dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The cartography of non-canonical antigen presentation revealed in our study arose from a harmonized large-scale analysis of immunopeptidomic data mapped to the human genome. Our innovations over the most recent trends in computational MS identified a diversity of peptides mapping to canonical and non-canonical translation products. We mapped deviations away from the reference proteome as mass shifts (PTMs) and applied a sequential approach to tackle the non-canonical immunopeptidome. Our proteogenomic pipeline allowed the identification of thousands of ncMAPs (8,601) derived from non-coding regions of protein-coding genes with an FDR of 1%. This was accomplished by analyzing a large collection of publicly available studies using COD-dipp, a highly modular large-scale pipeline that bypasses the challenge of multi-omics requirements and large MS databases when identifying ncMAPs.
Twenty-five percent of the identified ncMAPs accounted, on average, for 81. 36% of intersections when compared with three other high-profile studies  (6, 13, 16). In addition, COD-dipp revealed 6,433 new ncMAPs from protein-coding genes. Considering the high-quality and rigorous computational validation, the identification rate discrepancy is partly due to the performance of COD-dipp and the size of our dataset collection, making it the most exhaustive noncanonical library of MHC class I-associated peptides. Our survey of the possible sources of ncMAPs revealed that 70. 1% could be attributed to nORFs, IR, or frameshift mutations. We identified 597 ncMAPs downstream of known frameshift mutations in COSMIC, an understudied source of antigens in immunopeptidomic studies. Certainly, other biological processes not accounted for in this study could generate ncMAPs.
Simultaneously, the samples are analyzed using a novel de novo approach to identify non-canonical peptides. The de novo strategy trains a model per sample using quality-controlled peptide-spectrum matches from the MS-GF+ search engine to learn the direct interpretation of sample-specific mass spectra. The MS-GF+ results are split into three groups: training and testing to tune the hyperparameters and account for overfitting, and a validation group to approximate the accuracy per sample. De novo predicted peptides with an accuracy of at least 90% are sequentially mapped against the Human proteome (HP) then a 3-frame translation (3FT) database of protein-coding genes (1 mismatch allowed between leucine/isoleucine, i. e. , Xle). Predicted de novo peptides matching any known protein are labeled "canonical".
Peptides mapping to the 3FT database with at least 3 amino acids mismatches from any known protein sequence are labeled "non-canonical". Lastly, a second-round search is performed as a validation approach. Four of the most abundantly identified PTMs and a custom database consisting of ENSEMBL proteins and non-canonical peptides are considered. The resulting canonical and non-canonical peptides are controlled to an FDR of 1% and aligned to the hg38 human genome. Figure legends.
2 search engine was used to conduct an open search analysis against the ENSEMBL r94 protein database in combination with PTMiner (44) v1. 1. 2, to apply a transfer FDR and a false localization rate of 1% (FLR, the rate of falsely localizing Downloaded from http://aacrjournals. org/cancerimmunolres/article-pdf/doi/10. 1158/2326-6066. CIR-22-0621/3285889/cir-22-0621. pdf by guest on 03 April 2023 the site of modification). Unspecific cleavage generating peptides 8 to 25 amino acids long with no fixed/variable PTMs was considered. Further analysis revealed that the frequent unexplained mass shifts observed during the open-search annotations were caused by nonspecific cleavage. To address this issue, an open-search post-processing algorithm, PTMiner, was employed to effectively corrects for mass shifts introduced by in-source fragmentation, nonspecific digestion, or missed cleavage, by adding or deleting amino acids from the peptide N-or C-termini.
results of peptide-PRISME byErhard et al. 2020 (50). Our comparison was based on three common studies (1, 14, 34)  and resulted in 3,453 at 1% FDR from COD-dipp along with 4,576 ncMAPs at 10% FDR from Erhard et al. We first aligned Erhard et al. 's ncMAPs to the human proteome and eliminated a small fraction (1. 4%) that matched the canonical protein sequences (Fig. 4a, left-hand side). Since the COD-dipp ncMAPs were restricted to the 3FT of protein-coding genes, we aligned the remaining ncMAPs from Erhard et al. to the same 3FT database for comparison purposes. Fig. 4a (left-hand side) shows that 68. 25% of ncMAPs were successfully mapped to the 3FT database. The rest (30. 35%) that did not align to any of the human proteome or the 3FT database are shown in yellow on Fig. 4a lefthand side.
De novo predicted peptides with an accuracy of at least 90% are sequentially mapped against the Human proteome (HP) then a 3-frame translation (3FT) database of protein-coding genes (1 mismatch allowed between leucine/isoleucine, i. e. , Xle). Predicted de novo peptides matching any known protein are labeled "canonical". Peptides mapping to the 3FT database with at least 3 amino acids mismatches from any known protein sequence are labeled "non-canonical". Lastly, a second-round search is performed as a validation approach. Four of the most abundantly identified PTMs and a custom database consisting of ENSEMBL proteins and non-canonical peptides are considered. The resulting canonical and non-canonical peptides are controlled to an FDR of 1% and aligned to the hg38 human genome.
). (a)Percentage of ncMAPs that were solely in healthy and/or tumor samples by MS (blue) and ncMAPs undetected in healthy samples by MS (red). b) Parent gene expression of ncMAPs in TPM in 29 healthy tissues from 17,382 samples (GTEx v8 dataset). ncMAPs are distributed over two groups: (I) ncMAPs detected in healthy samples by MS in blue, (II) ncMAPs undetected in healthy samples by MS in red. c) Parent gene expression of ncMAPs in TPM in 29 healthy tissues from 17,382 samples (GTEx v8 dataset). A limit on the gene expression (y-axis) of 1. 2 TPM was set to visualize cancerselective ncMAPs in black.
Acknowledgments This work was supported by the  International Centre for Cancer Vaccine Science (Fundacja na rzecz Nauki Polskiej :  MAB/3/2017 ) project is carried out within the  International Research Agendas programme of the Foundation for Polish Science co -financed by the  European Union  under the  European Regional Development Fund. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  101017453. This work was supported by the  United Kingdom Research and Innovation  (grant  EP/S02431X/1 ),  UKRI Centre for Doctoral Training in Biomedical AI at the University of Edinburgh, School of Informatics. For the purpose of open access, the author has applied a creative commons attribution (CC BY) licence to any author accepted manuscript version arising.
received support from  Fundacja na rzecz Nauki Polskiej (FNP)  (grant ID:  MAB/3/2017 ). D. R. G. received support from  Genome Canada & Genome BC  (grant ID:  264PRO ). D. J. H. received support from  NuCana plc  (grant ID:  SMD0-ZIUN05 ). H. A. received support from  Swedish Cancer Foundation  (grant ID:  211709 ). H. G. received support from  United Kingdom Research and Innovation (UKRI)  (grant ID:  EP/S02431X/1 ). C. P. received support from  Fundação para a Ciência e a Tecnologia (FCT)  through  LASIGE Research Unit  (grant ID:  UIDB/00408/2020  and  UIDP/00408/2020 ). A. L. F. M. Z. , C. P. , A. R. , A. P. , and  J. A. A. received support from  European Union 's  Horizon 2020 research and innovation programme  (grant ID:  101017453 ). C. B. received support from  Agence Nationale de la Recherche (ANR)  through  GRAL LabEX  (grant  ID :  ANR-10-LABX-49-01 ) and  CBH-EUR-GS 32  (grant  ID :  ANR-17-EURE-0003 ).
). (a)Percentage of ncMAPs that were solely in healthy and/or tumor samples by MS (blue) and ncMAPs undetected in healthy samples by MS (red). b) Parent gene expression of ncMAPs in TPM in 29 healthy tissues from 17,382 samples (GTEx v8 dataset). ncMAPs are distributed over two groups: (I) ncMAPs detected in healthy samples by MS in blue, (II) ncMAPs undetected in healthy samples by MS in red. c) Parent gene expression of ncMAPs in TPM in 29 healthy tissues from 17,382 samples (GTEx v8 dataset). A limit on the gene expression (y-axis) of 1. 2 TPM was set to visualize cancerselective ncMAPs in black. 486 samples Open search strategy De novo strategy MSFragger search engine MS-GF+ search engine DeepNovoV2 interpreter PTMiner Scavager tool tool False Discovery Rate (FDR) ≤ 1% False Localization Rate (FDR) ≤ 1% False Discovery Rate Validation accuracy ≥ 90%.
3 , and experimental details in Table  S5 ). n = 1 independent experiments have been recorded. The dots and triangles represent fitted relaxation-rate constants R 1ρ ( 15 N) ± 2 times the standard deviation (see material and "Methods" for details on R 1ρ ( 15 N) measurements and Supplementary Fig. 3c, d  for individual fits). The error bars are derived from a bootstrap procedure. Source data are provided in the Source Data file. The larger the rate constant, the larger the molecular dynamics on the microsecond time scale. g Experimentally elucidated dynamics plotted on the apo-capsid structure (PDB 1qgt  13  , left structure) and on a CAM-A-bound structure (PDB 5d7y  37  with HAP18 in yellow spheres, right structure), where red indicates high amplitude motions on the microsecond time scale and blue low amplitude motions.
Acknowledgements This work was supported by the  CNRS (CNRS-Momentum 2018 , L. L. ), the  French Agence Nationale de Recherches sur le Sida et les hépatites virales (ANRS ,  ECTZ71388 , &  ECTZ100488 , both A. B. ; L. B. ANRS ECTZ158948 ), and a PhD grant from the  Chinese Scientific Council  to  S. W. Financial  support from the  IR-RMN-THC Fr3050 CNRS  for conducting the research is gratefully acknowledged (A. B. ). B. H. M. was supported by an  ERC Advanced Grant  (grant number  741863 , Faster, B. H. M), and by the  Swiss National Science Foundation  ( 200020_188711 , B. H. M). We thank the  Centre d'Imagerie Quantitative Lyon-Est (CIQLE)  for support at the EM platform (A. B).
Antigen design The complete polyprotein sequences of DENV were retrieved from the sequence database (NCBI)  (38) , aligned with ClustalX  (39) , and used to construct a phylogenetic tree with Mega 7 software  (40). A smaller set of sequences chosen to represent the genetic variability of DENV included five sequences of DENV-1 (GenBank accession N°s: ADC92350. 1, AJQ21317. 2, ABG75761. 1, AIG59667. 1, and AAQ19665. 2), DENV-2 (ALI16136. 1, AAD18036. 1, AUZ41807. 1, AHA42535. 1, and ANT47239. 1), and DENV-3 (ACV04798. 1, BAE48725. 1, AIH13925. 1, ALS05358. 1, and AIO11765. 1), and four sequences of DENV-4 (AVA30162. 1, ALI16138. 1, AEJ33672. 1, and ARN79589. 1). MAFFT online service was used to align sequences of known and predicted T-cell epitopes and DENV polyprotein sequences  (41). Alignments were visualized with BioEdit sequence editor  (42)  to further facilitate the selection of epitope-containing regions.
All 8-, 9-, 10-, and 11-mer peptides with a total positive score were retained and combined in a single peptide pool. Predictions on the DTU Bioinformatics server were done using the netCTLpan tool (46) for 9-mer peptides predicted to bind to the 20 most prevalent HLA alleles and retaining those with a consensus rank of less than or equal to 1. 0. The distribution of known and predicted T-cell epitopes was compared and conserved regions containing the maximal number of such epitopes were selected. A 75% majority consensus sequence of each DENV genotype, as well as a master consensus sequence representing all four genotypesthat served as a base for DENV poly-antigenwere created using the Consensus Maker software tool available at the Los Alamos HIV database website  (47). Consensus sequences corresponding to the chosen polyprotein fragments were assembled together as a linear polyprotein and then epitope predictions were repeated to verify that all T-cell epitopes located close to the junction sites were predicted to form correctly and that no junctional non-specific immunodominant epitopes were artificially created.
4G2, BD Biosciences), FITC-anti-CD8 (clone 53-6. 7, Millipore), eF450-anti-CD4 (clone RM4-5, eBioscience), APC-eF780-anti-CD3ϵ (clone 17A2, eBioscience), AF700-anti-CD62L (clone MEL-14, BD Biosciences), and PE-anti-CD44 (clone IM7, eBioscience) were used. Samples were incubated with appropriate mAb mixtures for 25 min at 4°C, washed with FACS buffer, and fixed with Cytofix (BD Biosciences) for 20 min at 4°C. Cells were acquired in an Attune NxT cytometer system (Invitrogen) and data analysis was performed using the FlowJo software (Treestar, OR, USA).
Statistical analysis Statistical analysis was performed with the statistical tests implemented in GraphPad Prism 9. 5. 0  (53). For pairwise comparisons, an unpaired parametric t-test with or without Welch's corrections, depending on the standard deviation (SD) determined from the dataset, was used. Comparative analysis of small sample groups was performed using an unpaired non-parametric Mann-Whitney test. Multiple comparisons were performed either using a one-way ANOVA test (ordinary or Welch, depending on standard deviation) or the Kruskal-Wallis test. Data were considered significant when p-values were less than 0. 05.
Design of a T-cell DENV poly-antigen To design a poly-antigen to be used in a tetravalent DENV vaccine candidate, a phylogenetic tree was first constructed using 240 complete polyprotein sequences of the four DENV serotypes derived from the nucleotide sequence database  (38). The tree was used to select a smaller set of DENV sequences in which distinct phylogenetic sub-lineages of each serotype were represented by a single sequence (Figure  1 ). Mapping of the regions conserved between distinct DENV serotypes, performed by plotting the similarity score along the polyprotein length, demonstrated that most conserved regions were located in the NS3, NS4B, and NS5 proteins (Figure  2A ). To select T-cell epitope-containing regions in DENV protein sequences, all HLA class I-or II-restricted epitopes, so far annotated in the Immune Epitope Database and Analysis Resource (IEDB) as positive in T-cell assays, were aligned to the representative DENV sequence set  (54).
bioinformatics. babraham. ac. uk/projects/ fastqc/). The reads for each condition were mapped the human reference genome hg19 from UCSC using TopHat2 version 2. 0. 7 and stored as BAM files. Differential expression values were evaluated using Cufflinks 2. 1. 1 package  (31). The three MDM replicates treated with C18:0 belonging to the different timepoints (3 hours and 16 hours) were tested for differential expression versus the control condition (BSA 3 hours and 16 hours respectively). Genes with a q value below 0. 05 were considered as significant for differential expression.
Glucose and lactate measurements The NMR spectra were recorded at 298 K on a Bruker Neo spectrometer (Bruker) operating at 500. 13 MHz for proton and equipped with a TCI cryoprobe (Bruker). Deuterium oxide (99. 96% D) and trimethylsilyl-3-propionic acid-d4 (TMSP) were purchased from Eurisotop (St-Aubin, France), phosphate buffer powder 0. 1 M and calcium formate were purchased from Sigma-Aldrich. Deuterated solvents were used as the internal lock. The data have been processed with Bruker TOSPIN 4. 1 (Bruker) software with standard parameter set. Phase and baseline correction were performed manually over the entire range of the spectra and the d scale was calibrated to 0 ppm using the internal standard TMSP. For extracellular lactate and glucose dosage, 400 ml of collected culture media supernatants were supplemented with 47 ml of deuterated phosphate buffer (pH 7.
4), 24 ml of a 10 mM deuterated solution of calcium formate and 70 ml of a 5mM deuterated solution of TMSP. The solution was distributed into 5mm tubes for NMR measurement. 1H NMR spectra were acquired using a 1D NOESY sequence with presaturation and 64 transients and 4 dummy scans. Based on the formate peak (8. 46 ppm), lactate and glucose concentration were then obtained by using the Chenomx NMR Suite software (version 9. 02, Edmonton, CA).
2-NBDG uptake The fluorescent glucose analogue 2-(N-(7-nitrobenz-2-oxa-1,3diazol-4-yl)amino)-2-deoxyglucose (2-NBDG, Invitrogen) was used to measure glucose uptake by MDMs. After treatment of the macrophages for various times, the medium was replaced by RPMI 1640 without glucose supplemented with 2-NBDG (30µM). The MDMs were maintained in culture for 30 min before being gently scrapped with EDTA (10mM, pH 6. 14). Following two PBS washes, labeling with 7-aminoactinomycin D (7-AAD, Thermo Scientific) was carried out according to the company's instructions in order to assess cell viability. Results are acquired on a FACS Verse flow cytometer (Becton Dickinson) and analyzed using FlowJo 9. 3. 2 software (Tree Star Inc. , 645 Ashland, OR).
Statistical analyses All statistical analyses were carried out using GraphPad Prism 7 for Windows (GraphPad Software, Inc. , San Diego, USA) and presented as the means ± standard deviation (SD). When one independent variable was involved, two-tailed Student's t-test was performed to compare two groups and one-way ANOVA with Dunnett's multiple comparisons test to compare more than two groups. When two independent variables were involved, two-way ANOVA was used with Sidak's or Tukey's multiple comparisons test. The statistical test used and the number of biological replicates (n) are described in each figure legend.
In LPS-treated B C A FIGURE 3 Glycolysis fuels the production of pro-inflammatory cytokines in C18:0-and LPS-stimulated human macrophages. MDMs were pre-treated or not with 2-DG (10 mM) for 1h before being stimulated with BSA, C18:0 (100 µM), LPS (10 ng/ml) or maintained in RPMI alone for 24h. The concentrations of IL-6 (A), IL-1b (B) and IL-8 (C) were measured in the supernatants by ELISA. Data are mean ± SD. Unpaired t-test (n=3). *p < 0. 05, **p < 0. 01. macrophages, HIF-1a drives the glycolytic switch by inducing the expression of several genes including SLC2A1, HK2, PFKP and PDK1. To investigate whether C18:0 treatment also leads to HIF-1a activation, we proceeded to the detection of HIF-1a in the nuclear extracts of BSA-and C18:0 -treated MDMs. We used LPSstimulated MDMs as positive controls.
Acknowledgments This work was supported by the  Belgian F. R. S. -FNRS  and the  Leon Fredericq  funds for Biomedical Research from the  University of Liege, Belgium. SL-P, JP, and PT are Research Associate, Honorary Research Director, and Research Director of the  Belgian F. R. S. -FNRS , respectively. MI received a PhD fellowship from  Teĺevie (Belgian F. R. S. -FNRS ). The authors thank the  GIGA transcriptomic Platform  and the  CIRM NMR-Santéplatform. We are grateful to  Marco Gianfrancesco  ( Laboratory of Immunometabolism and Nutrition, GIGA, ULiège, Belgium ),  Matthieu Schoumacher  ( Clinical Metabolomics group, CIRM, ULiège, Belgium ) and  Ferman Agirman  ( Metastasis Research Laboratory, GIGA, ULiège, Belgium ) for technical assistance.
Study design The present study is a part of an ancillary study project within the West Africa network of the International epidemiological Databases to Evaluate AIDS (IeDEA) of the US National Institutes of Health (https://www. iedea. org/regions/west-africa/)  (Egger et al. , 2012). The study took place between October 2019 to March 2022 (end of 3-month follow-ups), with an interruption of 4 months due to the COVID-19 pandemic. In this cross-sectional descriptive study, we compared pre-and post-intervention data (depressive symptoms, functioning and HIV-related stigma) in PLWH treated for depression with group IPT. Assessments were conducted preintervention, post-intervention, and at 3-month follow-up.
Flow chart In total, 559 PLWH were screened for depression using PHQ-9 and 165 had a PHQ-9 score ≥ 5 (Supplementary Figure  S1 ). Of these 165 patients, 79 had a diagnosis of depression subsequently confirmed by the study's psychiatrist. Three patients had to be excluded from the study: one had received individual IPT and the other two lived too far away from the clinic and required several hours to commute. Participation in group IPT was offered to 76 patients. Of the patients invited to participate in the study, two refused to participate in group IPT and three accepted but did not participate in any session, thus limiting the number of participants to 71. Subsequently, the data of two patients were excluded from analysis due to incorrect completion in their consent to participate. Therefore, a total of 69 patients were included in the present study.
Acknowledgments. The authors would like to thank the  IeDEA West Africa  region: Site investigators and cohorts: Adult cohorts:  Marcel Djimon Zannou ,  CNHU ,  Cotonou ,  Benin ;  Armel Poda ,  CHU Souro Sanou ,  Bobo Dioulasso ,  Burkina Faso ;  Fred Stephen Sarfo ,  Komfo Anokeye   Teaching Hospital, Kumasi, Ghana ;  Eugene Messou ,  ACONDA CePReF , Abidjan,  Cote d'Ivoire ;  Henri Chenal ,  CIRBA , Abidjan,  Cote d'Ivoire ;  Kla Albert Minga ,  CNTS ,  Abidjan ,  Cote d'Ivoire ;  Emmanuel Bissagnene  and  Aristophane Tanon ,  CHU Treichville ,  Cote d'Ivoire ;  Moussa Seydi ,  CHU de Fann ,  Dakar ,  Senegal ;  Akessiwe Akouda Patassi ,  CHU Sylvanus Olympio , Lomé, Togo. Pediatric cohorts:  Sikiratou Adouni Koumakpai-Adeothy ,  CNHU , Cotonou,  Benin ;  Lorna Awo Renner ,  Korle Bu Hospital , Accra, Ghana;  Sylvie Marie N'Gbeche ,  ACONDA CePReF , Abidjan,  Ivory Coast ;  Clarisse Amani Bosse ,  ACONDA_MTCT+ ,  Abidjan ,  Ivory Coast ;  Kouadio Kouakou ,  CIRBA , Abidjan,  Cote d'Ivoire ;  Madeleine Amorissani Folquet ,  CHU de Cocody, Abidjan, Cote d'Ivoire ;  François Tanoh Eboua ,  CHU de Yopougon, Abidjan ,  Cote d'Ivoire ;  Fatoumata Dicko Traore ,  Hopital Gabriel Toure ,  Bamako ,  Mali ;  Elom Takassi ,  CHU Sylvanus Olympio ,  Lomé , Togo; Coordinators and data centers:  François Dabis ,  Renaud Becquet ,  Charlotte Bernard ,  Shino Chassagne Arikawa ,  Antoine Jaquet ,  Karen Malateste ,  Elodie Rabourdin  and  Thierry Tiendrebeogo ,  ADERA ,  Isped  &  INSERM U1219, Bordeaux, France.
A large outbreak of Monkeypox virus (MPXV) infections has arisen in May 2022 in nonendemic countries. Here, we performed DNA metagenomics using nextgeneration sequencing with Illumina or Nanopore technologies for clinical samples from MPXV-infected patients diagnosed between June and July 2022. Classification of the MPXV genomes and determination of their mutational patterns were performed using Nextclade. Twenty-five samples from 25 patients were studied. A MPXV genome was obtained for 18 patients, essentially from skin lesions and rectal swabbing. All 18 genomes were classified in clade IIb, lineage B. 1, and we identified four B. 1 sublineages (B. 1. 1, B. 1. 10, B. 1. 12, B. 1. 14). We detected a high number of mutations (range, 64-73) relatively to a 2018 Nigerian genome (genome GenBank Accession no.
NC_063383. 1), which were harbored by a large part of a set of 3184 MPXV genomes of lineage B. 1 recovered from GenBank and Nextstrain; and we detected 35 mutations relatively to genome ON563414. 3 (a B. 1 lineage reference genome). Nonsynonymous mutations occurred in genes encoding central proteins, among which transcription factors and core and envelope proteins, and included two mutations that would truncate a RNA polymerase subunit and a phospholipase D-like protein, suggesting an alternative start codon and gene inactivation, respectively. A large majority (94%) of nucleotide substitutions were G > A or C > U, suggesting the action of human APOBEC3 enzymes. Finally, >1000 reads were identified as from Staphylococcus aureus and Streptococcus pyogenes for 3 and 6 samples, respectively. These findings warrant a close genomic monitoring of MPXV to get a better picture of the genetic micro-evolution and mutational patterns of this virus, and a close clinical monitoring of skin bacterial superinfection in monkeypox patients.