dataesr/scientific-paragraphs-categorization huggingface dataset:
This is consistent with the idea that SidH is toxic for host cells and persistent SidH toxicity during infection with the ΔlubX strain may be unfavorable for LP replication  18. The reasons of the toxicity of SidH and the basis for the selective ubiquitination of SidH by LubX are unknown. Because SidH and the paralogs share no notable sequence similarity with proteins of known function, biochemical characterization of these proteins remained difficult. Here we determined the cryo-EM structure of SidH at a nominal resolution of 2. 7 Å. SidH folds into a large network of helical bundles that does not share a notable similarity to any other protein in the protein data bank (PDB). Interestingly, we found that SidH interacts with bacterial tRNA and EF-Tu as it comes bound to these molecules during the purification.
Accordingly, we could morph the model of tRNA phe -EF-Tu complex (PDB ID-1TTT) into a part of the obtained cryo-EM density (Fig. S4 ). As SidH does not have any homology to proteins of known structure, we built the model de novo using a combination of Phenix autobuild package and manual building in Coot. A small region spanning residues 903-1040 of SidH was modeled using the AI structure prediction tool AlphaFold  25. The final refined structure contains one molecule each of LP SidH and E. coli EF-Tu/tRNA complex (Fig. 1b ). The structure of SidH is composed of a set of discrete bundles of helices that are intricately arranged with respect to each other, resulting in the overall shape of SidH resembling the letter "I". For the sake of describing the structure of SidH and its interaction with t-RNA and EF-Tu, we defined 8 distinct helical bundles (Hb) in SidH (Fig.
tRNA and SidH share a buried surface area of 500 Å 2 which is mostly composed of ionic interactions. The T-loop situated at the kink of the "L" shaped tRNA molecule inserts itself into the positively charged groove formed at the intersection of Hb1, Hb3 and Hb5 in the N-terminal region of SidH (Fig. 2b, c ). Interestingly, the T-loop is one of the most conserved features among tRNAs both in terms of sequence and structure explaining why SidH does not show specificity to one particular tRNA (Fig. S8 ). The acceptor arm of the tRNA does not make any direct contact with SidH and reaches into the β-barrel domain 2 of EF-Tu in a similar configuration as it does in the EF-Tu/Phe-tRNA/ GDPNP ternary complex (PDB: 1TTT). The anticodon arm of the tRNA extends into the solvent away from SidH and has poorly resolved electron density (Fig.
Evolution of the sidH gene within the species of Legionella pneumophila We noticed that the sidH gene in the LP strain Paris is split into two genes lpp2886 and lpp2883 coding for amino acids 1-1025 (SidH Paris Nterm) and 1026-2225 (SidH Paris C-term), respectively, due to the presence of an insertion sequence (Fig. 4a ). Moreover, we found that the sidH gene is absent in strains Lens  29  and Corby  30. Intrigued by these observations, we undertook a comparative and evolutionary analyses of the sidH gene within the species L. pneumophila. We retrieved 973 LP genomes from the RefSeq database and searched for sidH in these strains using sidH Philadelphia (lpg2829) as reference and blastn. sidH was present and highly conserved (more than 95% nucleotide identity) in 775 strains, absent in 174 strains and shorter or fragmented in 24 strains.
S13C ). 3D Focused classification in Relion helped us get the map of SidH bound to LubX with an overall nominal resolution of 3. 1 Å (Fig. S14 ). The full length LubX crystal structure (PDB:4WZ3, chain B) and the SidH-EFTu/tRNA complex structure could readily be morphed into the SidH-LubX EM map. We then real-space refined the structure against the EM map in PHENIX  35  which resulted in the model of the SidH-EFTu/tRNA-LubX complex structure (Fig. 6a )  34. SidH does not undergo even minor conformational changes upon binding to LubX. LubX bound to UBE2D2 and LubX in complex with SidH superposes well overall, with an r. m. s. d of 1. 1 Å over 196 C-α's. Interestingly, the C-terminal helix of LubX which was unresolved previously in the crystal structure of LubX-UBE2D2, now becomes resolved when bound to SidH (Fig.
Mass-photometry, cryo-EM sample preparation To prepare grids for cryo-EM data collection, purified SidH FL was first checked for the presence of any heterogeneity using mass photometry. First of all, the SEC buffer was used to determine the background. For calibration, we used Nativemark TM Unstained Protein Standard (LC0725-ThermoFisher Scientific) which contains known molecular weight marker proteins of 66, 146, 480, and 1048 kDa. Immediately prior to mass photometry measurements, protein stock was diluted directly in SEC buffer to 50 nM concentration. The diluted protein sample was applied onto the instrument, data was collected, processed and a graph was generated between counts and mass. Quantifoil Au 300 1. 2/1. 3 grids were glow-discharged for 30 s using a Pelco EasyGlow on both sides at 30 mA current.
cryo-EM data acquisition For the SidH FL, cryo-EM movies were collected on a Titan Krios (FEI) electron microscope equipped with a K2 Summit direct electron detector and a GIF Quantum energy filter (Gatan) at the EMBL Heidelberg cryo-EM platform. The operational acceleration voltage for the microscope was 300 kV. The defocus values ranged from -0. 8 to -2. 0 μm. The dose rate on the camera was set to be about 3 e -/pixel/ second in electron counting mode. The total exposure time for each movie was 8 s with a total exposure dose of 41. 93 e/ Å 2 on the sample. Each movie was fractionated into 40 frames, with 0. 2 s per frame. 9020 images were collected with a pixel size of 0. 81 Å. For SidH_LubX complex, 9195 images were collected with a pixel size of 0. 827 Å on Titan Krios (FEI) electron microscope at the ESRF Grenoble cryo-EM platform CM01 equipped with K2 direct electron detector  39.
cryo-EM data processing For SidH FL Cryo-EM data, motion correction and contrast-transfer function (CTF) estimation, with subsequent particle picking using the BoxNet2Mask_20180531 were performed in WARP. Coordinates of 1,370,015 particles were imported into Relion 3. 1 and initially extracted with a 2-fold binning factor. After two rounds of reference-free 2D classification 1,271,922 particles were included for 3D classification. One round of 3D classification gave rise to 6 different 3D classes. A subset of 367,741 particles from the best class were picked and reextracted un-binned and 3D refined 4 times to 2. 76 Å resolution. After post-processing, we had a map of 2. 72 Å resolution. The reported overall resolution of 2. 7 Å was calculated using the gold-standard Fourier shell correlation (FSC) 0.
Proteins were eluted by boiling with 2x Laemmli buffer for 10 min. To obtain quantitative information, the samples were digested using trypsin (sequencing grade, Promega) and labelled with TMT10plex Isobaric Label Reagent (ThermoFisher). The fractionation was carried out on an Agilent 1200 Infinity high-performance liquid chromatography system, equipped with a Gemini C18 column (3 μm, 110 Å, 100 × 1. 0 mm, Phenomenex). The peptides were introduced into the Fusion Lumos and data dependent acquisition was performed. Data analysis was done with MSFragger v3. 7  41  In brief, samples were searched against the Swissprot Homo sapiens database supplemented with GFP-apo and GFP-SidH sequence (IP samples) and eventually quantified by the TMT10plex option. Log2 transformed raw TMT reporter ion intensities were normalized.
Acknowledgements We thank the  EMBL Mass spectrometry core facility , especially  Mandy Rettel  and  Frank Stein  for the data acquisition and analysis,  Prof. Hiroki Nagai  ( Osaka University ) for the kind gift of LubX and SidH expression plasmids,  Martin Pelosse  for training and maintenance of the EMBL eukaryotic expression facility,  Eva Kowalinksi  and her group for help with RNA sequencing data analysis and providing in vitro synthesized RNA and the cryo-EM platform of the  Structural and Computational Biology Unit at EMBL Heidelberg. We thank the  EMBL Genomic Core Facility  for the RNA sequencing. We acknowledge the  European Synchrotron Radiation Facility  for provision of beam time on CM01. This work used the platforms of the Grenoble Instruct-ERIC center (ISBG; UAR 3518  CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology (PSB ), supported by  FRISBI  ( ANR-10-INBS-0005-02 ) and  GRAL , financed within the  University Grenoble Alpes graduate school (Écoles Universitaires de Recherche)   CBH-EUR-GS  ( ANR-17-EURE-0003 ).
The work was supported by a grant from the  French Agence Nationale de la Recherche  ( ANR-21-CE11-0013 ) to S. B. and by grants of the  Fondation pour la Recherche Médicale  ( EQU201903007847 ) and the  French Agence Nationale de la Recherche  ( ANR-10-LABX-62-IBEID ) to C. B.
Data availability Mass spectrometry data is available from the Proteomics Identification (PRIDE) database with the dataset identifier PXD044950. The RNA sequencing data is available from the ENA (European Nucleotide Archive) database with the dataset identifier ERR11775543. Cryo-EM structure coordinates are available from the PDB and the Electron Microscopy Data Bank (EMDB) for the SidH-EF-Tu-tRNA complex under accession codes 8QFS and EMD-18383, respectively. The coordinates and cryo-EM density for the LubX-SidH-EF-Tu-tRNA complex are available using accession codes 8QHC and EMD-18407. Models of tRNAphe-EF-Tu complex (PDB ID-1TTT) and LubX-UBE2D2 (PDB: 4WZ3) were used in this study. Source data are provided with this paper.
RESULTS ivRIC, a new approach to analyse the composition of the RNPs assembled into virions Proteomic analysis of purified viral particles has revealed that hundreds of cellular proteins are incorporated into virions, including cellular RBPs  7. Comparison of these datasets revealed sparse overlapping (Figure  S1A ), raising question about the biological significance of the identified proteins. The limited consistency of these studies is likely because total viral particle analysis is unable to discriminate between bystander proteins captured passively during virion assembly and RBPs actively interacting with the HIV-1 gRNA. To exclusively identify RBPs bound to gRNA in HIV-1 particles, we developed a new approach referred to here as in virion RNA interactome capture (ivRIC). In brief, viral particles are purified in a sucrose cushion, followed by protein-RNA 'zero distance' ultraviolet (UV) crosslinking, lysis under denaturing conditions and isolation of the polyadenylated HIV-1 gRNA with oligo(dT) magnetic beads via denaturing washes (Figure  1A ).
The different prevalence of GO terms between the two datasets is expected as the ivRNP is a subset of the total HIV-1 particle. RBPs can display different modes of RNA binding, ranging from universal to very selective, and from long-lived to transitory RNA binding. The RNA-binding properties of an RBP do not only impact its function (i. e. global vs specific and transitory vs stable)  34  , but also its ability to crosslink to RNA. Indeed, UV-induced, RNA-to-protein crosslinking is enhanced by long-lived and geometrically optimal interactions between nucleotides and amino acids  35. To assess the binding properties of the ivRBPs, we compared the protein intensity in eluates (ivRNP) and inputs (viral particles) of the ivRIC experiment. This analysis revealed two groups of proteins (Figure  2C ): i) ivRBPs with high eluate/input protein intensity ratios (i.
Elucidating the Rev interactome in infected CD4+ T cells The presence of many nuclear RBPs (Figure  2A ) in the ivRNP is surprising as HIV-1 particle assembly occurs at the plasma membrane. We hypothesised that these proteins may associate with the gRNA in the nucleus and remain bound to it until its packaging into viral particles. To test our hypothesis, we focused on a key component of the HIV-1 nuclear genomic ribonucleoprotein (gRNP), called Rev 2. Therefore, the protein interactome of Rev is expected to reflect the composition of the gRNP to a high extent. There have been several attempts at establishing the Rev interactome, however, these approaches were limited because Rev was expressed in non-infected cells and/or using cell types that are not infected by HIV-1, leading to very poor overlap between datasets (Figure  S4A )  2, [36] [37] [38].
However, 43% of the proteins identified here were also reported in these earlier studies (Figure  3I ). Our results thus reconcile the contradictions in these datasets, while still adding many additional Rev interactors. We also observed a strong overlap between our Rev interactome and the annotation in the NCBI HIV database, with 25% of the proteins being already annotated as Rev interactors and a further 42% previously linked to HIV-1 in some capacity (Figure  3J ). Altogether, these results highlight the consistency of our Rev interactome with previous data, while adding new Rev-host protein interactions with unknown roles in HIV-1 infection.
The HIV-1 ivRNP heavily overlaps with the Rev protein-protein interactome Nuclear RBPs present in the ivRNP may associate with the HIV-1 gRNA in the nucleus of the infected cell. To determine to what extent the ivRNP resembles to the nuclear gRNP, we compared the ivRIC (Figure  1 ) and Rev IP results (Figure  3 ). Surprisingly, 68% of the components of the ivRNP are also interactors of Rev (Figure  4A ). Indeed, we observed that the proteins identified in both the Rev interactome and the ivRNP displayed similar fold changes in both datasets, showing a striking Pearson correlation of >0. 61 (Figure  4B ). This suggests that the stoichiometry of these proteins in the Rev interactome and ivRNP is likely similar. Proteins shared between these two datasets are mostly involved in RNA transcription, splicing, nuclear export, and translation (Figure  4C ).
Moreover, several ivRBPs and Rev interactors were previously found associated with HIV-1 RNA in infected cells (Figure  S5B )  42, 43. These findings strongly suggest that a wide set of RBPs engage with HIV-1 gRNA during the nuclear and cytosolic life of the gRNA and remain bound to it during the formation of the viral particles. The striking functional connection between nuclear proteins and plasma membrane-assembled virions warrants further investigation. To test whether RBPs in the ivRNP and Rev interactome are specific to HIV-1 gRNA or have widespread binding activity across viral RNAs, we collected all the viral RNA interactomes available to date comprising 21 datasets across 11 viruses  44. Our results show that the ivRNP and the Rev interactome are both enriched in proteins with widespread binding to RNAs from different viruses (Figure  4D  and S5C).
To assess if this broad-spectrum viral RNA-binding activity is linked to widespread regulatory activity in virus infection, we compiled all siRNA and CRISPR-cas9 screenings of viral fitness generated to date, which include 36 viruses and 18 viral families  45. Interestingly, ~20% and ~13% of the components of the ivRNP and Rev interactome that were tested in the screenings caused phenotypes in at least three different viral families (Figure  4E  and S5D). The observed binding of ivRBPs and Rev interactors to the RNAs of different virus species, together with their potential master-regulatory roles in infection, could be derived from low binding specificity that enable them to engage with a wide range of RNA sequences. To test this, we examined the RNA-binding preferences of all the RBPs in these two datasets for whose enhanced crosslinking and immunoprecipitation (eCLIP) sequencing data is available in ENCODE  46.
The specific enrichment of these proteins in eluates was confirmed by silver staining and Western blotting (Figure  S7D  and E ). Proteomic analysis of the eluates revealed hundreds of proteins that specifically co-precipitate with PURA-eGFP and PURB-eGFP (Figure  5F  and S7F). Both datasets largely overlap, with 93% of the PURA interactors being also present in PURB IP (Figure  5G ), suggesting that their functions are either linked or mutually exchangeable. PURB-eGFP IP dataset was larger, probably due to the higher expression of this protein when compared to PURA-eGFP (Figure  S7B, D  and E ). While we observed differences in overall protein intensity (Figure  S7G ), the two IPs correlated well, suggesting that the composition of eluates was very similar (Figure  S7H ). Interestingly, PURB was the protein with highest fold change in PURA-eGFP IP and vice versa (Figure  5F  and S7F), suggesting that both proteins associate in heteromeric complexes.
Involvement in these processes is also supported by the high incidence of components of the translation and splicing apparatus in PURA and PURB protein-protein interactome (Figure  5I ). It is also noticeable that both PURA and PURB datasets grouped together in the principal component analysis, with HIV-1 and mock treatment and not the protein identity as the main differential factor (Figure  S8D ). Moreover, 81% of the identified RNAs are targets for both proteins, displaying near identical binding patterns across their mRNA targets and a high degree of overlap of their binding sites (Figure  6A-F  and S8F ). These results reinforce the idea that PURA and PURB play cooperative or redundant roles in RNA metabolism. To test if PURA and PURB recognise specific sequences, we examined the binding sites for enriched motifs.
The high performance of ivRIC is supported by the high number of previously established regulators of HIV-1 particle formation and infectivity that are top candidates in our datasets (e. g. PACSIN2  27  , UPF1  14  , EEF1A 15,19 , MOV10  23, 24  , PDCD6IP [ALIX]  12  , APOBEC  13  amongst others), as well as all proteins tested functionally here that showed effects in HIV-1 infection (i. e. MSN, FAM120A, PURA and PURB). Strikingly, we observed a strong overlap between the ivRNP and the Rev interactome, invoking an exciting link between the nuclear life of the HIV-1 gRNA and the formation of the viral particles. How these nuclear ivRBPs are captured and retained in the HIV-1 gRNP and whether they play a role prior to or after virus assembly requires a case-by-case study. In the present work we show that the ivRBPs MSN has no effect in virus infectivity but regulate viral gene expression and/or viral particle formation in the producer cell (Figure  5 ).
Our data show several lines of evidence suggesting that PURA and PURB associate primarily with the unspliced gRNA: i) they are assembled with the gRNA into viral particles; ii) their binding sites span intron-exon junctions that are only found in unspliced HIV-1 RNAs; and iii) their binding density is similar in the Gag and Pol ORFs (present only in gRNA) to other parts of the genome (present also in spliced HIV RNAs). Since PURA and PURB bind to nearly all splice sites within the viral genome and interact with several splicing factors, it is plausible that these proteins regulate intron/exon recognition, probably inhibiting gRNA splicing. A role promoting the expression of unspliced gRNA, is compatible with the reduction of Gag and Gag-Pol levels observed in PURA KO cells. Moreover, PURA and PURB also bind to the Gag and Gag-Pol start and stop codons, the Gag/Pol frameshift site and several proteins involved in translation, suggesting an additional role in translation control.
1:1, 1:10 and 1:100 dilutions were evaluated for each sample. HIV-1 RNA copy number was calculated in reference to a standard curve of purified HIV-1 gRNA, considering that each virion contains 2 copies of viral RNA. The standard curve was prepared as follows: 1) linearize pcR-Blunt-NL4. 3-mCherry-T2A-Nef with MluI restriction endonuclease; 2) transcribe HIV-1 genomic RNA in vitro with HiScribe T7 ARCA mRNA kit (New England Biolabs, #E2065S); 3) add poly(A) tail with E. coli Poly(A) Polymerase (New England Biolabs, #M0276S); 4) purify RNA using oligo(dT) 25 beads  58   RT-qPCR data was analysed with CFX Manager Software v3. 1 (Bio-Rad).
LC-MS/MS analysis Tryptic peptides were analysed using Ultimate 3000 ultra-HPLC system (Thermo Fisher Scientific) connected to a Q-Exactive mass spectrometer (Thermo Fischer Scientific) through an EASY-Spray nano-electrospray ion source (Thermo Fischer Scientific). The peptides were first trapped on C18 PepMap100 pre-column (300 µm inner diameter x 5 mm, 100 Å, Thermo Fisher Scientific) in solvent A (0. 1% FA in water), and were then separated on the analytical column (75 µm inner diameter x 50 cm packed with ReproSil-Pur 120 C18-AQ, 1. 9 µm, 120 Å, Dr. Maisch GmbH) using 30 min (ivRIC) or 60 min (PPI & SupT1 whole cell proteome) 15-35% (vol/vol) acetonitrile linear gradient at a flow rate of 200 nl/min. Full-scan mass spectra were acquired in the Orbitrap (scan range 350-1500 m/z, resolution 70000, AGC target 3 x 106, maximum injection time 50 ms) in a data dependent mode.
Quantitative analysis of proteomics data The proteinGroup files of MaxQuant search results were imported in RStudio (R Project) for further processing. Protein intensities were log 2 transformed. In each dataset proteins with less than 2 valid intensity measurements across experimental conditions were removed prior to downstream analysis. Batch effects in each comparison were assessed using principal component analysis (PCA). Normalisation was performed to ivRIC inputs ad PPI analyses using variance stabilisation normalisation (vsn) method  66. Missing values were imputed with deterministic minimum method (R package version 2. 0. https://CRAN. R-project. org/package=imputeLCMD) using 1% quantile of global intensities. All missing values were imputed in ivRIC and Rev interactome data. For PURA/PURB interactome data, only proteins with all values missing in one condition were imputed as described in  67.
Mapping and comparing gene IDs For all datasets analyses, including mass spectrometry data, genes were mapped to Hugo gene nomenclature committee IDs (HGNC) using R package BiomaRt. Any genes that could not be mapped were manually curated by manual addition or were removed if it was a pseudogene or its HGNC entry was missing. For HIV-1 analysis, the HIV-1 NCBI database was downloaded from the NCBI web server and parsed to gene IDs following the same strategy. Upset plots and Euler diagrams were generated using the R packages upsetR and venneuler respectively. ). E) Odds ratio of proteins reported to cause phenotypes in virus infection in the siRNA and CRISPR/cas9 screenings collected in 45. We consider a 'master regulatory' protein to any candidate whose perturbation causes phenotypes in viruses from three or more families.
ACKNOWLEDGEMENTS AC is funded by the  European Research Council (ERC)   Consolidator Grant  ' vRNPcapture ' N#  101001634 , the  Career Development Award  # MR/L019434/1 , and the  John Fell Funds  from the  University of Oxford. M. G. M is funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie-Sklodowska-Curie  grant agreement N#  700184. R. T. is funded by a  BBSRC   DTP scholarship   DD01. 20. L. I. is funded by the  BBSRC   DTP scholarship  number  BB/M011224/1. C. E. L. is funded by the  Department of Biochemistry Graduate Scholarship  and the  Hertford College Graduate Studies Scholarship  of the  University of Oxford. T. J. M. S is funded by  Fondation pour la Recherche Medicale  ( FRM -FDT202001010798 ). W. K. is funded by the  European Union 's  Horizon 2020 research and innovation programme  under  Marie-Sklodowska-Curie  n#  842067.
We consider a 'master regulatory' protein to any candidate whose perturbation causes phenotypes in viruses from three or more families. F) Analysis of the binding preferences of ivRBPs and Rev interactors using the eCLIP database (ENCODE)46. Odds ratio of binding sites in 5' UTRs, CDSs, introns and 3' UTRs was estimated for each protein in the ivRNP and Rev interactome and the overall preferences of each protein group was represented as a bar plot.