dataesr/scientific-paragraphs-categorization huggingface dataset:
The total 2519 epitopes in IEDB for the HCV GP, 256 are 9-mer linear peptides and 324 are 15-mer linear epitopes (23%). Some of these validated epitopes are not present in the selected sequences (150 9-mers and 202 15-mers), for a total of 106 9-mer and 122 15-mer validated epitopes present in P26664 and P27958 which were characterized with data from SILVI's final output table (S1 and S2 Figs). For Pf CSP (example#2), 294 epitopes are present in IEDB from several CSP and CSPrelated antigens from different P. falciparum isolates, including 41 9-mer and 30 15-mer validated epitopes (24%). Among these, 36 9-mer epitopes are from the CSP antigen (excluding related proteins), 24 of which are present in the selected P19597 protein (S1 Fig) Interestingly, the strongest binding validated HLA-class I epitopes from the HCV GP and Pf CSP proteins have higher overall predicted binding affinity (IC50 value calculated by NetMHCpan, "scoreN"), with over 75% and 62% of validated epitopes under 1100 nM predicted IC50, respectively, whereas for Mtb EsxA, only 19% of validated epitopes are below this cutoff (S1 Fig).
Results show the nearly 100% of validated epitopes present a promiscuity ranging from 1 to 6 alleles, comprising 46-52% of epitopes predicted to bind to 5 or 6 alleles (S2 Fig). Validated HLA-class I epitopes in final output table ("res_classI. csv"). For HCV GP, the highest predicted IC50 corresponds to peptide ADTAACGDI, 18184. 5 nM binding affinity to alleles from the B44 supertype. This epitope in IEDB (ID 775) has 2/2 positive experimental results but for binding to mouse H2-Kk alleles (Mamu-A1 � 011:01 and H2-Kk) (S1 Table ). For epitope ID 6435 (CINGVCWTV), the best predicted binding affinity is specific to alleles belonging to the A02 supertype (321 nM), which agrees with the experimental data regarding the HLA restriction of this epitope in the IEDB database (303 positive assays) (S1 Table)  [46, 47].
All algorithms predict these epitopes to bind with moderate or low affinity to other HLA-class I alleles (B07 and B62, A01, A03, respectively). For Pf CSP, the maximum predicted IC50 value for a validated 9-mer epitope is 13837. 8 nM, corresponding to an extremely low binding affinity for peptide NEEPSDKHI to B44 supertype alleles (representative allele B � 40:01). However, IEDB MHC-I binding predicts a binding affinity of 6510. 14 nM for the same allele (I_B4001_6510. 14). This is also the lowest predicted IC50 for this peptide among the 11 predictions available. In IEDB, this epitope (ID 43604) only contains positive experimental validation assays for binding to mouse H2-Kk alleles and 2 negative MHC binding assays for mouse Mamu-A1 � 011:01 and human HLA-B � 44:02 (S1 Table ). Experimental data are extensive for Pf CSP epitope ID 74841 (YLN-KIQNSL) including 20 (out of 22) positive assays and show this epitope is immunogenic, binds to HLA-B � 07:02, HLA-C � 03:03 and mouse H2-b class I alleles, but mostly to the HLA-A � 02:01 allele inducing cytotoxicity (chromium-51 cytotoxicity assays), IFN-γ and IL-2 release (ELISA and ELISpot assays) (S1 Table)  [50, 51].
5 nM, corresponding to a low binding affinity for peptide AWGGSGSEA (ID 189582) to the B62 supertype (representative allele B � 15:01). For the same allele, IEDB MHC-I binding predicts a binding affinity of 13739 nM. Experimental data in IEDB shows negative results in 7 assays out of 10 performed, and only 3 positive MHC binding assays (S1 Table ) show this epitope binds to HLA-A � 30:02 molecules, included in the A01 supertype  [53] , and the algorithms predict a very low binding affinity of 15981. 71 nM (MHC-I binding) and 43641. 5 nM (NetMHCpan). On the contrary, experimental data is robust for Mtb EsxA epitope 3064 (AMASTEGNV) with 7/7 positive T-cell and MHC binding assays showing it binds to HLA-A � 02:01, HLA-A � 30:02, and HLA-A2 alleles and induces IFN-γ release (S1 Table)  [53, 54].
2), with predicted affinity to A24 alleles of 29334 nM. Similarly, IEDB MHC-I binding indicates exceptionally low-affinity binding to A24 alleles (28888. 98 nM to HLA-A � 23:01 and 28049. 05 nM to allele HLA-A � 24:02). Validated HLA-class II epitopes in final output table (res_classII. csv). For HCV GP, the maximum predicted IC50 value for a validated 15-mer epitope is 21723. 9 nM, corresponding to a low-binding affinity for peptide PPLEGEPGDPDLSDG to allele HLA-DRB3 � 01:01 (core LEGEPGDPD). This epitope has experimental data indicating it is restricted to HLAclass II alleles and induces IFN-γ release (ELISpot) in the IEDB database albeit with only one positive assay out of 5 (S1 Table)  [56]. This peptide has two other prediction results in the final output table-extremely low-affinity binding to alleles HLA-DRB1 � 07:01 (core LEGEPGDPD, 28796.
32 nM, corresponding to a very low binding affinity for peptide WGGSGSEAYQGVQQK (core EAYQGVQQK) to allele HLA-DRB5 � 01:01. This example illustrates a disagreement between predictors, as IEDB MHC-II binding predicts this full peptide/core combination binds to DRB5 � 01:01 allele with an affinity of 1170. 1 nM. Furthermore, evidence in the IEDB database showed this epitope (ID 226404) induces IFN-γ release (ELI-Spot) and is restricted to HLA-DRB1 alleles but with only one positive assay (S1 Table)  [61]. The prediction algorithms again disagree on full_peptide/core combinations binding to DRB1 alleles. IEDB MHC-II binding predicts the combination WGGSGSEAYQGVQQK/ WGGSGSEAY binds with extremely low affinity to alleles DRB1 � 03:01 (33097. 7 nM) and DRB1 � 15:01 (15465. 1 nM). NetMHCIIpan has no predictions for DBR1 alleles for the full_peptide/core combination WGGSGSEAYQGVQQK/WGGSGSEAY, and the two prediction algorithms do not agree on the peptide core EAYQGVQQK binding to DRB1 alleles (only NetMHCIIpan has low binding affinity predictions for allele DRB1 � 03:01, 20821.
Discussion The ability to compare different data sources and to synergistically combine various algorithms in epitope prediction remains challenging. The potential of the datasets generated by omics technologies is limited by the lack of appropriate computer-based tools to manage and integrate the vast amount of epitope prediction data. Experimental validation steps restrict the number of peptide candidates to test, meaning that these candidates must be carefully selected to increase the chances of selecting immunogenic peptides. The successful identification of immunogenic epitopes depends on both the quality of used immunoinformatic algorithms and on the rationale behind epitope selection criteria. These criteria must be adjusted to the biological question at hand; they should be permissive enough not to reject immunogenic epitopes falsely, and sufficiently restrictive to filter HLA-binding prediction data effectively.
SILVI (short for: from in silico to in vivo) is a workflow written in R language that was developed to assist the selection of epitopes predicted by state-of-the-art HLA-binding affinity algorithms using also other sequence-specific features such as conservation among pathogenic species and homology to host. To help researchers make use of SILVI, the README file (available at github. com/JoanaPissarra/SILVI2020 and https://doi. org/10. 5281/zenodo. 6865909) includes in-depth explanations on the necessary downloads and dependencies, input file preparation and script utilisation. Researchers are free to adapt the script to particular conditions and interests. The SILVI pipeline was adapted to integrate data from the most widely used epitope prediction algorithms with high accuracy HLA-binding predictions  [62] , and homology alignments  [45].
We recommend the use of databases such as EUpathDB  [63]  or UniProtKB  [64]  and transcriptomic and proteomic information to design the initial protein candidate list and integrate gene expression data for the protein annotation from the start. Once the protein antigen pool is selected, protein sequences in fasta format are retrieved and directly uploaded in the online servers to perform HLA-binding predictions. SILVI allows users to perform an epitope-based selection, aiding the diversification of antigen sources. SILVI is designed to analyse several species-specific sequences per protein antigen (1 seq_num = 1 species-specific sequence). In the first selection step (step A), SILVI uses homology as a positive selection filter for highly conserved epitopes among pathogenic species (each protein can include several species-specific sequences in fasta format).
All subsequent steps add information without filtering out peptides. Also, even if the same linear sequence is predicted as an epitope, many will not be assigned the same HLA restriction and in this case "promiscuity" will equal 0. This reveals the limitations of available algorithms and the importance of using combinatorial approaches for epitope prediction. SILVI will accept virtually all available algorithms so long as the input data is correctly formatted. For HLA-class II binding predictions, the total epitope size is 15-mer, which includes the 9-mer motif for HLA binding: the epitope core or register. The algorithms' predictive power correlates better with the core prediction  [29]. By comparing core predictions, we compare predictors and consider all potential cores within a 15-mer peptide, selecting the best full_peptide/core combination according to predicted IC50.
Since SILVI currently only accepts 9-mer and 15-mer epitopes, we selected the total 9-mer and 15-mer validated epitopes from the IEDB database, which we used to analyse the frequency distribution of the several properties included in SILVI's output table (molecular weight, hydrophobicity, isoelectric point, total mismatches, promiscuity, HLA restriction, binding affinity, and anchor mismatches to HLAclass I predictions). While with a highly variable number of total assays and positivity rates in the IEDB, most validated epitopes have corresponding favourable prediction data. In contrast, others are predicted to be non-binders by the algorithms, disagreeing with experimental data in the IEDB database; this again highlights limitations of such HLA-binding prediction algorithms in epitope prediction.
Overall, there is a high concordance between IEDB and NetMHCpan (promiscuity � 1). Additionally, IC50 predictions are reliable, depending on allele restriction and the general representation of pathogens in the algorithms' databases, providing a useful filter to manage epitope prediction data. Still, as some validated epitopes with experimental evidence in the IEDB for cytotoxicity are predicted to have very high IC50 values, it illustrates current algorithms may fail to predict binding affinity and/or immunogenicity, so low stringency filters should be used. The filters applied to SILVI's output allowed a pronounced reduction of the epitope lists. Despite this broad selection, and particularly for HCV GP and HPV proteome, too many peptides are selected, which may be an issue for the experimental validation at reasonable cost.
Some experimentally validated epitopes present ambiguous experimental data and low HLA-binding predictions, revealing current limitations. Users may also add more data from other immunoinformatic tools by integrating those results into the SILVI dataframe. Present limitations of this pipeline version are: epitope size restricted to 9-mer and 15-mer peptides, which correspond to only around a quarter of total validated epitopes; user-dependent preparation of. csv files with HLA-binding prediction data; and adaptations needed to adjust the common_among_seq_nums filter to analyse multiple proteins without species-specific sequences. However, as SILVI is written in the R language, a free software environment widely used by researchers across different research fields, all existent included features and criteria can be modified.
Conclusions SILVI uses available high-performing HLA-binding predictors and relevant rational criteria associated with immunogenicity, allowing a swift selection of T-cell epitopes from large datasets and thereby restricting the total number of peptides to test at the bench while increasing the chances of selecting the most conserved and immunogenic epitopes. This pipeline also helps epitope-mapping experiments by identifying in silico high immunogenicity regions in several antigen candidates. The integration of BLASTp data is a great advantage since it is the most well-established method to find homologous sequences in all host organisms and provides excellent insight regarding the cross-reactive potential of an epitope. SILVI applies to any pathogenic organism, allele restriction and prediction algorithm.
It allows users to integrate diverse outputs with the freedom to select the most relevant criteria in a fast and reproducible manner. Finally, SILVI is customisable allowing for additional development (automated BLASTp, refined and/or extra criteria, synthetic summary, etc). We believe that this open-source tool will significantly help future epitope-based vaccines and immunotherapies design. programming language (https://www. r-project. org/) and is freely available to download from GitHub and Zenodo (see github. com/JoanaPissarra/SILVI2020 or https://doi. org/10. 5281/ zenodo. 6865909 for instructions). The R package dependencies are tidyverse  [65] ; stringr  [66] ; and Peptides  [67].
Script development Input data. Input data consists of 9-or 15-mer predicted epitopes and respective predicted binding affinity to several HLA alleles, according to the target population (allele list upload files for world population coverage are available), generated by HLA-binding prediction algorithms. For HLA-class I prediction data, users must prepare the. csv files containing results from at least two non-redundant and well-performing HLA class-I binding prediction algorithms (e. g. NetMHCpan 4. 0  [68] , IEDB MHC-I binding  [69] , and SYFPEITHI  [70] ). A low stringency score filter can be applied to select the top percentile of predicted epitopes (e. g. 10-50%) to reduce initial epitope lists. HLA-class I peptide. csv files should be named "protein-code_predictorcode. csv" and should contain the columns: "allele"; "peptide" (9-mer); "ic50" or "score"; "seq_num" (sequence_number).
For HLA-class II prediction data, users must prepare. csv files containing results from at least two non-redundant and well-performing HLA class-II binding prediction algorithms (e. g. NetMHCIIpan  [71] , IEDB MHC-II binding  [30, 33] ), with the desired cut-off value (e. g. top 10-50%). HLA-class II peptide. csv files should be named "proteincode_predictorcodeii. csv" and contain the columns: "allele"; "full_peptide"; "core"; "seq_num"; "ic50"; "rank" (Fig  1a  and 1b ). Users may check the file code/db_headers to confirm the correct column names, for both HLA-class I and class II predictions. First selection step (step A). The script imports and integrates all data into a single data frame class in R and, to address conservation among pathogenic species, by default, it directly compares all 9-mer peptides or cores among different sequences from the same protein, selecting only the ones that are 100% identical (common_among_seq_nums filter).
To assign HLA restriction, the comparison is made per supertype (11 supertypes), which allows the comparison among predictors  [72]. The correspondences between allele and supertypes  (11)  are in the file /code/ map_supertype_alleles. csv, where users may add new alleles. HLA-class II epitope selection: users open the file 'Fire_classII. R', introduce the directory pathway to input. csv files and run the first code lines (step A). To assign HLA restriction, SILVI compares per allele, so it is important to perform predictions with the same allele lists. As an intermediary output, the script generates a. csv file for the initial epitope list ("1_com-mon_I/II. csv") with all collected information thus far (source protein, all predicted allele/ supertype restrictions, peptide sequence, number of predictors, number of seq_nums, scores and raw data file).
Step A also generates a. txt file with all 9-mer peptides or cores in FASTA format, to be uploaded for online short-BLASTp analysis ("1_blast_me. fasta"). Users choose the host reference dataset (e. g. Homo sapiens taxid: 9606, RefSeq) and desired alignment parameters (e. g. default for short sequences). We recommend users to reduce the size of the alignment result file by selecting a maximum of 10 aligned sequences to display, in the 'Max target sequences' option in algorithm parameters, general parameters tab. Users download the short-BLASTp alignment result in. txt file format to be imported again in R (Fig  5c ). Second step (B). Users introduce the path and name of the short-BLASTp result file and run the code (step B). To verify the alignment result file is complete, SILVI confirms all peptides were included in the alignment result file.
Third step (C). Once the new dataframe is generated by the 2 nd step, users run the last code lines and SILVI adds extra information: molecular weight, hydrophobicity using the GRAVY (grand average of hydropathy) hydrophobicity index, calculated by adding the hydropathy value for each residue and dividing by the length of the sequence  [73] , and pI using the Bjellqvist pK scale with the Peptides package  [67]. Moreover, SILVI calculates the "promiscuity", the total number of alleles/supertypes to which a given epitope is predicted to bind to, and duplicates the rows according to this information, allowing selections based on predicted HLA restriction. Also, SILVI highlights the predicted IC50 to specific alleles from the NetMHCpan algorithm ("scoreN") to help the user select the top predicted binders ( As the final output, SILVI generates a.
Script implementation Example proteins. The IEDB database includes thousands of validated pathogen-specific epitopes, and the following search strategy was used to find extensively validated T-cell epitopes: Linear epitopes + Positive assays only + No B cell assays + Human host + Infectious Disease (S1 Table ). The Hepatitis C Virus (HCV) genome polyprotein (GP), Plasmodium falciparum's circumsporozoite protein (CSP), Mycobacterium tuberculosis's 6 kDa early secretory antigenic target and the HPV proteome were used as example proteins to undergo T-cell epitope selection with SILVI. HLA-class I binding predictions were performed on: 1) NetMHCpan 4. 0  [68]  predictions for 11 supertype representative alleles, 2) IEDB MHC-I binding  [69]  (Prediction Method Version 2013-02-22, recommended predictions for 36 alleles), and 3) SYFPEITHI predictions (default predictions for 22 alleles).
Flow-cytometry analysis of surface Sca1 protein distribution confirmed a higher expression on AGMs isolated from female mice when compared with males (Figure  S2G ). Among the four macrophage clusters, we found a transcriptional diversity, suggesting a particular developmental origin, localization, or function for each cluster (Figure  2C ; Data S1 and S2). The macrophage clusters 2 and 3 expressed Cx3cr1 mRNA, while clusters 4 and 5 appeared to have lower Cx3cr1 expression (Figure  2D ). Of interest, Lyve1 and Timd4 mRNA expression were restricted to macrophage cluster 4 (Figure  2D ). Tissue-resident macrophage numbers are regulated by monocyte recruitment from the blood circulation or local self-proliferation  (Ginhoux and Guilliams, 2016). Thus, we next sought to determine the contribution of monocytes to AGM pool size.
However, in older 16-week-old animals, the percentage of TdTomato + macrophages was significantly increased in female mice compared with in males (Figure  2J ). This result further supports our observation that monocyte recruitment to adrenal glands is more robust in females than males. Finally, in order to determine whether monocyte recruitment is mandatory to sustain AGM populations, we quantified macrophages in CCR2-deficient mice, which have severely reduced blood monocyte counts  (Serbina and Pamer, 2006). We observed that macrophage counts remained similar in CCR2-sufficient and -deficient mice (Figure  2K ). This suggests that a compensatory mechanism occurs to ensure the maintenance of tissue macrophage density in the absence of optimal monocyte recruitment. These data suggest that monocyte recruitment plays an important role in the maintenance of AGM (E) Flow-cytometry plot showing Ly6C and CCR2 GFP expression among adrenal gland CD45 + CD11b + CD64 + cells in male and female CCR2 GFP/+ mice.
See also Figure  S2. pool size but also sheds light on the existence of alternative mechanism(s) that actively contribute to this process. Embryonically seeded macrophages are renewing through self-proliferation in adults. Our scRNA-seq data indicated that few macrophages were proliferating in adrenal glands as reflected by low Mki67, Ccna2, and Top2a mRNA expression (Figure  S2I ; data not shown). A recent study reported that peritoneal macrophages show a sex-specific proliferation rate  (Bain et al. , 2020). Therefore, we assessed the macrophage proliferating rate in male and female CCR2-sufficient and -deficient mice using intracellular Ki67 staining. Flow-cytometry analysis revealed a similar proliferation rate between male and female CCR2-sufficient mice (Figure  2L ). However, loss of CCR2 led to increased macrophage proliferation in female mice, while this was not the case in CCR2deficient males (Figure  2L ).
Embryonic and monocyte-derived AGMs co-exist at homeostasis Our flow-cytometry data demonstrated that most (around 90%) of AGMs expressed CX3CR1 (Figure  1E ). Macrophage Cx3cr1 expression appeared restricted to clusters 2 and 3 in our scRNAseq data (Figure  2D ). To address the mechanisms governing AGM maintenance, we analyzed CCR2 creERT2 x R26 TdTomato x CX3CR1 GFP mice 48 h post TAM administration. CCR2 + cells were found to preferentially give rise to CX3CR1 + macrophages upon their entry into adrenal glands (Figure  3A ). Indeed, 48 h post TAM injection, 80% of R26 TdTomato+ macrophages were CX3CR1 GFP positive (Figure  3A ). To determine whether CX3CR1 + monocyte-derived macrophages could give rise to CX3CR1 -macrophages, we took advantage of double reporter CX3CR1 creERT2/GFP R26 TdTomato mice, allowing us to assess present and past CX3CR1 expression.
The numbers of MHC class II high and class II low macrophages were also unchanged between sham-operated and OVX animals (Figure  S4D ). Taken together, these data demonstrated that ovaries-derived hormones are unlikely regulators of AGM sex dimorphism. X-zone regression occurs between P28 and P35 in male mice  (Huang and Kang, 2019). Microscopy analysis revealed that the X-zone was present in 4-week-old females but absent in males in our experimental conditions (Figure  S4E ). We observed that the percentage of MHC class II low macrophages was similar in 4-week-old females and males (Figure  5D ). Strikingly, in 5-week-old males, the percentage of MHC class II low macrophages decreased to 5%, while it remained around 20% in females (Figure  5D ). These data suggest that X-zone disappearance precedes macrophage sex-dimorphism establishment and correlated with the loss of MHC class II low macrophages in males.
To further establish a causal link between X-zone presence and AGM sex dimorphism, males were castrated at 3 weeks of age to prevent X-zone degradation. We analyzed their adrenal glands when the mice were 7-week-old. MHC class II staining demonstrated an increase in the population of MHC class II low macrophages in castrated mice when compared with shamoperated animals (Figure  5E ). Interestingly, when we analyzed the localization of AGM, we found that MHC class II high macrophages had a very particular distribution at the border with the X-zone in castrated mice (Figure  5F ). In this same location, we found an increased number of MHC class II low macrophages in castrated mice compared with sham-operated controls (Figure  5F ). These data demonstrated that X-zone maintenance is sufficient to maintain MHC class II low macrophages in castrated males.