dataesr/scientific-paragraphs-categorization huggingface dataset:
Metabolism and immunity genes Metabolic pathways were reconstructed combining the CycADS database and a so-called gap-filling procedure (see the "Material and method" section). Gap filling allowed improving annotation by detecting 29 putative additional functions, associated with 39 genes (Additional File 1: Table  S4 ). For example, this includes a candidate gene for phosphopantothenoylcysteine synthetase (DV3025962. 1, EC: 6. 3. 2. 5), an enzyme of the coenzyme A biosynthesis pathway, and a candidate gene for nicotinamidase (DV3000063, EC: 3. 5. 1. 19), an enzyme involved in nicotinamide metabolism (Additional File 1: Fig. S7 ). Thus, the DakviCyc database contains a metabolic network reconstruction of the phylloxera genome. Metabolism was found to be largely conserved between grape phylloxera and the aphids M.
Automatic annotation and manual curation Gene predictions were generated using MAKER2  [104]. Within MAKER2, a first gene set was predicted by similarity to known proteins, or contigs of RNA-Seq (see below). This gene set was used thereafter for training both Augustus  [105]  and SNAP  [106] , in two steps, using results from an initial training to retrain again the software. Transcriptomic evidence came from two previous RNA-Seq projects  [107, 108] , which included whole bodies of leaf-galling adults (gallicoles), whole bodies of root-feeding adults (radicicoles), and eggs from radicicoles. Proteomic evidence came from SwissProt (release 2016_10) and a protein set from various hemipteran species, including A. pisum (NCBI), M. persicae Clones G006 and O (AphidBase), D. noxia (NCBI), Cimex lectularius (NCBI), and Rhodnius prolixus (EnsEMBL).
An Apollo  [59]  server was set up to allow manual curation of a set of genes from the automatic annotation. As many as 4815 genes were curated and checked based on guidelines defined by BIPAA [https://bipaa. genouest. org/ is/how-to-annotate-a-genome/]. Curated genes were merged with the automatic annotation using a custom script [https://github. com/abretaud/ogs-tools/tree/master/ogs_merge]. Putative functions of predicted proteins by the above pipeline were identified with blastp (v2. 6. 0) against Genbank NR (non-redundant GenBank CDS translations+PDB+SwissProt+PIR+PRF, release 09/2017), and interproscan v5. 13-52. 0 against Interpro. Associated GO terms were collected from blast NR and interproscan results with blast2GO (v2. 5). Transmembrane domain signal peptides were identified by tmhmm v2.
0c and signalP (euk v4. 1), respectively. All genome resources and the Apollo server were made available online on BIPAA, within the AphidBase section [http:// bipaa. genouest. org/is/aphidbase/daktulosphaira_vitifoliae/] [21]. This system was rolled out using different projects from the GMOD tool suite (JBrowse  [109] , Chado  [110] , Tripal  [111] ) and developments from the Galaxy Genome Annotation project [https://galaxy-genome-annotation. github. io/] [112].
Horizontal gene transfers To determine if genes for carotenoid biosynthesis were present in the phylloxera genome, we used genes of this pathway previously characterized in A. pisum as query sequences for blastp searches on the predicted proteins of the phylloxera genome. The A. pisum genome also contains genes from bacterial sources  [17, 18] , and, again using the A. pisum sequences as queries, we performed blastp searches on protein databases for the genomes of D. vitifoliae, A. pisum, Myzus persicae, Diuraphis noxia, Aphis glycines, Rhopalosiphum padi, Diaphorina citri (Psyllidae), Pachypsylla venusta (Psyllidae), and Bemisia tabaci (Aleyrodidae). A blastp search was also conducted in the NCBI non-redundant protein sequence database, in order to identify other species where these genes might be present.
As outgroups, we selected four insect taxa: D. melanogaster Meigen, 1830 (Diptera, Drosophilidae), Nasonia vitripennis (Ashmead, 1904) (Hymenoptera, Pteromalidae), Frankliniella occidentalis (Pergande, 1895) (Thysanoptera, Thripidae), and Pediculus humanus (Linnaeus, 1758) (Phthiraptera, Pediculidae). Genome versions are indicated in Additional File 1: Table  S18. Phylomes were reconstructed using the PhylomeDB pipeline  [118]. For each protein encoded in the grape phylloxera genome (25,567 annotated proteins, Official Gene Set version 3. 2) (http://bipaa. genouest. org/sp/dak-tulosphaira_vitifoliae/), we performed a blastp search against the custom proteome database built from the genomes listed above, which included a total of 252,530 proteins. Results were filtered using an e value of 1e-05 and a minimum of 50% overlap between the query and the hit sequences.
These genes were further scrutinized through functional annotations with InterProScan v. 5. 19  [129]  as well. GO term enrichment FatiGO  [131]  was used to check for enrichment in GO terms between the phylloxera genes and the rest of the database (i. e. , the sum of the genes belonging to the other species included in the phylome). Sets of enriched GO terms were summarized and visualized in REVIGO  [132]. GO enrichment was explored for phylloxera-specific genes, as well as for genes duplicated in each of the nodes to evaluate potential specific adaptation at different time points of the evolution of this species and group.
Synonymous distance-based assessment of duplication ages To remove potentially spurious gene models from the official gene set, we first used a filtering step, eliminating genes which had very weak support: these were defined as genes with no manual annotation, no hit to the nr database of GenBank, and very low RNA-Seq support (< 0. 5 CPM for the average of expression counts between radicicoles, gallicoles, and eggs). This left us with n = 21, 863 genes (a filtering of nearly 4000 genes). To evaluate synonymous distances (dS) among paralogs, we used a Reciprocal Best Hit approach (RBH) by blasting gene collections against themselves, determining pairs of genes that matched the RBH criteria. Doing this in just one step would lead us to focus on terminal branches in expanded gene families, neglecting deeper nodes and thus missing the ancient dynamics in the history of duplications.
CycADS annotation and DakviCyc database generation We used the Cyc Annotation Database System (CycADS  [136] ), an automated annotation management system, to integrate protein annotations from different sources into a Cyc metabolic network. Using the CycADS pipeline, proteins were annotated using Blast2GO  [137] , Inter-ProScan  [129] , KAAS  [138] , PRIAM  [139] , and Phylo-meDB  [118]  to obtain EC and GO numbers. These data were processed in the CycADS SQL database and automatically extracted to generate appropriate input files to build or update BioCyc databases  [140]  using the Pathway Tools software  [141]. The DakviCyc database, representing the metabolic protein-coding genes of phylloxera, was thus generated and is now included in the ArthropodaCyc database, a collection of arthropod metabolic network databases  [142]  (http://arthropodacyc.
cycadsys. org/). Metabolic pathway gap filling Metabolic reconstructions from the ArthropodaCyc databases for D. vitifoliae, A. pisum, and M. persicae (clone G006) were exported in the SBML format and imported into the PSAMM software  [143]. First, metabolic pathway gaps were identified using the "gapcheck" function, which reports a list of all metabolites not produced in the metabolic network. Then, the objective function was defined for each non-producing metabolite, and a gap-filling procedure was performed for each objective function through individual rounds of simulations using the PSAMM implementation of the fastgapfill algorithm  [144]. In the gap-filling step, results from A. pisum and M. persicae were used as candidates for identifying potentially missing annotations. Following the gap-filling predictions, candidate missing genes were identified through the identification of homologs to annotated genes in A.
pisum and M. persicae. This was achieved with manual curations using evidence from blast alignments, Pfam protein domain identifications  [65] , phylomeDB  [118] , transcriptomic support of gene expression, and literature review. Two rounds of annotation were performed with the above procedure, and predictions in the DakviCyc database were updated through these iterations. External links to resources that include the comprehensive enzyme information system: BRENDA (https://www. brenda-enzymes. org/), InterPro  [129] , KEGG orthology (https://www. genome. jp/kegg/), PhylomeDB  [118] , and crosslinks to the AphidBase  [21]  genome browser were added for all predicted genes.
Immunity genes Immune genes were annotated using bidirectional blastp analyses. We first used the phylloxera gene set to identify proteins with similarity to genes of the IMD and TOLL pathways. These putative phylloxera proteins were then blasted against D. melanogaster reference proteins. This approach was then extended to a complete collection of D. melanogaster immune genes. For reciprocal best hits (RBH) between phylloxera and D. melanogaster, the D. melanogaster annotation was directly transferred to phylloxera. In other cases (non-RBH relationship), a manual curation was performed, using the genomic information for arthropods with well-annotated immune pathways (Nasonia vitripennis, Plautia stali, Rhodnius prolixus, Tribolium castaneum) or for other aphid genomes (A. pisum and M. persicae) available in Genbank, ArthropodaCyc, and ImmunoDB  [145]  databases.
For RR-2 proteins, only the extended 69 amino acids RR domain (pfam00379) was used for phylogenetic analysis, the rest of the sequences being too divergent to align. Alignments were made with Clustal Omega  [150] , and phylogenetic analyses were made using the Phylogeny. fr platform  [151]  where alignments were cleaned with Gblocks and a maximum likelihood method as implemented in the PhyML program was used to infer a phylogenetic tree.
Effectors To identify genes underlying effector proteins active when grape phylloxera interacts with Vitis host plants, we modified a bioinformatics pipeline from  [157]. This pipeline was designed based on four features of effectors: (1) secretory, (2) small-sized (≤ 500 amino acids, and this only applied on the initial screening), (3) herbivore-only, and (4) gene-duplicating  [157, 158]. Testing of this pipeline on the genome dataset of the Hessian fly (Mayetiola destructor), a plant manipulating herbivore  [81] , showed that 95% of the predicted effector genes matched (blastp e value < 1e-5) the salivary gland-derived Hessian fly effector genes. We therefore screened the 24,585 automated gene models (OGS3. 0_20161223_proteins) and predicted a first set of 354 effector genes that classified (using OrthoMCL) into 78 clusters according to sequence similarity.
Populations from introduced areas (France, Germany, Hungary, Austria, Romania, Armenia, Uruguay, Australia) were collected from galls on leaves of Vitis vinifera cv. Details on this sampling are presented in Additional File 1: Table  S19. For each pool, which comprised between 30 and 100 individuals (adult insects), a DNA library was prepared with the TruSeq Nano Illumina kit, and sequenced on one lane of an Illumina HiSeq3000 sequencing machine at the Genotoul platform (reaching a genome coverage of ~60X for each pool). The reads (paired-end 2 × 150 bp) were mapped on the genome reference with BWA mem v0. 7. 10, with default parameters. Only primary alignments of properly paired reads were kept using samtools, and PCR duplicates were removed using Picard tools (https://github. com/broadinstitute/picard).
Acknowledgements The  International Aphid Genomics Consortium (IAGC)  has been a key player for the coordination of the annotation process. The  1KITE consortium  is acknowledged for providing transcriptomic data on the whole insect from the grape phylloxera. The  i5k consortium  is acknowledged for its support on Apollo training and for expert genome annotation. The bioinformatic platforms GenOuest and  BIPAA (Bioinformatics Platform for Agroecosystem Arthropods) in Rennes (France)  and  Bioinfo Genotoul in Toulouse (France)  are acknowledged for their support in bioinformatics. We thank  Fabien Gagné  (Canada),  Mauro Jermini  (Switzerland),  Ann-Kristin Jung (Germany), Laszlo Kocsis (Hungary) ,  Roberto Lopez  (Argentina), and  M. Andrew Walker  ( USA ) for collecting phylloxera populations-although not all populations could be included in the present study.
Funding This work has been funded by  INRAE (France)  and by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no. 764840  for the ITN  IGNITE  project. Rosa Fernandez  was funded by a  Juan de la Cierva-Incorporación Fellowship (Government of Spain ,  IJCI-2015-26627 ) and a  Marie Skłodowska-Curie Fellowship  ( 747607 ). Angela Douglas  was supported by the  US National Institute of Food and Agriculture  Grant  12216941. Honglin Feng  was supported by a  University of Miami Maytag Fellowship, William H. Evoy Graduate Research Support Fund , and a  Molecular Biosciences Graduate Research Award  from the  Department of Biology.
Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information files. Reads produced in this study are available at the NCBI Short Read Archive (SRA) under accession PRJNA588186  [161]  for reads used for the reference genome, and under accession PRJNA588387  [162]  for reads used for the population genomics study. Other datasets (assembled sequence, official gene sets, microRNAs, mitochondrial genome) are available at the Aphidbase repository  [163].
Human genetic variants predicted to cause loss-of-function of protein-coding genes (pLoF variants) provide natural in vivo models of human gene inactivation and can be valuable indica tors of gene function and the potential toxicity of therapeutic inhibitors targeting these genes 1,2. Gain-of-kinase-function variants in LRRK2 are known to significantly increase the risk of Parkinson's disease 3,4 , suggesting that inhibition of LRRK2 kinase activity is a promising therapeutic strategy. While preclinical studies in model organisms have raised some on-target toxicity concerns 5-8 , the biological consequences of LRRK2 inhibition have not been well characterized in humans. Here, we systematically analyze pLoF variants in LRRK2 observed across 141,456 individuals sequenced in the Genome Aggregation Database (gnomAD) 9 , 49,960 exome-sequenced individuals from the UK Biobank and over 4 million participants in the 23andMe genotyped dataset.
After stringent variant curation, we identify 1,455 individuals with high-confidence pLoF variants in LRRK2. Experimental validation of three variants, combined with previous work 10 , confirmed reduced protein levels in 82. 5% of our cohort. We show that heterozygous pLoF variants in LRRK2 reduce LRRK2 protein levels but that these are not strongly associated with any specific phenotype or disease state. Our results demonstrate the value of large-scale genomic databases and phenotyping of human loss-of-function carriers for target validation in drug discovery. New therapeutic strategies are desperately needed in Parkinson's disease (PD), one of the most common age-related neurological diseases, which affects about 1% of people over the age of 60 years  11, 12. Around 30% of familial and 3-5% of sporadic PD cases have been linked to a genetic cause 13.
NAtURE MEDiciNE mechanism by which LRRK2 variants mediate their pathogenicity remains unclear, a common feature is augmentation of kinase activity associated with disease-relevant alterations in cell models  3, 17, 18. Discovery of Rab GTPases as LRRK2 (ref. 19 ) substrates highlighted the role of LRRK2 in regulation of the endolysosomal and vesicular trafficking pathways implicated in PD  19, 20. LRRK2 kinase activity is also upregulated more generally in patients with PD (with and without LRRK2 variants)  21. LRRK2 has therefore become a prominent drug target, with multiple LRRK2 kinase inhibitors and suppressors  22  in development as disease-modifying treatments for PD  21, 23, 24. There are three LRRK2 therapeutics currently in early clinical testing from both Denali (small molecules DNL201, ClinicalTrials.
gov Identifier: NCT03710707 and DNL151, ClinicalTrials. gov Identifier: NCT04056689) and Biogen (antisense oligonucleotide BIIB094, ClinicalTrials. gov Identifier: NCT03976349). Despite these promising indications, there are concerns about the potential toxicity of LRRK2 inhibitors. These mainly arise from preclinical studies, where homozygous knockouts of LRRK2 in mice and high-dose toxicology studies of LRRK2 kinase inhibitors in rats and primates, have shown abnormal phenotypes in the lung, kidney and liver  [5] [6] [7] [8]. While model organisms are invaluable for understanding the function of LRRK2, they also have important limitations, as exemplified by inconsistencies in phenotypic consequences of reduced LRRK2 activity seen among yeast, fruit flies, worms, mice, rats and nonhuman primates  25.
NAtURE MEDiciNE requires very large collections of genetically and phenotypically characterized individuals, combined with deep curation of the target gene and candidate variants  32. Although previous studies of pLoF variants in LRRK2 have found no association with risk of PD  10  , no study has assessed their broader phenotypic consequences. We identified LRRK2 pLoF variants and assessed associated phenotypic changes in three large cohorts of genetically characterized individuals. First, we annotated LRRK2 pLoF variants in two large sequencing cohorts: the gnomAD v. 2. 1. 1 dataset, which contains 125,748 exomes and 15,708 genomes from unrelated individuals  9  and 46,062 exome-sequenced unrelated European individuals from the UK Biobank  33. We identified 633 individuals in gnomAD and 258 individuals in the UK Biobank with 150 unique candidate LRRK2 loss-of-function (LoF) variants, a combined carrier frequency of 0.
48%. All variants were observed only in the heterozygous state. Compared to the spectrum observed across all genes, LRRK2 is not significantly depleted for pLoF variants in gnomAD (LoF observed/expected upper bound fraction 9 = 0. 64). We manually curated the 150 identified variants to remove those of low quality or with annotation errors suggesting that they are unlikely to cause true LoF (Fig. 1a  and Supplementary Tables  1  and 2 ). We removed 16 variants identified as low confidence by the LoF transcript effect estimator ((LOFTEE); 6 variants in 409 individuals)  9  or manually curated as low quality or unlikely to cause LoF (10 variants in 129 individuals). One additional individual was excluded from the UK Biobank cohort as they carried both a pLoF variant and the G2019S risk allele.
Our final dataset comprised 255 gnomAD individuals and 97 UK Biobank individuals with 134 unique high-confidence pLoF variants (Fig. 1a ) and an overall carrier frequency of 0. 19%; less than half the frequency estimated from uncurated variants, reaffirming the importance of thorough curation of candidate LoF variants  32. A subset of 25 gnomAD samples with 19 unique LRRK2 pLoF variants with DNA available were all successfully validated by Sanger sequencing (Supplementary Table  3 ). Second, we examined LRRK2 pLoF variants in over 4 million consented and array-genotyped research participants from the personal genetics company 23andMe. Eight putative (LOFTEE high confidence) LRRK2 LoF variants were identified. After manual curation, all putative carriers of each variant were submitted for validation by Sanger sequencing and variants with <5 confirmed carriers were excluded.
The resulting cohort comprised 749 individuals, each a Sanger-confirmed carrier for one of three pLoF variants (Fig. 1a  and Supplementary Table  4 ). The high rate of Sanger confirmation for rs183902574 (>98%) allowed confident addition of 354 putative carriers of rs183902574, from expansion of the 23andMe dataset, without Sanger confirmation. Analyses with and without these genotyped-only carriers were not significantly different (Supplementary Table  5 ). Across the two most frequent pLoF alleles we observed an extremely small number (<5) of sequence-confirmed homozygotes; however, given the very small number of observations, we can make no robust inference, except that homozygous inactivation of LRRK2 seems compatible with life. For the remainder of this manuscript we focus on heterozygous pLoF carriers.
The three combined datasets provide a total of 1,455 carrier individuals with 134 unique LRRK2 pLoF variants. These variants are found across all major continental populations (Fig. 1b  and Extended Data Fig. 1 ) and show neither any obvious clustering along the length of the LRRK2 protein, nor specific enrichment or depletion in any of the known annotated protein domains (chi squared P = 0. 22; Fig. 1c ), consistently with signatures of true LoF  32. To confirm that LRRK2 pLoF variants result in reduced LRRK2 protein levels, we analyzed total protein lysates from cell lines with three unique pLoF variants. We obtained lymphoblastoid cell lines (LCLs) from two families with naturally occurring heterozygous LoF variants and a third variant was CRISPR/Cas9-engineered into embryonic stem cells (Extended Data Fig.
2 ), which were differentiated into cardiomyocytes. In all instances, LRRK2 protein levels were visibly reduced compared to noncarrier controls (Fig. 2 ). These results agree with a previous study which assessed three separate pLoF variants and found significantly reduced LRRK2 protein levels  10. Together, these six functionally validated variants represent 82. 5% of pLoF carriers in this study (1,201 of 1,455). Although heterozygous pLoF carriers have LRRK2 protein remaining, we believe that this state represents a plausible genetic model for therapeutic inhibition of LRRK2, as target engagement by pharmacological inhibitors is unlikely to be complete. We next sought to determine whether lifelong lowering of LRRK2 protein levels through LoF results in an apparent reduction in lifespan. We found no significant difference between the age distribution of LRRK2 pLoF variant carriers and noncarriers in either the gnomAD or 23andMe datasets (two-sided Kolmogorov-Smirnov P = 0.
NAtURE MEDiciNE The 23andMe dataset includes self-reported data for thousands of phenotypes across a diverse range of categories. We performed a phenome-wide association study comparing LRRK2 pLoF carriers to noncarriers for 366 health-related traits and found no significant association between any individual phenotype and carrier status (Fig. 3b ). In particular, we found no significant associations with any lung, liver or kidney phenotypes (Supplementary Tables  5  and 8 ). The UK Biobank resource includes measurements for 30 blood serum and four urine biomarkers. We found no difference in any of these biomarkers between pLoF carriers and noncarriers (Supplementary Table  9  and Supplementary Fig. 1 ). In particular, there was no difference between carriers and noncarriers for urine biomarkers transformed into clinical measures of kidney function (Fig.
Letters NAtURE MEDiciNE samples) will be required to answer this question. As such, our study focuses entirely on whether partial genetic LRRK2 inactivation has broader phenotypic consequences that might correspond to adverse effects of chronic administration of LRRK2 inhibitors. We acknowledge multiple limitations to this work. First, we relied on heterogeneous phenotype data mostly derived from self-reported questionnaires. Both 23andMe and gnomAD record only age at recruitment, which is an imperfect proxy for lifespan and participants are relatively young compared to the typical age of onset for PD. In addition, at current sample sizes we are only powered to detect a strong effect on lifespan. Our ascertainment of LRRK2 pLoF variants in 23andMe was necessarily incomplete, due to the availability of targeted genotyping rather than sequencing data; this means that a subset of the 23andMe individuals treated as noncarriers could be carriers of LRRK2 pLoF variants not genotyped or imputed in this dataset.