dataesr/scientific-paragraphs-categorization huggingface dataset:
A further functional insight into the ubiquitylated merozoite proteins was obtained from a pathway enrichment analysis using the manually curated Malaria Parasite Metabolic Pathways (MPMP) database  [28]  (Supplementary Figure  2 ), which suggested a potential crosstalk between ubiquitylation and other post translation modifications such as phosphorylation, myristoylation, and nitrosylation with histone acetylation and methylation as the most enriched term. On the basis of the GO and functional enrichment analyses we focused on some groups of proteins, and three examples will be described here: proteins of the pellicle, exported proteins and histones, as well as ubiquitin itself.
This is reflected by the fact that within a radius of 5 Å from MLN7243 the rms deviation between the model of the PfUBA1-ubiquitin-MLN7243 complex and the ScUBA1-ubiquitin-MLN7243 structure drops to 0. 76 Å. Surface representations of the ATP binding pockets of human (PDB entry 6DC6), yeast (PDB entry 5L6J) and the PfUBA1 homology model (Figure  3C ) indicate that the regions occupied by Mg 2+ -ATP are nearly identical. The only difference is the formation of a weak salt bridge between Lys604 and Asp629 in PfUBA1 where these two residues are within 5. 4 Å of each other, in contrast to ScUba1 and HsUBA1; in the latter the Lys is replaced by Arg, and the minimal distance between these oppositely charged residues is in excess of 7 Å. This apparently large discrepancy in surface representation is due to a different side chain rotamer of the positively charged residue in yeast and human UBA1 (1 of -132° and -180°, respectively) compared with PfUBA1 (1=-80°).
0, 2 mM EDTA, 1. 6% Triton X100, 0. 16% saponin, 5x SYBR Green I [Life Technologies]) and incubated in the dark for 2 h. Parasite growth was determined by measuring the fluorescence of the samples using a FLUOStar Omega plate reader (BMG Labtech) with excitation and emission filters of 485 nm and 520 nm, respectively  [69]. EC50 values were calculated from a four-parameter logistical fit of the data using the Prism software (GraphPad Software, Inc. ).
Inducible truncation of the uba1 gene: A conditional deletion of the last exon of uba1 was generated using CRISPR-Cas9 to target nuclease activity to the uba1 locus in a parasite line expressing a rapamycin-inducible diCre recombinase  [24]. Guide RNAs were designed using the Protospacer software (www. protospacer. com) [70]  that would target Cas9 to introduce a doublestranded break at nucleotide 4576, 4674, or 4787 in the penultimate exon of the uba1 gene. Pairs of complementary oligonucleotides, 107for/rev, 109for/rev, or 111for/rev were phosphorylated with T4 polynucleotide kinase, annealed, and ligated into pDC2-Cas9-hDHFRyFCU digested with BbsI as described in  [24]. A synthetic DNA construct containing 500 bp of the uba1 gene upstream of its second intron, followed by a sera2 intron containing a loxP site (described in  [23] , and the recodonized final exon of uba1 was generated in the pMK vector (Life Technologies).
The maximum ion injection time utilized for MS2 scans was 120 ms, the HCD normalized collision energy was set at 28, the dynamic exclusion was set at 10 s, and the peptide match and isotope exclusion functions were enabled. Protein and peptides were identified by the Andromeda search engine  [71]  integrated in the MaxQuant (Version 1. 5. 3. 8) proteomics analysis software  [26]. The protein database selected for the MS/MS searches contained P. falciparum protein sequences from PlasmoDB version 28 (http://plasmodb. org/common/downloads/release-28/) and human proteins from Uniprot (downloaded May 2016 https://www. uniprot. org/proteomes/UP000005640) supplemented with frequently observed contaminants. Andromeda search parameters for protein identification were set to a precursor mass tolerance of 20 ppm for the first search and 6 ppm for the main search.
Gene ontology (GO) analysis: Gene ontology (GO) analyses were performed with the software package Ontologizer  [73] , with the gene association components downloaded from http://www. geneontology. org: gene ontology. Overrepresented GO terms for the merozoite ubiquitome and intracellular ubiquitome relative to the background of the P. falciparum proteome (∼5500 proteins) were computed by the parent-child union approach and corrected for multiple testing by the Benjamini and Hochberg method and were considered significant for adjusted pvalues lower than 0. 05. The results were visualised in an enrichment map with the Cytoscape plugin Enrichment Map v1. 2  [74]. Pathway enrichment analysis: Enrichment for Malaria Parasite Metabolic Pathways (MPMP)  [28]  (http://mpmp. huji. ac. il/) pathway terms of the merozoite unbiquitome relative to all 5500 P.
Thermal Stability Measurements: The thermal unfolding profile of purified pfUBA1 was measured using nanoDSF. Standard, nanoDSF grade Prometheus NT. 48 capillaries (NanoTemper Technologies) were filled with ~10 μL measurement sample at 100 µM. Back reflection (light scattering) and nanoDSF (nano-differential scanning fluorimetry) measurements were performed using a Prometheus NT. 48 (NanoTemper Technologies) at 5% excitation power and with a thermal ramp from 25 to 95°C with a heating rate of 2. 5 °C/min. Fluorescence data for protein unfolding were analysed using the GraphPad Prism software. The fluorescence (F) ratio F330nm/F350nm was used for inflection point determination, which represents the melting temperature (Tm). Unfolding transition temperatures (Tm) were automatically determined by the software using the Boltzman equation.
Samples were analysed by SDS-PAGE on 4-12% gradient gels (NuPAGE, Thermo Fisher). Gels were scanned using a Typhoon FLA 9500 scanner (GE Healthcare) to visualize the Cy5-Ub containing bands. In E1 inhibition assays 30 nM Cy5-Ub and 1 µM UBA1 were incubated with a two-fold serial dilution of the inhibitor (MLN7243 or MLN4924), from 40 µM to 0. 039 µM, in reaction buffer with 2% DMSO. 100 µM ATP was added and the reaction mixtures were incubated for 30 min at room temperature before they were mixed with 2 x LDS sample buffer without DTT. Samples were analysed by SDS-PAGE and gels scanned as described above. Band intensities were quantitated using the ImageQuant software (GE Healthcare). The percentage intensities of the Cy5-Ub-E1 thioester band was plotted versus inhibitor concentration. IC50 values were determined by non-linear least square fitting using the Hill equation in Prism 7 (GraphPad).
falciparum proteins was determined by a one-tailed Fisher's exact test (FET) adjusted for multiple testing correction by the Benjamini-Hochberg method. PfUBA1 homology model, purification of recombinant protein and analysis of activity UBA1 homology model: Using the published structure of ScUBA1 in complex with MLN7243 covalently linked to the C-terminus of ubiquitin (PDB: 5LDJ) a homology model of the PfUBA1ubiquitin-MLN7243 complex was generated using MOE [Molecular Operating Environment (MOE), 2018. 01; Chemical Computing Group ULC, 1010 Sherbrooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2018]. The structure was corrected and protonated using the Protonate3D tool.
Acknowledgements This work was supported. by the  Francis Crick Institute  which receives its core funding from  Cancer Research UK  ( FC001097 ), the  UK Medical Research Council  ( FC001097 ), and the  Wellcome Trust  ( FC001097 ). AP was funded by a  British Council Newton Fund PhD Placement. SB and NT were supported by fellowships from  GK 2243  funded by the  Deutsche Forschungsgemeinschaft. BS was supported by a  Biotechnology and Biological Sciences Research Council   Project grant  ( BB/R003750/1 ). We gratefully acknowledge  Dr Hagai Ginsburg  for providing MPMP data enabling the pathway enrichment analysis.
At 120 s or 150 s after stimulation, time-lapse imaging was resumed every 30 s until 5 min after stimulation, then every 60 s until 30 min and continued every 5 min until 60 min after stimulation. Image data analysis. Time-series image data of reporter mRNAs was analyzed by kymographs. Dendritic 40-µm segments at ≥20 µm from the cell body were selected and straightened in ImageJ. The KymographTracker plugin of the ICY Bioimaging software  57, 58  was used to generate kymographs and to trace and extract single tracks. Only movements longer than 1. 5 µm were considered for analysis. Tracks were terminated when a particle stopped, changed direction or left the region of interest (ROI). Average speed and displacement were obtained by calculating the mean. Anterograde and retrograde tracks were counted to calculate the percent of anterograde transport.
The sum of anterograde and retrograde displacement lengths was used to calculate the percent of total anterograde displacement. Dualcolor kymographs were generated by overlaying identical regions of interest from two separate channels. Events in dual-color kymographs were manually selected and distances were manually measured in ImageJ (line tool), aided by a custom written ImageJ macro script (available upon request). Data were processed and subjected to statistical analyses in R  59. RNA signal intensity of PSD-95 labelled postsynaptic densities was analyzed within the synaptic masks generated by the xsParticle Tracker ImageJ plugin  60, 61. Time series of the measured, background corrected reporter RNA signal was fitted with a series of constants using the rpart package of R  62. Minimum duration of a single constant fit was set to five frames (~1 s).
The 5th percentile of the RNA signal intensity distribution measured in every 100 frames (for correction of photobleaching, Supplementary Fig. 6e, f ) was used as the signal corresponding to a single reporter RNA molecule. Changes between two adjacent fitted constants, whose absolute value exceeded this threshold were quantified as docking and undocking events, depending on the sign of the change. For deconvolution, z-stacks with 25 images at an interval of 0. 26 µm were acquired, covering a total distance of 6. 24 µm. Z-stacks were subjected to deconvolution using the constrained iterative quantitative restoration method of the Zeiss ZEN software deconvolution module. For analysis of time-lapse series with glutamate uncaging, individual frames from image stacks were median filtered (5 × 5 pixel window) and maximum intensity projections generated.
Dendritic spine size (tdTomato fluorescence) was determined as integrated fluorescence within a ROI containing the spine and subtracting integrated background signal from a ROI of the same size placed outside of any structure. To control for stimulus specific changes in spine size, the size of neighboring unstimulated spines or of an adjacent dendritic region was determined in the same way. Data were analyzed with custom routines written in MATLAB (version R2018b, MathWorks, Natick, MA, USA). mRNA granules were manually quantified in a 5-µm dendritic radius centered at the stimulated spine. To compensate for fluctuations due to ongoing transport two time points before (~7-4 and ~2 min) and after (40 and 45 min) uncaging were averaged respectively. Statistical analysis. R software or DeltaGraph Prism v8.
Acknowledgements We thank  Sabine Thomas  and  Jessica Olberz  for primary neuron culture preparation;  Renate Dombi ,  Ulrike Kring  and  Ilaria Brentari  for assistance;  Dieter Edbauer  for the tdTomato plasmid,  Daniela Rieger  for antibody purification;  Tobias Straub  and  Florian Müller  for assistance in image analysis and statistics;  Teja Sharangdhar ,  Marco Tolino  and  Georgia Vendra  for initial experiments;  Robert H Singer ,  Simon Bullock  and  Magdalena Götz  for general support and  Peter Scheiffele ,  Andre Fischer ,  Jernej Ule ,  Walter Neupert  and  Rico Schieweck  for critical comments on the manuscript. This work was supported by grants from the  DFG  ( Großgeräteantrag INST86/1581-1FUGG ,  SPP1738 ,  FOR2333 ,  SFB870 ) and the  FWF  (I  590-B09 ,  F4314-B09   SFB RNA-seq ) (all to M.
Multidimensional prognostic index The MPI was calculated with the information from eight different domains of the CGA as previously reported 6 : 1. Functional status was evaluated through the Katz's activities of daily living (ADL) index,  21  which defines the level of independence in six daily personal care activities (bathing, toileting, feeding, dressing, continence, and transferring in and out of bed or chair). 2. Independence was measured by means of the Lawton's instrumental activities of daily living (IADL)  22  scale, which assesses independence in eight activities that are more cognitively and physically demanding than the ADL, that is, managing finances, using a telephone, taking medications, shopping, using transportation, preparing meals, doing housework, and washing. 3. Cognitive status was determined through the short portable mental status questionnaire, 23 a 10-item questionnaire investigating orientation, memory, attention, calculation, and language.
The sum of the calculated scores from the eight domains was divided by 8 to obtain a final MPI risk score ranging from 0 = no risk to 1 = higher risk of mortality. The MPI requires between 15 and 25 minutes for its complete execution and the results can be automatically obtained through the MPI calculator software downloadable from the www. mpiage. eu website. For this study, the MPI was calculated after a trained health care professional performed a CGA.
Acknowledgment Funding source: Funding for this study was provided by the project '' EFFICHRONIC 738127 ,'' which has received cofunding from European Union's  Health Programme  ( 2014-2020 ). The contents of this article are the sole responsibility of the abovementioned authors and can under no circumstances be regarded as reflecting the position of the  European Union. The funding agencies had no role in design or conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the article. The study was based on administrative data sets, and the participants were not identifiable to the authors. The members of the  EFFICHRONIC Consortium  are as follows:  Marta Pisano Gonzalez  ( Health Service of the Principality of Asturias-SESPA, Spain );  An L.
tRNA fragments (tRFs) are a class of small non-coding RNAs (sncRNAs) derived from tRNAs. tRFs are highly abundant in many cell types including stem cells and cancer cells, and are found in all domains of life. Beyond translation control, tRFs have several functions ranging from transposon silencing to cell proliferation control. However, the analysis of tRFs presents specific challenges and their biogenesis is not well understood. They are very heterogeneous and highly modified by numerous post-transcriptional modifications. Here we describe a bioinformatic pipeline to study tRFs populations and shed light onto tRNA fragments biogenesis. Indeed, we used small RNAs Illumina sequencing datasets extracted from wild type and mutant Drosophila ovaries affecting two different highly conserved steps of tRNA biogenesis: 5'pre-tRNA processing (RNase-P subunit Rpp30) and tRNA 2'-O-methylation (CG7009 and CG5220).
Indeed, mutations of these tRNAs methyltransferases in Drosophila lead to lifespan reduction, small non-coding RNA pathways dysfunction and increased sensitivity to RNA virus infections, besides tRFs accumulation (M. Angelova, 2019). Despite their abundance, only a very limited subset of RNA modifications can be detected and quantified by current powerful high-throughput analytical techniques such as ARM-seq, and big efforts are being invested for the development of this field  (Cozen et al. , 2015; Dai et al. , 2017). Indeed, modifications such as 2'-O-methylation impact on classical sequencing techniques during library preparation (RT blocking), introducing a biases in the analyses  (Motorin and Helm, 2019). Nevertheless, thousands of small RNA datasets have been already generated with Illumina sequencing techniques and are available for a wide range of mutants from different species (www.
ebi. ac. uk/ena and www. ncbi. nlm. nih. gov/geo). Since tRFs biogenesis remains obscure, we developed and described a "user-friendly" pipeline based on Galaxy environment to study all types of tRFs, including those that have a CCA tag. To do so, we took advantage of several Drosophila datasets generated in our laboratories: Rpp30 mutants, which affect tRNA processing, and CG7009 and CG5220, which affect tRNA methylation  (Molla-Herman et al. , 2015; M. Angelova, 2019). We believe that this study will help to better understand the known pathways of tRFs biogenesis as well as to uncover new tRFs biogenesis factors and unexpected crosstalks between different RNA regulatory mechanisms, crucial for gene expression.
Small RNA sequencing RNA samples of 3-5 µg were used for High-throughput sequencing using Illumina HiSeq, 10% single-reads lane 1×50 bp. (Fasteris). 15-29 nt RNAs sequences excluding rRNA (riboZero) were sequenced. All the analyses were performed with Galaxy tools https://mississippi. snv. jussieu. fr. Data set deposition is described in  (Molla-Herman et al. , 2015; M. Angelova, 2019). European Nucleotide Archive (ENA) of the EMBL-EBI (http://www. ebi. ac. uk/ena), accession numbers: PRJEB10569 (Rpp30 mutants), PRJEB35301 and PRJEB35713 (Nm mutants).
Clipping and concatenation Raw data were used for clipping the adaptors [Clip adapter (Galaxy-Version 2. 3. 0, owner: artbio)] and FASTQ quality control was performed (FastQC Read Quality reports (Galaxy-Version 0. 72)). Since replicates were homogeneous in quality and analysis (replicates for CG7009* heterozygous and homozygous, triplicates for CG7009*-CG5220* double mutants) we merged them [Concatenate multiple datasets tailto-head (Galaxy-Version 1. 4. 1, owner: artbio) to have single fasta files. CG7009*/Def9487 was used to obtain normalization numbers but is not shown in the figures.
small RNA annotation Small RNA reads files were first depleted from rRNA by discarding reads aligning to rRNA genome reference. Then, we annotated the small RNAs by iterative alignments to various references using the tool [Annotate smRNA dataset (Galaxy-Version 2. 4. 0, owner: artbio)] and allowing 0 mismatches. Iterative alignments were performed in the following order: tRNA, tRNA-CCA-edited, miRNA, TE-derived, all-ncRNA, all genes and all intergenic. Number of alignments for each class were visualized with Pie-Charts whose sizes reflect the respective depth (total aligned reads) of the libraries (see  Sup. Fig. 5).
tRFs global size distribution, coverage and tRF Logo All-tRFs, non-CCA-tRFs or 3'CCA-tRFs datasets were used to generate small RNA maps and read size distributions taking into account the normalization factors for the different genotypes. Read coverage of tRNA sequences was generated using the tool [BamCoverage (Galaxy-Version 3. 1. 2. 0. 0, owner: bgruening) ]. Briefly, we first used sR_bowtie with the options "matched on DNA, multiple mappers randomly matched at a single position" and "0 mismatch allowed" and tRNA-CCA-edited as a reference. Bam alignment files from this step were used with the BamCoverage tool to generate BigWig coverage files, using the library normalization factors as scale factors. The tool [computeMatrix (Galaxy-Version 3. 1. 2. 0. 0, owner: bgruening)] was then used to prepare the data for plotting heatmaps or a profile of given regions.
0. 0) to create the expression profile. We used Log2(value+1) and Blue-White-Red colors to reflect reads from minimal to maximal expression. To detect big changes of tRFs between genotypes, we cut columns corresponding to counts of white-and Rpp30 18. 2 mutants, or CG7009*/TbSb heterozygous and CG7009* homozygous mutants. We calculated the ratio of tRFs expression between them, using Compute an expression on every row tool (Galaxy-Version 1. 2. 0). Data were treated with Microsoft Office Excel to better observe ratio differences by using conditional formatting tool, obtaining a three color code (Blue-White-Red from minimal to maximal value). See Sup. Fig. 6.
How to study different tRFs categories. In this study we have developed a Workflow using Galaxy (https://mississippi. snv. jussieu. fr) to extract all principal classes of tRFs (Fig. 1  and Methods): Type-I tRFs correspond to fragments derived from mature tRNA transcripts (5'-tRFs, 3'CCA-tRFs and intermediate i-tRFs) and Type-II, formed by RNase-Z cleavage of tRNA precursors ; spanner tRFs that span CCA region created before CCA addition, and transcription associated tRFs (taRFs) due to problems in transcription termination. Thanks to this pipeline we can study them separately or altogether. Intermediate tRFs formed in or around the anticodon region are considered as non-CCA tRFs.
, 2013; Goodarzi et al. , 2015; Karaiskos et al. , 2015a Karaiskos et al. , , 2015b;; Kumar et al. , 2015; Grigoriev and Karaiskos, 2016; Göktaş et al. , 2017; Schorn et al. , 2017; Shen et al. , 2018; Siira et al. , 2018; Kuscu et al. , 2018; Liu et al. , 2018; Su et al. , 2019; Guan et al. , 2019). Some studies have modified the genome reference to have pre-tRNAs sequences, but the number of downstream sequences can vary; some have removed introns from the genome reference; some studies have added CCA tag to the genomic reference to recover 3'CCA-tRFs but others have allowed 3 mismatches, then trimmed the CCA tag from reads to match them against the mature tRNA genome reference. Consequently, several studies are lacking some tRFs populations (3'CCA-tRFs, Type-II, spanner, taRFs). We thus designed a "user-friendly" Galaxy workflow, working with available small/mid RNA datasets, and which can be analyzed by most laboratories.
, 2015a (Karaiskos et al. , , 2015b;; Grigoriev and Karaiskos, 2016; Angelova et al. , 2018). It is known that high throughput Illumina sequencing of small RNA libraries could introduce biases in tRFs detection, since tRNAs are highly modified molecules and very few techniques are able to properly describe these modifications in a transcriptome-wide way, such as ARM-seq  (Cozen et al. , 2015). However, a huge number of datasets are already available with valuable information to extract. By analyzing different mutants from distinct pathways we will be able to increase our knowledge on tRFs biogenesis pathway and the interactions between these different mechanisms. For example, it has been recently shown that snoRNAs association with fibrillarin can cause tRNA-CAT 2'-O-methylation at position 34 in mammalian cells, similarly to CG7009  (Vitali and Kiss, 2019).
Conversely, tRNA methylation could have an impact on snoRNAs biogenesis as observed in this study. Thus, this workflow will help to analyze past, present and future small RNA sequences obtained by different means. It will be interesting in the future to obtain a tRF cartography in different tissues, organs and different species, to determine tRFs biogenesis, targets and their functions in gene expression regulation. It will be also very interesting to compare datasets obtained from classical Illumina sequencing with other techniques such as ARM-seq, which provides a read out of some modifications and may reveal additional tRFs population. Hopefully, our study will participate in the discovery of specific important nuclear tRFs or mito-tRFs that could help to understand the etiology of a wide range of human pathologies.
Annotate datasets: Annotate smRNA dataset by iterative alignments with sRbowtie (Galaxy Version 2. 4. 0). 0 mismatch allowed. Alignment steps: in the following order tRNA, tRNA-CCA-edited, miRNA, TE-derived, all-ncRNA, all genes, all intergenic. Generation of a cascade annotation analysis and a barplot. small RNA reads input fasta les depeleted from rRNA.