dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION Investigation of checkpoint inhibitor plus targeted therapy combinations in BRAF-mutant melanoma was motivated by the observation that targeted therapy may lead to tumor microenvironmental changes favorable for immunotherapy. 7 8 However, the results of three key clinical trials of such combinations have been reported at the time of this writing, showing only modest benefits vs targeted therapy alone. 9-11 While these data collectively do not support routine use of first-line checkpoint inhibitor plus targeted therapy combinations, biomarkers that identify patient subgroups more likely to benefit remain an intriguing possibility. The results from COMBI-i Open access reported here represent, to our knowledge, the largest prospectively collected biomarker data set from patients with metastatic melanoma and highlight several characteristics that may inform treatment selection, pending replication in prospective studies.
Acknowledgements This manuscript represents original work that is not under consideration in whole or in part for publication in another journal. We thank the patients and their families for their participation, as well as study site staff and additional investigators. We thank  Noëlle Hanoteau  ( Novartis Pharmaceuticals Corporation ) for biosample management and  Steven Green ,  Kelly Biette ,  Radha Ramesh ,  Bin Fu ,  Steven Wilkes ,  Annette Quail ,  Nan Fei Jiang ,  Yumeng Wang ,  Christine Unitt ,  Rebecca Leary , and  Catarina D. Campbell  ( Novartis Pharmaceuticals Corporation ), and  Naveen Dakappagari ,  Shyam Sarikonda ,  Reinhold Pollner ,  Ju Young Kim , and  Thai Tran  ( Navigate BioPharma, a Novartis subsidiary ) for support with biomarker data generation and analyses. We thank HistoGeneX (now CellCarta),  BioAgilytix , and  Foundation Medicine, Inc.
Integration of these datasets with open chromatin profiles demonstrated prominent active DNA demethylation of regulatory regions in both examined organisms, thus strongly supporting the notion that invertebrate deuterostomes use TET proteins and 5hmC for developmental gene regulation. Moreover, we found that vertebrate orthologs of genes marked by 5hmC in these animals are associated with 5hmC enrichment and defined spatiotemporal regulatory logic in zebrafish. Together, our work demonstrates that at least in some nonvertebrate lineages, 5mC is not merely a by-product of gene activity required for the prevention of spurious transcriptional initiation but a bona fide regulatory mark that is being actively remodeled at regulatory regions associated with key developmental processes.
DNA methylation content and dynamics of sea urchin and lancelet genomes To quantify the amount of genomic 5mC, which can act as substrate for TET oxidation and 5hmC formation, we generated base-resolution 5mC profiles (WGBS)  (31)  corresponding to the following stages of sea urchin development: 24 hours post fertilization (hpf) (blastula), 48 hpf (gastrula), 72 hours post fertilization (hpf) (pluteus), and adult (tube feet). In addition, we analyzed WGBS datasets of lancelet embryogenesis from 8 hpf (blastula, G3), 15 hpf (gastrula, G6), 36 hpf (neurula, T0), and adult (liver, A) time points (Fig. 2A  and table  S1 )  (29, 41, 42). In agreement with previous studies  (29, 43) , average methylation values (sea urchin = 23 to 28% and lancelet = 21 to 25%) (Fig. 2A ) were consistent with canonical invertebrate methylome patterns, where the majority of genomic 5mC is located within gene bodies of expressed genes (fig.
5hmC-marked regulatory regions in deuterostome genomes Next, we wanted to assess to what extent does 5hmC correlate with sites of developmental hypomethylation and regulatory regions in general, identified through open chromatin profiling. We first plotted the 5hmC signal over previously identified adult hypomethylated DMRs (Fig. 2 ) and found a notable overlap between the two datasets (fig. S3D ). Of 16,753 DMRs, 5585 (33%) formed a 5hmC-enriched cluster in the sea urchin, whereas in the lancelet, that overlap was 46% (2072 of 4507 DMRs). To obtain more comprehensive insight into the relationships between 5hmC and gene regulation, we generated open chromatin profiles Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq), corresponding to 48 hpf (sea urchin) and 60 hpf (lancelet) (  29 ) stages (Fig.
MethylC-seq data analysis Sea urchin and lancelet MethylC-seq libraries were sequenced on the Illumina HiSeqX platform [150 bp, paired end (PE)]. Sequenced reads in FASTQ format were trimmed using Trimmomatic (ILLUMINACLIP: adapter. fa:2:30:10 SLIDINGWINDOW:5:20 LEADING:3 TRAILING:3 MINLEN:50). Trimmed reads were mapped to Spur_v3. 1, Bl71nemr and danRer10 genome references for sea urchin, lancelet, and zebrafish, respectively, (all containing the  phage genome as chrLambda) using WALT with the following settings: -m 10-t 24 -N 10000000 -L 2000. Mapped reads in SAM format were converted to BAM and indexed using SAMtools  (75). Optical and PCR duplicates were removed using Picard Tools function MarkDuplicates REMOVE_DUPLICATES = true (http://broadinstitute. github. io/picard/). Methylated and unmethylated cytosines at each genomic CpG position were called using MethylDackel v0.
3. 0 (https://github. com/dpryan79/MethylDackel). Additional MethylDackel extract parameters --minOppositeDepth 5 --maxVariantFrac 0. 5 were used for the genotype correction (to exclude C-to-T nucleotide transitions). In addition, sequencing read boundaries obtained from MethylDackel mbias were reviewed and fed into MethylDackel extract --OT a,b,c,d, --OB a,b,c,d parameters. For MethylC-seq, bisulfite conversion efficiency was estimated from the  phage spike-in control. Global DNA methylation at different developmental stages was estimated using two approaches: (i) 5mC/C ratio per single CpG site and averaged across all CpG sites in the genome and (ii) sliding window approach where average 5mC/C was calculated per 10-kbp windows with 2-kbp sliding intervals. Sliding windows were generated using bedtools makewindows -w 10000 -s 2000.
ACE-seq data analysis Sea urchin, lancelet, zebrafish, and mouse ACE-seq libraries were sequenced on Illumina HiSeqX and NovaSeq 6000 platforms (150 bp, PE). Adapter clipping and read quality trimming was performed using Trimmomatic (ILLUMINACLIP:NEXTflex. fa:2:30:10 SLIDINGWINDOW:5:20 LEADING:5 TRAILING:5 MINLEN:50 CROP:140 HEADCROP:10 ). These settings allow for the removal of low-complexity Adaptase tails. Trimmed reads were mapped to Spur_v3. 1, Bl71nemr, danRer10, and mm10 genome references for sea urchin, lancelet, zebrafish, and mouse  (32) , respectively, (all containing the  phage genome as chrLambda and pUC19 plasmid genome as chrPUC) using WALT with the following settings: -m 10 -t 24 -N 10000000 -L 2000. Mapped reads in SAM format were converted to BAM and indexed using SAMtools.
Identification of DMRs Differentially methylated CpG sites between consecutive embryonic and adult stages were identified using the DMLtest function of the DSS package. Differentially methylated CpG sites located within 50 bp from each other were joined into regions (DMRs), which were filtered to harbor at least five differentially methylated CpGs that span at least 50 bp. DMRs containing more than 25% of CpGs within them with sequencing coverage less than 5× were excluded from the analysis. The remaining DMRs were defined as hypomethylated or hypermethylated when both replicates displayed at least 20% average methylation difference between consecutive developmental stages. hMeDIP-seq data analysis hMeDIP-seq libraries from sea urchin 72-hpf, lancelet 36-hpf, and zebrafish 24-hpf embryos were sequenced on Illumina HiSeq X platform.
hMeDIP-seq raw data were processed using the NGSANE framework v0. 5. 2. 0. Briefly, sequenced hMeDIP-seq reads in FASTQ format were trimmed using Trimmomatic (ILLUMINACLIP: TruSeq3-PE. fa:2:30:10 SLIDINGWINDOW:5:20). Trimmed reads were mapped to Spur_v3. 1, Bl71nemr and danRer10 reference genomes for sea urchin, lancelet, and zebrafish, respectively, using bowtie2 v2. 1. 0 with the following parameters: bowtie2 --end-to-end -R 2 -p 10 -N 1 --very-sensitive -X 1000 --no-mixed --no-discordant  (76). Optical and PCR duplicates were removed using Picard Tools function MarkDuplicates REMOVE_DUPLICATES = true. hMeDIP-seq signal enrichment over ATAC-seq peaks was plotted using deep-Tools2 computeMatrix reference-point and plotHeatmap functions.
ATAC-seq data analysis Sea urchin ATAC-seq data were generated as part of this study, whereas lancelet and zebrafish ATAC-seq data were downloaded from GSE106428. Sequenced ATAC-seq reads in FASTQ format were trimmed using Trimmomatic software (ILLUMINACLIP:TruSeq3-PE. fa:2:30:10 SLIDINGWINDOW:5:20 LEADING:3 TRAILING:3 MINLEN:25). Properly paired trimmed read pairs were mapped to Spur_v3. 1, Bl71nemr, and danRer10 genome references for sea urchin, lancelet, and zebrafish, respectively, using bowtie2 v2. 1. 0 with the following parameters: bowtie2 -p 10 -N 1 --very-sensitive -X 2000 --no-mixed --no-discordant. Mapped reads in SAM format were converted to BAM and indexed using SAMtools. Optical and PCR duplicates were removed using Picard Tools function MarkDuplicates REMOVE_DUPLICATES = true.
RNA-seq data analysis Sea urchin RNA-seq data were downloaded from PRJNA81157, whereas lancelet and zebrafish RNA-seq data were downloaded from GSE106430/PRJNA416866. Illumina adapter trimming was performed using TrimGalore v0. 4. 0 (https://github. com/FelixKrueger/ TrimGalore). Trimmed sequence reads were aligned to the Spur_ v3. 1, Bl71nemr and danRer10 reference genomes for sea urchin, lancelet, and zebrafish, respectively, using STAR v2. 4. 0d. Quantification of transcript abundance was performed using RSEM v1. 2. 2. TET genes analyzed in this study correspond to the following IDs: WHL22. 387614 (SPU_027517) for sea urchin; BL00006 for the European lancelet; and ENSDARG00000075230, ENSDARG00000076928, and ENSDARG00000062646 for zebrafish.
scRNA-seq data analysis Zebrafish 24-hpf embryo scRNA-seq InDrop data were downloaded from GSE112294  (63). The data were converted to a file format that resembles a 10X Chromium CellRanger output using the indRop R package (https://github. com/caleblareau/indRop) and processed using the standard Seurat pipeline. Briefly, single cells with less than 200 detected genes, more than 4000 detected genes, indicative of Downloaded from https://www. science. org on January 09, 2023 empty droplets and duplicate cells in the droplet, respectively, were discarded. Cells with total Unique molecular identifier (UMI) (transcript) counts less than 1000 and more than 15,000 were excluded from the analysis. Read counts were normalized by a factor of 10,000 and log-transformed. Highly variable features were identified using FindVariableFeatures function.
scRNA-seq downstream analysis FindAllMarkers function was used to define gene markers for every cluster in the 24-hpf zebrafish embryo. Top 20 genes identified as cluster features  (63)  were matched with the obtained gene markers and used to assign cell clusters to different tissue types. AddModuleScore function was used to define module scores for a set of genes harboring a 5hmC peak and a phylo-DMR (5hmC/phylo-DMR), and genes harboring a 5hmC peak not overlapping a phylo-DMR (5hmC/no phylo-DMR), and genes without a 5hmC peak and without a phylo-DMR (no 5hmC/no phylo-DMR). Positive module score indicates that a given module of genes is more highly expressed in a certain cell than what would be expected from the average expression of this gene module across all cells in the population. Module score was then z-transformed and used in the FeaturePlot.
To generate "upset" plots, PrctCellExpringGene function was used to calculate the percentage of cells expressing each of the genes in the 5hmC/ phylo-DMR, 5hmC/no phylo-DMR, and no 5hmC/no phylo-DMR gene sets. Genes with nonzero expression counts in more than 25% of cells in any given cluster were deemed expressed in that cluster. UpSetR package upset function was used to display the number of genes in the gene set expressed per cluster, as well as the number of genes expressed across different numbers of clusters.
Acknowledgments: We thank  A. de Mendoza  for critical reading of the manuscript,  P. Leahy  for providing adult sea urchins, and  D. Caramiello  for sea urchin care. Drawings of adult sea urchin, lancelet, and zebrafish were created with BioRender. com. Funding: The  Australian Research Council (ARC)   Discovery Project  ( DP190103852 ),  Ramón y Cajal fellowship  from the  Spanish Ministry of Science and Innovation  ( RYC2020-028685-I ), and funding from  CEX2020-00108 - M Unidad de Excelencia María de Maeztu  to O. B. supported this work. Author contributions: O. B. and K. S. designed the study. M. I. A. , H. E. , and J. L. G. S. contributed to concept and study design. Sea urchin embryo and adult tissue collection and DNA extraction were performed by D. V. and  M. I. A. Lancelet  embryo and adult tissue collection and DNA extraction were performed by S.
B. and H. E. MethylC-seq library preparation and sequencing were carried out by P. E. D. and S. E. R. ACE-seq library preparation and sequencing were carried out by K. S. Sea urchin ATAC-seq library preparation was carried out by M. S. M. , D. V. , M. I. A. , and  I. M. , and sequencing was carried out by M. S. M. and I. M. K. S. performed data analysis. O. B. and R. J. W. participated in data analysis. K. S. and O. B. wrote the manuscript. All authors discussed the results and commented on the manuscript. Competing interests: The authors declare that they have no competing interests. Data and materials availability:  MethylC-Seq , ACE-seq, and ATAC-seq data generated in this study are available from  NCBI Gene Expression Omnibus  (GEO SuperSeries accession number  GSE188334 ). Specifically, sea urchin and lancelet MethylC-seq is available under GSE188333.
Sea urchin, lancelet, and zebrafish ACE-seq data are available under GSE188331. Sea urchin, lancelet, and zebrafish hMeDIP-seq data are available under GSE188332. Sea urchin ATAC-seq data are available under GSE186363. Public lancelet and zebrafish ATAC-seq data are available under GSE106428. Public sea urchin RNA-seq data are available under PRJNA81157. Public lancelet and zebrafish RNA-seq data are available under  GSE106430/PRJNA416866. Public scRNA-seq data of zebrafish 24-hpf embryo are available under GSE112294. All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Submitted 12 November 2021 Accepted 19 October 2022 Published 2 December 2022 10. 1126/sciadv. abn2258.
Methods Pseudomonas aeruginosa strain collection and patient characteristics. From June 2016 to August 2018, we prospectively collected isolates for all monomicrobial urine cultures positive for P. aeruginosa (regardless of the level of bacteriuria and leucocyturia) in the Rouen University Hospital. Clinical data were prospectively collected from the hospital's computerized medical records. The diagnosis of UTI or AB was assigned according to the diagnosis retained by the physician in charge of the patient, and confirmed by a committee made up of microbiologists and infectious disease physicians. A total of 120 patients were consecutively included, 40 with UTI and 80 with AB (Table  S4 ). For each urine culture, two (in case of low-level bacteriuria, i. e. 2. 10 2 CFU/mL) to five single colonies representative of all morphotypes observed were selected.
MLST. MLST was performed for two (in case of low-level bacteriuria) to three isolates per urine culture selected to represent the phenotypic diversity (in terms of morphotype and AMR profile). MLST was carried out as previously described  27  , except for the use of newly designed primers (Table  S5 ). Briefly, bacterial DNA was extracted by InstaGene Matrix kit (Bio-Rad) according to the manufacturer's recommendations. PCR products were purified and sequenced by GENEWIZ Europe (Leipzig, Germany). Sequencing data were aligned with BioEdit software (http:// www. mbio. ncsu. edu/ BioEd it/ bioed it. html). Allelic profiles and corresponding sequence types (STs) were assigned using the international PubMLST database (https:// pubml st. org/ paeru ginosa/). New alleles and STs were submitted to this database.
Clinical and microbial data were upload into BioNumerics software (Version 7. 6, Applied Maths, Belgium) in order to construct minimum spanning trees based on concatenated sequences. Statements on study approvals. We confirm that all methods and protocols used in this study were carried out in accordance with relevant guidelines and regulations. Clinical data and isolates were anonymised so that they were irretrievably unrelated to an identifiable subject. According to French regulation on observational database analyzes, no specific informed consent was required from patients. The study was approved and registered by the Clinical Research and Innovation Delegation of the Rouen University Hospital under number 2018/413/OB. Ethical approval for this study was obtained from the Ethics Committee for Research of the Rouen University Hospital, Rouen, France (No.
The two default criteria applied by SILVI and additional filtering allow the fast selection of the most conserved, promiscuous, strong binding T-cell epitopes. Users may adapt the script at their discretion as it is written in open-source R language. To demonstrate the workflow and present selection options, SILVI was used to integrate HLA-binding prediction results of three example proteins, from viral, bacterial and parasitic microorganisms, containing validated epitopes included in the Immune Epitope Database (IEDB), plus the Human Papillomavirus (HPV) proteome. Applying different filters on predicted IC50, hydrophobicity and mismatches with host proteins allows to significantly reduce the epitope lists with favourable sensitivity and specificity to select immunogenic epitopes. We contemplate SILVI will assist T-cell epitope selections and can be continuously refined in a community-driven manner, helping the improvement and design of peptidebased vaccines or immunotherapies.
Introduction In the post-genomic area, available databases and -omics datasets have been extensively explored to discover antigens for the development of vaccines or immunotherapies  [1]. Reverse vaccinology (RV) approaches hold the promise for breakthrough contributions to vaccine development, as in the case of the meningococcal vaccine  [2]. Yet, there are still a myriad of pathogens and conditions for which no vaccine has yet been successfully developed, particularly in the case of Neglected Infectious Diseases (NIDs), wherein current tools, innovation and funding are lacking, and would greatly benefit of new preventive or therapeutic candidates with low development costs  [3, 4]. To induce protective immunity, epitope-based vaccines require identifying the minimal immunogenic units for optimal recognition by the host's immune system and induction of protective immunity  [5, 6].
Also, epitope-based selections offer great advantages when compared to empirical antigen selection, and can be applied whenever cellular immune responses are relevant, whether protective or detrimental  [7] [8] [9] [10]. Yet, epitope selection from databases remains a challenging step to optimise and diversify the antigens to be tested in an experimental setting  [10]. Epitope immunogenicity relies firstly on high-affinity binding to HLA-class I or -class II molecules and on antigen abundance and kinetics of expression, conservation, low homology to the host, intracellular processing, and the presence of T cells with specific TCRs  [11] [12] [13]. To inform on these features, databases such as Immune Epitope DataBase (IEDB) and other tools are available  [14]. Nevertheless, in silico-based approaches have not supported a significant increase in the total number of licensed products in the last decades and several challenges remain  [9, 15, 16].
Optimised T-cell epitope selection relies on knowledge of several disease-specific variables: host susceptibility/resistance factors, HLA variability, environmental factors  [21, 22]  and pathogen-related characteristics such as virulence, tropism, immunomodulation and species conservation  [23]. Several open-access immunoinformatics tools and databases are available to evaluate a set of characteristics associated with immunogenic epitopes  [6, [24] [25] [26]. T-cell epitope prediction can be performed via direct prediction (predicting T-cell receptor, TCR recognition) or indirect methods (predicting epitope binding to HLA molecules), the latter extensively more accurate than the former  [27]. HLA-binding affinity has become the first criterion when trying to predict if a given peptide sequence constitutes an epitope, since it is the first requirement for T-cell activation and it correlates with peptide linear sequences  [26, 27].
The script reads epitope binding prediction data from different predictors, processes and compares data, assimilates BLASTp alignment results  [45]  and feeds a final output table with all relevant information to perform the tailored epitope selections with available information, thus helping to refine the search of the most immunogenic epitopes. We demonstrated the relevance of SILVI's workflow with epitope selection from the Genome Polyprotein (P26664 and P27958) from Hepatitis C Virus (HCV), the Circumsporozoite Protein (P19597) from Plasmodium falciparum, the 6kDa early secretory antigenic target (P9WNK7) from Mycobacterium tuberculosis, and the Human Papilloma Virus (HPV) proteome (uniprot_HPV_proteome_UP000126093). SILVI helps the process of epitope selection from a vast amount of data produced by different open-source third-party algorithms, and to add extra relevant information, in a non-restrictive, user-friendly manner.
HLA-class II predictions were performed for all 15-mer full peptides (Fig 2 ). With SILVI's step A applied to the example proteins, the peptides shared between speciesspecific protein sequences (seq_nums) are selected (common_among_seq_nums filter), and only the epitopes predicted by at least two predictors are selected (common_among_predictors filter). In the examples, proteins were analysed with only one seq_num (Pf CSP, Mtb EsxA and HPV proteome), or two seq_nums (HCV genome polyprotein). SILVI integrated data from all 9-mer or 15-mer epitopes and respective HLA-binding prediction information, meaning only repeated epitopes (illustrated by Pf CSP) or epitopes not shared by species-specific sequences (illustrated by HCV genome polyprotein) were excluded from the analysis. In the BLASTp online server, the human RefSeq proteins were used as the host proteome (Homo sapiens, Taxid: 9606).
Comparison of SILVI's outputs with IEDB-validated epitopes Previously validated epitopes specific to the selected example proteins were retrieved from the IEDB 3. 0 database to exemplify SILVI's workflow. From full epitope lists from the example proteins, all 9-mer and 15-mer validated epitopes were retrieved (S1 Table ). Information from the final output files was used to characterize the validated epitopes in the IEDB database (S1 Table ) and possibly hint at the most relevant filtering criteria users can utilize to reduce epitope lists (S1 and S2 Figs). For HCV GP (example#1), from the total 3003 9-mer peptides present in both protein sequences, 2109 are shared among the two seq_nums P26664 and P27958. Similarly, among the 2997 15-mer peptides present, 1692 are shared among the two seq_nums.