dataesr/scientific-paragraphs-categorization huggingface dataset:
The 18S ribosomal DNA sequences were obtained by sequencing the complete 18S ribosomal DNA from strains available in our laboratory, and for non-available strains, the sequences were retrieved from the NCBI GenBank database or directly from the Acanthamoeba draft genome sequences. Nucleotide sequences alignments were performed with the MUSCLE program  (Edgar, 2004). Phylogenetic trees were constructed using FastTree  (Price et al. , 2010). We investigated more specifically any possible occurrences of horizontal gene transfers (HGT), i. e. , the gene trafficking between this amoeba and giant viruses. A. polyphaga proteins which had as significant hit a giant virus sequence were used as queries to search into the NCBI GenBank non-redundant protein sequence database (nr). Phylogenetic analyses were performed to confirm suspicion of HGT for the genes showing the highest level of sequence similarity with a viral homolog.
Amino acid sequences alignments were performed with the MUSCLE program. Phylogenetic trees were constructed using FastTree. Ancestral major capsid protein (MCP) sequences were predicted using the MEGA6 program 3. Additionally, similarity searches were performed by using the tBLASTn program for all ORFs of giant viruses, virophages and transpovirons against the draft genome sequences of A. polyphaga. The following criteria were used: a percentage of amino acid identity ≥ 30%; an e-value ≤ 1e-2; and a percentage of coverage of aligned sequences ≥ 30%. Finally, results from similarity searches were formatted to create networks of gene trafficking using the Cytoscape tool  (Smoot et al. , 2011). This software was also used to generate a network between protein sequences from two giant viruses of amoebae, Pandoravirus dulcis and Pandoravirus salinus, and from the draft genome sequences of A.
polyphaga and A. castellanii ATCC 50370. Finally, a 'rhizome' of genes was determined for a few A. polyphaga genes whose best hit was a giant virus. This information was obtained by performing BLASTp searches with fragments of this gene obtained by fenestrating its amino acid sequence with a window of 40 amino acids and a sliding step of 20 amino acids. The representation of the mosaicism of these genes was built using the Circos tool 4. We identified 115 ORFs (i) best matching with a giant virus gene, (ii) larger than 100 amino acids, and (iii) present in scaffolds that harbor a majority of 'non-viral' ORFs. For these 115 ORFs, we performed BLASTp searches against the GenBank protein sequence database nr and a tBLASTn search against all 14 Acanthamoeba draft genome sequences. Then, we merged the results of these two BLAST searches by creating a database with significant sequence hits (e-value < 1e-4, length of query and subject sequence alignments > 100 amino acids), and performed another BLASTp search for the 115 ORFs against this database.
A total of 262 A. polyphaga ORFs had as best hit a coding sequence of a giant virus of amoeba, among which 134 are larger than 100 amino acids and 115 are present in scaffolds harboring a majority of 'non-viral' ORFs. For these 115 ORFs, results from BLAST searches against the non-redundant GenBank protein sequence database and all 14 Acanthamoeba draft genome sequences showed different patterns of best hits, including sequence sets comprising a majority of sequences from giant viruses or from Acanthamoeba spp. Subsequent phylogenetic reconstructions enabled us to infer that the most parsimonious evolutionary scenario was, in at least three cases, a gene sequence transfer from giant viruses of amoebae to Acanthamoeba spp. , although alternative scenarios could not be ruled out (Figures  3A-C ).
Moreover, the iridovirus homolog 1 sequence is clustered with a sequence from Mollivirus sibericum, the nearest neighbor of the pandoraviruses for which no gene encoding a capsid protein has been identified  (Legendre et al. , 2015)  (Supplementary Figure  S8a ). A BLAST search was performed against the NCBI non-redundant protein sequence database. The query used was the ancestral sequence inferred for the MCP protein detected in the draft genome sequences of the different Acanthamoeba species and the MCP of Mollivirus sibericum. This search retrieved MCP-encoding sequences from phycodnaviruses, which are DNA viruses that infect algae and are classified with giant viruses of Acanthamoeba in the proposed order Megavirales (Supplementary Figure  8b ). The G + C content was homogenous along the Acanthamoeba scaffolds carrying these virus-related genes.
DISCUSSION We give here new insights into the possible interactions between Acanthamoeba species and giant viruses. Only one A. castellanii genome has been analyzed so far  (Clarke et al. , 2013). Our analyses primarily focused on A. polyphaga, for which the body of data regarding the isolation and propagation by culture of giant viruses is the greatest among amoebal species (along with that of A. castellanii). Great similarities were suggested between A. polyphaga and A. castellanii Neff, as A. polyphaga was found to harbor the majority of the 14,974 genes previously predicted through genome and transcriptome sequencing in A. castellanii Neff  (Clarke et al. , 2013). In contrast, it is worthy to note that A. castellanii Neff and A. castellanii ATCC 50370, whose draft genome sequences were analyzed here, did not cluster together based on our phylogenetic analyses.
Furthermore, there is possibly an incorrect identification of sequences presented as originating from ATCC 30872 but belonging to A. polyphaga species. Indeed, the 18S ribosomal DNA previously described (AY026244) and part of the draft genome sequences analyzed here were not the closest among sequences from different Acanthamoeba species, and were not clustered in the phylogenetic analysis. These issues deserve clarification in future studies. More than half of the genes predicted from the A. polyphaga draft genome sequences analyzed here had no homologs in the NCBI non-redundant sequence database. This suggests that a large part of the genome sequence of this amoeba remains unknown. Yet, a significant proportion of the predicted ORFs contained less than 100 codons. They should be considered carefully, especially when identified as ORFans or annotated as hypothetical proteins.
castellanii Neff and a sequence of Mollivirus sibericum, and, secondly, the other sequences which are related to those of a phycodnavirus (genus Raphidovirus), Heterosigma akashiwo virus 01. The G+C content was found to be homogeneous along the Acanthamoeba scaffolds carrying these genes best matching MCPs. However, this does not completely rule out the possibility that these sequences are the result of an exchange of sequences between these organisms because the GC% in some giant viruses is relatively close to that of their host, for example for pandoraviruses and A. castellanii (60. 6% vs. 58. 3%) (Antwerpen et al. , 2015). These results also confirm previous observations concerning the presence of homologous sequences of MCPs in the genome of some eukaryotes  (Maumus et al. , 2014). Thus, these data suggest nucleotide sequence transfers between giant viruses and Acanthamoeba, and raise the question of the significance of homologies between genes present in giant virus and Acanthamoeba genomes.
Phylogenetic tree using the complete draft genome sequences of the 16 Acanthamoeba strains was represented aside the synteny distribution of genes best matching with giant virus genes in 10 selected genomic regions from Acanthamoeba spp. Phylogenetic reconstructions were performed using the alignment of 16 draft genome sequences of Acanthamoeba strains classified in 14 species including two A. polyphaga strains and two A. castellanii strains, by using the progressive Mauve program  (Darling et al. , 2010)  the gene nucleotide sequence transfers remained undetermined in a large majority of cases. This is due to an insufficient number of matches, or an absence of significantly delineated cluster in the phylogenetic trees. Nevertheless, for some of the phylogenies, a transfer from Acanthamoeba to giant viruses was the most parsimonious evolutionary hypothesis, indicating that giant viruses are not only bags of genes but contribute to the gene sequence flow.
FUNDING This work was supported by a grant from the  French State  managed by the  National Research Agency  under the " Investissements d'avenir (Investments for the Future)" program  with the reference  ANR-10-IAHU-03  ( Méditerranée Infection ). NC was financially supported through a grant from the  Infectiopole Sud foundation. This work was supported by  Région Provence Alpes Côte d'Azur and European funding FEDER PRIMI.
Methods: In this study PMCAs were examined in breast cancer datasets and in a variety of breast cancer cell lines representing different subtypes. We investigated how estrogen receptor alpha (ER-α) and histone deacetylase (HDAC) inhibitors regulate the expression of these pumps. Results: Three distinct datasets displayed significantly lower ATP2B4 mRNA expression in invasive breast cancer tissue samples compared to normal breast tissue, whereas the expression of ATP2B1 and ATP2B2 was not altered. Studying the protein expression profiles of Ca 2+ pumps in a variety of breast cancer cell lines revealed low PMCA4b expression in the ER-α positive cells, and its marked upregulation upon HDAC inhibitor treatments. PMCA4b expression was also positively regulated by the ER-α pathway in MCF-7 cells that led to enhanced Ca 2+ extrusion capacity in response to 17β-estradiol (E2) treatment.
In addition, we showed that inhibition of mutant B-Raf enhanced PMCA4b expression in BRAF mutant melanoma cells, and that PMCA4b abundance (induced either by overexpression or drug treatments) was coupled with decreased migration and metastatic activity of BRAF mutant melanoma cells  [31]. These observations also highlight the importance of PMCA4b in the development and progression of these tumor types. In this study we investigated the expression of the ATP2B genes in publicly available breast cancer gene expression datasets and studied the modulation of the expression of various Ca 2+ pumps at the protein level by HDAC inhibitor and/or 17β-estradiol (E2) treatments in a variety of breast cancer cell lines. The examined cell line panel represents the non-tumorigenic breast epithelium, the luminal A, luminal B and HER2 expressing, as well as the triple negative, basal subtype breast tumors (see Additional file 1: Table  S1 )  [2, 3, [32] [33] [34].
Signals of the secondary, HRP-conjugated anti-mouse or anti-rabbit antibodies (Jackson ImmunoResearch) were detected using the Pierce ECL Western Blotting Substrate (Thermo Scientific) and luminography on CL-XPosure Film (Thermo Scientific) or Amersham Hyperfilm ECL (GE Healthcare) films. Densitometric analyses were carried out using the ImageJ software v1. 51j8, and data were processed with the Prism 4 software v4. 01 (GraphPad Software) and expressed as means ± SEM.
Immunocytochemistry The procedure of immunocytochemical staining was performed as described previously  [29]  with a mouse monoclonal anti-PMCA4b (JA3) primary antibody  [37]  and an Alexa Fluor 488-conjugated anti-mouse IgG secondary antibody (Invitrogen). Images were taken by a Zeiss LSM710 confocal laser scanning microscope equipped with a Plan-Apochromat 63×/1. 40 oil immersion objective and Zeiss ZEN software. Images of control and treated wells from the same cell line were taken with the same microscope settings.
40 oil immersion objective. Time lapse images were recorded every 0. 3 s at room temperature with the Zeiss ZEN software. Data were analyzed with the ImageJ software v1. 51j8 and Prism 4 software v4. 01 (GraphPad Software). The relative fluorescence intensities were calculated as F/F 0 (where F 0 was the average initial fluorescence) and are expressed as means ±95% CI. Statistical significance was calculated by t-test, *** means P < 0. 001, ** means P < 0. 01, * means P < 0. 05, n. s. means not significant.
Analysis of publicly available ChIP-seq data ChIP-seq data analysis was performed using the Cistrome Data Browser  [40, 41]. Used raw data from the Gene Expression Omnibus (GEO)  [42, 43]  are: GSM798428 (ER-α Chip-seq data in T-47D cells, this data sample is in the GEO data series GSE32222  [44] ), GSM798424 (ER-α Chip-seq data in MCF-7 cells, this data sample is in the GEO data series GSE32222  [44] ), GSM986089 (ER-α Chip-seq data in ZR-75-1 cells, this data sample is in the GEO data series GSE40129  [45] ), GSM986063 (ER-α Chip-seq data in MCF-7 cells, this data sample is in the GEO data series GSE40129  [45] ), GSM2040043 (ER-α Chip-seq data in MCF-7 cells  [46] ), GSM2467223 (ER-α Chip-seq data in MCF-7 cells [Dzida et al. , unpublished]). Chip-seq data were visualized using the UCSC Genome Browser  [47, 48]  on Human Dec.
ATP2B4 gene expression is downregulated in breast carcinomas In our previous study immunohistochemical staining of breast tissue indicated the presence of PMCA4 (ATP2B4) protein in normal breast ductal epithelium  [29]. Here, we evaluated the expression of the ATP2B family members in breast cancer tissues analyzing data from three individual gene expression datasets using the Oncomine database  [35]. We found that the expression of the ATP2B4 gene was downregulated in invasive breast carcinoma or invasive ductal breast carcinoma samples compared to normal breast tissues (Fig. 1 ). In contrast, the expression of the ATP2B1 and ATP2B2 genes was not altered in breast cancer tissues. Next we studied Ca 2+ pump expressions in vitro in a set of breast cancer cell lines representing all breast tumor types, at the protein level.
The human ATP2B4 locus (chromosome 1, 1q32. 1) in the UCSC Genome Browser (Human Dec. 2013 (GRCh38/hg38) Assembly)  [47, 48]. The RefSeq ATP2B4 transcript contains the untranslated exon 1 and 22 protein coding exons. Arrows mark transcriptionally important sites according to histone methylation and acetylation patterns. Binding site of ER-α (obtained from ChIP-seq data of Cistrome database  [40, 41] ) is marked with red rectangle. ChIP-Seq data were sourced from GSM2467223 [Dzida et al. , unpublished], GSM2040043  [46] , GSM986063 and GSM986089 from series GSE40129  [45] , GSM798424 and GSM798428 from series GSE32222  [44]  intracellular Ca 2+ mobilization  [70] , GPER/GRP30 can affect metastasis progression, as it was to regulate the cell-matrix adhesion of MCF-7 cells through the ERK1/ 2-calpain pathway  [71].
Acknowledgements The authors are grateful to  Krisztina Lór  for her excellent technical assistance. We are much obliged to  Tamás Orbán  ( Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences ) for providing the SB-CAG-GCaMP2-CAG-Puro and the SB100× transposase constructs. We sincerely thank  Prof. Kuniko Horie-Inoue  ( Research Center for Genomic Medicine, Saitama Medical University ) for valuable advice and thought-provoking discussions.
S2E ). Similarly, only 63 peaks overlapped between OPCs, OLs, and GNPs, with 4% of OPC peaks shared with GNPs (SI Appendix, Fig. S2F ). The Chd7 chromatin binding is thus mostly cell-type-and lineage-stage-specific. We next examined whether Chd7 preferentially binds enhancer elements in OPCs, as has been reported in ESCs  (17, 30). To identify enhancer elements in OPCs, we took advantage of oligodendroglia-specific ChIP-seq datasets for Sox10 and Olig2, key TFs activating oligodendrogenesis  (15, 28). In oligodendroglial cells, Sox10/Olig2-commonly bound regions (12,094) (SI Appendix, Fig. S2G ) display strong H3K27ac marks (SI Appendix, Fig. S2I ), characteristic of active regulatory elements  (31) , and are mainly located outside promoter regions (31) (SI Appendix, Fig. S2H ) and thus correspond to active enhancers.
We found that Chd7 binds many H3K27ac + Sox10/Olig2-active enhancers both in OPCs and OLs (Fig. 1I ). Unexpectedly, in OPCs Chd7 binding was especially enriched in promoter regions defined by either H3K4me3/H3K27ac active promoter marks or the Genomatix portal (Fig. 1J  and SI Appendix, Fig. S2  J and K), contrary to rare Chd7 binding to promoters in other cell types, such as OLs, GNPs  (21) , and ESCs  (30). Taken together, our data suggest that Chd7 binding to promoters is temporally restricted to the OPC stage while Chd7 binds to enhancers both in OPCs and OLs. Chd7 Regulates Genes Involved in OPC Proliferation, Differentiation, and Survival. To investigate whether the expression of Chd7bound genes in OPCs was modified upon Chd7 LOF, we induced Chd7-conditional deletion (Chd7iKO) in neonatal OPCs, using PDGFRa-CreER T and Chd7 Flox alleles (32, 33) (Fig.
Indeed, Chd7 and Chd8 proteins showed similar expression patterns in postnatal brain oligodendroglia; Chd8 protein was detected in PDGFRα + OPCs (Fig. 3  A and C, arrowheads) and found at higher levels in differentiating OLs (CC1 high cells) (Fig. 3  A and C, arrows), and its levels decreased in mature MOG + OLs (Fig. 3  B and C, arrows), in agreement to Chd8 transcript pattern in postnatal brain cells shown by RNA-seq databases  (34, 35). Furthermore, upon LPC-induced focal demyelination of the adult corpus callosum (CC), Chd8 was detected at high levels in PDGFRα + OPCs and iOLs (CC1 + /Olig1 + cells) in/around lesions during remyelination (SI Appendix, Fig. S4 A-E ), similar to Chd7 expression (SI Appendix, Fig. S1 J  and K ). Chd8 protein may thus compensate for Chd7 LOF in differentiating OPCs/iOLs.
2, Olig1, Olig2, Sox10), oligodendroglial markers (Pdgfra, Cspg4, Gpr17, Apc, Itpr2, Mbp), R checkpoint regulators (Ccnd1, Cdk4, Cdk6), and apoptotic/survival genes (p53, Bax, Apaf1) were all bound by both Chd7 and Chd8 (Fig. 3 G  and I ). Of note, Chd7 and Chd8 share very few binding sites with Brg1 (Fig. 3D ), another chromatin remodeler that cooperates with Olig2 to control OL differentiation  (15) , suggesting that Chd7/Chd8 and Brg1 may regulate distinct OPC differentiation processes. Given that we integrated ChIP-seq datasets obtained from rat and mouse, we asked whether binding is conserved between species. To address this question, we compared the Chd8-binding profile between our in vivo mouse-purified O4 + cells with rat OPC primary cultures  (37)  and observed a large overlap (87%) (SI Appendix, Fig.
Finally, we assessed whether Chd7 and Chd8 binding to OPC genes was enriched among 201 ASD risk genes described previously  (40, 41). A large number of them were Chd7/Chd8-bound genes in OPCs (90 of 201 and 136 of 201, respectively) (Fig. 3H  and Dataset S1), but only Chd7 was found to be significantly enriched over its genome-wide binding to protein-coding genes (P < 0. 0034, hypergeometric test), suggesting that OPCs may contribute to brain developmental defects in ASD patients displaying mutations in either CHD8, CHD7, or their targets among ASD-risk genes. Chd7 LOF Reduces Brain OPC Pool Without Impacting OPC Proliferation. To determine the consequences of Chd7 LOF in OPC population, we quantified PDGFRα + OPCs in P7 brains and found that Chd7iKO brains display less numerous OPCs in the CC and cortex, compared with controls (∼30% and ∼60%, respectively) (Fig.
4 A  and B ). Chd7 deletion thus affects either OPC proliferation or survival. To assess the possible impact of Chd7 deletion on OPC proliferation, we examined proliferationassociated genes in our OPC RNA-seq datasets and found an upregulation of key regulators of both cell-cycle checkpoints (Ccnd1, Cdk6, Ccne1, Ccna1, Cdk2, Ccnb1, Cdk1) and proliferation markers (Mki67, Mcm2) (Fig. 4C ). Therefore, we assessed the proliferative status of Chd7iKO OPCs in P7 brains. Surprisingly, the number of Mcm2 + or K i 67 + proliferating OPCs in the CC and cortex was similar between Chd7iKO and controls (Fig. 4  D and E and SI Appendix, Fig. S9 A-E ). We then addressed possible changes in cell-cycle length measuring the proportion of OPCs in S-phase (labeled by short pulse of BrdU) among cycling OPCs (MCM + /PDGFRα + cells) and found no changes in cell-cycle length between Chd7iKO and controls at P7 (SI Appendix, Fig.
S11 ). Taken together, these data indicate that Chd7 deletion impairs OPC capacity to start differentiation but not the normal progression once differentiation has started (Fig. 6F ). To assess whether OPC apoptosis was sufficient to account for the reduction of differentiating OLs in Chd7iKO brains, or if Chd7 functions in survival and differentiation could be separated, we rescued OPC survival and numbers by PFT treatment (Fig. 5 I  and K ). Remarkably, the number of iOLs (Nkx2. 2 + cells) in the CC was the same in PFT-treated and untreated Chd7iKO mice (Fig. 6 D  and E ). Therefore, the rescue of OPC survival is not sufficient to recover normal differentiation of Chd7iKO OPCs, indicating that Chd7 functions in OPC survival and differentiation can be dissociated (Fig. 6F ). To address Chd7 remodeling function in the loci involved in OPC differentiation, we compared their chromatin accessibility in Chd7iKO OPCs and controls.
7 B  and C ). These results thus indicate that Chd7 and Chd8 genetically interact in the process of OPC differentiation and suggest that Chd8 may partially compensate the loss of Chd7 during OPC differentiation. Chd7/Chd8 and Olig2/Sox10 Bind to Regulatory Elements of Active Stage-Specific Genes in Oligodendroglia. Finally, we further investigated the mechanisms by which Chd7/Chd8 control gene expression in a timely manner during the course of OL cell lineage differentiation, integrating different datasets. An oligodendroglia single-cell transcriptome database (35) was used to classify genes according to their expression timing (Fig. 8A ). Focusing on oligodendroglial stage-specific gene expression, we classified oligodendroglial-genes into three groups: (i) "OPCgenes" expressed in OPCs and down-regulated upon differentiation (e.
8-mg/mL concentration at postnatal stages P3, P4, and P5 (5 μL per day). Brains were collected at P7. OPC MACSorting. Dissociation of cortex and CC from Ctrl, Chd7iKO, or wildtype P7 mice was done using neural tissue dissociation kit (P) (130-093-231; Miltenyi Biotec) and dissociator (130-096-427). Magnetic sorting was done using anti-O4-coupled-beads (130-094-543) and the MultiMACS Cell24 Separator Plus (130-098-637). O4 + -cells were collected in PBS before being process to RNA-seq, ATAC-seq or ChIP-seq. ChIP-Seq, RNA-Seq, ATAC-Seq, and Data Analysis. A detailed description of the methods used can be found in SI Appendix.
ACKNOWLEDGMENTS. We thank  Grace Houser  for her help revising the English writing of the manuscript;  Michel Mallat  for his advice all along the project;  Dwight Bergles  for the PDGFRα::CreER T mice;  Matthieu Gerard  for validated Chd8 shRNA sequence;  Damien Ulveling  for help generating Bigwig files; and  Emmanuelle Huillard  for the shScramble lentivirus. All animal work was conducted at the  Institut du Cerveau et de la Moelle Épinière (ICM) PHENOPARC Core Facility. Data generated relied on ICM Core Facilities: bioinformatics (ICONICS), sequencing, CELIS, histology, and ICM Quant; we thank all personnel involved for their contribution and help. The Core Facilities were supported by the " Investissements d'avenir"  ( ANR-10-IAIHU-06  and  ANR-11-INBS-0011-NeurATRIS ) and the  "Fondation pour la Recherche Médicale.
| Collection of density estimates We obtained population density estimates for terrestrial amphibians, reptiles, birds and mammals from the TetraDENSITY database  (Santini, Isaac, & Ficetola, 2018). We did not consider density estimates for reptiles living in ponds or lakes (whose densities are strictly dependent on the water surface area and shore length) and for breeding aggregations of amphibians (which may last only a few days or weeks). For birds, we considered density estimates collected only in areas where the species are resident all year according to the International Union for Conseration of Nature (IUCN) range polygons. Many density estimates for birds were expressed as the pair density rather than individual density; following previous studies, these records were doubled for analyses  (Cotgreave & Harvey, 1994).