dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, we aim to understand the genetic structure of a montane lineage that might well correspond to a cryptic evolutionary radiation. Specifically, we tackle this by assessing the diversification of the undata complex by conducting a regional analysis that encompasses most of its distribution across the Neotropics. We do this by analysing the most extensive genomic dataset in any species complex of tropical montane plants. This dataset consists of 419 samples from 62 different locations (spanning a gradient of 30 • of latitude and 2400 m in elevation) of the three more widespread species within the undata complex -G. lehmannii, G. orbignyana and G. undatathat are all closely related and very difficult to distinguish morphologically from one another; and an extended target sequencing approach covering 1,684,987 variant sites across the genome for the population genetic analyses and over 4,000 genes for the phylogeny.
Taxon sampling We sampled leaf tissue from 419 adult individuals located in 62 sites across three species from the undata complex: G. undata, G. orbignyana, and G. lehmannii. Geonoma undata and G. orbignyana are the most variable, abundant, and widespread species of the group, whereas G. lehmannii occurs in small and disjunct populations from Panama to Peru. These distributions explain why our sampling includes only eight samples of G. lehmannii, 243 of G. undata and 168 of G. orbignyana. We were unable to sample the locally endemic species G. trigona and G. talamancana. We aimed to cover the latitudinal and elevational gradients of the clade, and thus sampled populations from ca. 800 m to 3,400 m in elevation. As outgroups, we used 21 individuals from closely related groups like G. macrostachys, and species from 14 different palm genera (Appendix C in Supplementary Material).
Reads were trimmed using CONDETRI V2. 2 (Smeds and Künstner 2011) selecting 20 as the high-quality threshold. We used BOWTIE2 v2. 2. 5  (Langmead and Salzberg 2012)  to map reads to the G. undata pseudo reference genome (NCBI project: PRJNA482221). Only reads mapping at a unique location in the genome were kept for analyses. PCR duplicates were masked using PICARD TOOLS v1. 119 (https://broadin tatute. github. io/picard). GATK v3. 8  (McKenna et al. , 2010)  was used to base-recalibrate and realign reads around indels. SNPs were called for target regions and their surrounding 1,000 bp using UNI-FIEDGENOTYPER also from GATK v3. 8 to finally obtain the VCF dataset.
Filtering of the VCF file We filtered the VCF file to obtain a reliable dataset in which variants occur in a significant number of individuals and are covered by a suitable number of reads. Sites were filtered with VCFtools v0. 1. 16 using the options: no indels allowed, minimum quality score of 30, minimum mean depth at 10 × and maximum mean depth at 100 × per site, and a maximum of 50% missing data allowed per site. This resulted in a set of 1,684,987 variant sites with an average depth of 32X and 13. 5% missing data. The whole data set containing 440 samples including the outgroup samples were used for phylogenomic analyses, while a subset of only the 419 ingroup samples of undata complex were used for downstream population analyses.
PCA analysis To explore the distribution of genetic groups in our dataset we performed a Principal Component Analysis (PCA) -We used PLINK 1. 9beta6. 21 to prune variant sites in linkage disequilibrium by selecting a window size of 50 Kb. We then used a window step size of 10 bp and we pruned any variables that showed an r 2 equal or greater than 0. 1 within windows. Filtered variants were used to calculate the eigenvectors and eigenvalues and produce a PCA.
Divergence and diversity parameters We calculated population genetic parameters including pairwise nucleotide diversity (π)  (Nei and Li 1979) , average number of pairwise differences (D xy )  (Nei and Li 1979 ) and pairwise F ST  (Reynolds et al. , 1983). All parameters were calculated using customised python scripts (from  Martin et al. , 2019)  both per genetic cluster inferred from admixture analyses, as well as for geographical sampling point (populations). Since our approach consist of targeting capture and not whole genome sequencing, relative mean parameters (π, D xy and F ST ) were calculated from the average between genomic windows of 500 bp containing at least one variant site. By excluding windows without variant sites, π values might in general be high but this increase is proportional in all measurements which allows the relative comparison between all clusters and populations from this study.
Environmental variation Since the undata complex occurs along elevational gradients, we expected that the major environmental differences between populations and clades will be related to differences in temperature. We obtained the values for the mean annual air temperature from the CHELSA V2. 1 database  (Karger et al. , 2017)  for each sample and used them to analyze the variance (ANOVA) in temperature between phylogenetic clades. We did the same analysis to test for the differences in elevation between clades.
Data accessibility Sequences for 162 samples used in this study were publicly available on NCBI (NCBI bioprojects PRJNA482221, PRJNA541164, PRJNA707300 and PRJNA689999 from  de La Harpe et al. , 2019; Loiseau et al. , 2019; Sanín et al. , 2022b; and Sanín et al. , 2022c). Sequences for the other 278 samples are available under the following NCBI numbers: BioProject PRJNA707300, BioSamples SAMN37951972 to SAMN37952249.
Acknowledgements We thank  Thelma Barbara ,  Frédérique Fardin ,  Libertad Silva ,  Benoit Villain ,  Anna Weigand , and all collaborators of the  POPCORN project in Colombia , Ecuador, Peru, and  Bolivia  for assistance during fieldwork, permit applications, and lab work,  Henrik Balslev ,  Rodrigo Bernal ,  Angela Cano ,  Gloria Galeano  and  Julissa Roncal  for sharing samples. To  Andrew Henderson  for interesting discussions, his encouragement to proceed with this study, and for the confirmation of a few sample identifications. To all members of the  Shimizu group  for their collegiality,  Epifanía Arango ,  Masaomi Hatakeyama  and  Stefan Milosavljevic  for bioinformatics support. To  Nuria Melissa Morales  from  Science Graphic Design  for the design of Figures 2 and4. We especially thank the late  Christian Lexer , Professor at the  University of Fribourg , who conceptualised and initiated this population genomics study together with  Michael Kessler  and  Nicolas Salamin.
Acknowledgements This work was funded by the  European Research Council  ( NOVEL  project, grant # 648321 ) and  ANR TE-MoMa  (grant  ANR-18-CE02-0020-01 ). AL's PhD thesis was funded by the  ERC  and the  University of Lille. The authors thank  Barbara Mable  for sharing seeds of A. lyrata and  Camille Roux  for discussions. This work was performed using infrastructure and technical support of the  Plateforme Serre , cultures et terrains expérimentaux - Université de Lille  for the greenhouse/field facilities. The authors thank the  UMR 8199 LIGAN-MP Genomics platform (Lille, France ) which belongs to the 'Federation de Recherche' 3508  Labex EGID (European Genomics Institute for Diabetes ;  ANR-10-LABX-46 ) and was supported by the  ANR   Equipex 2010 session  ( ANR-10-EQPX-07-01 ; ' LIGAN-MP ').
Data Availability All sequence data are available in the NCBI Short Read Archive (SRA; https://www. ncbi. nlm. nih. gov/sra) with accession codes: PRJNA744343, PRJNA755829. All scripts developed are available in Github (https://github. com/leveveaudrey/dominance_and_sheltered_load https://github. com/leveveaudrey/analysis-of-polymorphism-S-locus ).
Analyses of neuronal migration and centrosome movement Analyses were performed using ImageJ (NIH Image; National Institutes of Health, Bethesda, MD) software and MTrackJ plugin (Meijering, Dzyubachyk, et Smal 2012). The nucleus and the centrosome (when centrin-RFP was co-injected) were tracked manually on each time frame during the whole movie. We considered a NK as a movement superior to 6 μm between two consecutive time points (3 minutes-interval). For cell migration, calculation of speed, percentage of pausing time, sinuosity, directionality, NK distance and frequency was performed using the x,y,t coordinates of the nucleus of each cell. Cells tracked for less than 30 minutes and cells that did not perform any NK during the whole tracking were excluded. A CK was defined as a forward movement superior to 2 μm followed by a backward movement superior to 2 μm.
Acknowledgements We thank  Isabelle Dusart  for expert reading as well as  Caroline Dubacq  and  Oriane Trouillard  for technical help. The experiments were performed in IBPS imaging facility and the mice were housed in IBPS animal facility. migration or maturation of granular cells arriving at the OB (Scotto-Lomassese et al. , 2011). In summary, the authors do not show the functional consequences of aberrant neuroblast migration in the Fmr1 KO mice, making weaker the assumption that the study is important for the understanding of FXS pathophysiology.
Extrapolation to unanalyzed SPINK1 coding SNVs Finally, we addressed a critical question: Can we reasonably interpret the potential splicing effects of the 658 SPINK1 coding variants that have not yet undergone functional analysis, based on insights derived from the cross-correlation of FLGSA data and SpliceAI predictions of the 27 known and 35 newly analyzed SNVs? To accurately answer this question, we initially evaluated whether the Illumina precomputed scores significantly deviated from those calculated using a distance of 10,000 bp and the hg38 sequence. Consequently, we manually acquired these latter scores from  [40]  for all SNVs that underwent FLGSA. While we did observe slight disparities between the two datasets for many SNVs (see Supplementary Table  S1 ), it's crucial to emphasize that these variations were not expected to result in any changes to the predicted splicing outcomes.
Results U2AF2 variant carriers have a neurodevelopmental phenotype. Phenotype and genotype data from 46 unrelated individuals with an NDD and a heterozygous missense variant in U2AF2  (OMIM 191318)  were ascertained through GeneMatcher and Matchermaker Exchange  (26, 27). The cohort consisted of 27 female and 19 male probands spanning from 6 months to 24 years of age at the time of data collection, including 1 individual (individual 3) who was recruited from the Deciphering Developmental Disorders (DDD) study (www. ddduk. org) and 1 (individual 27) who was described elsewhere  (28). The affected individuals had substantial overlapping features, which are summarized in Table  1  and described in more detail in Supplemental Table  1  (supplemental material available online with this article; https://doi.
Here, using integration of clinical phenotyping, exome/ genome sequencing, protein structure analysis, modeling in flies and human pluripotent stem cells (hPSCs), and transcriptomics, we mapped the genetic architecture of 3 NDD-associated genes, determined their roles in neurodevelopment and behavior, and These data reveal a neurodevelopmental phenotype associated with U2AF2 de novo missense variants, with core features of global developmental, speech, and motor delays, mild-to-severe ID, hypotonia, seizures, frequent brain malformations, autistic features, behavioral disturbances, and a shared facial gestalt. De novo U2AF2 variants cluster in RNA recognition motifs. The U2AF2 variants (NM_ 001012478. 1) of the 46 probands were identified via exome or genome sequencing and confirmed to be de novo except in 2 instances due to lack of parental DNA.
Individuals P1-P5 all have speech/language and motor delays, and individuals P1-P4 have a formal diagnosis of autism. Moreover, hypotonia was present in 3 individuals (P1, P2, and P4). Three individuals (P1, P4, and P5) had ID and 1 was normal (P3). One individual was too young for ID assessment (P2), and P6 was a fetus with multiple congenital malformations (Table  1  and Supplemental Table  3 ), substantially overlapping the core features of U2AF2-related NDD. All variants were confirmed by Sanger sequencing and were absent in population genomics resources. Prediction of their deleterious effects is summarized in Supplemental Table  2. PRPF19 is intolerant to both LoF (probability of LoF intolerance [pLI] = 1) and missense (Z = 3. 89) variants according to the gnomAD population database.
Analysis of U2af50 RNAi RNA-Seq ants (Gly404Ser and Arg422Cys) are in the WD40 beta-propeller architecture 5 and 1 (Leu499Phe) in WD40 beta-propeller architecture 7 (Figure  3A ). 3D structural modeling (Supplemental Figure  2A , PDB: 4LG8) showed that these substitutions, whose side chains are bigger or smaller than WT residues  (49) , are expected to destabilize the beta-propeller folding and in turn impair association with NTC-associated proteins. We induced expression of the WT and 3 PRPF19 variants in HEK293T cells, and, as expected, cells transfected with PRPF19 His273Thrfs*37 showed a complete loss of binding with CDC5L (Figure  3B ), suggesting that the variant p. (His273Thrfs*37) is a complete LoF allele. PRPF19 pathogenic variants lead to social behavior deficits. To dissect the pathophysiological mechanism of how PRPF19 variants contribute to disease, we analyzed fly ortholog Prp19 with 2 independent RNAi lines (Prp19 RNAiBL32865 and Prp19 RNAiv41438).
All RBFOX1 variants were confirmed to be de novo, with the p. (Arg118Gln) variant being recurrent in 4 unrelated individuals (Figure  5A ), and other 2 individuals having the c. 460G>T, p. (Gly154Cys) and c. 559G>A, p. (Glu187Lys) variant. RBFOX1 is not directly involved in the core spliceosome complexes but has a single RRM domain that binds to specific sequences [5′-(U)GCAUG-3′] to promote inclusion or exclusion of exons  (24, 25)  and is expressed mainly in the nervous system, heart, and muscle  (25, 54). All identified missense variants occurred in the only RRM domain, and multiple algorithms predicted potential deleterious effects (Supplemental Table  2 ). Moreover, inspection of the protein structure (Supplemental Figure  2A , PDB: 2N82) demonstrated that all 3 missense variants are in a region of close contact with RNA, as for many U2AF2 variants, suggesting that the RNA-binding affinity may be impaired.
Mis-splicing events further revealed by hPSC RNA-Seq. To isolate the effect of a single variant change, we performed transcriptomic profiling of the hPSC isogenic model with 24 samples (10 colonies of Arg149Trp, 4 colonies of Arg150Cys, and 10 WT colonies; Supplemental Figure  6H ). Interestingly, we found 62 diffUE events (|log 2 FC|>0. 7 and P adj ≤ 0. 01) in 39 unique genes (Supplemental Table  6  and Supplemental Figure  6, I-K ). Gene enrichment analysis of these 39 genes revealed that only RNA binding under molecular functions was enriched (P adj = 0. 017). Real-time reverse transcription quantitative PCR (RT-qPCR) confirmed 3 randomly selected exon targets, i. e. , RPL37, DNAJC21, and OTX2 (Supplemental Figure  6, I-K ). These data suggest that the 2 pathogenic variants (Arg149Trp and Arg150Cys) residing in the interaction surface with RNA could result in aberrant mRNA splicing in stem cells, a relevant cell type to model the pathophysiology.
Methods Statistics. The data shown represent the results of at least 3 separate experiments. All results are presented as the mean ± SEM or the mean ± SD. Comparisons between 2 groups were analyzed by t test, and comparisons between 3 groups were done by 1-way ANOVA followed by Dunnett's test or 2-way ANOVA followed by Turkey's or Šidák's test using GraphPad Prism (GraphPad Software). A P value of less than 0. 05 was considered statistically significant. Study approval. Informed consent was obtained from all the families according to protocols approved by local IRBs and human research ethics committees. Specific written consent was also retained for all individuals whose photographs appear in the manuscript. Data availability. RNA-Seq data generated from flies and hPSCs are available in the NCBI's Gene Expression Omnibus (GEO) database (GEO GSE246137).
Acknowledgments We thank the patients and their families for participating in this research. We acknowledge  Clara L. Kielkopf  ( University of Rochester, Rochester, New York, USA ) for providing the pyPY minigene construct. This study was supported in part by a  CHOP Roberts Collaborative Functional Genomics Rapid grant  (to DL, MB, and YS);  Institutional Development Funds  (to HH); an  Eagles Autism Foundation  grant (to DL);  NIH  grant  R01NS107392  (to YS); and a  Pennsylvania Department of Health  grant  4100088540  (to YS). Work for U2AF2 individual 30 was supported by the  NIH Common Fund , through the  Office of Strategic Coordination/Office of the NIH Director  under Award  U01HG007672  (to  Duke University ). Work for U2AF2 individual 27 was conducted as part of the  Clinical Sequencing Evidence-Generating Research (CSER) consortium , which is funded by the  National Human Genome.
14 to 0. 59  15, 19, 21, 22, [31] [32] [33] [34] [35] [36] [37] [38] [39]. Similarly, the overlap of the detected loci influencing mRNA (eQTL) and protein (pQTL) abundance greatly differed across the datasets. It ranges from a very weak overlap of 5. 5% in a study on 97 inbred and recombinant mice to nearly 35% in human (n = 62) and mice (n = 192)  6, 15, 40. Part of the diverging results might have been driven by technical limitations. For instance, it has been shown that by selecting the most representative peptides in prior proteomic methods, the overall correlation of global transcript and mRNA abundance improves significantly  37, 41. A key difference is also whether the goal of the survey is to correlate absolute number of transcripts and proteins, or relative changes in protein or mRNA levels, which differ between samples.
Integration of proteomic and transcriptomic datasets acquired in parallel under similar conditions allowed comparison of accurate quantification of the mRNA and protein abundance of 629 genes across 889 natural isolates  44. Based on these unique datasets, we clearly demonstrated that the degree of withingene correlation between protein and mRNA abundance is very low (r = 0. 165). Consistently, we found that the genetic variants influencing protein and mRNA abundance are very dissimilar. Our study highlights that population-scale proteomes are essential and add a new dimension to the characterization of the genotype-phenotype relationship when integrated with genomic and transcriptomic information.
Quantitative proteomes of a large collection of natural isolates We generated a quantitative proteomic dataset for strains of the 1,011 strains collection  43  from cells cultivated in synthetic complete medium with amino acids in order to match the growth medium used for RNA sequencing  44  (Figure  1A ). We had previously acquired a proteome dataset of the 1,011 strains collection, measured with microflow chromatography and SWATH MS  45. For the acquisition of this new dataset we used a proteomic method that allows for an even higher throughput, using analytical flowrate chromatography and Scanning-SWATH MS with a 3 min gradient  46. After cultivation of the yeast isolates in 96 well plates, proteins were extracted, and subjected to reduction, alkylation, and trypsination in a semi-automated workflow using liquid handling robotics  47.
Peptide preparations were separated using a 3-minute high-flow rate (800 µl/min) chromatographic gradient using an Infinity II chromatographic system (Agilent Technologies), coupled to a 6600 Triple TOF instrument (Sciex). Data was recorded using Scanning SWATH acquisition  46  and the raw data was processed using the DIA-NN software (version 1. 8), which was specifically developed for large scale proteomic exploration  48. We applied several quality filters where poor-quality samples were removed from the analysis, and we excluded peptides that were not detected in more than 80% of the samples (see Methods). The generated dataset hence encompasses protein abundance quantification for 630 proteins among 942 isolates (Table  S1  and Table  S2 ). This dataset therefore covers the overall genetic diversity of the species and captures the subpopulations that were defined as part of the 1,011 yeast genomes project, including both domesticated and wild clades  43  (Figure  S1A ).
We combined the proteomic dataset with transcriptomic data obtained from the 1,011 strains collection  44  , which gave access to the quantified expression of both levels for 629 genes across 889 isolates (Figure  1B , Table  S1 ). To be able to properly compare these two datasets, we normalized them with quantile normalization after imputing the missing values using the KNN method (Table  S3 , Figure  S1B-C ). To characterize the quantified proteins in our study, we first compared the level of transcription of both the identified and unidentified proteins. Low abundance transcripts are less likely to be quantified by proteomics as compared to high abundant transcripts (Figure  S1D ). Indeed, 489 out of 629 consistently quantified proteins fall into the 20% highest transcribed genes (n = 1,304).
In total, 537 out of 629 quantified proteins were found in the two highest abundance deciles as defined in a recent yeast protein abundance meta-analysis 49 (Figure  S1E ). Overall, proteins related to essential genes and involved in molecular complexes were both significantly enriched in the set of proteins quantified by Scanning SWATH (odd-ratio = 3. 5 and 2. 2 respectively, Fisher's exact test, p-values < 2. 2x10 -16 )  [50] [51] [52]. Functionwise, we found that metabolism-related genes were overrepresented among the 629 genes included in our study (Table  S4 ). We then investigated the level of variation in protein abundance by calculating the coefficient of variation (CV) for each protein using the non-normalized dataset. We found an average CV of 31%, varying between 12% and 98% and one high outlier reaching 300% (PDC5, a pyruvate decarboxylase).
The precursor-level CVs across quality control samples (15. 15%) were much lower than the precursorlevel CVs across the natural isolate samples (34. 21%), confirming that a biological signal was observed across the isolates (see Methods). Gene set enrichment analyses (GSEA) were performed using the CVs and significant enrichment of genes related to amino acid metabolism, respiration or pyruvate metabolism was found for proteins with a high CV, indicating that they vary the most (Table  S5 ). By contrast, proteins with a low CV were significantly related to genes involved in tRNA aminoacylation or protein degradation. A. The proteomic dataset was generated on isolates grown in synthetic complete (SC) medium with amino acids using a semi-automated sample preparation workflow, and Scanning-SWATH MS (see Methods).
The overlap between this dataset and the recently generated transcriptomic dataset on the same population in the same condition  44  resulted in 629 protein/transcript abundances across 889 isolates. B. Phylogenetic trees of the isolates used in this study. Colors correspond to previously defined subpopulations  43. C. Gene-wise correlation coefficients (Spearman correlation test) between the proteome and the transcriptome. D. and E. mRNA-protein within-gene correlation across isolates for the RPL38 and GLR1 genes (ρ corresponds to the Spearman correlation coefficient with p-values of 4. 8x10 -7 and 2. 3x10 -153 , respectively). Transcript and protein abundances are weakly correlated at the gene level across isolates As proteomes and transcriptomes were obtained using the same growth media, our dataset allowed us to characterize the different types of correlation between mRNA and protein abundance across a natural population.