dataesr/scientific-paragraphs-categorization huggingface dataset:
002) for the population size parameter θ, which corresponds to a mean theta of 0. 001 and a mean root age of one million years, assuming a constant mutation rate of 7 × 10 -9 substitutions per site per generation. The rooted species tree was defined as (((A_East, A_Italia), (B_East, B_West)), C_Italia), as inferred by  Stritt et al. (2022). The MCMC was run four times independently, each time with 408,000 iterations, including a burn-in of 8,000 iterations. Highest posterior density intervals (HPDI) were estimated with the R package HDInterval version 0. 2. 4 (Meredith and Kruschke 2022). Relative cross-coalescence extracted from the output of Relate were also used to estimate divergence times, with relative cross-coalescence < 0. 5 considered as populations being fully separated.
Ecological niche modeling Raster maps were downloaded for all bioclimatic variables from WorldClim 2. 1  (Fick and  Hijmans 2017) at a 30 sec resolution (about one kilometer). Global aridity index raster maps were obtained from https://cgiarcsi. community/data/global-aridity-and-pet-database/. We used the geolocations of the 332 accessions to extract bioclimatic variables at each locality with the R package raster 3. 5-29. The 19 classical WorldClim variables (bio1 to bio19) and elevation were used unmodified. New variables were created from monthly values for precipitation, solar radiation, average temperature, maximum temperature, minimum temperature, and aridity, by calculating the averages of four months of putative biological interest (March to June, and November to February) (Table  S1 ).
Elevation data were downloaded using Google Earth. Correlations among bioclimatic variables were plotted as a bubble plot with the R package corrplot Version 0. 92  (Wei and Simko 2021). We used ecological niche models (ENMs) to estimate the climatic niches of the five genomic clades of B. distachyon and to investigate the degree of climatic niche overlap in both current and past conditions. As inputs for the models, we used the geographical coordinates of sequenced individuals and a set of environmental variables that were selected based both on biological relevance and on minimum collinearity between them: minimum temperature averaged from November to February, precipitation levels averaged from March to June, solar radiation levels averaged from March to June and elevation. The ENMs were produced with the MaxEnt software version 3.
Sardinia, Corsica and the Balearic Islands were excluded. The dataset also included three sample points in Northern Iran and Iraq. No suitable administrative region could be found to effectively delineate these areas. We therefore created a buffer (1 decimal degree radians) around the sampling points. To study past clade distributions, we projected current climatic niches onto paleoclimate maps using variables with a resolution of 2. 5 arc-min for the Last Glacial Maximum (LGM) obtained from WorldClim1. 4  (Fick and Hijmans 2017). The atmospheric general circulation models used in this study are the Community Climate System Model version 4 (CCSM4)  (Gent et al. 2011 ). Past Maxent model projections were 'clamped' which restricts them to the range of climatic conditions that are seen in the current climate conditions (where the model was trained).
This is recommended to avoid spurious model extrapolations when projecting into novel environments  (Elith et al. 2010). Clamped areas can be seen coloured grey in Fig. S6. The program ENMTools version 1. 3  (Warren et al. 2010; Warren et al. 2021 ) was used to test whether there are statistically significant ecological differences between the clades, based on the ENMs produced by Maxent. Niche similarity was first calculated between each clade combination for the empirical data. 99 pseudoreplicate niche comparisons between each clade were then produced by pooled and randomized occurrence points of the empirical data. The overlap between ENMs generated from the empirical data for each clade comparison was then compared to the null distribution obtained using the pseudoreplicates, using Schoener's D and the I statistic  (Warren et al.
5 kb overlap, using the R package rehh version 3. 2. 2  (Gautier et al. 2012 ). The selected windows contained at least two SNPs above the FDR threshold. We then used the BEDTOOLS version 2. 26. 0 intersect option  (Quinlan and Hall 2010)  to extract genes located in or overlapping with significant regions based on the v. 3. 2 annotation of the B. distachyon (https://phytozome-next. jgi. doe. gov).
Scans of selection The X T X analysis was performed with BayPass using the five genetic clades as focal populations  (Gautier 2015). We generated the input file by using vcftools --count to calculate the allele frequency of each SNP present in our vcf (no filtering on minimum allele frequency). We then ran Baypass on our actual dataset with the following parameters: -pilotlength 500 -npilot 15 -burnin 2500 -nthreads 6. Top 0. 1% outliers were chosen as a threshold of significance, as more conservative than the threshold calculated with a pseudo-observed dataset simulated with the co-variance matrix  (Gautier 2015). We also used a sliding window approach, with window size of 5 kb and 2. 5 kb overlap, using the R package rehh  (Gautier et al. 2012).
Age estimates and gene ontology enrichment The age of each single SNP was computed with GEVA  (Albers and McVean 2020)  to estimate the average SNP age for each annotated gene (https://phytozome-next. jgi. doe. gov). All private SNPs to the combined A and B lineages were polarized using the ancestral C lineage using custom R scripts. GEVA was run on the five main scaffolds (corresponding to the five chromosomes) using the genetic map produced by  (Huo et al. 2011 ) and the polarized SNP dataset. The average age of genes associated with the 32 bioclimatic variables described above or outliers in the X T X analysis was compared to the average age of genes at the genome wide level with Wilcoxon test. We only used the gene lists produced by the GEA and the X T X analyses if they contained at least fifteen genes.
DATA AVAILABILITY Seeds will be distributed through GRIN but can in the meanwhile be obtained in small quantities upon request. Illumina paired-end sequencing data generated for this project are archived on the European Nucleotide Archive, project number PRJEB61986. Archive numbers for the reads produced by Gordon et al. (2018 Gordon et al. ( , 2020)) ,  Skalska et al. (2020)  and  Stritt et al. (2022)  are available in the respective publications. Scripts are available at https://github. com/nminad/env_genomics.
Mutation of R99 impairs ARF1 GTPase activity and association with the COPI complex ARF1 is a small GTPase, and both GTP-GDP cycling and co-factor binding are known to be crucial for its enzymatic activity  18. Analyses of ARF1 structures, based on PDB: 2J59  41  , indicated that residue R99 is not directly involved in binding to GTP or other ARF1 interaction partners. Instead, R99, located on helix α5, forms salt bridges with residue D26, thereby stabilizing the loop β1-α3 which is part of the GTP-binding site of ARF1 (Fig. 4a ). D26 is not directly involved in the coordination of GTP binding (Fig. 4a ). However, correct positioning of the loop β1-α3 is crucial for the stability of ARF1 and GTP hydrolysis  41. To experimentally assess the effects of the R99C mutation on protein stability, GTP binding and GTP hydrolysis, we purified recombinant ARF1 WT, ARF1 R99C and ARF1 Q71L (Supplementary Fig.
4f  and Supplementary Fig. 4f ). This analysis suggested that major protein components of COPI complex, COPA, COPB1, COPB2, COPG1, showed 1. 18, 1. 32, 1. 36 or 1. 41-fold lower association with ARF1 R99C. Panther DB aided Gene Ontology (GO) term analysis of the top 30 proteins binding with lower affinity to ARF1 R99C showed that interaction with proteins of the COPI pathway was significantly reduced by the mutation R99C (GoTerm: COPI vesicle coat (GO:0030126), >100-fold enrichment in analysed list, p = 7. 42E-09; COPI-coated vesicle membrane (GO:0030663), >100-fold enrichment in analysed list, p = 2. 27E-08; COPI-coated vesicle (GO:0030137), 100-fold enrichment in analysed list, p = 5. 42E-08) (Fig. 4g , Supplementary Data 3). All human genes in the PantherDB database were used as a reference list.
Ethics statement Clinical information and samples were obtained with informed consent and consent to publish this research. Consent to participate in research and consent to publish the findings were obtained either from the patients or, in the case of underage patients, from their respective parent(s) or guardian(s). The study was approved by the Comite de Protection des Personnes (ID-RCB/EUDRACT: 2014-A01017-40) in France, and the Leeds (East) Research Ethics Committee (REC reference: 10/H1307/2 IRAS project ID: 62971) in the UK. ARF1 patient cells were handled with approval of the Ethics Committee at Ulm University (Approval 530/21). Sex and/or gender was not considered in the study design and is of no relevance top the study. Information on each individual patient is provided in the Supplementary Information file.
Both were normalized to cell viability determined by the CellTiter-Glo Luminescent Cell Viability Assay (Promega, G7570). Luciferase and cell viability measurements were performed using an Orion II microplate Luminometer and the Simplicity software (Berthold), SEAP activity was measured at 650 nm by using a Vmax kinetic microplate reader (Molecular Devices) and the SoftMax Pro 7. 0. 3 software.
Full scans ranging from m/z 370 to 1700 were acquired in the Orbitrap at a resolution of 30,000 (at m/z 400) with Automatic gain control (AGC) enabled and set to 10 6 ions and a maximum fill time of 500 ms. For fragmentation in the linear ion trap the AGC was set to 10,000 ions and a maximum fill time of 100 ms. For MS/MS fragmentation of the top 20 most intense ions, a normalized collision energy of 35% with an activation q of 0. 25 and an activation time of 30 ms was used. The resulting fragments were analysed using the LTQ part at rapid scan speeds. Database search was performed using MaxQuant Ver. 1. 6. 3. 4 (www. maxquant. org)  70. For peptide identification and quantitation, MS/MS spectra were correlated with the UniProt human reference proteome set (www. uniprot. org, Version on March 16th 2021), supplemented with the ARF1 sequences, employing the build-in Andromeda search engine  71.
Go-term analysis The top 30 genes less associated with ARF1 R99C compared to ARF1 WT according to the SILAC experiment were submitted to PantherDB  72, 73. Analysis Type: PANTHER Overrepresentation Test (Released 20220202). GO Ontology database DOI: 10. 5281/ zenodo. 6399963, Released 2022-03-22. Reference List:Homo sapiens (all genes in database).
25 µg/ml; rabbit anti-GM130, 0. 585 µg/ml; rabbit anti pTBK1, 1. 42 µg/ml; rabbit anti-STING, 3. 5 µg/ml; mouse anti-FLAG M2, 0. 35 µg/ml; conjugated to equal amounts (µg/ml) of Zenon Alexa Fluor 647 rabbit IgG labelling reagent (Z25308), Zenon Alexa Fluor 568 rabbit IgG labelling reagent (Z25306), Zenon Alexa Fluor 488 rabbit IgG labelling reagent (Z25002) or Zenon Pacific Blue mouse IgG 2a labelling reagent (Z25313, Thermo Fisher Scientific) and 500 ng/ml DAPI (Invitrogen, D1306) for 2 h at 4 °C in the dark. Next, the samples were washed with PBS/0. 1% Tween 20 and water and the coverslips were mounted onto microscopy slides. Images were acquired using a Zeiss LSM 710 confocal laser scanning microscope with ZEN 2010 imaging software (Zeiss). Images were analysed with ImageJ (Fiji). The number of ERGIC-53 positive particles and the particle size was analysed using a custom ImageJ macro (Fiji).
STED imaging Images were captured with a home-built dual-colour 3D-STED microscope with home-built software  77. Typically, an average power of ~1 μW for each excitation beam (568 nm and 633 nm, respectively) and ~1. 5 mW for each depletion beam (710 nm and 750 nm, respectively) was used. STED images were captured at a pixel size of 20 nm and a dwell time of 300 μs with a typical peak photon number of ~150 counts. Images were analysed by ImageJ (Fiji). For better visualization, a Gaussian blur of σ = 1 was applied in each channel.
EM stereology According to the Delesse principle, stating that the volume density of an organelle or component in a tissue can be estimated by measuring the area fraction of the intersections of the component within a random section of the tissue  82, 83  , volume fractions of luminal structures and small vesicles were determined by counting grid points on predefined classes within square test fields of 16 µm2 size using the recursive grid option implemented in the open-source software JMi-croVision image analysis system. Since it is not possible to evaluate the entire cell, the precondition for stereological evaluation was that the section through the cell contained centrioles. The test fields were chosen so that the centrioles were located in their centre. This ensured that only similar areas in the cell were evaluated, as the distribution of organelles may differ depending on the cell area.
Intact single cells were gated using SSC-A/FSC-A and FSC-A/FSC-H, respectively. Raw fluorescence-activated cell sorting (FACS) data were analysed using FlowJo 10. Median fluorescence intensity shifts of all samples were calculated by subtracting the LC3B-GFP-MFI of vectortreated samples from the ARF1 WT and ARF1 R99C samples.
Acknowledgements We thank  Jana-Romana Fischer ,  Birgit Ott ,  Regina Burger ,  Daniela Krnavek ,  Kerstin Regensburger ,  Martha Meyer  and  Nicola Schrott  for excellent technical assistance. This study was supported by  DFG (German Research Foundation ) grants  SP1600/4-1  (to K. M. J. S. ),  SPP1923  (to K. M. J. S. and F. K. ),  CRC1279  (to K. M. J. S. , F. K. , J. M. , C. R. , S. W. and P. W. ), as well as the  BMBF  to F. K. (Restrict  SARS-CoV-2 ) and K. M. J. S. ( IMMUNOMOD 01KI2014 ) and the  DFG   Emmy Noether Programme   458004906  to CCOM. Y. J. C. and K. M. J. S. are additionally supported by a  DFG/Agence Nationale de la Recherche (ANR, France)  joint grant ( 504830917 ). Y. J. C. acknowledges the  European Research Council  ( 786142 E-T1IFNs ), a  UK Medical Research Council   Human Genetics Unit core grant  ( MC_UU_00035/11 ), a state subsidy from the  Agence Nationale de la Recherche (France)  under the ' Investissements d'avenir' programme  bearing the reference  ANR-10-IAHU-01 , and  MSDA-VENIR  ( Devo-Decode Project ).
All GD lines displayed a significant reduction of the cell count compared to the healthy counterpart (Fig. 3a ). This is also observed in measuring cell proliferation and metabolic activity through MTT assay, where the reduction of viable and metabolically-active cells is particularly evident and significant for the analysed nGD NPCs and for CBE-treated cells (Fig. 3b ). These data confirm the hypothesis that the GCase enzyme deficit induces a decrease in cell growth due to a defective neural cell proliferation possibly coupled with increased cell death. To assess cell death pathways, early and late apoptosis rates were measured through flow cytometry. After 9 days in culture, NPCs from GD lines showed an overall cell death rate higher than control lines, with a significantly increased percentage of PI-positive cells when compared with the matched control in the GD-2 line (Fig.
Quantification of the YAP nuclear localization in NPCs and neurons is shown for all the analysed lines (see Supplementary Fig. 5  for the other lines representative pictures) and was performed with the Intensity Ratio Nuclei Cytoplasm Tool plug-in of the Fiji software (n = 3). YAP nuclear localization is strongly reduced in GD-NPC and neurons. g qRT-PCR results of YAP transcriptional targets CTGF and CYR61 in CTRL NPCs after 24 h VP 0. 5 µM treatment expressed as fold changes and normalized with respect to three housekeeping (n = 2). h MTT assay results, measuring cell proliferation and metabolic activity after 24 h of VP 0. 5 µM treatment in NPCs (n = 2). All graphs show mean values and SEM. Statistical significance is indicated as p value (Student's t-test), *p < 0. 05, **p < 0. 01, ***p < 0.
Only the experiments involving the Minute and engrailed flies were performed at 29 °C. Adult retina fluorescence evaluation. Using GMR > Gal4 as specific retina promoter and UAS-mCD8::GFP, as GFP driver, the head of the adult flies of GBA-KO, UAS-GBA-RNAi (one copy each), and UAS-luc-RNAi as control, were cut at different time points, in order to evaluate the fluorescence associated with ommatidia degeneration. The fluorescent retinas were imaged using a Nikon Eclipse 90i microscope, taking advantage of the Z-stack acquisition tool present in the Nis-element Software, and the quantification used the Corrected Total Cell Fluorescence (CTCF) method. Wing hair count and evaluation of fat body cells area. Adult wings from Minute, engrailed > Gal4; UAS-GFP/UAS-GBA-RNAi (M,en; GBA1b-RNAi) (experiment population) and Minute, engrailed > Gal4; UAS-GFP/UAS-luc-RNAi (M,en; luc-RNAi) individuals were mounted in Fluoromount media and observed under the Nikon Eclipse 90i microscope.
Eight images were taken for each genotype using the 60X objective, always focusing on the distal vein area. For each picture, the hairs included in two random 3 × 3 cm squares, were counted. For the fat body cells area, 12 images for CTRL and GBA-KO samples were taken; in each one, three cells were measured using ImageJ Software. Climbing assay and survival rate. CTRL, GBA-KO, and GBA-KO, GBA > Gal4 flies for the climbing assays were separated at birth into males and females and kept 25 per vial at 25 °C. The climbing test was performed on days 3, 7, 15, and 20 after birth. An equal number of female and male flies was placed inside a 50 ml transparent glass cylinder and, once acclimated, the cylinder was tapped down hard enough to knock all the flies down to the bottom; after 10 s, the number of flies able to reach three pre-established levels (below 5 cm-between 5-7.
5 µM Verteporfin (Selleckchem), NPCs have been treated for 24 h before detecting cell proliferation through MTT assay. Cell death and apoptosis were analysed through flow cytometry (MACSQuant Analyzer, Mylteni); NPC were detached with Accutase, washed once with PBS, and stained with propidium iodide (PI) and AnnexinV (FITC) for 30 min in PBS 4% FBS before data acquisition. Flow cytometry data were analysed with the FlowJo software. RNA extraction and RT-PCR. RNA extraction from Drosophila tissues was performed using the TRI Reagent® (Sigma-Aldrich), after mechanical disintegration and homogenization of the tissues. To avoid genomic contamination, the RNA samples underwent DNase treatment. To obtain cDNA, 250 ug of DNase-free RNA was reverse transcribed using the iScript Sigma Bio-Rad kit. RPL32 was used as a housekeeping gene  22.
Library preparation and whole transcriptome sequencing. Total RNA was extracted from three different biological replicates of Drosophila CTRL and GBA-KO brains each, at 3 and 15 days from hatching with TRI Reagent® (Sigma-Aldrich). cDNA libraries were synthesized starting from 500 ng total RNA with TruSeq stranded mRNA Sample Prep Kit v2 (Illumina, San Diego, CA, USA) following the manufacturer's instructions. Whole transcriptome sequencing was performed on Nextseq500 sequencer (Illumina) at 80 bp read length in paired-end mode, yielding a total of 76 GB and an average of 79,5 million reads per sample. Read pairs were mapped on the Drosophila melanogaster genome reference dm6 using the alignment program hisat2 (http://daehwankimlab. github. io/hisat2/). Raw counts were computed with htseq-count (https://www.
ncbi. nlm. nih. gov/pmc/articles/PMC4287950/) using the Drosophila dm6 annotation Drosophila_melanogaster. BDGP6. 95 from ENSEMBL (http://ftp. ensembl. org/pub/release-95/gtf/drosophila_melanogaster/). Raw counts were then normalized as count per million (CPM) by the R-Bioconductor package edgeR (https://bioconductor. org/packages/release/bioc/html/edgeR. html). The same package was adopted to estimate the differentially expressed genes (DEG) between conditions and time points. Upregulated and downregulated biological pathways were evaluated with the web tool FlyEnrichr (https://maayanlab. cloud/ FlyEnrichr/). GCase enzyme activity. For the measurement of the enzymatic activity of GCase, the fluorescent substrate 4-methylumbeliferyl-N-acetyl-β-glucosamine (Sigma M-3633) was used. The test was performed on protein lysates, obtained by sonication in PBS + Triton X-100 0,01%.
The magnifications used the Nyquist theorem not to exceed the zoom and capture false signals. The Fluoview software was used for acquisition and the Fiji (ImageJ) software for analysis. iPSC-derived NPC and neurons were seeded on cover-glass at the density of 100,000 cells/well, fixed in 4% paraformaldehyde for 15 min at room temperature (RT), rinsed twice with PBS, and permeabilized using 0. 25% Triton X-100 in PBS for 10 min at RT and blocked with 10% normal goat serum in PBS + 0. 1% Triton X-100 for 1 h. After washing in PBS, the cells were subjected to a block of unspecific binding of the antibodies using 1% bovine serum albumin (BSA) prepared in PBS solution for 30 min at RT and subsequently incubated with primary antibody at 4 °C overnight (on). Then, samples were rinsed and stained with the appropriate species of Alexa Flour Fluor 488/568/ 546 conjugate secondary antibody (1:250; Invitrogen by Thermo Fisher Scientific) for 1 h at 37 °C in the dark.
Visualization was done by using Amersham ECL Western Blotting Detection Reagent (GE Healthcare). Densitometry analyses on the immunoblots were performed by ImageLab software (Bio-Rad). Statistics and reproducibility. Statistical testing involved two-tailed Student's t-test, multiple t-test, one-way ANOVA, and log-rank test as indicated in each related figure legend. Data were expressed as mean ± SEM. The significance level was set at p value <0. 05. To determine reproducibility, analysis involving flies, iPSC-derived NPC and neurons have been performed on at least three different biological replicates, except for VP treatments on NPC, where analysis on two biological replicates have been carried out. qPCR samples were run in three technical replicates. Sample size and replicates are stated in the corresponding figure legends.
Acknowledgements This work was supported by grants from  Fondazione del Monte di Bologna e Ravenna. We would like to acknowledge the AGEOP association for the opportunity to S. S. to be financially supported during the Ph. D. program;  Sanofi-Genzyme  for support in the participation to national and international conferences, the  University of Bologna  and the Ph. D. programs in Cellular and  Molecular Biology  and in Oncology, Hematology, and Pathology; the  Bloomington Drosophila Stock Center and Vienna Drosophila RNAi Center for Drosophila stocks ;  Harvey K. F. and  Fumic K. Labs  for the scientific inspiration and technical improvements; Ignesti M. and Poppi L. for the fundamental technical support.
Statistical analysis Before the analysis, we log-transformed data of realized fecundity. We used a Mixed Generalized Additive Model (GAMM) with R software  (Team, 2013)  and the Mgcv package  (Wood et al. , 2016)  to analyze population growth rate, realized fecundity and hatching success (data in supplementary informations; Table  A. 1, Table  A. 2) with quasi-Poisson (log link function), Gaussian and quasi-binomial (logit link function) distribution, respectively. No overdispersion of the data was observed. Population growth rate, realized fecundity, and hatching success were analyzed as a function of the number of transfers (as a continuous variable), irradiation treatment (i. e. , control, low and high irradiation) and their interaction as fixed effects. ID of the replicate and measurement group ID (only for population growth rate and realized fecundity) were added as random effects.