dataesr/scientific-paragraphs-categorization huggingface dataset:
For the high heritability trait, predictions using SNP effects of HOL, RED and JER yielded accuracies of 0. 411, 0. 275 and 0. 114, respectively, when the region size was 1 SNP (Fig. 1 ). For the same scenario and region size, estimating SNP effects separately for each breed, but accounting for breed origin of alleles in the prediction of breeding values (HOL/RED/JER) of MIX, improved accuracy up to 0. 531. Combining MIX data with pure breeds' data assuming common SNP effects for all breeds (HOL+RED+JER+MIX), improved accuracies compared to combining only three pure breeds' data (HOL + RED + JER) for the accuracy of admixed individuals (0. 827 vs 0. 560). Models that can use MIX data with breed origin of alleles (HOL + RED + JER + MIX uncor/cor), improved accuracies compared to combining all available data, i.
e. , combining all purebred data or all purebred and admixed individuals' data, although it depended on the correlation in the QTL scenario (see Additional file 2: Tables  S1-S4 ). Accounting (0. 877) or not (0. 876) for correlations between the SNP effects of different pure breeds did not make any difference (Fig. 1 , MIX). For a QTL correlation of 1. 00 and predictions in MIX, (HOL+RED+JER+MIX) led to higher accuracies than (HOL + RED + JER + MIX uncor). Among the different region sizes considered here (1 SNP, 100 SNPs and the whole genome (WG)), the WG region size generally yielded the lowest accuracies for pure breeds and the admixed population (Fig. 3 ). The efficiency of the methods considering breed origin of alleles in model training became more apparent as the correlation of the simulated QTL effects between the breeds decreased (see Additional file 2: Tables  S1-S4 ).
Within-and across-breed predictions A simple approach for avoiding the unfavourable impact of the difference in marker effects among different purebred populations is to carry out separate evaluations for each of those pure breeds, as is the case for genomic evaluations in many countries  [19]. Such an approach, however, comes with the cost of a potential loss of data information, and therefore, in the accuracy of SNP effect estimation. This is a limitation for genetic improvement in populations with a small genomic reference population. In this study, accuracies from within-breed predictions were higher for HOL and RED, compared to JER. Although there could be other reasons, one explanation is the small reference population size (660 vs 3150) set for JER. The accuracies for pure breeds differed between the two heritability levels for any QTL effect correlation scenario, with accuracies being higher for the high heritability trait than for the low heritability trait.
Combined data from multiple pure breeds If the studied population is small, it might be challenging to establish a large reference population, and in turn the accuracy of genomic prediction might also be limited  [6]. For breeds with a limited reference population size, incorporating data from other breeds may yield higher accuracies  [26, 40, 51] , although it depends on the relatedness between those breeds  [9, 19]. When HOL and RED individuals were included in the reference population of JER (HOL+RED+JER), accuracies generally dropped. Similarly, using the combined reference population, accuracies for HOL and RED also generally dropped, but less compared to those for JER. When multiple purebred populations are combined to form a reference population, SNP effects are dominated by the breeds that contribute more to the reference population.
Genomic prediction including data from admixed individuals If a large amount of commercial farm data for admixed populations becomes available, it can help to improve selection accuracy by expanding the data size for each pure breed population  [12]. Such data can also allow to exploit heterosis due to dominance and can accelerate performance of crossbred animals in commercial farms  [54]. How to use those data in genomic evaluations is still an open question. Naturally, all purebred and admixed individual data can be combined together, when homogeneous SNP effects are assumed. Including the data of an admixed population (MIX) along with the data of pure breeds in the reference population led to higher accuracies than the combined reference population of pure breeds. The JER benefited relatively more from adding MIX data.
Because a full rotation cycle of three generations (G6-G8) was considered when forming the reference populations, each pure breed was represented in the MIX data almost equally. Thereby, the reference population size increased almost equally for all breeds by adding MIX data to the combined data of three breeds, HOL + RED + JER + MIX. As one would expect, JER benefited more from this increase in data size, since it is the breed with the smallest pure breed reference population. It should be noted that the validation individuals of RED had the grandsires which were also the sires of MIX at G8, and G8 was included in the reference population. Hence, although the data size increased almost equally for each breed, the added value of this may not be equal for all the breeds. More elaborative ways to include individuals with admixed genetic background in the genomic evaluations, were proposed.
Genomic prediction considering breed origin of alleles Models accounting for breed origin of each SNP allele, rather than genome-wide breed proportions estimated from pedigree or markers, have been proposed, and were shown to improve genomic predictions for simple 2 or 3-way crosses. Those studies applied either univariate whole genome regression models at the SNP level ignoring that the SNP effects might be correlated between the pure breeds  [27, 55] , or rather computationally demanding multi-trait genomic BLUP models with "partial" relationship matrices at the individual level  [22, 56, 57]. It was claimed that considering genomic correlations between pure breeds had limited relevance in models for predicting crossbred performance  [57]. Our results did not show any clear evidence of the benefit of accounting for correlations among the breeds when MIX data were used with the BOA approach, even for the breed (JER) with a small reference population, for which one would expect more gain in accuracy compared with breeds with a large reference population (HOL and RED).
A possible explanation of the unobserved benefit for JER could be due to this breed being genetically distinct from HOL and RED  [58] , and therefore, the pattern of SNP effects along the genome being different from HOL and RED. In addition, the information in the data may be too weak to estimate correlations among the breeds. The MIX data also increased the within-breed data size to some extent, which may lower the importance of correlated information from other breeds  [41]. For the scenario with a QTL effect correlation of 1. 00, analysis with HOL+RED+JER+MIX was competitive with or even superior to the analysis using BOA without accounting for correlations between the breeds, particularly in predicting breeding values for individuals of MIX. This may be due to the MIX individuals being included in the reference population, which simply increases the data size in a combined analysis, whereas BOA with uncorrelated analysis uses only the information in a single breed (component).
Hence, OGs were tabulated into three different categories: "two-algae" (at least one complex alga from two different groups or at least one complex alga and one primary alga, n = 11,775), "onealga" (at least one alga, n = 18,844) and "zero-algae" (no algae, n = 16,647) using the script classify-mcl-out. pl. In order to address the issue of multiple-copy genes (paralogs), we developed a strategy to isolate subtrees ("clans") of interest, i. e. , including only plastid-bearing organisms. Briefly, we computed trees for the 11,775 "two-algae" OGs when possible (i. e. , ≥ 3 sequences, n = 11,499) with IQ-TREE  [7]  and developed a tool for identifying and pruning subtrees fulfilling user-specified taxonomic filters (tree-clan-splitter. pl). This way, we obtained 31,784 "photosynthetic" clans (Data set 5) only composed of plastid-bearing organisms (including species with a nonphotosynthetic plastid, such as Plasmodium falciparum).
Acknowledgements We are grateful to  Ugo Cenci  for sharing the protein sequences of Goniomonas avonlea ( Cenci U ,  Sibbald SJ ,  Curtis BA ,  Kamikawa R, Eme L, Moog D , et al. Nuclear genome sequence of the plastid-lacking cryptomonad Goniomonas avonlea provides insights into the evolution of secondary plastids. BMC Biology (2018)  16:137. https:// doi. org/ 10. 1186/ s12915-018-0593-5 ).
pdf ) Figshare https:// doi. org/ 10. 6084/ m9. figsh are. 13312 622. v1 [24] Data set 4 Orthogroups FASTA files, YAML configura- Figshare https:// doi. org/ 10. 6084/ m9. figsh are. 13573 658. v3 [25] tion file (. tar. gz) Data set 5 Clans FASTA files (. tar. gz) Figshare https:// doi. org/ 10. 6084/ m9. figsh are. 13573 415. v1 [26] Data file 4 Organelle database Text file (. tsv) Figshare https:// doi. org/ 10. 6084/ m9. figsh are. 13246 841. v1 [27] Data file 5 Plastid-targeted proteins Spreadsheet (. xlsx) Figshare https:// doi. org/ 10. 6084/ m9. figsh are. 13246 784. v1 [28] Data file 6 eggNOG OG annotations Text file (. tsv) [30] hare https:// doi. org/ 10. 6084/ m9. figsh are. 13415 048. v1[29]Data file 7 eggNOG clan annotations Text file (. tsv) Figshare https:// doi. org/ 10. 6084/ m9.
Cells release diverse types of extracellular vesicles (EVs), which transfer complex signals to surrounding cells. Specific markers to distinguish different EVs (e. g. exosomes, ectosomes, enveloped viruses like HIV) are still lacking. We have developed a proteomic profiling approach for characterizing EV subtype composition and applied it to human Jurkat T cells. We generated an interactive database to define groups of proteins with similar profiles, suggesting release in similar EVs. Biochemical validation confirmed the presence of preferred partners of commonly used exosome markers in EVs: CD81/ADAM10/ITGB1, and CD63/syntenin. We then compared EVs from control and HIV-1-infected cells. HIV infection altered EV profiles of several cellular proteins, including MOV10 and SPN, which became incorporated into HIV virions, and SERINC3, which was re-routed to non-viral EVs in a Nef-dependent manner.
Pellets were recovered to obtain CCM subfractions F1, F2 and F3. In parallel, a "reference" fraction from SILAC heavy-labelled cells was obtained by a single 100K spin (equivalent to all subfractions  Appendix Fig S1C). Three biological replicates (A, B and C) were performed, and the subfractions were mixed with their cognate reference fractions prior to mass spectrometric analysis and quantification. Over 3,300 proteins were quantified in all three replicates (Dataset EV1). Mass spectrometry analysis was also performed on the corresponding releasing cells, to generate a quantitative whole proteome (Dataset EV1). Protein abundance profiles across subfractions F1-F3 allows definition of groups of proteins that have similar fractionation behaviour, i. e. that are present in extracellular structures or EVs that have identical pelleting (i.
Furthermore, we calculated the median enrichment of organellar markers detected in each of the Jurkat CCM subfractions relative to their abundance in Jurkat full proteomes  (Fig 2C; Dataset EV1). This revealed strong depletion of mitochondrial (ca. 30-fold in F3) and ER proteins (5-fold in F3), but strong enrichment of plasma membrane proteins (ca. 20-fold enrichment in F2 and F3). These observations indicate that the Jurkat CCM has a composition substantially different from whole Jurkat cells. While it contains components from different intracellular origins, it is highly enriched in plasma membrane-derived components. To identify groups of proteins with similar profiles, we then built a "neighbourhood network predictor" (NNP; see Table  EV1 , for the data with basic analysis functionality, and http://evprofiler.
institutcurie. org for the complete interactive database with network generation features). This interactive tool can be used to obtain a classified list of proteins with similar profiles against a query protein. By modelling the local distance distributions of proteins close to a query, and taking into account the reproducibility across biological replicates, we derived variable distance cut-offs to define the query's "neighbourhood". Furthermore, by scoring profile similarity among members of the query's neighbourhood, the NNP generates network plots that reveal clusters of tightly co-profiling proteins. Since some proteins are shared between different types of EVs, it cannot predict hard cluster boundaries or pure population compositions. Nevertheless, the NNP allows discernment of groups of proteins with similar profiles at steady state, suggesting preferential incorporation into the same type(s) of EVs or extracellular structures.
Abundance profiles of the same groups of proteins as in Fig  2A  showed the same clustering behaviour, although in this second dataset, the proportion of proteins recovered in the F2 was more variable. These differences, however, did not affect the results of the NNP or movement analysis. C Unbiased identification of significant translocation events triggered by HIV-1 infection. Each protein is scored for magnitude of translocation (M score, x-axis) and reproducibility of translocation direction (R score, y-axis) across the two replicates. MR plot analysis reveals significant translocations in the top right quadrant. Proteins with M > 1 and R > 0. 9 are candidate hits for changing localization (estimated FDR = 8%). D Movement of the 26 proteins identified as top hits for significant and reproducible translocation upon HIV-1 infection (from Fig  2C  and Table 2)  consistently between 0.
Changes in sEV protein composition upon HIV-1 infection of Jurkat cells We next used our approach to understand changes in the release of EVs from Jurkat cells during HIV-1 infection. This pathological situation involves the partial usurpation of the cellular machinery of EV biogenesis by the virus. Two biological replicates for control and two for NL4-3 EGFP-Nef + HIV-infected Jurkat cells were processed in parallel, as for the previous analysis  (Appendix Fig S1). By mass spectrometry, we profiled over 3,000 proteins in the CCMs from control and HIV-1infected samples, with 2,805 proteins common to all four sets  (Fig 4A, Dataset EV2). In CCMs from infected cells, HIV-1 proteins show very closely matched profiles with high abundance in the last (F3) fraction, but these were clearly distinguishable from the profiles of host EV proteins CD81,  ITGA4 and ITGB1 (Fig 4B).
We then compared infected and uninfected conditions to identify host proteins that changed distribution between the subfractions upon infection, and, consequently, position in the PCA map. We performed an outlier test quantifying the movement (M) and reproducibility (R) of the profile shifts across biological replicates  (Itzhak et al, 2016). We thereby identified 26 candidate proteins (high stringency filtering, FDR = 8%) that undergo significant movements  (Table 2, Fig 4C, Dataset EV2  [including the extended list of hits, medium stringency, FDR = 20%]). Consistent with MR analysis results, the profile of MOV10 (the top hit, with M = 10. 55) substantially changed upon HIV-1 infection towards an increase of abundance in the F3 fraction, whereas, for instance, CD55 profiles (M = 0. 01) did not change significantly between control and HIV-1 infected situations  (Appendix Fig S2B).
As with any experimental setup, biological and technical noise can mask real changes, and we stringently prefiltered our profiling data to exclude poorly reproducible shifts (see Materials and Methods). For example, we did not include CD63 in the comparative +/À HIV analysis, as the data were insufficient in this case, due to the low number of CD63 peptides quantified in the comparative dataset. So while our gradient analysis in Fig 1 suggests that CD63 shifts upon HIV infection, we could not address this point by profiling. Nevertheless, the MR analysis identified numerous proteins whose secretory behaviour is strongly affected by HIV infection, of which we selected three for validation. Other proteins with identified MR shifts, or with more subtle changes that were not highlighted, may also be important.
All pellets were washed in 37 ml of PBS and recentrifuged at the corresponding speed before being resuspended in sterile PBS. Cells recovered from the 300 g pellet were stained with a live/dead dye (Fixable Viability Dye eFluor 780, eBioscience). Cells were then fixed and analysed on MACSQuant instrument (Miltenyi) to assess viability and percentage of infection (GFP expression). We have submitted all relevant data of our experiments to the EV-TRACK knowledgebase (EV-TRACK ID: EV190107)  (Van Deun et al, 2017). Iodixanol gradient separation 100K pellets obtained by ultracentrifugation of conditioned medium from 60-120 million cells (Jurkat NL4-3 Nef + -infected or uninfected) were resuspended in 1 ml of PBS and layered on top of iodixanol (Optiprep TM , Sigma-Aldrich #D1556) gradients. Gradients were prepared in PBS in 4% increments ranging from 6 to 18% and centrifuged for 1 h at 4°C at 200,000 g (35,000 rpm) in a SW 41 Ti rotor (Beckman Coulter), stopping without brake.
Western blotting Vesicles recovered from conditioned medium from 20 × 10 6 cells (2K, 10K and 100K pellets) or the iodixanol gradient fractions from 60-120 × 10 6 cells (6-12 fractions, respectively) were resuspended in in 4x Laemmli Sample buffer (Bio-Rad) diluted in PBS and loaded on 4-15% Mini-Proteanâ TGX Stain-Free TM gels (Bio-Rad), under non-reducing conditions. Transferred membranes (Immuno-Blot PVDF Bio-Rad) were developed using Clarity western ECL substrate (Bio-Rad) and the ChemiDoc Touch imager (Bio-Rad). Intensity of the bands was quantified using ImageLab Software (Bio-Rad). Antibodies for WB were anti-human: CD63 (clone H5C6, BD Bioscience 557305), CD9 (clone MM2/57, Millipore cbl162), CD45 ( clone HI30, BD Bioscience 557748, GP96 (clone 9G10, Stressgen ADI-SPA-850-D), AChE (Abcam ab31276), actin (clone C4, Millipore MAB1501), syntenin-1 (clone C2C3, GeneTex GTX10847), CD81 (clone 5A6, Santa Cruz sc-23692), SERINC3 (Abcam 153748), SPN (clone MEM-59, Abcam 9088) and MOV10 (clone EPR14478, Abcam ab189919), ADAM10 (clone 163003, R&D Systems MAB1427), CD3 (clone EPR4517, Abcam ab134096) and anti-HIV-1 p24 monoclonal (183-H12-5C, NIH AIDS reagent program 1513).
Secondary antibodies included HRP-conjugated goat anti-rabbit IgG (H + L) (Jackson 111-035-144), HRP-conjugated goat anti-mouse IgG (H + L) (Jackson 111-035-146) and HRP-conjugated donkey anti-goat IgG (H + L) (Jackson 705-035-147). Images were obtained with the Chemidoc Imaging System (Bio-Rad), and quantifications were done using Image Lab software (Bio-Rad).
Flow cytometric analysis was performed with Aurora analyser (Cytek) and data analysed with FlowJo software (v10, FlowJo LLC). The 39 single bead populations were gated to allow determination of the APC signal intensity on the respective bead population and median fluorescence intensity (MFI) for each capture bead was measured. Out of the 37 markers analysed, only those detected in the proteome of Jurkat are shown. 100K pellets from control cells, SERINC3 KD cells and control cells after NL4-3 EGFP-Nef + infection (Fig 6G ) were analysed by the MACSPlex Exosome Kit as above, but with detection by a mix of APC-conjugated anti-CD9/CD81/CD63 antibodies provided by the manufacturer, and including a fixation step with 4% PFA for 1 h to inactivate the virus. 3-5 × 10 8 fixed EVs and 15 µl of MACSPlex Exosome Capture Beads were used.
1% formic acid) to 30% buffer B over 95 min at 300 nl/ min flow rate and 55°C. Runs were separated by 5 min wash-outs with 95% buffer B and re-equilibration. The LC was coupled to a Q Exactive HF-X Hybrid Quadrupole-Orbitrap Mass Spectrometer via a nanoelectrospray source (Thermo Fisher Scientific). MS data were acquired using a data-dependent top 15 method, dynamically excluding precursors picked during the last 30 s. MS1 survey scans were acquired at a resolution of 60,000 in a 300-1,650 Th range, with a maximum injection time of 20 ms and 3e6 target ions, as determined with predictive automatic gain control. Sequencing was performed via higher energy collisional dissociation fragmentation of ions isolated from a 1. 4 Th window, with a maximum injection time of 28 ms and 1e5 target ions.
MS2 fragment scans were acquired at a resolution of 15,000 in a 200-2,000 Th range. The minimum predicted ion count to be reached per injection was 2. 9e3. Raw data have been deposited to the ProteomeXchange Consortium via the PRIDE (Vizca ıno et al, 2016) partner repository with the dataset identifier PXD019074. Processing of proteomic data Raw files were analysed with MaxQuant Version 1. 6. 10. 43  (Cox & Mann, 2008)  with default settings, except for the following. SILAC mixed samples and full proteome samples were separated into two parameter groups for relative SILAC quantification and label-free quantification using the MaxLFQ algorithm  (Cox et al, 2014) , respectively. For SILAC samples, the multiplicity was set to 2, with Arg6 and Lys4 as light labels, and Arg10 and Lys8 as heavy labels; re-quantification was enabled.
For full proteomes, only the light SILAC labels were specified. The minimum ratio count was set to 1 for both SILAC and LFQ, and matching between runs was enabled. Only identifications from the same cellular fraction and adjacent SDB-RPS peptide fractions were matched across replicates. The human SwissProt protein reference database was downloaded from UniProt (2019_11_29, canonical and isoform data). Comparison of control and HIV-infected Jurkat cells (the "comparative set") Sample processing SILAC protein mixed samples were separated by polyacrylamide gel electrophoresis (PAGE), and separated proteins were recovered in 7 bands and in-gel digested as described in standard protocols. Briefly, following the SDS-PAGE and washing of the excised gel slices, proteins were reduced by adding 10 mM dithiothreitol (DTT) (Sigma-Aldrich) prior to alkylation with 55 mM iodoacetamide (Sigma-Aldrich).
Processing of proteomic data Raw files were analysed with MaxQuant Version 1. 6. 1. 13 (Cox & Mann, 2008) , with default settings, except for the following. Multiplicity was set to 2, with Arg6 and Lys4 as light labels, and Arg10 and Lys8 as heavy labels. Requantify, match between runs and iBAQ were enabled. The minimum ratio count for SILAC quantification was set to 1. For the experimental design, equivalent slices from the SDS-PAGE gels were labelled with consecutive numbers (1-7 for control fractionation profiling samples, 11-17 for HIV-1 infected fractionation profiling samples, and 101-107 for whole proteome samples (1, 11 and 101 correspond to the top slices of the gels). Hence, only identifications from equivalent and directly adjacent slices were matched. The human SwissProt protein reference database was downloaded from UniProt (2019_07_29, canonical and isoform data), augmented with second a custom-made HIV protein Fasta file.
Bioinformatic analysis of proteomic data The protein groups output files from MaxQuant were first processed in Excel (separate analyses for the "NNP dataset" from untreated Jurkat cells, and the "comparative set" from untreated and HIVinfected cells): • Remove protein groups marked as reverse hit/potential contami- nant/only identified by site. • Normalize each sample for channel loading: Divide SILAC ratios by median ratio of the sample. • Normalize intensities (full proteome samples, and SILAC medium intensities from F1-F3) for equal loading: Divide all intensities by the summed intensity of the sample and multiply with largest sum. This master dataset was then further subdivided for different types of analyses. The SILAC ratio data were grouped into threedata point profiles, i. e. for each protein, the SILAC ratios in the F1, F2 and F3 fractions of a given replicate and biological condition.