dataesr/scientific-paragraphs-categorization huggingface dataset:
The mechanism of inhibition of the second step of splicing To gain further insights into the preferential inhibition of the second step of splicing by group II intron inhibitors, we solved the crystal structure of OiD1-5 at 3. 6 Å resolution, in the presence of intronistat B and of Mg 2+ and Na + ions. This condition forces the intron active site into a transiently inactive, so-called "toggled" conformation, which is achieved via rotation of residues 287-289 of J2/3 junction around their backbone, and which is mechanistically important because it favors the transition from the first to the second step of splicing  (Manigrasso et al. , 2020; Marcia and Pyle, 2012). In our new sodium structure with intronistat B, the intron retains the same overall structure as in the apo form (RMSD = 1. 2 Å with respect to PDB id: 4FAX).
9σ. (C) Fo-Fc simulated annealing electron density omit map (green mesh) for the structure reported in panel A, calculated by omitting intronistat B, the J2/3 junction residues and both M1 and M2 and contoured at 2. 7σ. This omit map shows that the J2/3 junction adopts the triple-helix conformation and not the toggled conformation. For reference, the toggled conformation is modeled here as light grey sticks from PDB id: 4FAX. without intronistat B in the presence of potassium provide a precise mechanistic explanation for how the compound inhibits active site toggling. Specifically, multiple ~500 ns-long MD replicas show that the presence of intronistat B prevents the formation of the catalyticallyessential interaction between the active site potassium ion K1 and the N7 atom of the conserved G288 nucleotide [Figure  3F ,  (Manigrasso et al.
The spliceosome active site derives evolutionarily from that of group II introns and preserves its key structural, chemical and functional properties  (Marcia et al. , 2021). Not surprisingly, by superimposing the intron and spliceosome active site structures, we note that compounds analogous to intronistat B could fit within the catalytic core of spliceosomal complexes formed at different stages of catalysis, including the B* complex (i. e. before the first step of splicing, PDB id: 5Z56, 5Z57, 5Z58) and the Ci complex (i. e. after the first step of splicing, PDB id: 7B9V). Here, the compounds could anchor at the M1-M2-K1 site with their pyrogallol or an analogous moiety, as our work now proves possible. In this binding pose, the compounds would be able to explore multiple conformations, interacting with catalytic nucleotides, metal ions or protein splicing factors (Figure  S13 ).
Structure determination Diffraction data were collected at beamlines ID30A-1 and ID30B at ESRF (Grenoble, France) and processed with the XDS suite  (Kabsch, 2010). The structures were solved by molecular replacement using Phaser in CCP4  (McCoy et al. , 2007)  and the RNA coordinates of PDB entry 4FAR and 4E8M (without solvent atoms) as the initial model  (Marcia, 2016; Marcia et al. , 2013a; Marcia and Pyle, 2012). The models were improved automatically in Phenix  (Adams et al. , 2010; Adams et al. , 2004; Adams et al. , 2002)  and Refmac5  (Murshudov et al. , 2011)  and manually in Coot  (Emsley, 2004) , and finally evaluated by MolProbity  (Davis, 2007). The figures depicting the structures were drawn using PyMOL Molecular Graphics System (Version 1. 5. 0. 4, Schrödinger).
Equilibrium molecular dynamics simulations We used four structural models to perform MD simulations. Specifically, we modelled the holosystems based on the structure of the intronistat B bound to the free intron (Figure  2 , PDB id: 8OLS), and that of the ligand in complex with the 5'exon-bound intron (Figure  4 , PDB id: 8OLZ). We modelled the corresponding apo-systems by removing the ligand from the holomodels, thus obtaining the free intron and 5'-exon-bound intron structures. We used the AMBER force-field RNA. OL3 to parametrize the RNA molecules  (Perez et al. , 2007; Zgarbova et al. , 2011). We used Joung and Cheatham  (Joung and Cheatham, 2009) , Panteva 12-6-4 non-bonded fixed-point charge  (Panteva et al. , 2015) , and TIP3P models  (Jorgensen et al. , 1983)  to parametrize ions, divalent ions and water, respectively.
cerevisiae (PDB id: 7B9V). Notably, in both cases, a two-metal-ion binding compound similar to intronistat B would locate in proximity of the splice junctions or the intron nucleotides, suggesting that sequence-specific contacts between small molecules and the spliceosomal complex can be possibly engaged upon having anchored its structurally-conserved active site. a, b, c (Å)  89. 0, 95. 0, 223. 9 90. 0, 94. 7, 225. 0 88. 1, 94. 1, 222. 9 88. 5, 94. 7, 222. 9 87. 7, 94. 0, 223. 4 88. 8, 94. 3, 223. 9 α, β, γ (°) 90, 90, 90 90, 90, 90 90, 90, 90 90, 90, 90 90, 90, 90 90, 90.
ACKNOWLEDGEMENTS We thank the scientists at the  ID30-A1  and  ID30B ESRF beamlines  for support with data collection,  Dr Isabel Chillon  ( CNRS, IGMM Montpellier ) for help with initial splicing kinetic assays, and  Ombeline Pessey  and  Ines Dieryck  for excellent technical assistance. We thank  Prof Annalisa Pastore ,  Prof Kristina Djinovic-Carugo ,  Dr Isabel Chillon ,  Michela Nigro  and  Shekhar Jadhav  for critical reading of the manuscript. We also thank all members of the  Marcia and De Vivo labs  for helpful discussion. Work in the Marcia lab is partly funded by  ITMO Cancer  ( 18CN047-00 ),  Région Auvergne Rhône Alpes  (project  R21105CC ; allocation  RPH21004CCA ),  FINOVI  ( AAP15 ),  Canceropole CLARA  (Oncostarter), and by the  Fondation ARC pour la recherche sur le cancer  ( PJA-20191209284 ).
The Marcia lab uses the platforms of the  Grenoble Instruct Center  (ISBG: UMS 3518  CNRS- CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology (PSB). MDV thanks the  Italian Association for Cancer Research (AIRC)  for financial support ( IG 23679 ).
+ and di-Br-intronistat B Oi5eD1-5 with Ca 2+ , K + and intronistat B Oi5eD1-5 with Mg 2+ , K + and intronistat B OiD1-5 with 5'- exon, Mg 2+ , K + and intronistat B OiD1-5 with Mg 2+ , Na + and intronistat B PDB code 8OLS 8OLV 8OLW 8OLY 8OLZ 8OM0 Data Collection Wavelength 0. 96 0. 92 0. 96 0. 96 0. 96 0. 96 Resolution range a 39. 7 -3. 0 (3. 1 -3. 0) 48. 4 -2. 8 (2. 9-2. 8) 48. 64 -4. 0 (4. 1 -4. 0) 48. 02 -3. 1 (3. 2 -3. 1) 39. 8 -3. 3 (3. 4 -3. 3) 42. 3 -3. 6 (3. 7 -3. 6) Space group P 21 21 21 P 21 21 21 P 21 21 21 P 21 21 21 P 21 21 21 P 21 21 21 Cell dimensions:.
RESULTS & DISCUSSION A cohort of 1,016 people with albinism underwent testing of ≤19 albinism-related genes; these individuals were not known to be related and had predominantly European ancestries (Supplementary Data 1). A further 105 probands with albinism were identified in the Genomics England 100,000 Genomes Project dataset. 22  A 'control' cohort of 29,451 unrelated individuals that had no recorded diagnosis or features of albinism was also identified in this resource (Fig. 1 , Supplementary Table  1 , Methods). A case-control analysis was performed to gain insights into the contribution of genotypes involving heterozygosity for the TYR:c. 1205G>A (p. Arg402Gln) [rs1126809] and OCA2:c. 1327G>A (p. Val443Ile) [rs74653330] variant combination in albinism. These two missense changes were selected as they are the commonest albinism-related changes in European populations.
104 The majority of participants in the 'case' cohort (1,016/1,121) were identified through the database of the University Hospital of Bordeaux Molecular Genetics Laboratory, France. All these probands had at least one key ocular feature of albinism, i. e. nystagmus or prominent foveal hypoplasia. The remaining 105/1,121 cases were identified through the Genomics England 100,000 Genomes Project dataset and had a diagnosis of albinism or partial/ocular albinism. The 'control' cohort included 29,451 unrelated individuals from the Genomics England 100,000 Genomes Project The primary analysis involved using logistic regression to study the subsets of the case and control cohorts that (i) had European ancestries and (ii) did not carry any other rare genetic changes that had been labelled as disease-causing (DM) in the Human Gene Mutation Database (HGMD) v2021.
2. The following two covariates were used: sex; number of common albinism-associated alleles, i. e. relevant DNA sequence alterations in the genomic locations corresponding to TYR c. -301C>T [rs4547091] and TYR c. 575C>A (p. Ser192Tyr) [rs1042602]. To assess the robustness of the findings we performed additional regression analyses in the same cohort using broader inclusion criteria (e. g not just individuals of European ancestries) and incorporating additional covariates. Validation studies in an independent cohort (UK Biobank) were also conducted. 100K_GP, Genomics England 100,000 Genomes Project; HGMD, Human Gene Mutation Database v2021. 2; het, heterozygous; UKB, UK Biobank. TYR and OCA2 variant numbering was based on the transcripts with the following identifiers: NM_000372. 5/ENST00000263321.
1205G>A and OCA2:c. 1327G>A variant combination affects the probability of receiving a diagnosis of albinism ("risk of albinism"). This logistic regression approach has been designed to handle small, imbalanced datasets (which are common in studies of rare conditions) and allows for adjustment of key covariates (which is not possible in contingency table methods) (Fig. 1 ). We performed two different regression analyses. The first one involved a direct casecontrol comparison between those individuals who were heterozygous for the TYR:c. 1205G>A and OCA2:c. 1327G>A variant combination and those who were not. The focus of this primary analysis was the subset of participants who had European ancestries and carried no other rare albinism-implicated variants (i. e. , low-frequency genetic changes that had been labelled as disease-causing (DM) in the Human Gene Mutation Database (HGMD) v2021.
A log 10 scale with 1 as the reference point is used. This analysis focused on case and control individuals that (i) had European ancestries and (ii) did not carry any other rare genetic changes that had been labelled as disease-causing (DM) in the Human Gene Mutation Database (HGMD) v2021. 2. Two covariates were factored in (see Fig. 1 ). Group A was used as the reference group to which all other groups were compared using Firth regression analysis (i. e. the odds ratio for this group was fixed at 1). Further information including numerical data can be found in Supplementary Table  2. To increase confidence in these observations, we performed additional analyses in an independent cohort, the UK Biobank. 40  We found that UK Biobank volunteers who were heterozygous for the TYR:c. 1205G>A and OCA2:c.
University Hospital of Bordeaux albinism cohort Individuals with albinism were identified through the database of the University Hospital of Bordeaux Molecular Genetics Laboratory, France. This is a national reference laboratory that has been performing genetic testing for albinism since 2003 and has been receiving samples from individuals predominantly based in France (or French-administered overseas territories). Information on the dermatological and ophthalmological phenotypes was available for all affected individuals, and each of these cases had at least one of the key ocular features of albinism, i. e. infantile nystagmus or absence of a foveal pit (prominent foveal hypoplasia). No pre-screening based on genotype was undertaken other than selecting individuals in whom position c. -301 of TYR was sequenced.
Genomics England 100,000 Genomes Project cohort Clinical and genomic data from the Genomics England 100,000 Genomes Project were accessed through a secure Research Environment that is available to registered users. This dataset was collected as part of a national genome sequencing initiative. 46  Enrolment was coordinated by Genomics England Limited, and participants were recruited mainly at National Health Service (NHS) Hospitals in the UK. 22  Clinical information was recorded in Human Phenotype Ontology (HPO)  47. The multi-sample VCF was then split into 1,371 roughly equal chunks to allow faster processing and the loci of interest were queried using bcftools v1. 9  50  (see https://researchhelp. genomicsengland. co. uk/display/GERE/ for further information). Only variants that passed all provided site quality control criteria were processed.
In addition, we filtered out genotypes with: genotype score <20; read depth <10; allele balance <0. 2 and >0. 8 for heterozygotes; allele balance >0. 1 or <0. 9 for homozygotes (reference and alternate, respectively). Genomic annotation was performed using Ensembl VEP  51  ; one additional annotation was included: presence of a variant in HGMD v2021. 2  35  with a "disease-causing" (DM) label. Ancestry inference was performed in this cohort using principal component analysis. Data from the 1000 genomes project (phase 3) dataset was used and five broad super-populations were projected (European, African, Admixed American, East Asian, South Asian) (further information on this can be found online at https://research-help. genomicsengland. co. uk/display/GERE/Ancestry+inference). We focused on a pre-determined subset of the Genomics England 100,000 Genomes Project dataset that includes only unrelated probands (n=29,556).
Case-control analysis to estimate the probability of an albinism diagnosis The effect of heterozygosity for the TYR:c. 1205G>A (p. Arg402Gln) [rs1126809] and OCA2:c. 1327G>A (p. Val443Ile) [rs74653330] variant combination on the probability of receiving a diagnosis of albinism (i. e. the albinism risk) was estimated using data from the University Hospital of Bordeaux albinism cohort and the Genomics England 100,000 Genomes Project dataset. A casecontrol analysis of a binary trait (presence/absence of albinism) was conducted assuming a recessive model. Logistic regression using the Firth bias reduction method  33, 34  was utilised (as implemented in "logistf" R package). The following parameters were considered: number of rare albinism-implicated variants, number of common albinism-associated alleles, ancestry and sex.
53   ) and performed comparisons between group A (which was used as the reference group) and all the other groups; sex and the number of common albinism-associated alleles were used as covariates (Fig. 3a ; Supplementary Table  4 ). Given that reduced visual acuity and increased central retinal thickness (due to underdevelopment of the fovea) are two hallmark features of albinism we investigated the impact of selected TYR/OCA2 variant combinations on these quantitative traits. We first obtained data on the left LogMAR visual acuity for UK Biobank volunteers (data field 5201, "instance 0" datasets). These visual acuity measurements were then used to compare visual and Social Care). The 100,000 Genomes Project is funded by the NIHR and NHS.
ACKNOWLEDGEMENTS We acknowledge the following sources of funding: the  Wellcome Trust  ( 224643/Z/21/Z ,  Clinical Research Career Development Fellowship  to P. I. S. ;  200990/Z/16/Z ,  Transforming Genetic   Medicine Initiative  to G. C. B. ); the  UK National Institute for Health Research (NIHR)   Clinical Lecturer Programme  ( CL-2017-06-001  to P. I. S. );  Retina UK and Fight for Sight  ( GR586 , RP  Genome Project -UK Inherited Retinal Disease Consortium  to G. C. B. );  Christopher Green  (D. J. G. ); the  French Albinism Association (Genespoir)  and the  French National Research Agency (Agence Nationale de la Recherche ;  ANR-21-CE17-0041-01  to B. A. ). The  UK Biobank Eye  and Vision Consortium is supported by funding from the  NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology , the  Alcon Foundation  and the  Desmond Foundation.
Notably, only 24 UK Biobank participants have been assigned a diagnosis of albinism (data field 41270; ICD-10 term "Albinism" [E70. 3]) of which only 7 had visual acuity measurements and none had OCT imaging; 19 additional individuals had a diagnosis of albinism in their primary care record data (resource 591). Aiming to assess the robustness of the findings in the combined University Hospital of Bordeaux and Genomics England 100,000 Genomes Project dataset, we performed case-control analyses in UK Biobank volunteers. First, genotyping array data were used to obtain genotypes for the TYR:c. 1205G>A variant (data field 22418 including information from the Applied Biosystems UK Biobank Axiom Array containing 825,927 markers). In contrast to this change, the OCA2:c. 1327G>A variant was not directly captured by the array.
The levels were ordered, with the reference level corresponding to a chamois living in a weakly exposed area during a cold and weakly disturbed day. We defined the unique annual id-year ID of each chamois as a random effect. We set the maximum number of knots for the smoothing functions to 24. We also fitted the null model, which was similar to the model described above but excluded the temperature-disturbance variable, for comparison purposes. We used the predictions of the model to investigate the effect of temperature and hikers disturbance on several activity metrics derived from this model (described below). We controlled for the effect of day length by fixing it to the median of the complete dataset (i. e. 963 minutes) when predicting chamois activity for each level of the temperature-disturbance combination.
Acknowledgments This study was funded by  Agence Nationale de la Recherche (French National Research Agency) (ANR)  Grant  Mov-It  No. 16-CE02-0010  coordinated by  Anne Loison ,  Centre National de la Recherche Scientifique (Centre National de la Recherche Scientifique) (CNRS). The authors thank all participants for helping with the capture and marking of animals, for the recovery of GPS collars, and for collecting GPS data on human activities: volunteers and professionals from the  Office Français de la Biodiversité  (formerly  Office National de la Chasse et de la Faune Sauvage) , the  Office National des Forêts , the  Groupement d'Intérêt Cynégétique des Bauges , the  Parc Naturel Régional du massif des Bauges , the  Observatoire Grande Faune et Habitats , and the  laboratory Environnements Dynamiques Territoires Montagnes (EDYTEM).
Acknowledgments We thank  Fabrice Blanc  for facilitating the administrative organization of this PhD studentship. We would also like to thank  Dr. Gabriel Scalliet ,  Dr. Stephanie Bedhomme  and  Dr. Mato Lagator  for their sound comments about our findings. AB was supported by a PhD studentship funded by the  French Ministry of Higher Education, Research and Innovation , and  Syngenta France , through the  CIFRE program , supervised by the National IPO323 Ancestral susceptible strain (negative control) MDR Field type I MDR 2 isolate (positive control) H152R Ancestral SDHI-resistant isolate (negative control) G31 Ancestral DMI-resistant isolate (negative control)  PopR_Alt_PB_I Evolved under Alt_PB selection regime PopR_Alt_PB_II Evolved under Alt_PB selection regime PopR_Alt_PB_III Evolved under Alt_PB selection regime PopR_Mixt_PB_I Evolved under Mixt_PB selection regime PopR_Mixt_PB_II Evolved under Mixt_PB selection regime PopR_Mixt_PB_III Evolved under Mixt_PB selection regime PopR_Alt_MF_I Evolved under Alt_MF selection regime PopR_Alt_MF_II Evolved under Alt_MF selection regime PopR_Alt_MF_III Evolved under Alt_MF selection regime PopR_Mixt_MF_I Evolved under Mixt_MF selection regime PopR_Mixt_MF_II Evolved under Mixt_MF selection regime PopR_Mixt_MF_III Evolved under Mixt_MF selection regime PopR_Alt_CPB_I Evolved under Alt_CPB selection regime PopR_Alt_CPB_II Evolved under Alt_CPB selection regime PopR_Alt_CPB_III Evolved under Alt_CPB selection regime PopR_Alt_CPB_IV Evolved under Alt_CPB selection regime PopR_Mixt_CPB_I Evolved under Mixt_CPB selection regime PopR_Mixt_CPB_II Evolved under Mixt_CPB selection regime PopR_Mixt_CPB_III Evolved under Mixt_CPB selection regime PopR_Mixt_CPB_IV Evolved under Mixt_CPB selection regime 2.
Data availibity Data and scripts were deposited on the Research Data Gouv repository under DOI https://doi. org/10. 15454/DCVDZN. Citation : Ballu, A. , et al. (2022). Dataset of an experimental evolution on the effect of fungicide-resistance composition of populations on the durability of anti-resistance strategies on the wheat pathogen Zymoseptoria tritici, Recherche Data Gouv, https://doi. org/10. 15454/DCVDZN. Association for Research and Technology (ANRT). This work was supported by the Plant Health division of INRAE through the STRATAGEME project.
Briefly, genotyping was performed using GATK v. 4. 1 Haplotype Caller for all individuals separately, followed by joint genotyping with GenotypeGVCFs. Genotyping was performed using the flag --all-sites to genotype both polymorphic and monomorphic positions. For the Z-chromosome scaffolds we applied a filter of GQ >30 for male samples and GQ >15 for female samples, since females have only one copy of the Z-chromosome in birds. Additionally, we removed any sites with heterozygous genotypes in females after applying genotype filters. Finally, after performing all filters, we removed sites with more than 10% missing data in any of the four species. Our final dataset included 51,424,863 SNVs within a set of 566,724,393 callable sites in total on the autosomes from  (Chase & Mugal, 2022) , combined with 2,662,105 SNVs within a set of 30,171,840 callable sites in total on the Z-chromosome.
Acknowledgements The authors thank  Laure Ségurel  for feedback on an earlier version of the manuscript. The computational infrastructure and data storage were enabled by resources provided by the  Swedish National Infrastructure for Computing  (SNIC), partially funded by the  Swedish Research Council  through grant agreement No. 2018-05973. MAC and CFM have conducted their work with financial support from the  Knut and Alice Wallenberg Foundation  ( 2014/0044  to Hans Ellegren) and the  Swedish Research Council  ( 2013-8271  to  Hans Ellegren ).
Data accessibility Sequencing data for all samples are available at the EMBL-EBI European Nucleotide Archive (ENA; http://www. ebi. ac. uk/ena) with the following accession numbers: PRJEB43825 (taiga and red-breasted flycatchers), PRJEB22864 (collared flycatcher), and PRJEB7359 (pied and snowy-browed flycatchers). VCF files used for analyses will be deposited on dryad upon publication. Scripts used for analysis will be publicly available on GitHub upon publication.
Acknowledgements Work in the C. B. laboratory is financed by the  Institut Pasteur  and funding has been received from the  French Government  (grants  ANR-10-LABX-62-IBEID  and  ANR-15-CE17-0014-03  to C. B. ) and the  "Fondation pour la Recherche Médicale"  (grant  EQU201903007847  to C. B. ). S. S. is a scholar in the  Pasteur-Paris University (PPU)   International Ph. D. program and received stipends from the  Institut Pasteur  and the " Fondation pour la Recherche Médicale " ( FDT202204015116 ). We thank the  Institut Pasteur UTechs CB  platform for cytometry and biomarkers for assistance with SP-X ELISA measurements. We gratefully acknowledge the kind financial support of the  Institut Pasteur (Paris)  and the  Région Ile-de-France  (program  DIM1Health ). Further, we want to thank  Hayley Newton  for kindly gifting us the Llo ΔdotB mutant and  David Bikard  for sfGFP.
ACKNOWLEDGEMENTS We thank members of the  Structure and Instabilités des génomes and LPTMC laboratories  for constructive comments during the realization of this work. Work was supported by core funding from  CNRS ,  INSERM  and  MNHN. ALJ and ER were supported by a doctoral fellowship from the french ministry for Education , Research and Technology. EP was funded by grant  ANR-15-CE11-0023-03  ( HiResBac ).