dataesr/scientific-paragraphs-categorization huggingface dataset:
The RNA binding protein Ssd1 is highly enriched by eIF2A independently of RNA To gain further insight into the mechanistic role of eIF2A, we performed an in vivo affinity purification of proteins using eIF2A-TAP as bait. After purification on IgG coupled magnetic beads, the eIF2A-associated complex was analyzed on a silver-stained polyacrylamide gel (Fig 5A ) and eIF2A interaction partners were identified and quantified by mass spectrometry (S3 Dataset). A total number of 1509 proteins were identified in either input or purified samples but only 1037 were quantified in both. The enrichment level for each protein is indicated in the S3 Dataset. A volcano plot (Fig 5B ) represents analysis of the label-free quantitative (LFQ) MS data. This analysis highlighted that three groups of proteins were significantly enriched by eIF2A.
Comparison of Ssd1-associated mRNAs identified by CRAC analysis  [46]  with previous RIP and transcriptome data  [47] [48] [49]  showed that 11 mRNAs were systematically bound by Ssd1 (Table  1 ). Among these 11 mRNAs, 8 were also highly enriched by eIF2A, including CTS1, SUN4 and SRL1 (Fig 1  , Table 1 , S5 Table and S1 Dataset). As mentioned above, Ssd1 directly binds its mRNA targets  [46] , while currently none of our results demonstrated that this is the case for eIF2A. Given that Ssd1 and eIF2A seem to share the regulation of several targets, we tested the hypothesis that eIF2A binds some of its mRNA targets through Ssd1. For that purpose, we performed a RIP experiment of eIF2A in the presence or absence of Ssd1 and evaluated the associated mRNA by Northern Blot analysis. In agreement with our   and S1 Dataset), we confirmed that SUN4, CTS1, SRL1 mRNAs were strongly enriched by eIF2A-TAP in the wild-type strain, while SSD1 deletion dramatically decreased eIF2A association with these mRNAs (Fig  6 ).
Acknowledgments We are grateful to the proteomics platform of the  Pasteur Institute  for the availability of the Orbitrap Q Exactive Plus mass spectrometer and the transcriptomic platform for the sequencing experiments. We thank  Elodie Zhang  and  Frank Feuerbach  for providing the Xrn1 mutant strains. We thank  Cosmin Saveanu  for discussions and criticisms on the manuscript and  Madison Lenormand  for polishing our english. We are grateful to  Lucia Oreus  for providing media and buffers used in this study and  Christelle Lenormand  for her administrative assistance.
Funding: This work was supported by the  ANR-17-CE11-0049-01 , the  ANR-17-CE12-0024-02  and the  ANR-18-CE11-0003-04  grants from the  Agence Nationale de la Recherche. Financial support and facilities were provided by the  Institut Pasteur  and the  Centre National de la Recherche Scientifique. -MFR, AN, GB and AJ ( ANR-17-CE11-0049-01 ,  ANR-17-CE12-0024-02 ). -AN and  AJ  ( ANR-18-CE11-0003-04 ). LM and BC were supported by  ANR-17-CE11-0049-01 ,  ANR-17-CE12-0024-02  and  ANR-18-CE11-0003-04. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Three different genome assembly strategies, using Canu 5 , FALCON  6  , and MaSuRCA 7 , yielded comparable results (Supplementary Table  1 ), with >90% representation of the 255 Benchmarking Universal Single Copy Orthologs (BUSCO 8 ) of the eukaryota_odb10 dataset (Fig. 2 ), indicating high completeness. For downstream analyses, we opted to use the FALCON assembly because it was the most contiguous of the three, with the majority of the contigs (59 out of 78 primary contigs) bearing telomeric repeats at both ends. In addition, 14 of the remaining contigs had telomeric repeats at one end, leading to an estimation of 66 pairs of chromosomes in the M. sphyraenae nucleus. Biallelic single nucleotide polymorphism (SNP) frequencies cluster around a ratio of 0. 5/0. 5 for each major/minor allele (Fig.
2 ). We inferred the phylogenetic relationships of our species within the CRuMs clade using publicly available data to reconstruct a dataset of 182 conserved protein markers. Consistent with previous studies, our maximum likelihood (ML) and Bayesian inference (BI) phylogenetic trees recovered the monophyly of CRuMs with high BI posterior probability (0. 99) and ML bootstrap support (95%), although it is worth noticing that the outgroup is highly reduced since resolving the position of CRuMs in the tree of eukaryotes is outside the scope of this paper. The monophyly of Mantamonas received full support from both methods. We found Mantamonas sphyraenae to be sister to a maximally-supported clade containing M. plastica and M. vickermani (Fig. 4 ). To explore the gene content diversity of our new mantamonad species, we first annotated their genes with EggNOG mapper  9.
77% and 66% of the predicted proteins from M. sphyraenae and M. vickermani could be annotated, respectively. A total of 1,718 orthogroups were found to be conserved among all CRuMs taxa (Fig. 5a ), while 4,378 were identified as shared between the three Mantamonas species, representing the minimal core proteome of the genus Mantamonas as currently known (Fig. 5b ), among which 2,161 orthogroups are not found in the other two CRuMs lineages. Most of the proteins conserved among the CRuMs taxa (99. 6%) were found to have an ortholog in the EggNOG database and to belong to at least one Cluster of Orthologous Groups (COG)  10, 11  functional category, where the most highly represented were "Function unknown" and "Post-translational modification, Translation, and Intracellular trafficking" (Fig.
5c ). By contrast, a substantial amount of orthogroups conserved among mantamonads (12%), but absent in other CRuMs lineages, could not be assigned to any cluster in the EggNOG database. In addition, most orthogroups conserved in mantamonads but absent in other CRuMs that could be connected to an existing EggNOG cluster were annotated as "Function unknown" (Fig. 5c ). Altogether, this large number of Mantamonas-specific genes of unknown function suggests that many genetic innovations occurred during the early evolution of this group. Finally, as an additional way of assessing the M. sphyraenae and M. vickermanii sequence data completeness and to capture a sense of the complexity of the cellular systems in these organisms, we interrogated the complement of one well-studied set of proteins, the membrane-trafficking system.
6 ). Notably, Mantamonas encodes some rarely retained proteins, such as the AP5 complex  13  and syntaxin 17  14. We also identified several jotnarlogs (Fig. 6 ), including a near-complete TSET complex, and the SNAREs NPSN and Syp7. The identification of homologs of the majority of the protein complement associated with the membrane trafficking system corroborated the high completeness of our genomic and transcriptomic datasets. Interestingly, the presence of proteins previously reported with patchy distributions is consistent with their ancient nature, and, more speculatively, with the hypothesis that free-living flagellates may have retained them from a complex LECA. Overall, our new Mantamonas nuclear genome and transcriptome sequences provide high quality data for a major, yet poorly known, eukaryotic supergroup.
A custom library of repetitive elements was generated for the polished and cleaned nuclear genomic sequence by combining the results of RepeatModeler2 21 and Transposon-PSI (http://transposonpsi. sourceforge. net/) pipelines. The gathered repeat sequences from both analysis were merged and clustered to generate a single consensus and refined repeat library that was further compared against the Dfam database 22 to classify the repetitive elements using RepeatModeler  21  refiner and classifier modules. Repetitive elements identified by this procedure were then masked out of the nuclear genome using RepeatMasker 21 before the prediction of protein-coding genes. Subsequently, the RNA-seq libraries were mapped against the genome sequence with HISAT-2  23  to generate spliced alignments, and BRAKER2  24  was employed to predict the nuclear protein coding genes integrating the extrinsic evidence from the RNA-Seq data.
Mantamonas vickermani proteome prediction Reads coming from possible cross-species contamination were removed using CroCo v1. 1  29. The M. vickermani transcriptome was then assembled de novo using Spades v3. 13. 1  30  with the rna mode and default parameters specified. Transcripts were then screened to identify remaining contaminants using the Blobtools2 31 pipeline and homology searches against a custom database (see technical validation section). Predicted proteins were obtained from the clean transcripts using Transdecoder v2 (http:transdecoder. github. io) with default parameters. Subsequently, CD-HIT  32  clustering was employed (with a threshold of >=90% of identity) to produce a non-redundant data set of proteins for M. vickermanii, and to eliminate falsely duplicated proteins stemming from alternatively spliced transcripts.
Phylogenomic analyses The dataset of 351 conserved protein markers from Lax et al. 3  was updated by BLASTP searches  33  against the inferred proteomes of representatives of other eukaryotic lineages, including the proteomic data for our two new mantamonad strains. Each protein marker was aligned with MAFFT v. 7  34  and trimmed using TrimAl  35  with the -automated1 option. Alignments were manually inspected and edited with AliView 36 and Geneious v6. 06  37. Single-protein trees were reconstructed with IQ-TREE v1. 6. 11  38  under the corresponding best-fitting model as defined by ModelFinder 39 implemented in IQ-TREE  38. Each single-protein tree was manually inspected to discard contaminants and possible cases of horizontal gene transfer or hidden paralogy. At the end of this curation process, we kept a final taxon sampling of 14 species, including members of Ancyromonadida, Malawimonadida, Opisthokonta, and CRuMs (concatenated alignment and supplementary trees are available at https://doi.
CRuMs orthologue analysis Orthologous gene families were identified among the predicted proteomes of Mantamonas sphyraenae, Mantamonas vickermani and the publicly available proteomes of Mantamonas plastica, Diphylleia rotans and Rigifila ramosa as obtained from the EukProt v3 database  43  using OrthoFinder v2. 5. 4  44. For this, we used DIAMOND  45  ("ultra-sensitive" mode, and query cover >= 50%), an inflation value of 1. 5, and the MCL clustering algorithm.
Analysis of the conservation of the membrane-trafficking system complement To assess the complement of the membrane trafficking system encoded in our Mantamonas genome and transcriptome datasets, we performed homologous searches of a selection of protein query sequences from the genomes of Homo sapiens, Dictyostelium discoideum, Arabidopsis thaliana and Trypanosoma brucei available at the NCBI (National Center for Biotechnology Information) database (Supplementary Table  3 ). These proteins included components of the machinery for vesicle formation (HTAC-derived coats, ESCRTs, and ArfGAPs) and vesicle fusion (SNAREs and SM proteins, TBC-Rab GAPs, and Multi-subunit tethering complexes)  12. BLASTP and TBLASTN were used to search the predicted proteomes and nucleotide coding sequences, respectively, of M.
sphyraenae and M. vickermani. The HMMER3 package was used to find more divergent protein sequences using the hmmsearch tool  46. In cases in which only TBLASTN hits were retrieved, these were translated using Exonerate  47. Potential orthologs (i. e. , hits with an E-value below 0. 05) were further analyzed by the Reciprocal Best Hit (RBH) approach, using the Mantamonas candidate orthologs as queries against the H. sapiens, D. discoideum and A. thaliana proteomes. If the best hit was the protein of interest and had an E-value two orders of magnitude lower than the next non-orthologous hit, this was considered as orthology validation. Forward and reverse searches were performed using the AMOEBAE tool  48.
Identification and filtering of contaminant sequences Mantamonads grow in non-axenic cultures with co-cultured prokaryotes on which they feed. Therefore, various methods were employed to ensure the correct identification and filtering of contaminant sequences of the genomic and transcriptomic datasets of M. sphyraenae and M. vickermani. For the genomic dataset of M. sphyraenae, we first identified the main bacterial contaminants from the initial genome assemblies  51. In addition, we established a customs database consisting of contigs assembled from Illumina sequencing data from bacteria only enrichment cultures derived from the lab's several xenic protist cultures. These were used to screen PacBio reads using BLASR v5. 1  52  and Illumina reads using Bowtie2 v2. 3. 5. 1  27. Only Illumina reads in which reads from neither pair aligned to the bacterial database were retained for further assembly.
After genome assembly using the filtered reads, remaining contaminant contigs were identified by using MyCC v1  53  , which bins contigs based on their tetranucleotide frequencies and coverage. Clusters were formed using the affinity propagation (AP) algorithm and visualized in a 2-dimensional Barnes-Hut-SNE plot. BLASTN searches using default parameters were conducted against the 'nt' database from the NCBI to taxonomically classify the bins. Contigs were identified as contaminants if they contained no hits other than to prokaryotes, and were clustered away from the main eukaryotic bin. Finally mitochondrial sequences were screened out from the short and long read libraries of M. sphyraenae by mapping them against the mitochondrial genome using bwa-0. 7. 15  19  and minimap2  17  respectively.
The assembled transcriptome of M. vickermani was decontaminated with the Blobtools2 pipeline  31. Briefly, this approach helps to identify contaminant sequences based on their biases in coverage and GC content, as well as on a taxonomic classification established by DIAMOND searches  45  against the 'nt' and Uniprot databases  54. In addition, a second cleaning step was done by performing DIAMOND searches against a database containing all the proteins of the prokaryotic Genome Taxonomy Database (GTDB)  55  and the eukaryotic-representative EukProt v3 database  43. A protein was considered as a probable contaminant and excluded from further analyses if its best hit corresponded to any protein from GTDB, with strict cutoffs of identity ≥ 50% and query coverage ≥ 50%. Finally, a blobplot was generated for the final genomic and transcriptomic contigs of M.
Completeness analysis To assess the completeness of the decontaminated genome and transcriptome datasets, we employed the BUSCO v5. 3. 2 pipeline. We identified the percentage of near-universal single copy orthologs of the eukaryote_odb10 database  16  on the predicted proteomes of M. sphyraenae and M. vickermani, as well as those of other species belonging to the CRuMs supergroup available in the EukProt v3  43  database for comparison purposes.
Acknowledgements The authors thank  Drs. J. Z. Xiang ,  D. Xu , and  H. Shang  at the  Weill Cornell's Genome Resources Core Facility  for their assistance with Illumina sequencing. They also thank  Dr. S. Goodwin  in the  NGS sequencing core at Cold Spring Harbor Laboratory  for her help with PacBio sequencing, and  Drs. J. A. Burns  and  A. A. Pittis  for their initial data analysis efforts. This work was funded by the  Simons Foundation   Grant  awards to EK ( SF-382790  &  SF-876199 ). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( ERC Starting  Grant No  803151  to L. E. , and  ERC Advanced  Grants No  322669  and  787904  to P. L. -G. and D. M. , respectively). L. J. G. was funded by the  Horizon 2020 research and innovation programme  under the  European Marie Skłodowska-Curie Individual Fellowship   H2020-MSCA-IF-2020  (grant agreement no.
) have been deposited to the NCBI under the Bioproject accession PRJNA886733. molecule M. sphyraenae STR306 DNA Illumina HiSeq 2500 Paired SRR21818798 M. sphyraenae STR306 DNA Illumina HiSeq 2500 Mate pair SRR22188164 M. sphyraenae STR306 RNA Illumina HiSeq 2500 Paired SRR21818794 M. vickermani CRO19MAN RNA IlluminaHiSeq 2500 Paired SRR21818793 Species and strain name Type Platform Read type SRA accession number M. sphyraenae STR306 DNA PacBio RS II Single SRR21818797.
Examination and imaging The amber pieces have been trimmed and polished to facilitate the observation of the specimens, using thin silicon carbide papers on a grinder polisher (Buehler MetaServ 3000). Observations and photographs were conducted with a Leica DMC4500 camera attached to a Leica M205C stereomicroscope. All images are digitally stacked photomicrographic composites of several focal planes, which were obtained using Helicon Focus 6. 7. Adobe Illustrator CC2019 and Photoshop CC2019 software were used to compose the figures and ImageJ 1. 53 for measurements  (Schneider et al. 2012). Habitus or details of the body have been redrawn digitally from drawings made with a camera lucida attached to the stereomicroscope.
Phylogenetic analyses were performed in parsimony using TNT 1. 5  (Goloboff and Catalano 2016). Implied-weighting and equal-weighting searches were conducted under the 'Traditional search' method, using the following parameters: space for 99 999 trees was reserved in memory, collapsing rules as Tree Bisection Reconnection and 10 000 replicates performed. For implied-weighting analyses, the setk. run script written by Salvador Arias (unpublished) was used to calculate the appropriate value of K  (Goloboff et al. 2008 ) and returned a value of 4. 375, which was subsequently used. When several trees were produced, a strict consensus was computed. To measure the robustness of the most parsimonious trees, subsequent symmetric resampling was performed for 1000 replicates. Characters were mapped on the trees using the Winclada software.
Acknowledgments We are most grateful to Dr Christoph Öhm-Kühnle (Herrenberg, Germany) and Corentin Jouault (MNHN, Paris, France) for donating three of the specimens studied herein; and to Carsten Gröhn (Glinde, Germany) and Patrick Müller (Zweibrücken, Germany) for access to two other specimens with subsequent repositories in institutional collections. We sincerely thank Dr. Celso Azevedo (Espírito Santo, Brazil) for his precious comments on the first version of the draft and Dr. Wesley Colombo (Espírito Santo, Brazil) for his help on the construction of the matrix. We thank Prof. Denis J. Brothers and two anonymous reviewers for their constructive comments on the manuscript, as well as the subject editor, Dr. Ricardo Pérez-de la Fuente, for his guidance during the submission process. Finally, we thank the Willi Hennig Society for making available the TNT software for free.
From these smoothed 5 Ma intervals, smoothed spatial climate distributions were generated for each 166. 7 ka time step using linear interpolation. Further, delta temperature values were calculated for each time step by linearly interpolating the 1 Ma interval values provided by and applied to the new geographically smoothed time steps. Finally, corrections were made to account for climatic fluctuations associated with recent glacial maxima  [79]. Cost distance values between habitable cells in the reconstructed landscapes were calculated using the transition() function in the gdistance package in R  [80]. The shortest path between each pair of cells was calculated and the distance between all pairs was stored in a distance matrix. Paths were calculated using an 8-direction adjacency scheme whereby cells are deemed adjacent if they are in contact vertically, horizontally, or diagonally.
A phylogeny object from this cluster divergence matrix was calculated using a hierarchical clustering approach implemented by hclust() in the R stats package  [92]. From this cluster phylogeny, phylogenetic diversity is calculated using the pd() function in the phylomeasures R package  [83]. The mean value from each simulation was then taken to make values comparable to the species-level phylogeny. Similarly, the mean pairwise distance was calculated as the mean pairwise distance between these geographic clusters of cells. We focus on three different measures of diversity: richness, phylogenetic diversity (PD), and mean pairwise distance (MPD). Despite these metrics being conceptually related and occasionally correlated, they capture different aspects of biological diversity  [57]. The relationship between the species and population levels of these diversity metrics, or the continuity across levels, was calculated.
These model variables were then reduced using a forward and backward stepwise model selection based on Akaike Information Criterion scores using the step() function in the R stats package  [92]. Finally, we correlated the continuity metrics to the calculated clade properties: species range, thermal and niche trait evenness, weighted endemism, species turnover, and diversification rate. This was done with the hmisc package in R using Spearman's Rank Correlation Coefficient to capture non-linear relationships between variables. p-values were Bonferroni corrected for multiple testing. This was visualised using a scaled PCA implemented in the R stats package  [92].
Funding Open access funding provided by  Swiss Federal Institute of Technology Zurich Explicit  funding for the project was provided by the  ETH  research grant financing the salary of  Thomas Keggin  ( ETH Research  Grant  ETH-34 18-1 ). Additionally, O. H. gratefully acknowledges the support of  iDiv  funded by the  German Research Foundation  ( DFG-FZT 118 ,  202548816 ).
Previous analyses of this dataset at a site-level showed that vegetation composition was primarily structured by landscape and soil, and secondarily by agricultural intensification  (Andrade et al. , 2021; Fried et al. , 2018). Composition was driven by margin management and fertilization, while species richness responded to herbicide use. However, these previous analyses did not explore the role of scale in their results, or whether the relative influence of agricultural practices on margin plant communities were consistent across regions. Building on these first findings, here we investigated the potential influence of spatial extent and resolution of analysis on the drivers of field margin plant communities. Because agricultural practices are site-specific whereas landscape, soil and climatic conditions tend to have a greater range of values within larger grid cells, we hypothesized that the effects of the former would dominate at finer resolutions, while the latter would stand out at coarser resolutions.
Vegetation surveys We used vegetation data from a national monitoring effort, the 500-ENI network, which is funded by the French Ministry of Agriculture (see details in  Andrade et al. , 2021). Note that raw data access requires a request to the Ministry and is conditional on confidentiality of some information, such as site coordinates; however, all datasets used in the following analyses are available in a repository (see the data accessibility statement). Agricultural field margins covering continental France, were surveyed yearly since 2013, representing a total of 543 unique sites between 2013 and 2019 (including some site turnover between years, see Appendix A, Fig. SA. 1). Here we selected a subset of sites that had at least five years of botanical data between 2013 and 2019, resulting in a total of 462 sites for the rest of the analysis.