dataesr/scientific-paragraphs-categorization huggingface dataset:
Crystallisation and structure determination Crystallisation trials were carried out by sitting drop vapour diffusion method at 293 K. Freshly purified STARD10 was concentrated to w38 mg/mL and centrifuged to remove insoluble material before crystallisation. Single crystals appeared in condition containing 50% (v/ v) PEG 200, 100 mM sodium phosphate dibasic/potassium phosphate monobasic (pH 6. 2), and 200 mM NaCl after one month. Cryo-freezing was achieved by stepwise soaking the crystals in reservoir solution containing 10, 20, and 30% (w/v) glycerol for 3 min and flash freezing in liquid nitrogen. X-ray diffraction data were collected on beamline I03 at the Diamond synchrotron X-ray source and were integrated and scaled with the Xia2 system  [15]. The structure was determined by molecular replacement using the START domain structure from human STARD5 protein (PDB ID: 2R55) as a search model in CCP4, followed by rigid-body refinement by Refmac5  [16].
Solution of STARD10 3D structure and molecular docking identify PI and PI(3)P as potential ligands To assess which membrane lipids may be bound by STARD10, we sought to obtain a crystal structure of the purified protein. We generated and purified the recombinant protein from a bacterial expression construct comprising the human STARD10 coding sequence fused with a 6His-MBP (Maltose Binding Protein) tag. Following the recombinant protein production in E. coli and protein purification, STARD10 generated a well-diffracting crystal. The crystal structure was then resolved at 2. 3 Å resolution by molecular replacement using the structure of STARD5 (PDB ID: 2R55) (Figure  3A ; Supplemental Movie 1; Supplemental Table  2 ).
ACKNOWLEDGMENTS The authors are grateful to  Thomas Di Mattia  and  Laetitia Voilquin  from the  Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), France , for the construction of the STARD10-GFP and GFP-STARD10 plasmids. We thank  Rhodri M. L. Morgan  for assistance with data collection for the determination of the STARD10 structure, and we acknowledge the  Diamond Light Source  for access to beamlines I03. We would like to thank the  Electron Microscopy Facility of the MPI-CBG  for their support. This work was supported by the  Electron Microscopy and Histology Facility , a Core Facility of the  CMCB Technology Platform at TU Dresden.
This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Starting Grant  715884  to D. J. H. ) and from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No. 115881  (RHAPSODY) to G. A. R. and M. S. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA. A. T. was supported by a project grant from the  MRC  ( MR/R010676/1 ). V. S. is supported by a  Diabetes UK Harry Keen Clinician Scientist   15/ 0005317. D. W. was supported by the  Medical Research Council and Cancer Research UK. The X-ray Crystallography Facility at  Imperial College London  is partly funded by the  Wellcome Trust. The Facility for Imaging by  Light Microscopy  (FILM) at the  Imperial College London  is partly supported by funding from the  Wellcome Trust  (grant  104931/Z/ 14/Z ) and  BBSRC  (grant  BB/L015129/1 ).
The  London Metallomics Facility  was funded by a  Wellcome Trust   Multi-User Equipment Grant  ( 202902/Z/16/Z ). M. S. was supported by funds from the  German Ministry for Education and Research (BMBF)  to the  German Centre for Diabetes Research (DZD). N. S. A. was supported by the  NUS Research Scholarship. A. K. K. T. was supported by the  Institute of Molecular and Cell Biology (IMCB) ,  A*STAR.
Statistical Analysis Statistical analysis was performed using the GraphPad Prism version 6. 0 software for Windows (San Diego, CA, USA). Data are expressed as mean and mean standard error. For between-group comparisons, the one-way ANOVA was performed, followed by Bonferroni post hoc test or two-way ANOVA, followed by the Bonferroni post hoc test, as appropriate. Differences were considered significant when p < 0. 05. Survival analyses were performed using the Kaplan-Meier curve and were compared using the log-rank test for trend. Differences were considered significant when p < 0. 05.
11 he solvatochromism observed on going from acetonitrile to water suggests that H-bonds and the dielectric constant a major role in M photophysics. To distinguish the general solvent effects from specic solute-solvent effects, the electronic transitions of M were also calculated in bulk water. The positions of the lowest energy absorption band, and of the N* and T* emission bands obtained by PBE0-D/TZVP method are at 354, 424 and 540 nm, respectively. This shows that, the dielectric constant alone has a negligible effect on the absorption position maximum but dramatically affects the quality of the predictions for the positions of the emission bands as evidenced by the large shis with respect to the experimental data (16 and 25 nm for the N* and T* bands, respectively). These results highlight the key importance of specic intermolecular H-bonding with water molecules to account for the photophysics of M in aqueous media, a behaviour already observed with other 3HC derivatives.
Acknowledgements C. A. K. acknowledges the  Abdus Salam Inter-national Centre for Theoretical Physics (ICTP)  for its support to CEPAMOQ through the  OEA-AC-71  project and the  High Performance Computing Center of the University of Strasbourg  for supporting this work by providing scientic support and access to computing resources. Part of the computing resources was funded by the  Equipex Equip@Meso project  ( Programme Investissements d'Avenir ). YM and AB thank  ANR Fluometadn ,  ANR Pico2  and the  FRM  for nancial support. YM is grateful to the  Institut Universitaire de France (IUF)  for support and providing additional time to be dedicated to research.
Thus, uvaol directly affects fibroblast and endothelial cells by enhancing their migration. Representative photomicrography images showing the scratched area of fibroblasts (A) and endothelial cells (B) treated with medium (control) or uvaol, and cell migration towards the cell-free area after 24 h (magnification 40×). The percentage of the scratch covered was measured by quantifying the total distance that cells moved from the edge of the scratch towards the center of the scratch, using ImageJ software, followed by conversion to a percentage of the wound that was covered (C,D). Values represent mean ± SD from three independent experiments. (+) p < 0. 05 and (++) p < 0. 01 compared with medium-treated cells after 24 h.
Treatment with 50 µM uvaol increased the immunofluorescence staining of cytoplasmic laminin. Image analysis showed a 36% increase in laminin levels after treatment (Figure  4D ). Unlike the proteins laminin and fibronectin, basal collagen type I expression in fibroblasts did not change after treatment with 50 µM uvaol for 24 h (Figure  4E, F ). Representative photomicrography images showing the scratched area of fibroblasts (A) and endothelial cells (B) treated with medium (control) or uvaol, and cell migration towards the cell-free area after 24 h (magnification 40×). The percentage of the scratch covered was measured by quantifying the total distance that cells moved from the edge of the scratch towards the center of the scratch, using ImageJ software, followed by conversion to a percentage of the wound that was covered (C,D).
40× ). The percentage of the scratch covered was measured by quantifying the total distance that cells moved from the edge of the scratch towards the center of the scratch, using ImageJ software, followed by conversion to a percentage of the wound that was covered (B,D). The bars represent mean ± SD of three independent experiments. Statistical differences were detected with two-way ANOVA followed by the Bonferroni test. (+) p < 0. 01 compared to medium-treated cells, (*) p < 0. 05 compared to uvaol-treated cells. Compared to medium-treated cells (control), uvaol accelerated the migration of fibroblasts towards the scratched area after 24 h (Figure  7A ). Treatment with the p38-MAPK inhibitor (SB203580, 10 µM) did not cause any significant alteration in uvaol-induced fibroblast migration (Figure  7B ).
However, the accelerated migration of endothelial cells in response to uvaol treatment was significantly inhibited in 37% by the inhibitor for p38-MAPK (Figure  7C, D ). Thus, the enhanced migration of endothelial caused by uvaol is associated with both PKA and p38-MAPK signaling pathways. However, uvaol-induced migration in fibroblasts seemed to be dependent on the PKA pathway but not the p38-MAPK signaling pathway. 40× ). The percentage of the scratch covered was measured by quantifying the total distance that cells moved from the edge of the scratch towards the center of the scratch, using ImageJ software, followed by conversion to a percentage of the wound that was covered (B,D). The bars represent mean ± SD of three independent experiments. Statistical differences were detected with two-way ANOVA followed by the Bonferroni test.
40× ). The percentage of the scratch covered was measured by quantifying the total distance that cells moved from the edge of the scratch towards the center of the scratch, using ImageJ software, followed by conversion to a percentage of the wound that was covered (B,D). Bars represent mean ± SD of three independent experiments. Statistical differences were detected with two-way ANOVA followed by the Bonferroni test. (+) p < 0. 01 and (+++) p < 0. 001 compared to medium-treated cells, (***) p < 0. 001 compared to uvaol-treated cells.
Thus, uvaol exhibited healing properties in vivo, in parallel to directly affecting the functioning of fibroblasts and endothelial cells in vitro. 40× ). The percentage of the scratch covered was measured by quantifying the total distance that cells moved from the edge of the scratch towards the center of the scratch, using ImageJ software, followed by conversion to a percentage of the wound that was covered (B,D). Bars represent mean ± SD of three independent experiments. Statistical differences were detected with two-way ANOVA followed by the Bonferroni test. (+) p < 0. 01 and (+++) p < 0. 001 compared to medium-treated cells, (***) p < 0. 001 compared to uvaol-treated cells.
After 24 h of treatment, the cells were photographed using invert microscopy (Olympus IX70, Tokyo, Japan) with a digital camera to measure wound closure (magnification, 40×). Cell migration was analyzed using ImageJ software (version 1. 51k), and was expressed as the percentage of closure of the initial wound (scratch width at time zero) using the following formula: [(scratch width at time 0scratch width after an identified culture period)/(scratch width at time 0)] × 100%.
Tube-Like Structure Formation Assay The Matrigel-based tube formation assay was used to evaluate the angiogenic potential of uvaol. In brief, 80µL Matrigel solution (BD Biosciences) was applied to 98-well plates 2 h before cell seeding. After matrix formation, endothelial cells (104 cells) were placed on the matrix gel and cultured in the presence or absence of uvaol (10 µM) with RPMI medium containing 2% serum at 37 • C in a 5% CO 2 incubator. After 16 h, the formed network of tubes was visualized at 100× magnification by light microscopy. The number of tube-like structures per field was measured using angiogenesis Analyzer plugin in ImageJ. Each experiment was performed in triplicate. All data analyses were performed in a blinded manner.
Wound Contraction Measurements Macroscopic evaluation of the wounds was performed using a digital camera on day 0 (before the start of treatment) and on days 3, 7, and 10 after injury. The Optical SteadyShot DSC-W350 digital camera (14. 1 megapixels) was positioned at a distance of 15 cm of wound. The data were analyzed using ImageJ software. The wound measurements on various days were expressed as the percentage of wound closure. The values were expressed as percentage values of day 0 measurements, and were calculated by the formula: [(A0 -AI)/A0 × 100], where A0 is the initial wound area (day 0) and AI is the wound area on days 3, 7, and 10 after the initial wound.
Statistical Analysis Data were reported as mean ± standard deviation (SD), and were analyzed using GraphPad Prism software, version 5. 0 (San Diego, CA, USA). One-way analysis of variance (ANOVA) followed by the post hoc Bonferroni test multiple comparison test was applied for the comparison among various groups and Student's t test to determine the significance of differences between two means. p values less than 0. 05 were considered statistically significant.
INTRODUCTION Mannosides occur in many living organisms, from microbes to plants and animals, in the form of homo-or heteropolymers or glycoconjugates. They play a critical role in several cellular processes, such as cell wall structuring in plants  [5] , cell signalling, as key components of proteoglycans  [6] , lipopolysaccharides and capsular polysaccharides in bacteria, yeasts, mammals and plants  [7] , and also as a carbohydrate storage polymer in certain parasites  [8]. Mannoside synthesis is mostly carried out by glycoside transferases (GTs) from mannose diphosphonucleotides, whereas mannoside degradation is carried out by glycoside hydrolases (GHs). These enzymes are very diverse in terms of structures, mechanisms and specificities, as indicated by their respective membership of 13 and 15 different GT and GH families, respectively, in the classification of carbohydrateactive enzymes (CAZy database, http://www.
Their large diversity of specificities, the tolerance of some enzymes towards acceptors, and their ability to generate αMan1P through phosphorolysis of cheap substrates for the synthesis of various heteromannosides, makes GH130 glycoside phosphorylases attractive enzymes for the in vitro synthesis of β-mannosidic linkages, which are considered to be some of the most challenging glycosidic linkages in synthetic carbohydrate chemistry  [24]. However, the functional and sequence diversity of GH130 enzymes, and their role in the functioning of ecosystems, are underinvestigated. Less than 1 % of the 2142 GH130 enzymes listed in the CAZy database (release 8 January 2020) have been biochemically characterized. In order to predict the specificity of enzymes in this family that are still uncharacterized, in 2013 we proposed a classification into two subfamilies, namely GH130_1, strictly acting on β-1,4mannosyl-glucose, and GH130_2, targeting β-1,4mannosyl-N,N′-diacetylchitobiose (the core oligosaccharide of N-glycans), or β-1,4mannooligosaccharides  [20].
This subfamily analysis also revealed one heterogeneous GH130_NC sequence cluster, which was later shown to include enzymes of various specificities and mechanisms, such as β-1,2mannosidases  [12, 13] , β-1,2mannobioseand β-1,2oligomannan-phosphorylases  [21, 22] , and a β-1,3mannobiose-phosphorylase  [23]. Even though these early subfamilies are still used  [17] , we wished to perform a new analysis based on the greater sequence diversity available today. Indeed the number of GH130 sequences listed in the CAZy database alone is now almost six times larger than in 2013. Because the CAZy database only lists sequences from daily releases from GenBank, we also wished to include a number of metagenomic GH130 sequences not present in CAZy in our novel analysis, as these may reveal a diversity not yet present in GenBank.
The Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST) was developed in 2015 (http:// efi. igb. illinois. edu/ efi-est/  [25] ) to rationalize the search for sequences of interest in large datasets. This web tool automatically generates sequence similarity networks (SSNs) to visualize sequence relationships in protein families  [26]. SSNs have recently been used to search for new functions in the glycyl radical enzyme superfamily, a class of prominent enzymes in the human gut microbiome  [27] , and to subdivide the multispecific GH16 family into 23 robust subfamilies  [28]. In the present study, we used SSNs to analyse the diversity of GH130 sequences listed in the CAZy database, and in four metagenome datasets selected from mammalian guts, as ecosystems that are rich in mannosides of various origins and structures  [29].
METHODS GH130 sequence mining in mammal gut metagenomes The GH130 sequences were identified from the metagenomic sequence datasets following a procedure previously described for other metagenomics analyses  [30]. The procedure consisted of a blast  [31]  search of the protein sequences against the full-length GH130 sequences included in the CAZy database (http://www. cazy. org) in November 2018, using a cut-off E-value of 10 -6. Sequences that aligned over their entire length with a GH130 sequence in the database with >50 % identity were assigned directly to the GH130 family. The remaining sequences were subjected to a sequence similarity search using the hidden Markov models (HMMER v3) built for each CAZy family, allowing identification of the CAZy family they belong to  [9].
Sequence similarity networks The web-based EFI-EST (http:// efi. igb. illinois. edu/ efi-est)  [25]  was used to construct SSNs from the GH130 sequences retrieved from the CAZy database in November 2018 (1592 sequences) and from the 4 metagenomes (4714 sequences), plus 2 sequences of recently characterized GPs (VCV21229. 1 and VCV21228. 1), with E-value thresholds between 10 -30 and 10 -90. The node networks were visualized using Cytoscape 3. 2 (https:// cytoscape. org/  [32] ).
Analysis of sequence similarity The CD-HIT program was used to analyse sequence redundancy within and between the datasets (http:// weizhong-lab. ucsd. edu/ cdhit-web-server/ cgi-bin/ index. cgi)  [33]. For each GH130 sequence selected for biochemical characterization, the most similar sequence was identified from the CAZy database using blast, with the NIH blastp default search parameters (https:// blast. ncbi. nlm. nih. gov). The conservation of active site residues was analysed using multiple sequence alignments performed using clustal Omega (https://www. ebi. ac. uk/ Tools/ msa/ clustalo/). The alignments were corrected in the region containing residue K199 (reference sequence AAO78885. 1), according to available structural information  [22].
Phylogenetic analysis First, a multiple structural alignment using mustang 3. 2. 2 [34]  was performed with the 11 GH130 sequences associated to a PDB code (1VKD, 3QC2, 3R67, 3TAW, 3WAS  [35] , 4ONZ, 4UDG  [36] , 5A7V  [12] , 5AYC  [16] , 5AYE  [16]  and 5B0R  [22] ). Then, 10 sequences for each of the 15 meta-nodes (150 sequences in total) were chosen, including the 18 characterized sequences and 132 randomly chosen sequences. The multiple structural alignment was used to guide the multiple sequence alignment of the whole dataset (150 sequences) using MAFFT and the G-INS-i strategy (iterative refinement, using WSP and consistency scores, of pairwise Needleman-Wunsch global alignments)  [37]. Finally, a phylogenetic tree was constructed by setting 1000 bootstrap replicates using the default neighbour-joining method  [37, 38]  and was visualized with iTOL  [39].
NMR spectroscopy All spectra were acquired using a Bruker Advance 500 MHz spectrometer with a 5 mm z-gradient TBI probe at 298 K, and an acquisition frequency of 500. 13 MHz for 1 H and 125. 75 MHz for 13 C NMR. The data were processed using TopSpin 3. 0 software. The freeze-dried reaction media and standards were resuspended in deuterated water. Deuterium oxide was used as the solvent and the chemical shift scales were internally referenced to sodium 2,2,3,3-tetrad euterio-3-trimethylsilylpropanoate (d4-TSP) for 1 H and to the non-deuterated acetone (singlet at 30. 89 ppm) for 13.
Mass spectrometry The reverse phosphorolysis reaction performed with U1 was analysed by mass spectrometry (MS) on a Synapt G2 Si HDMS (Waters Corp. , Manchester, UK) equipped with a LockSpray Exact Mass Ionization Source (electrospray ion source, ESI). The instrument was operated in positive polarity in the sensitivity mode. The parameters used for electrospray were as follows: capillary voltage: 2. 1 kV; sampling cone: 70; desolvation temperature: 120 °C; desolvation gas: 350 l h -1. The tandem MS (MS/MS) measurement was performed in the transfer cell of the triwave setting using an energy of 22. After drying, the sample was solubilized in H 2 O/MeOH and infused at a flow rate of 5 µl min -1. MS and MS/MS measurements were recorded for 30 s. Data were acquired using MassLynx 4. 1 and processed using Mmass 5.
GH130 sequence mining in mammal gut microbiomes In order to extend the GH130 diversity from the genomic to the metagenomic sequence space, 4 mammal gut metagenomes, totaling 33 962 473 genes, were searched for sequences containing at least 1 GH130 module. In total, 4714 GH130 sequences were retrieved from the human, mouse, pig and bovine gut metagenomes (1205, 467, 681 and 2361 sequences, respectively) (Table  1 ). The relative numbers of GH130 genes in these four datasets are of the same order of magnitude, varying from 6×10 -3 to 26×10 -3 % of the genes in each metagenome. The proportion of sequences that lack N-and/ or C-terminal ends varies between 31-66 %. Logically, the larger is the N50 assembled contig length in the metagenomic dataset and the smaller is the percentage of fragmentary sequences.
Most of the metagenomic sequences contain a single catalytic module. Only 19 combine a GH130 module with a GH2, GH43, GH92, GH127 or a GH142 module (Table  S2 ). This very low number of multi-modular sequences is probably not due to gene fragmentation in the metagenomic datasets, since only 6 of the 1592 sequences listed in the CAZy database as of November 2018 are multi-modular (GH130-GT81 and GH130-GH130 modular combinations). None of the CAZy or metagenomic sequences contains a CBM domain. Of the 4714 metagenomic sequences, only 0. 7 % (31 sequences) are fully identical to sequences already listed in the CAZy database. Among the metagenomic sequences, only 0. 5 % (24 sequences) are identical. With a threshold of 90 % sequence identity, sequence redundancy between each metagenome and the CAZy database does not exceed 10.
Sequence diversity in the GH130 family To analyse sequence diversity in the GH130 family, we constructed SSNs with the 1592 sequences from the CAZy database, plus 2 sequences corresponding to recently characterized GPs, and the 4714 metagenomic sequences. The low number of identical sequences does not affect clusterization, since they appear in the same nodes. We also checked that the presence of the four multi-modular GH130-GH130 sequences in our dataset (Table  S2 ) did not create erroneous edges between otherwise distinct metanodes. Taken independently, the sequences of each of the modules do indeed gather in the same meta-node. Different E-value thresholds (10 -30 and 10 -90 ) were tested to construct the SSNs (Figs  1  and S1 ). For each SSN, we mapped the known functions of the 18 characterized GH130 sequences on the meta-nodes obtained.
Conversely, the GPs acting on Man-β-1,4-GlcNAc and on β-1,4mannosides (sequences belonging to the former GH130_2 subfamily) are grouped in the same meta-node (C2), apart from the those of the meta-node C1 targeting Man-β-1,4-Glc (former GH130_1 subfamily). It was not possible to obtain an SSN representation grouping GPs that act on β-1,2mannosides   S1 ). In addition, even at the lowest E-value tested (10 -90 ), the characterized sequences targeting Man-β-1,4-GlcNAc and β-1,4mannosides are still grouped in the same meta-node. This may be due to the high promiscuity towards acceptors of these enzymes, as demonstrated for the RaMP2, BT1033 and UhgbMP enzymes  [14, 19, 20]. The optimal SSN contains 15 meta-nodes containing more than 20 sequences each; this is 1 of the criteria used to define subfamilies in the CAZy database  [28].