dataesr/scientific-paragraphs-categorization huggingface dataset:
RESULTS Queuine is incorporated into E. histolytica tRNA and induces C38 hypermethylation (m 5 C38) in the tRNA Asp  GUC anticodon loop. Given the established role of queuine as a precursor to queuosine (Q) in tRNA, we first set out to characterize the incorporation of queuine into amoebic tRNA. Here, we quantified Q in tRNA in the parasite using liquid chromatography-tandem mass spectrometry (LC-MS/MS). tRNA samples were prepared from untreated trophozoites and from trophozoites that were grown with queuine (0. 1 mM). This concentration of queuine was chosen because (i) it provides to the parasite the best protection against H 2 O 2 (see Fig. S1  in the supplemental material available at https://datadryad. org/stash/share/Y6qAAIhEzUcPNlK16zSmOvPJyjd0bqUcPUz3mNw1fCg) and (ii) because it has also been used in a study on the social amoeba Dictyostelium discoideum  (23).
S2A  available at https://datadryad. org/stash/share/Y6qAAIhEzUcPNlK16zSmOv PJyjd0bqUcPUz3mNw1fCg). Based on the annotation of the E. histolytica genome, homologs of hQTRT1 and hQTRTD1 exists in E. histolytica, namely, EhQTRT1 (XP_656142. 1) and EhQTRTD1 (XP_652881. 1). The EhQTRT1 gene shares a high degree of homology with human QTRT1 (52% sequence identity), whereas EhQTRTD1 shares a homology at the C-terminal domain (28. 3% sequence identity). Moreover, these two proteins also share a high degree of homology with the Zymomonas mobilis TGT (41. 6% and 35. 6%, respectively). The Z. mobilis TGT enzyme is a homodimeric  FIG 1  Queuine is efficiently incorporated into E. histolytica tRNA and leads to C38 hypermethylation (m 5 C38) in the anticodon loop of tRNA Asp  GUC. (A) Quantification of Q in tRNA of E.
05). The data are expressed as means 6 standard deviations (SDs) obtained from 20 clones each. zinc-binding protein, whereas the eukaryotic TGT is a heterodimeric zinc-binding protein. We identified four essential residues that are important for Zn 21 binding (Cys 305, Cys307, Cys310, and His342; E. histolytica numbering) to be conserved in both EhQTRT1 and EhQTRTD1 (see Fig. S2B  available at https://datadryad. org/stash/ share/Y6qAAIhEzUcPNlK16zSmOvPJyjd0bqUcPUz3mNw1fCg). To get information about the EhTGT structure, we built an in silico model of EhTGT subunits based on the Thermotoga maritima QTRT1 structure and murine QTRT2 structures. The prediction also suggested that EhQTRT1 and EhQTRTD1 are homodimers (see Fig. S3A  and B available at https://datadryad. org/stash/share/Y6qAAIhEzUcPNlK 16zSmOvPJyjd0bqUcPUz3mNw1fCg), which interact together to form a heterodimer like the eukaryotic TGT enzyme (Fig.
2-kDa and 44. 1 kDa-bands excised from the SDS gel. After mapping these peptide fragments against E. coli and E. histolytica databases, our samples were confirmed to be EhQTRT1 and EhQTRTD1 (see Fig. S3C  available at https://datadryad. org/stash/share/Y6qAAIhEzUcPNlK16zSmOvPJyjd0 bqUcPUz3mNw1fCg). To verify that the EhTGT is active, the activity of the complex was tested by determining its ability to displace guanine in the tRNA Asp  GUC anticodon with 8-[ 14 C]guanine. We observed that EhTGT is an active enzyme and that the EhQTRT1-EhQTRTD1 enzyme complex efficiently incorporated 8-[ 14 C]guanine with a specific activity of 2. 28 pmol/ min/mg of protein (Table  1 ) (Fig. 3D ). However, no activity was detected when tRNA Val CAC was used as a substrate (Table  1 ). The in silico model also predicted that the stretch of S102 to L125 in EhQTRT1 is essential for the binding of EhQTRTD1, and that the D267 residue in EhQTRT1 is a substrate-binding residue (Fig.
histolytica to OS (Fig. 9A  and B ). RNA-Seq experiments were performed as described previously  (10) , and the data (available at http://www. ncbi. nlm. nih. gov/geo under accession number GSE142211) were analyzed using the generalized linear model implemented in the DESeq2 package in R. This allowed us to perform pairwise comparisons of gene expression by the E. histolytica HM1:IMSS strain under the various tested conditions and to probe the interaction between queuine and OS (Fig. 9B ). We first used a principal-component analysis to explore the data structure (Fig. 9C ); this showed that the untreated trophozoite group (T) and the trophozoites that were grown with queuine (Q) form two distinct clusters that can be separated on the first principal component. The stress effect was mainly supported by the second component, and OS-treated and -untreated samples formed clusters under both T and Q conditions.
Our comparisons revealed that queuine has a profound impact on the E. histolytica transcriptome (C1), with 664 downregulated and 548 upregulated genes (Table  2 ). Details regarding these genes can be found in Table  S1  available at https:// datadryad. org/stash/share/Y6qAAIhEzUcPNlK16zSmOvPJyjd0bqUcPUz3mNw1fCg. Gene categories affected by queuine supplementation. The differentially regulated genes in Q versus T (comparison C1) were classified according to the protein class which they encode (Fig. 10A  and B ) using the Protein ANalysis THrough Evolutionary Relationship (PANTHER) sequence classification tool (version 14. 1)  (35). Of the upregulated genes in the presence of queuine (Fig. 10A ), genes that are associated with the following were significantly enriched according to the PANTHER statistical overrepresentation test: nucleic acid binding (GO:0003676) and nucleosome binding (GO:0031491) such as chromatin-specific transcription elongation factor (EHI_109860) and protein HIRA (EHI_131950); heterocyclic compound binding (GO:1901363) such as 60S ribosomal protein L4 (EHI_064710) and ATP-dependent DNA helicase (EHI_119290); ATPase activity (GO:0016887) such as heat shock protein 70 family (EHI_001950) and RecQ family helicase (EHI_028890); oxidoreductase activity (GO:0016491) such as peroxiredoxin (EHI_145840) and aldehyde-alcohol dehydrogenase 2 (EHI_024240); catalytic activity, acting on RNA (GO:0140098) such as regulator of nonsense transcripts (EHI_035550) and RNA lariat debranching enzyme (EHI_062730); actin binding (GO:0003779) such as coronin (EHI_122800) and villidin (EHI_150430); cytoskeletal protein binding (GO:0008092) and tubulin binding (GO:0015631) such as dynamin-1-like protein (EHI_013180) and kinesinlike protein (EHI_140230); protein-containing complex binding (GO:0044877) such as elongation factor 2 (EHI_189490) and chromatin-specific transcription elongation factor (EHI_109860); and catalytic activity, acting on DNA (GO:0140097) such as DNA replication licensing factor (EHI_076880) and RecQ family helicase EHI_028890 (Fig.
Next, we addressed the influence of queuine on E. histolytica's response to OS (comparison C5). We identified 932 genes (Table  2 ) and then performed a hierarchical clustering analysis (Fig. 9C ). The average profile of each of the four identified clusters was then established (Fig. 9C ) (see Table  S3  available at https://datadryad. org/stash/share/ Y6qAAIhEzUcPNlK16zSmOvPJyjd0bqUcPUz3mNw1fCg). Clusters 1 and 4 represent genes that were mostly upregulated in trophozoites exposed to OS but that were either mostly not differentially expressed (cluster 4) or downregulated (cluster 1) in trophozoites that were grown with queuine and exposed to OS. Clusters 2 and 3 represent genes that were mostly downregulated in control trophozoites exposed to OS but that were mostly not differentially expressed in trophozoites that were grown with queuine exposed to OS.
The analytical tools in PANTHER (http://pantherdb. org/) were used for functional classification and gene set overrepresentation tests. To identify the functional categories, we used the PANTHER tools, which include entries from E. histolytica for functional classification. The categories for functional classification within each cluster (using the subset of protein class) are shown in Fig. S4  and Table  S4  available at https://datadryad. org/stash/ share/Y6qAAIhEzUcPNlK16zSmOvPJyjd0bqUcPUz3mNw1fCg. The most abundant classes of proteins in cluster 1 were nucleic acid binding (PC00171) (20. 3%), enzyme modulator (PC00095) (16. 4%), and hydrolase (PC00121) (16. 4%) (see Fig. S4A ). The most abundant classes of proteins in cluster 2 were enzyme modulator Functional categories and classification of genes enriched in trophozoites that were grown with queuine versus control trophozoites.
Functional categories are upregulated genes in trophozoites that were grown with queuine (A) and downregulated genes in trophozoites that were grown with queuine (B). The number of genes related to each category was derived by processing the data (see Table  S1  available at https://datadryad. org/stash/ share/Y6qAAIhEzUcPNlK16zSmOvPJyjd0bqUcPUz3mNw1fCg) with PANTHER (http://pantherdb. org). (C) Enrichment test in queuine-treated trophozoites. The details of genes present in each cluster are given in Table  S3  available at https://datadryad. org/stash/share/Y6qAAIhEzUcPNlK16zSmOvPJyjd0bqUcPUz3mNw1fCg. (PC00095) (22%), hydrolase (PC00121) (17%), and nucleic acid binding (PC00171) (14%) (Fig. S4B ). The most abundant classes of protein in cluster 3 were nucleic acid binding (PC00171) (21%), hydrolase (PC00121) (19%), and transferase (PC00220) (15%) (Fig.
S4C ). Finally, the most abundant classes of protein in cluster 4 were nucleic acid binding (PC00171) (20. 3%), hydrolase (PC00121) (19%), and enzyme modulator (PC00095) (13%) (Fig. S4D ). Enrichment of molecular functions. We restricted our analyses to the families of factors enriched. 2. 0-fold in the overrepresentation test (see Fig. S5  and Table  S4  available at https://datadryad. org/stash/share/Y6qAAIhEzUcPNlK16zSmOvPJyjd0bq UcPUz3mNw1fCg). (i) Functional analyses of genes in cluster 1. The bioinformatics analysis of genes present in cluster 1 (Fig. S5A ) showed significant enrichment in 18 GO terms. It highlighted genes encoding proteins related to lipid transport (steroid-binding activity [GO:0005496], fold enrichment [FE] of 15. 4, and lipid transporter activity [GO:0005319], FE of 5.
histolytica genes (see Table  S5A  available at https://datadryad. org/ stash/share/Y6qAAIhEzUcPNlK16zSmOvPJyjd0bqUcPUz3mNw1fCg). Previously, GSCU analysis has been used to analyze all genes in a genome to identify those overusing specific codons translationally enhanced by wobble U modifications  (37) , and here, we have advanced the methodology to analyze transcriptionally regulated transcripts (Table  S5B ). We observed that some groups of genes have a distinct codon bias, but we were not able to link these gene clusters back specifically to codons decoded by Q (GUN anticodon) (see Fig. S6A  at the above-listed URL). Most likely, the use of all 62 codons in our analysis obscured the clustering, as strong biases in many codons can be observed for a group of queuine-regulated genes. We next stratified the transcripts upregulated in the parasite cultivated in the presence of queuine based on whether they overuse the four U (U-GUN)-or four C (C-GUN)-ending codons decoded by Q (Table  S5C ).
Quality control was performed on an Agilent Bioanalyzer. Sequencing was performed on a HiSeq 2500 system (Illumina) and produced 65-base single-end reads. RNA-Seq data analysis. Bioinformatics analysis was performed using the RNA-Seq pipeline from Sequana  (92). Reads were cleaned of adapter sequences and low-quality sequences using cutadapt version 1. 11  (93). Only sequences at least 25 nucleotides (nt) in length were considered for further analysis. Bowtie version 1. 2. 2 (94), with default parameters, was used for alignment on the reference genome (Entamoeba histolytica, from amoebaDB). Genes were counted using featureCounts version 1. 4. 6-p3 (95) from the Subreads package (parameters: -t gene -g ID -s 1). Count data were then analyzed using R version 3. 5. 3 (12)  and the Bioconductor package DESeq2 version 1.
Hierarchical clustering of the genes was based on the correlation distance and the Ward aggregation criterion. The average profile was established by computing the mean log 2 -normalized count over the genes in each of the four identified clusters. The functional classification, GO term analysis, and protein class analysis were performed with PANTHER tools (http://pantherdb. org) (97). To determine the gene-specific codon usage (GSCU) for each transcript upregulated in trophozoites cultivated in the presence of queuine, we obtained the protein-coding sequences for 7,930 E. histolytica genes from the Amoeba database (https://amoebadb. org/amoeba/app/) The E. histolytica open reading frames were each analyzed using GSCU scripts and methodology previously reported  (98). In brief, GSCU analysis determined the total number of times each codon was used in each gene.
GSCU analysis also determined the frequency that each codon was used in each gene, with the codon frequency relative to that of the other synonymous codons specific to each individual amino acid. For example, the summed frequency for the two glutamic acid codons (GAA and GAG) potentially used in a gene equal 1. 0. Note that methionine and tryptophan codons are omitted from GSCU analysis, as they each only have 1 specifying codon and the GSCU frequency is always 1. 0 for AUG (methionine) and 1. 0 for UGG (tryptophan). Whether a gene was over-or underrepresented with a specific codon from a synonymous set, relative to the genome average, was determined by calculating a codon specific Z-score, as described previously  (98). Gene-specific codon usage data (Z-scores) was analyzed by hierarchical clustering using Cluster 3.
0 and visualized as a heat map using Treeview. A spreadsheet containing the gene-specific codon usage data is presented in Table  S5  available at https://datadryad. org/stash/share/Y6qAAIhEzUcPNlK16zSm OvPJyjd0bqUcPUz3mNw1fCg. Immunofluorescence microscopy. E. histolytica trophozoites were transferred to microscope slides as described elsewhere  (87). The attached trophozoites were washed in warm (37°C) PBS, fixed with methanol, and then permeabilized with fluorescence activated cell sorting (FACS) buffer (2% bovine serum albumin in PBS that was supplemented with 0. 1% Tween) as described in reference 87. The slides were then blocked with 5% goat serum in FACS buffer for 15 min at ambient temperature. The samples were then probed with 1:1,000 polyclonal mouse EhTGT (complex) antibody. At the end of the incubation, the slides were washed 3 times in FACS buffer and then incubated with a 1:250 Alexa Fluor antibody (Jackson ImmunoResearch) for 3 h at 4°C.
Analysis of each sample condition was performed with 12 biological replicates, and data are presented as means and standard errors (SEMs). Bisulfite sequencing of tRNA Asp GUC. Total RNA isolation and bisulfite conversions were performed using a previously described protocol  (19). Bisulfite-treated tRNAs were reverse transcribed by using a tRNA 39-specific stem-loop primer and amplified with primers that bind only to the deaminated sequences at the 59 end (universal primer and a specific tRNA Asp GUC stem-loop primer). Amplicons  (20)  under each condition were subcloned into a pGEM-T Easy (Promega) vector and sequenced (Multi-Disciplinary Laboratories Unit, Bruce Rappaport Faculty of Medicine, Technion). APB Northern blotting to detect tRNA His GUG. Acryloyl aminophenylboronic acid gels were prepared and run with a few modifications according to Igloi and Kossel  (99).
org/index. php). Using the lowest energy structures, we performed the protein-protein docking experiment using the cluspro server (https://cluspro. org/login. php). We checked the docking for the all the possible conformations and defining the active site. The cluspro algorithm considers greedy clustering of ligand positions after obtaining the best poses by rotating the ligand using a grid. We have used the best poses from the top two clusters. The images were created using PyMOL software (https://pymol. org/2/). Data availability. RNA-Seq data have been deposited at the Gene Expression Omnibus under accession number GSE142211. Supplemental data are available at DRYAD (https://datadryad. org/stash/share/ Y6qAAIhEzUcPNlK16zSmOvPJyjd0bqUcPUz3mNw1fCg).
ACKNOWLEDGMENTS We thank  Yuko Umeki  at  National Institute of Infectious Diseases of Tokyo  for her technical assistance, the staff of the  Microscopy Imaging Laboratory at the Faculty of Queuine Regulates Entamoeba Virulence ® March/April   2021 Volume 12 Issue 2 e03549-20  mbio. asm. org 25 on March 9, 2021 at  INSTITUT PASTEUR-Bibliotheque  http://mbio. asm. org/Downloaded from  Medicine, Technion, Haifa , for their help with confocal microscopy resources and their excellent support in image recording and analysis, and  Udita Roy  and  Daniel Kornitzer ,  Faculty of Medicine, Technion, Haifa , for their help with the purification of the recombinant EhTGT by size exclusion chromatography. The work was supported by the  Israel Ministry of Health  within the  framework European Research Area NETwork Infect-ERA  ( 031L0004 ;  AMOEBAC  project), the  Israel Science Foundation  ( 260/16 ), the  ISF-NRF program  ( 3208/19 ), the  National Research Foundation of Singapore  through the  Singapore-MIT Alliance for Research and Technology Antimicrobial Resistance IRG , the  Rappaport Institute , the  US-Israel Binational Science Foundation  ( 2015211 ), and the  Niedersachsen program.
INTRODUCTION Excessive accumulation of senescent cells in body tissues has been associated with organismal aging and fatigue as observed in older individuals and patients treated with anticancer agents  (Sanoff et al. , 2014; Rajeevan et al. , 2018; Xu et al. , 2018). On the other hand, preclinical studies have reported that the selective elimination of senescent cells could alleviate not only some chemotherapy adverse effects but also various age-related symptoms, including muscle weakness, fatigue and frailty, suggesting a potential treatment modality for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)  (Demaria et al. , 2017; Short et al. , 2019; Kaur et al. , 2020; NCT03675724). Long lasting myalgia and exhaustion were reported in over 40% of COVID-19 patients, indicating that the SARS-CoV-2 virus may directly invade skeletal muscles, triggering myositis  (Chen G.
, 2019). Indeed, several senolytic drugs were recently tested, including dasatinib (FDA-approved for chronic myeloid leukemia), hyperoside, quercetin, fistein and the BCL-2 inhibitor, Navitoclax  (Kirkland et al. , 2017; Mohamad Anuar et al. , 2020). As ECs are dependent on BCL-2, Navitoclax should be tested for ME/CFS  (Zhu et al. , 2016; Wissler Gerdes et al. , 2020). Fistein, currently in phase 2 clinical trials for the frail elderly syndrome, has demonstrated good tolerability, indicating potential beneficial effects in ME/CFS (AFFIRM-LITE trial NCT03675724).
Studying the virus/host interactome and associated pathology will contribute to a better understanding of the largest immune compartment in the body, the GI tract, and its role in the immunological acceptance of gut microbes. This will likely contribute to the development of antigen-specific immunosuppressant therapies, such human Tregs. Indeed, allogeneic T cells are currently in clinical trials for COVID-19 hyperinflammatory syndrome (NCT04482699). Unlike non-specific immunosuppressive agents that impact many tissues and organs, generating adverse effects, Tregs offers specificity and precision that could benefit not only the patents with ME/CFS, but also those with allergies, transplants and infectious diseases. Furthermore, elucidation of the molecular underpinnings of microbial translocation will undoubtedly lead to more specific treatments for restoring the adequate permeability of the GI tract, that would benefit patients with inflammatory bowel disease (IBS).
Oligosaccharides were separated on a Dionex LC (Thermo Fisher Scientific) by high performance anion exchange chromatography (HPAEC), detected using pulsed ampherometric detection (PAD) as described by  Doblin et al. (2009) , and quantified from peak area (nC min) obtained using Chromeleon 6. 8 Chromatography Data System software (Thermo Fisher Scientific) against digested barley flour MLG standard (Megazyme). To compare content of leaves transformed with CSLF6 variant constructs, MLG was normalised to corresponding in vitro protein on immunoblot, relative to a BdCSLF6 wildtype control on each, and analysed by analysis of variance (ANOVA) using R v 3. 3. 0 (R Core Team, 2016).
, 2015). Analyses were performed on a CFX384 Touch Real-Time PCR system (BioRad) using KAPA Fast qRT-PCR chemistry. Quantitation against standards was performed using CFX Maestro Analysis software (BioRad), and normalisation of gene expression calculated using the GeNORM method as described by  Vandesompele et al. (2002). Normalised transcript abundance was analysed with two factor ANOVA with pairwise comparisons using R (R Core Team, 2016).
ACKNOWLEDGMENTS Mutants were obtained from the  TILLING Platform at INRAE-IPS2 , after screens were performed using the EPITRANS platform,  Paris-Saclay University, France. We wish to thank  Marion Dalmais ,  Richard Sibout ,  Abdelhafid Bendahmane , and  Sébastien Antelme  for their expertise which facilitated the mutant screen. Monoclonal CSLF6 antibody was generously characterised and provided by  Yin Ying Ho , The  School of Biosciences, The University of Melbourne, Australia.
The authors suggested that Cr 3+ can electrostatically neutralize this zone, pushing the T4 to bind TTR  [30]. Crystal structures of wt-TTR, in complex with rhenium, were obtained by soaking TTR crystals in cryoprotectant solutions rich in tris-carbonyl derivatives, following a strategy already reported in the literature  [31, 32]. The initial purpose of this investigation was to study and try to solve the phase problem during the single-wavelength anomalous diffraction (SAD) experiments. Briefly, crystals were obtained by sitting-drop vapor-diffusion method from a reservoir solution of 21% polyethylene glycol 4000 (PEG4K), 0. 14 M imidazole malate, pH 6. 0 (PDB id:5K1J) as well as from 21% PEG4K, 0. 14 M imidazole malate, pH 6. 0, 3. 6% polyethylene glycol monomethylether (MPEG5K), and 30 mM sodium acetate, pH 5.
5 (PDB id 5K1N). For data collection, the first crystal was cryoprotected by soaking for 10 min into cryoprotectant solution composed of 40% of SM2 (12. 5% ethylene glycol, Crystals 2021, 11, 354 4 of 12 12. 5% glycerol, 12. 5% 1,2-propanediol, 25% DMSO and 37. 5% 1,4-dioxane), 25% PEG 8K and 0. 2 mM of rhenium derivative (PDB id:5K1J). The second one was cryoprotected with a solution of 40% SM3 (25% diethylene glycol, 25% ethylene glycol, 25% glycerol, 25% 1,4-dioxane), 25% PEG 8K and 0. 5 mM of rhenium compound  [31]  (PDB id 5K1N). Interestingly, crystals soaked with a low concentration of rhenium derivatives gave structures that diffracted to 1. 7 Å resolution, showing a new wt-TTR conformation. Both structures show a Re atom that coordinates with His88 in monomer B and B', Figure  2a.
diffraction (SAD) experiments. Briefly, crystals were obtained by sitting-drop vapordiffusion method from a reservoir solution of 21% polyethylene glycol 4000 (PEG4K), 0. 14 M imidazole malate, pH 6. 0 (PDB id:5K1J) as well as from 21% PEG4K, 0. 14 M imidazole malate, pH 6. 0, 3. 6% polyethylene glycol monomethylether (MPEG5K), and 30 mM sodium acetate, pH 5. 5 (PDB id 5K1N). For data collection, the first crystal was cryoprotected by soaking for 10 min into cryoprotectant solution composed of 40% of SM2 (12. 5% ethylene glycol, 12. 5% glycerol, 12. 5% 1,2-propanediol, 25% DMSO and 37. 5% 1,4dioxane), 25% PEG 8K and 0. 2 mM of rhenium derivative (PDB id:5K1J). The second one was cryoprotected with a solution of 40% SM3 (25% diethylene glycol, 25% ethylene glycol, 25% glycerol, 25% 1,4-dioxane), 25% PEG 8K and 0.