dataesr/scientific-paragraphs-categorization huggingface dataset:
2018). MALDI-TOF MS has been used to identify ticks that had been frozen or preserved in alcohol, and collected from various countries  (Boucheikhchoukh et al. 2018; Boyer et al. 2019; Diarra et al. 2017; Karger et al. 2012; Kumsa et al. 2016; Yssouf et al. 2013). Currently, the challenges of this technique remain selecting the body part to be used, the methods of arthropod conservation in order to generate sufficiently reproducible and specific spectra, and the need to have a database of arthropod spectra formally identified by morphology and confirmed by molecular biology, if possible  (Laroche et al. 2017a). In this study, we morphologically identified six different species, four of which were collected from domestic pigs (Rh. complanatus, Rh. congolensis, Hae. muhsamae and I. cumulatimpunctatus) and two from wild pangolins and monitor lizards  (A.
The performance of tick identification is not particularly impacted by the alcohol 70% because with an adapted database, we obtained identification percentages very similar to those of fresh ticks, often around 100%  (Boucheikhchoukh et al. 2018; Diarra et al. 2017; Kumsa et al. 2016). The impact of alcohol on MS MALDI-TOF profiles, resulting in lower intensity and overall quality than fresh or frozen samples, has been reported in previous studies  (Diarra et al. 2017; Kumsa et al. 2016; Nebbak et al. 2017; Rothen et al. 2016). Despite the impact of the preservation method on the quality of the MALDI-TOF MS profiles, MALDI-TOF MS appears to be efficient, fast and less expensive for the identification of ticks if a specific database is created with good quality spectra of specimens preserved under the same conditions, as demonstrated by other studies  (Diarra et al.
2017; Kumsa et al. 2016). In this study, we observed no differences in MS spectra from male and female Rh. complanatus and Rh. congolensis. A previous study reported that it was not possible to definitively distinguish between male and female ticks on the basis of their MS spectra  (Yssouf et al. 2013 ). Nevertheless, differentiation of male and female mosquito MS profiles has been achieved  (Fall et al. 2021). More in-depth analysis of fresh tick spectra should be performed in order to assess whether MALDI-TOF MS is actually able to distinguish male and female specimens. Based on morphological identification, all six of these tick species were not represented in our home-made reference spectra MALDI-TOF MS database. With the exception of Hae. muhsamae, for which we did not have good quality spectra, the reference spectra of five species, namely  Rh.
complanatus, Rh. congolensis and I. cumulatimpunctatus, collected in 2015 and 2016 , and A. exornatum and A. compressum collected in 2015  were added to our MS MALDI-TOF home-made database. In the first step, 515/520 ticks were correctly identified at species level with LSVs > 1. 8, which is the threshold for reliable identification of arthropods  (Yssouf et al. 2013 ). The mean differences between the first and second species match scores in our study were > 0. 2, which is the minimum difference to distinguish two species  (Kumsa et al. 2016). The five unidentified specimens were due to morphological identification errors highlighted by the MALDI-TOF MS tool. MALDI-TOF MS incited us to re-examine the morphology of these ticks which were morphologically identified as Rh. complanatus. We were the able to identify one of the five specimens as Hae.
muhsamae. None of the three experts in tick identification involved in this study was able to morphologically identify the remaining four specimens of Rhipicephalus sp. to the species level. However, it should be noted that these ticks were engorged females, whose morphological characteristics were distorted or had disappeared, making identification impossible, highlighting the limits of morphological identification  (Walker et al. 2003). After temporarily adding the 1 3 spectrum of one of these specimens in the database, MALDI-TOF MS confirmed that all four belonged to the same species. Knowing that the species which were morphologically identified in our study were not yet represented in the GenBank database, we sequenced the specimens chosen for the creation of the MALDI-TOF MS database, as well as others with confirmed MALDI-TOF MS identification, by amplifying part of the 16s and 12s rDNA genes and the Cox1 mtDNA gene, as previously used to discriminate ticks  (Boyer et al.
2019; Diarra et al. 2017). This allowed us, on the one hand, to evaluate the percentage of similarity of sequences from the same tick species for the three genes, and, on the other, to compare the identity of all these sequences with those available in the GenBank database to reinforce the results. The high percentage of similarity between the sequences of different specimens of the same species (97% to 100%) using the three genes supports the robustness of our morphological and MALDI-TOF MS identification. However, the low identity of our sequences (between 85 and 95%) with the reference sequences of the 16s, 12s rDNA and Cox1 mtDNA genes of the tick species available in the GenBank database could be due to the absence of sequences of the tick species from our study in this database. The phylogenetic trees constructed with the sequences of the three genes clearly show that Rhipicephalus sp.
form a separate group from Rh. complanatus  (Figs. 4, 5 and 6) , and thus have different genomic sequences. Similarly, the three genes used did not allow us to identify the four specimens of Rhipicephalus sp. , which we could not identify down to the species level by either morphology or MALDI-TOF MS, thus showing the limitations of this technique, namely the insufficient number of reference sequences and the reliability of the morphological identification of tick species whose sequences already available on GenBank  (Yssouf et al. 2016). MALDI-TOF MS has been used in clinical laboratories in developed countries and capacity building was performed in Senegal, where staff and students was trained to use this technique for bacterial and entomological identifications. It can indeed be used in medical entomology, with almost zero additional costs, for the rapid and accurate identification of arthropods and determination of their trophic preferences and infection status  (Yssouf et al.
Acknowledgements The authors are grateful to the manager of the Kalongo Petrus IAT market slaughterhouse, who kindly helped us to collect our data at the slaughterhouse. We are also grateful to the  Centre de Surveillance de la Biodiversité of the University of Kisangani, Royal Institute of Natural Sciences of Belgium (Brussels, Belgium) , the  Boyekoli Ebale Congo 2010 expedition consortium (Belgian Development Aid , the  Belgian Science Policy  and the  National Lottery of Belgium) , and the teams working with profs. Horak ( South Africa ) and  Beati (USA)  for their help with the morphological identification of ticks.
1 3 Table 1 31 Ticks collected from domestic animals between September 2015 and September 2016 and from wild animals in 2010 in the Democratic Republic of the Congo (DRC), their hosts and the result of MALDI-TOF MS identification Mean (± SD) differences between LSV of first and second top species 0. 66 ± 0. 11 0. 62 ± 0. 10 0. 63 ± 0. 18 0. 85 ± 0. 10 0. 86 ± 0. 15 Tick species ID by MS Log score value (aver- (number) age) Rh. complanatus (434) 1. 813-2. 51 (2. 084) Rh. congolensis (57) 1. 955-2. 452 (2. 165) I. cumulatimpunctatus 1. 836-2. 262 (2. 011) (9) Hae. muhsamae (0) A. compressum (5) 1. 985-2. 224 (2. 087) A. exornatum (10) 1. 824-2. 298 (2. 121) Number added to database 5 4 3 1 2 15 Number tested (%) Host Number of high quality spectra (%) 456 (79. 3) Pigs 444 (97. 4) 61 (10. 6) Pigs 61 (100) 12 (2.
Results Characterization of the super-enhancers in ALCL. To identify in an unbiased manner cellular dependencies and vulnerabilities which could ultimately be tools for ALCL therapy, we performed genome-wide H3K27ac ChIP-seq for SE detection in 8 ALCL cell lines (5 ALK + , 3 ALK -). By applying the ROSE algorithm  22, 24  , we defined SEs, characterized by extensive H3K27ac, in all 8 cell lines (Fig. 1a , b and Supplementary Fig. 1a, b ). These were compared to published H3K27ac ChIP-seq datasets of CD3 + , CD8 + and CD4 + T-cell subsets from healthy donors as well as Tcell-derived Jurkat cell line (Supplementary Fig. 1b ). Globally, we defined 538 and 722 SEs in Karpas-299 (systemic ALCL, ALK + ) and Mac-1 (derived from cutaneous T-NHL later progressing to ALCL) cell lines (Fig. 1a, b ), respectively.
1f ). Among 21,000 assigned and expressed genes, CD30 and IL2RA were within the top 50 hits positively correlating with BATF3 (both P < 0. 001). Also, IL2RB positively correlated with BATF3 expression (P = 0. 027; Fig. 1f ). The same pattern was observed in an independent microarray dataset of 29 ALCL patients  30  (Supplementary Fig. 2e ). In line, BATF3 and IL-2Rα expression positively correlated (P = 0. 0017) by immunohistochemistry (IHC) of ALCL specimens using a tissue microarray (TMA) (n = 63; Fig. 1g , Supplementary Fig. 2f  and Supplementary Tables  3  and 4 ). IL-2Rα/β/γ are highly expressed in ALCL but not other T-NHL. To comprehensively examine expression of the IL-2R subunits, IL-2Rα (also called CD25), IL-2Rβ (CD122) and IL-2Rγ (CD132), in mature T-NHL, we performed immunostaining of TMA comprising angioimmunoblastic T-cell lymphoma (AITL, n = 8), peripheral T-cell lymphoma not otherwise specified (PTCL-NOS, n = 23), ALCL with (n = 22) or without (n = 23) ALK-translocation, and of non-neoplastic reactive lymph node controls (n = 11) as well as of pcALCL specimens (n = 24).
However, this finding reflects our functional studies and is in line with the correlation of high soluble IL-2Rα levels with more aggressive disease and lower EFS at 3 years in ALCL, ALK + children cohorts  35. As risk factor, IL-2Rα expression might be outcompeted by more intense treatment regimens with brentuximab-vedotin (BV)-containing regimens or SCT in our adult ALCL, ALK -cohort. Moreover, it is unlikely that DUSP22 and TP63 rearrangements, having been described in ALCL, ALK  -43,46  , are confounding factors in our survival analysis based on the following findings. In the first clinical report, the 5-year OS of 21 patients with DUSP22 rearrangement was 90%  7. However, in a later work from the British Columbia Cancer Lymphoid Cancer database, the 5-year OS of 12 patients with DUSP22 rearrangement was 40%  47.
Thus, the prognostic impact of DUSP22 rearrangement is currently unclear, and it may be more related to prognostic factors such as IPI. In addition, in pcALCL, no impact of DUSP22 rearrangement (present in about 30% of cases) on prognosis was observed  48. Despite that TP63 rearrangement has a confirmed poor prognosis in ALCL, ALK -, it is indeed very rare. We also revealed a BATF3-dependency of Fig. 3  IL-2 and IL-15 promote ALCL proliferation and activate STAT1, STAT5 and ERK1/2. a Indicated 7 ALCL cell lines were treated with rhIL-2 (100 ng/ ml). Control cell lines were SUP-M2 (ALK + , IL-2Rγ-negative) and Jurkat (T-cell leukemia-derived, IL-2Rα-and IL-2Rβ-negative). Resazurine assay was used to measure metabolic activity as a marker of cell proliferation. Data are means ± SD of biological triplicates given as a percentage of the untreated control.
P values were determined by two-tailed unpaired Student's t test; n. s. not significant. Structure of IL-2 bound to the trimeric IL-2R complex is shown (PDB ID: 2B5I). b In BATF3-KO cells, no IL-2-induced proliferation in Karpas-299 (ALK + ) and Mac-1 (ALK -) could be seen compared to CRISPR control. Proliferation was determined by resazurine assay. Data are normalized to untreated control and shown as means ± SD of biological triplicates. P values were determined by two-tailed unpaired Student's t test. c Immunoblot analysis of total STAT1, STAT5 and ERK1/2 proteins and their phosphorylated (pY, pT/Y) forms in Mac-2A (ALK -), TLBR-2 (BIA) and Jurkat control cells stimulated with rhIL-2 (50 or 100 ng/ml), Neo-2/15 (100 ng/ml) or PBS (-) as control. GAPDH serves as loading control. Blots shown are representative of two independent experiments with similar results in different cell lines.
d Limiting dilution of Karpas-299 (ALK + ), Mac-1 (ALK -) and Jurkat control cells with or without rhIL-2 (100 ng/ml) in a 96-well plate containing RPMI1640 and 10% FCS. At day 0, the indicated number of cells were seeded and incubated for 2 weeks. Representative example of biological triplicates is shown. e Inhibition of IL-2induced proliferation by the IL-2Rα-blocking antibody Basiliximab (1 μg/ml). Proliferation was determined by resazurine assay. Data are normalized to control-treated cells and shown as means ± SD of biological triplicates. P values were determined by two-tailed unpaired Student's t test. f Representative images of 9 patient samples measured of IL-15 IHC staining in fresh-frozen primary ALCL tissue. g Effect of IL-15 (50 ng/ml) on cell proliferation of indicated 4 ALCL and Jurkat cell lines.
Given the unsatisfactory treatment response of particular subgroups of systemic ALCL, ALK -patients, relapsed/refractory systemic ALCL patients or relapsed pediatric ALCL, ALK + patients to standard chemotherapy, there is a medical need for new effective treatment strategies  49. With implementation of chemotherapy regimens containing BV, which proved to be the first effective targeted treatment for ALCL in many years  4  , in frontline ALCL therapy, effective treatment options remain lacking in the second line. For ALCL, ALK + patients, pharmacological ALK-inhibition might be a treatment option in this setting  50, 51. IL-2Rα has been investigated as immunotherapeutic tool for other malignancies  52, 53. ADCT-301, which has entered clinical trials (e. g. , ClinicalTrials. gov Identifier: NCT02432235 and NCT04052997), killed ALCL cells with high efficacy  39.
FFPE tissues from systemic ALCL, ALK -cases collected through TENOMIC, a transnational research consortium on T-cell lymphomas involving several centers in France, Belgium and Switzerland, were used for immunohistochemical validation. The TENOMIC database is approved by the ethical committee "CPP Ile-de-France IX 08-009". Genome-wide occupancy analysis. ChIP-seq was performed as described  58, 59. Primary antibodies used for analysis are listed in Supplementary Table  7. In short, for each ChIP, 5 μg of antibody was conjugated to 2 mg of M-270 Epoxy Dynabeads (Thermo Fisher) and added to sonicated nuclear extract. Library construction was performed using the Swift 2S Turbo DNA library kit as per the manufacturer's instructions (Swift Biosciences). Illumina sequencing was performed using the NextSeq500 platform with single-end 75 bp reads.
SEs and typical enhancers from the same cell type as the TF ChIP-seq were used. Promoters were defined as 4 kb windows centered on the transcription start sites of RefSeq genes. Gene bodies were defined as the remaining portion of genes longer than 2 kb from 2 kb downstream of the promoter to the transcription end site. SE identification and assignment. SEs were identified using ROSE (https:// bitbucket. org/young_computation/rose) algorithm with some modifications. Reads overlapping ENCODE blacklist regions were removed using bedtools intersect. From the remaining reads, two sets of peaks of H3K27ac were identified using MACS with parameter sets -keep-dup=auto -p 1e-9 and -keep-dup=all -p 1e-9. H3K27ac peaks were stitched computationally if they were within 12500 bp of each other, though peaks fully contained within + /-2000bp from a RefSeq promoter were excluded from stitching.
These stitched enhancers were ranked by their H3K27ac signal (length * density) with input signal subtracted. SEs were defined geometrically as those enhancers above the point at which the line y = x is tangent to the curve. Stitched enhancers (typical enhancers and SEs) were assigned to the single active gene whose transcription start site is nearest the center of the stitched enhancer. Active genes were determined by taking the top two-thirds of all RefSeq promoters (+/-500 bp) ranked by their H3K27ac signal. H3K27ac signal in promoters was determined using bamToGFF (https://github. com/BradnerLab/pipeline) with parameters -e 200 -m 1 -r -d. RNA-seq and bioinformatic analysis. RNA isolation was performed using the RNeasy Mini Kit (Qiagen) and RNA quality was assessed on a Fragment Analyzer (Advanced Analytical Technologies) -SS Total RNA 15nt.
RNA-seq libraries were prepared using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (New England Biolabs) with Poly(A) selection according to the manufacturer's instructions. Library quantification was examined on a Fragment Analyzer -HS NGS Fragment 1-6000 bp and Qubit HS dsDNA Kit (Invitrogen). Libraries were pooled and sequenced to 75 bp single-end on the Illumina NextSeq 500 platform. Sequencing data were analyzed as described  60. CRISPR/Cas9 genome editing. Cloning of gRNA lentiCRISPR v2 plasmid, lentiviral packaging, lentiviral transduction and single clone isolation were performed as described  12. The lentiviral Cas9-containing plasmid lentiCRISPR v2  61  was a gift from F. Zhang (Addgene #52961, Cambridge, USA). Non-targeting and BATF3 gRNAs were designed using the E-CRISP program, version 5.
RNA preparation, cDNA synthesis, semiquantitative and quantitative PCR analyses were performed as described  9. Primers are listed in Supplementary Table  8. Flow cytometry. Surface protein expression of IL-2Rα, IL-2Rβ, IL-2Rγ, CD30 and IL-15Rα on ALCL and T-cell leukemia-derived cell lines was determined by flow cytometry. Primary (all 1:50) and corresponding isotype control (all 1:50) antibodies are listed in Supplementary Table  7. Apoptotic cell death was determined by Annexin V/PI staining according to the manufacturer's instructions (Bender MedSystems). Data were acquired with a BD FACSCanto II and analyzed with FACSDiva software 8. 0. 1 (Becton Dickinson). EMSA. Nuclear protein preparation and EMSA were performed as described  9. AP-1 motifs of the respective IL2R promoter (IL2RA) or enhancer (IL2RB) gene regions were identified using LASAGNA-Search 2.
009/0200-V/3b/2018 and Addendum Zl. 18/115-97/98, 2019). Statistical analysis. Statistical analyses were performed using GraphPad Prism v6. 0 and R v4. 0. 3 software. Data of protein expression in TMAs are presented in box-and-whisker plots with indication of medians, quartiles and ranges, each data point represents an individual score. Correlation of data was determined by the Pearson correlation coefficient. Other data are presented as means ± SD or SEM as stated in the respective figure legends. Differences between mean values of two groups were evaluated by the two-tailed unpaired Student's t test. Estimation of EFS and OS was performed using the Kaplan-Meier method using IBM SPSS Statistics Version 22 with the survival package of R (v3. 2-7; Therneau, 2020). Events were defined as relapse, progressive disease or death from any cause.
Acknowledgements We thank  Boris Tichý  (Brno) at the  Core Facility Genomics of CEITEC Masaryk University  ( LM2018132 ),  Eva Kaergel  and  Simone Lusatis  (both  Berlin ) for their outstanding technical assistance. We also thank  Gerda Egger ,  Heidi Neubauer ,  Inés Garces de los Fayos Alonso ,  Philipp Staber ,  Renato Arnese ,  Richard Moriggl ,  Robert Eferl ,  Sabine Lagger  (all Vienna) for helpful discussions. We are grateful to  Alan Epstein  ( Los Angeles ) for providing the BIA-ALCL cell lines. We also thank  ADC Therapeutics  for providing ADCT-301 and  B12-SG3249. This project received funding from the  European Union Horizon   2020 Marie Skłodowska-Curie Innovative Training Networks (ITN-ETN)  under grant agreement no. 675712. O. M. is supported by the  Austrian Science Fund (FWF)  project ( P32579 ).
B. J. A. receives support from the  American Lebanese Syrian Associated Charities. D. S. received funding from  FWF  grant  P32693. Š. P. is financially supported by the  Ministry of Education, Youth and Sports of the Czech Republic  under the project  CEITEC 2020  ( LQ1601 ). W. K. is supported by the  Kinder Krebs Initiative Buchholz-Holm-Seppensen (KKI). L. K. was supported by the  Christian-Doppler Lab for Applied Metabolomics  (CDL-AM). L. K. was supported by the  Austrian Science Fund , grants  FWF  grant  P 26011  and  P 29251  and by the  COMET Competence Center CBmed -Center for Biomarker Research in Medicine  ( FA791A0906. FFG). The  COMET Competence Center CBmed  is funded by the  Austrian Federal Ministry for Transport, Innovation and Technology (BMVIT) ; the  Austrian Federal Ministry for Digital and Economic Affairs (BMDW) ;  Land Steiermark  ( Department 12, Business and Innovation ); the  Styrian Business Promotion Agency (SFG) ; and the  Vienna Business Agency.
Data availability All RNA-seq and ChIP-seq datasets produced in this study are deposited in Gene Expression Omnibus (GEO) under SuperSeries GSE158916. Source data is provided for the Fig. 1a-d , Supplementary Fig. 1  and Supplementary Fig. 2a-d. The Crescenzo et al. 29  and Iqbal et al. 30  publicly available data used in this study are available in the Sequence Read Archive (SRA) and GEO database under accession code SRP044708 and GSE19069, respectively. ChIP-seq of H3K27ac and input DNA of normal T-cell subsets and Jurkat cell line used in this study are publicly available in GEO under the accession numbers GSM1058764, GSM1058789, GSM772835, GSM772916, GSM1102781, GSM1102806, GSM1296384 and GSM569086. The remaining data are available within the Article, Supplementary Information or Source Data file.
Imaging Process Micrographs were recorded using Hitachi's TM4000Plus tabletop SEM. The specificity of this microscope lies in its ease of use for sample visualization and micrograph acquisition compared to older SEMs that require much more expertise. TM4000Plus can ensure the necessary vacuum conditions in <2 min, significantly reducing the images acquisition time compared to other SEMs. At least 10 micrographs were acquired per condition. Accelerating voltages of TM4000Plus was at 15 kV, and the magnification ranged between 2,000 X and 10,000 X. Low magnifications were used for quantification, however high magnification was essential for ultra-structural and morphological investigations. We used the automated imaging function in the TM4000Plus software that enables automated screening and image acquisition.
Post-acquisition Analysis Strategy We looked for morphological modifications of bacteria incubated with or without IPM. Analysis of the bacterial morphology with regards to the shape, density, thickness, brightness signal, formation of blebs, bacterial length, and width were analyzed for all tested bacterial isolates. Measurement of bacterial length and width were performed on the acquired micrographs using Fiji's Image-J software. One hundred bacteria per condition and per isolate were measured by two independent operators on the three sets of data for the experiments carried out in triplicate. The length or width variation between different conditions were considered significant when it was more than 0. 2 µm and p ≤ 0. 001. Average size ± standard deviations (SD) were used to compare bacteria incubated with or without IPM at the different time points (30, 60, 90, 120 min).
Increase or decrease in the brightness signal was also analyzed on Image-J using the greyscale histogram option. We analyzed the contrast of 30 bacteria per condition, extracted the contrast histograms for each bacterium, and calculated the relative SD corresponding to the contrast variation in a single bacterium. Bacterial count was also realized on Image-J using the multi-point tool. We compared the average measurements by performing an ANOVA statistical analysis using ElliStat software. The reported error was set at 95% confidence interval.