a GO term only present in this condition of infection in fed ticks. Besides, other two GO term related to stress response were identified ("response to stress" and "response to chemical"). This suggests that the production of proteins that mediate cellular response to stimuli is being stimulated only when blood meal occurs.
R. bursa Cellular Machinery in Response to Feeding and Infection To build knowledge about the response of R. bursa SG, the datasets were analyzed to identify proteins commonly represented at the different conditions (Supplementary Table  S1 ). Figure  5  summarizes the proteins involved in infection, feeding, or both. The process of infection (when ticks are fed or unfed) modulate the representation of twelve proteins of R. bursa SGs, while blood meal (when ticks are infected or uninfected) influence ten proteins. From these, two proteins, a putative ubiquitin-protein ligase (UniProt ID: L7M1X7, UB2N) and an uncharacterized protein (UniProtID: L7MAU7, PCCA), were found in all comparisons, its representation being modulated positively when ticks are exposed to a single stimulus (INF/NINF and NIF/NINF) and negatively when ticks are exposed to both stimuli (IF/NIF and IF/INF) (Figure  5 , Supplementary Table  S1 ).
The Role of a Putative Ubiquitin-Protein Ligase in R. bursa and B. ovis Interface Ubiquitination is a biological process that affects proteins by adding to them ubiquitin moieties  [77]. This process could influence proteins by altering their cellular location, activity, and interaction with other molecules, being involved in pleiotropic roles such as protein degradation [78], cell-cell communication  [79] , pathogen invasion  [80] , and innate immune system  [80]. Ubiquitin addition involves the sequential action of three main groups of enzymes: ubiquitin-activating (E1s), ubiquitin-conjugating (E2s), and ubiquitin-ligase (E3s) enzymes  [79]. An E1 enzyme interacts with an E2 that subsequently coupled with a specific E3 leading to ubiquitin incorporation in a protein sequence. The putative ubiquitin-protein ligase, UB2N (UniProt ID: L7M1X7) possesses several domains that are common among E2s (CDD: cd00195, InterPro: IPR023313 and IPR000608) reflecting its function as an ubiquitin-conjugating enzyme.
Funding: This research was funded by  Fundação para a Ciência e Tecnologia (FCT)  under the project  TickOmic  ( PTDC/CVT-CVT/29073/2017 ). JC and JF are the recipients of Ph. D. grants supported by the  FCT  ( SFRH/BD/121946/2016 ,  SFRH/BD/122894/2016 , respectively).
HLA-H Transcriptional Expression in PBMC and Bronchial Epithelial Cells HLA-H transcriptional expression was investigated in PBMC and HBEC from healthy donors (N = 5 and N = 8, respectively). K562 cells (ACC86) were used as the negative control. Total RNA was isolated using the RNeasy kit (Qiagen, France). cDNA was reverse transcribed using Superscript III Reverse Transcriptase (Invitrogen), and Real-time PCR analyses were performed using TaqMan technology (Life Technologies) as previously described  (28). The primers/probes were designed using the Primer 3 v. 0. 4. 0 program (http://bioinfo. ut. ee/primer3-0. 4. 0/primer3/) to specifically target HLA-H as checked with the Human Genome Browser (http://genome. ucsc. edu/) (29) and IPD-IMGT/HLA database 3. 37. 0 (30) (Supplementary Table  1 ).
HLA-G and HLA-E were investigated as previously described  (28) , and ACTB (actinβ) was used as an endogenous control (ACTB Hs99999903_m1, Invitrogen). Each experiment was carried out in duplicate, and average Ct was calculated with StepOne 2. 1 software (Invitrogen), but Ct duplicates with a standard deviation above 0. 5 were excluded.
HLA-H Transcriptional Expression in Cell Line From the 1000 Genomes Project HLA-H expression was also investigated in RNA-sequencing data from 464 lymphoblastoid cell lines from the 1000 Genomes Project  (31). RNA sequencing data were analyzed using PolyPheMe software specially designed for HLA NGS data analysis (Xegen, France) for which accuracy was assessed at 99. 3%  (10, 32, 33). This software is based on a specific strategy to avoid bias raised by the use of a unique genome as a reference for HLA NGS data mapping  (34) (35) (36). First, RNAseq reads were selected for their specificity for HLA class I alleles. Then, all reads assigned to HLA class I were filtered out for their specificity for HLA Ia (A, B, C), Ib (E, F, G), and pseudogenes that showed the highest homology with HLA-H. Lastly, remaining reads were selected for their specificity for HLA-H using all exon sequences.
For HBEC, incubation at both interfaces (air and liquid) was performed at 37 • C for 16 h. Each experiment was carried out in duplicate. HLA-E expression at the cell surface was assessed by flow cytometry using a Mouse IgG1 antibody clone 3D12-PE (Invitrogen). The isotype control was a Mouse IgG1 antibody clone 679. 1Mc7-PE (Beckman Coulter). Data were acquired on a FACSCalibur machine (BD Biosciences) and analyzed with BD FACSDiva software 6. 1. 2. PBMC subtypes for HLA-E expression and overexpression after incubation with HLA-H were characterized with an antibody panel that included CD3-vioBlue (Miltenyi), CD19-AF700 (Biolegend), CD14-FITC (Biolegend), and viability Dye (eFluor506) (eBioscience) with or without a 3D12 antibody. Data were acquired on a Cytoflex machine (Beckman Coulter).
Statistical Analyses All association and correlation tests were performed with GRAPH PAD Prism 5 software (California USA). Differences between two modalities were tested using a Mann-Whitney U test. A Kruskal-Wallis one-way ANOVA followed by a Dunn post-hoc test was used to test more than two modalities. Q-PCR results were expressed as dCt (delta of cycle threshold, expression normalized by ACTB endogenous gene) with median and range  [min, max]. RNA seq data from 464 lymphoblastoid cell lines from the 1000 Genomes Project were presented as the number of reads. HLA-E protein expression without peptide incubation was estimated as a percentage of expressing cells (cells gated in R2 and defined according to isotype control staining). The HLA-E mobilization at the cell surface upon peptide incubation was estimated and compared to incubation with a solvent (DMSO).
RNAsequencing data from lymphoblastoid cell lines from the 1000 Genomes Project allowed us to show a hemizygous effect in the expression of HLA-H and to characterize different levels of expression according to HLA-H alleles; most interestingly, the HLA-H * 02:07 allele, which potentially encodes a full-length protein, presents the highest level of mRNA expression. HLA-H expression results from RNA-sequencing data, but this should, however, be taken with precaution and needs further confirmation; a part of the reported expression level, notably in the low-expression alleles, may be due to mismapped reads, as observed in the HLA-A * 23 and/or * 24 samples and as reported in different studies dealing with HLA NGS mapping  (34) (35) (36). We then analyzed HLA-E mobilization at the cell surface by peptides from the HLA-H signal sequence.
Results SPHK1 expression inversely correlates with survival after ICI therapy. Analysis of two different cohorts from the Oncomine database indicated that SPHK1 (encoding SK1) transcript levels were higher in human primary melanomas as compared to nevi (Fig. 1a , left panel); SPHK1 expression was further increased in metastatic melanomas (Fig. 1a , right panel), suggesting that SPHK1 expression might be associated with melanoma progression. In order to evaluate whether SPHK1 expression was related to the therapeutic outcome in advanced melanoma patients receiving anti-PD-1 therapy (Table  1 ), we analyzed SPHK1 messenger RNA (mRNA) expression in tumor biopsies by in situ hybridization using the RNAscope technology. Patients were separated in two groups according to the proportion of tumor cells positive for SPHK1 mRNA (Low SPHK1: ≤ 50% tumor cells positive and High SPHK1: > 50% tumor cells positive) (Fig.
1  SPHK1 expression inversely correlates with survival after ICI therapy. a SPHK1 expression in human nevi (n = 17) compared to primary (P) melanoma (n = 45), and in primary melanoma (n = 25) compared to metastatic (M) melanoma (n = 44) based on the Oncomine database. Data were compared using Mann-Whitney test. The bottom-most and topmost horizontal lines, the lower and upper hinges, and the middle line of the boxplots indicate the minimum and maximum values, the 25th and 75th percentiles, and the median, respectively. b Percentages of cancer cells positive for SPHK1 mRNA staining in metastatic melanoma tissues of 32 patients prior anti-PD-1 treatment (Low: ≤ 50% of tumor cells are positive (black points); High: > 50% of tumor cells are negative (red points)). c Representative mRNA staining of low and high SPHK1 expression.
To analyze the translational relevance of our findings in patients affected with metastatic melanoma, we correlated SPHK1 transcript levels with those of transcripts encoding immunosuppressive factors, using a publically available dataset (TCGA) of human melanoma biopsies  29. Interestingly, samples with high SPHK1 expression showed a higher expression of immune escape genes, including PTGES, FOXP3, TGFB1, IL10, CCL22, CCR4, IDO1, IDO2, CTLA4, PDCD1 (encoding PD-1), CD274 (encoding PD-L1), PDCD1LG2 (encoding PD-L2), TIGIT, LAG3 and HAVCR2 (encoding TIM-3) (Fig. 7a, b ). Altogether, these data indicate that SPHK1 overexpression is associated with an immunosuppressive TME in humans, in line with our preclinical findings.
This will be performed on a larger cohort of advanced melanoma patients treated with anti-PD-1 in combination or not with anti-CTLA-4, from a prospective clinical trial (IMMUSPHINX: NCT03627026) we are currently conducting in our institute. SK1 produces the phospholipid S1P, which exerts its functions through intracellular actions or ligation to five cell surface Gprotein-coupled receptors (named S1PR1-5) expressed both on cancer cells and cells of the TME  38, 39. The SK1/S1P/S1PR axis can regulate tumor cell behavior, as well as the TME composition  [8] [9] [10] [11]. For instance, it promotes tumor angiogenesis  40. Thereby, neutralization of extracellular S1P with an anti-S1P antibody significantly inhibited bFGF-and VEGF-induced angiogenesis, as well as growth of different tumors including melanomas  41.
Methods In silico analysis with Oncomine database. SPHK1 expression in human nevi or melanomas was analyzed using the Oncomine tool (www. oncomine. org) in 2 different cohorts  50, 51. In situ mRNA hybridization. In situ detection of SPHK1 transcripts in formalinfixed, paraffin-embedded tissues was performed using the RNAscope assay with RNAScope 2. 5 vs. Probe-Hs-SPHK1 and the ACD RNAscope 2. 0 Red kit (Advanced Cell Diagnostics). Assay specificity was assessed measuring the signal in positive and negative control samples. Positivity of endothelial cells was used as an intrinsic positive control. Cases with positive intrinsic control and no signal in tumor cells were considered as negative. We determined the percentage of positive tumor cells blinded to clinical response to treatment. Murine cell lines.
Peptides were analyzed by liquid nanochromatography (UltiMate 3000 RSLCnano system, Dionex, Amsterdam, The Netherlands) coupled to a LTQ-Velos mass spectrometer (ThermoScientific, Bremen, Germany) as described  53. Raw MS files were processed using MaxQuant (v 1. 5. 3. 30) for database search against Mouse entries of the Swissprot database, and validation through the target-decoy approach at a false-discovery rate of 5% and 1% for peptides and proteins, respectively. Label-free relative quantification of the proteins across samples was performed in MaxQuant using the LFQ algorithm, and the Perseus software was used for missing value imputation and statistical analysis. A two-sided Student-t-test with variance correction (S0 = 0. 1) was calculated between the shCtrl group (three replicates) and SK1-silenced group (shSK1 (1) and shSK1(2), six replicates), and the global false-discovery rate was adjusted at 1% using the Perseus permutation-based method to identify proteins significantly regulated between the two groups.
Quantification of eicosanoids. Eicosanoid concentrations in conditioned media or Yumm cell lysates were determined by ultra-performance liquid chromatography, using an Agilent 1290 system coupled to a G6460 triple quadripole mass spectrometer (Agilent Technologies). Analysis of SPHK1 expression in human melanoma. SPHK1 expression was analyzed using the TCGA melanoma cohort. TCGA genomic and clinical data were downloaded from the UCSC Xena platform (https://xenabrowser. net/). The analysis population consisted of 342 patients with distant metastasis for whom RNAseq and clinical data overlap. No formal sample size calculation was performed concerning TCGA analysis. Gene expression was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform and log2(x + 1) transformed. The strength of the relationship between SPHK1 transcript levels with those of transcripts encoding immunosuppressive factors was assessed using Spearman's rank correlation coefficient.
Patients still alive were censored at their date of last follow-up. Comparison between groups (low expression vs. high expression) was performed using log-rank test. All tests were two-sided and differences were considered statistically significant when p < 0. 05 (*p < 0. 05; **p < 0. 01; ***p < 0. 001, ****p < 0. 0001). Data were analyzed using GraphPad Prism (GraphPad Software, San Diego, CA) and STATA v13 (Stata Corporation, College Station, TX, USA). Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements This work was supported by  INSERM, Paul Sabatier University, Ligue Nationale Contre le Cancer  ( LNCC, Equipe Labellisée 2013), INSERM Transfert, Cancéropôle Grand Sud-Ouest, ROTARY Toulouse clubs ,  Fondation Toulouse Cancer Santé and Fondation ARC  ( IMMUSPHINX  project). The research leading to these results has received funding from the  Transcan-2 Research Program , which is a transnational R&D program jointly funded by national funding organizations within the framework of the ERA-NET Transcan-2. C. I. is a recipient of a fellowship from  LNCC  and  Fondation pour la Recherche Médicale (FRM). The  IBiSA Toulouse Proteomics facility  is supported by the following institutions:  Région Midi-Pyrénées ,  Fonds Européens de Développement Régional ,  Toulouse Métropole , and the  French Ministry of Research  with the ' Investissement d'Avenir Infrastructures Nationales en Biologie et Santé program ' ( Proteomics French Infrastructure  project,  ANR-10-INBS-08 ).
P. B. is supported by the LABEX TOUCAN. The authors would like to thank  Drs. J. Riond  and  L. Martinet  for critical reading ( CRCT, Inserm UMR1037, Toulouse, France ) and  Drs. N. Cénac  ( IRSD, Inserm U1220, Toulouse, France ),  JJ. Fournié , and  M. Ayyoub  ( CRCT, Inserm UMR1037, Toulouse, France ) for fruitful discussion. We are grateful to the animal (CREFRE, Toulouse, France) and flow cytometry (Inserm  UMR1037 , Toulouse, France) facilities. We also thank  Dr. JE Sarry  ( CRCT, Inserm UMR1037, Toulouse, France ) for the kind gift of NSG mice and  Dr. C. Lachaud  ( CRCM; Marseille, France ) for the generation of stable cell line using the CRIPSR/Cas9 technology. We acknowledge the assistance of  Drs. P. Le Faouder  and  J. Bertrand-Michel  from the  MetaToul-lipidomic facility  ( Inserm UMR1048, Toulouse, France ) and the assistance of  A.
Data availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD016535 [http://www. ebi. ac. uk/pride/archive/projects/PXD016535]. The oncomine data referenced during the study in Fig. 1  are available in a public repository from the oncomine website (www. oncomine. org). Talantov data can be accessed here. Xu data can be accessed here. The TCGA source data underlying Fig. 7  are available in UCSC Xena platform: https://tcga. xenahubs. net/download/TCGA. SKCM. sampleMap/HiSeqV2. gz. All the other data supporting the findings of this study are available within the article and its supplementary information files and from the corresponding author upon reasonable request. A reporting summary for this article is available as a Supplementary Information file.
Similar data have been obtained using the human TNBC cell line MDA-MB231 (Figure  S5 ). The potential anti-tumorigenic action of mime[4]CRM, together with the activation of immune cells within the tumor microenvironment, was then evaluated in vivo by performing a preclinical study. For this purpose, Tn expressing  (Solatycka et al. , 2012)  murine 4T1-luc cells (stably expressing Firefly Luciferase gene, see Supplemental Information) were implanted into the mammary fat pad gland of immunocompetent syngeneic mouse model (BALB/c mice). Of note, the use of immunocompetent mice properly allows to test the efficacy of immunomodulating compounds. Moreover, recent data showed that wild-type and huMUC1 transgenic mice produced equivalent antitumoral response against a native Tn-containing candidate vaccine  (Stergiou et al.
g. , PD1) in the tumor microenvironment of TNBC (data not shown). In contrast, the decrease of Tregs (FOXP3+ CD4+) in the same mime[4] CRM-treated mice was not substantially observed, thus inferring a loss of regulation of conventional T lymphocytes at T42 (results did not reach a statistical significance; data not shown). Altogether, immunofluorescence staining indicates that mime[4]CRM vaccine has a clear potential role in modulating the recruitment and phenotype of CD4+ T cells and, as a consequence, on the polarization status of TAMs in the TNBC tumor microenvironment. As 4T1 cells, used in this study as a cellular model of TNBC, are highly metastatic, the presence of lung metastases was evaluated by performing in vivo analyses 42 days after 4T1 TNBC cells were implanted. At T42, our in vivo BLI analyses indicated the presence of lung metastases in seven of nine controls and four of eight mime[4]CRM-treated mice (Figures  8A  and 8B ).
SAXS Recording Method and Results. SAXS data were recorded on the BioSAXS beamline BM29 at the European Synchrotron Radiation Facility (ESRF) Grenoble, France, using a 2D Pilatus detector at an X-ray wavelength of 1. 008 Å. Data processing and reduction were performed using an automated standard ESRF beamline software (BSxCuBE)  (Pernot et al. , 2013)  and PRIMUS  (Konarev et al. , 2003). Concentration series (0. 5, 1. 0, 2. 0, 5. 0 mg/mL) were recorded for both samples and individual samples and buffers were exposed for 10 times 1s frames and grouped to improve statistics. For CRM197 the radius of gyration RG was extracted by the Guinier approximation  (Guinier, 1939)  using PRIMUS. In Figure  S2  we report the SAXS spectra of CRM197 and mime[19]CRM sample measured at 37 °C. The SAXS data showed clear aggregation in the case of mime[19]CRM but globular and compact particles in the case of CRM197.