dataesr/scientific-paragraphs-categorization huggingface dataset:
The MycoLec Database of Fungal Lectomes The MycoLec database (https://www. unilectin. eu/mycolec/) shares the same architecture as the LectomeXplore database and is dedicated to Fungi. The prediction from the 1419 translated fungal genomes of the Mycocosm portal (February 2020 release) resulted in approximately 28,700 sequences of putative lectins with a score greater than 0. 25. The entry page of the database is displayed in Figure  2 , showing the different entry points: lectins can be accessed by clicking on the different classes (left panel, with number of sequences indicated for each class), or on a taxonomy sunburst (right panel). Alternatively, the "search by field button" enables the combination of search terms such as, genus, species, classes or selected keywords. Each candidate lectin is associated with sequence information including the alignment with the reference sequence of the predicted lectin class.
Identification of Lectin Domains in Fungal Proteomes The MycoLec database contains 33,518 putative lectin sequences (28,691 with filter at 0. 25 quality score). Fungal lectin prediction spreads the classification coverage from 12 to 27 folds and 20 to 63 classes (see Section 3. 1 and Figure  S2 ). The distribution of lectins by class, along with the score distribution and the structural origin is displayed in Figure  3 , illustrating that many more classes are predicted in genomes than characterized in structural studies. Notably, lectins are systematically found in fungal genomes. In 43% (12,290) of cases, the predicted lectins belong to a lectin class having a fungal origin in Unilectin3D (i. e. , created using fungal proteins but not restricted to it). Expectedly, the prediction score of lectins containing lectin motifs of fungal origin (Figure  3 , purple) is high, with the exception of the LysM-like lectin class.
Prediction of the Laccaria Bicolor Lectome Laccaria sp. are fungi pertaining to the Basidiomycota phylum and the Agaricomycetes class. It forms ECM with a wide range of trees. L. bicolor is one of the rare ectomycorrhizal fungus that can be genetically manipulated by using RNAi silencing or gene overexpression. This potentially allows investigation of the role of candidate lectin genes in further functional analyses. Only one strain for L. bicolor was publicly accessible at the time of this study and integrated in our dataset. A search with a 0. 25 cutoff identified 13 different classes of lectins in the genome (Table  S3 ). Filtering out house-keeping lectins and poorly scored predictions left eight lectins that are displayed in Figure  6. Two of them are proteins found in many organisms: Cyanovirin and Oyster lectin.
Discussion and Conclusions The aim of this project was to explore lectin composition and abundance across and in the context of the fungal kingdom. A large panel of 33,518 fungal lectins belonging to 63 distinct lectin classes were predicted in 1419 genomes from the MycoCosm database. This collection ranked by a quality score can be accessed, searched and browsed in MycoLec, an online interactive database available at unilectin. eu/mycolec. Each predicted lectin is available together with the identified lectin domain in which the binding site can be compared with a reference motif. Significant differences in the lectin content (lectomes) of translated genomes support the distinction between fungal taxonomic classes. Moreover, lectin occurrence could be correlated with ecological information available in some of the fungal species considered in this study.
Based on the Agaricomycete fungal class, we showed that lectomes vary with lifestyle and that saprophytic fungi living on decaying wood and litters have a larger variety of lectins than symbionts and pathogens. However, an in-depth statistical analysis is necessary to demonstrate the association of lectins with specific ecological traits. Previous analysis of glycoside hydrolases in the Agaricales order  [49]  led us to reconstruct the history of the corresponding gene family and investigate the expansion and contraction events. Such a study of lectin evolution would be challenging because of the observed gene dispersion within the dataset. Laccaria bicolor, a model fungus for studying ECM, includes interesting, predicted lectins with distinct folds and these proteins are upregulated in the presence of the associated host plant.
This provides a promising lead for further analysis. Overall, the present study opens up new prospects for appraising the broad diversity of fungal lectomes. Even though a large number of fungal lectins were already identified, we provide the tools to better appreciate the extent of this repertoire. We show that some lectin classes previously identified in invertebrates (H-lectins, oyster lectins) or in plants (jacalin, Ginkbilobin-like) turn out to be widely distributed in Agaricomycetes. Some of these new lectins could have useful application in biotechnology or as antiviral compounds  [50] , and the MycoLec database is readily available to be mined for novel lectins. S1 : Fungal species investigated in the exploration of the lectins induced and repressed during their mycorrhization with a compatible plant host, Table  S2 : Lectin content in the predicted proteomes of the Agaricomycetes fungal class sorted by ecological niche, Table  S3 : Details of lectins identified in the genome of Laccaria bicolor.
Funding: F. B. is funded by the ANR PIA Glyco@Alps (ANR-15-IDEX-02), A. L. is funded by a postdoctoral scholarship from the Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China. Written informed consent for publication must be obtained from participating patients who can be identified (including by the patients themselves). Please state "Written informed consent has been obtained from the patient(s) to publish this paper" if applicable.
The following are available online at https://www. mdpi. com/article/ 10. 3390/jof7060453/s1, FigureS1: Distribution of lectin folds and classes of fungal lectin with 3D structures in Unilectin3D database, Figure S2: Distribution of folds (left) and classes (right) of predicted lectin sequences in MycoLec, Figure S3: Distribution of predicted lectins by species in MycoLec, Figure S4: Impact of mycorrhization of 14 fungal strains with their corresponding host plant on lectin expression, Figure S5: Differential expression of lectins within mycorrhizal fungi upon plants interaction, Table.
Acknowledgments: The authors acknowledge support by the ANR PIA Glyco@Alps (ANR-15-IDEX-02), the Alliance Campus Rhodanien Co-funds (http://campusrhodanien. unige-cofunds. ch), the Labex Arcane/CBH-EUR-GS (ANR-17-EURE-0003), the Laboratory of Excellence ARBRE (ANR-11-LABX-0002-01), the Region Lorraine and the European Regional Development Fund.
Supplementary Materials: The following are available online at https://www. mdpi. com/article/ 10. 3390/jof7060453/s1, FigureS1: Distribution of lectin folds and classes of fungal lectin with 3D structures in Unilectin3D database, Figure S2: Distribution of folds (left) and classes (right) of predicted lectin sequences in MycoLec, Figure S3: Distribution of predicted lectins by species in MycoLec, Figure S4: Impact of mycorrhization of 14 fungal strains with their corresponding host plant on lectin expression, Figure S5: Differential expression of lectins within mycorrhizal fungi upon plants interaction, Table.
Several lists of NDD genes and databases have been designed to inform the scientific and medical communities about gene-or variation-disease pairs and the biological functions of these genes. They also help clinicians to provide the most accurate NDD diagnostic and genetic counselling for patients and their parents (Savatt and Myers, 2021) (Table  1 ). This chapter provides an overview of all the current databases, the genes associated with NDD as well as their main biological functions. Notably, we show that high-confidence NDD genes (described below) are expressed at very early stages of fetal brain development and are enriched in several biological pathways such as chromosome organization, cell cycle, metabolism and synaptic functions.
Curated databases for genetic studies on NDD Genes associated with NDD were first identified through targeted studies, e. g. medical case reports and region-based or candidate gene approaches, and more recently from large-scale genome wide studies, including whole genome SNP genotyping array and whole exome/genome sequencing. Some databases are dedicated specifically to autism research (e. g. SFARI) or ID (e. g. SysID) but others cover the broad category of NDD (e. g. DBD) or more generally genetic diseases (e. g. ClinVar, OMIM).
Gene database from the SFARI To date, the largest database that focuses on ASD was developed by the Simons Foundation Autism Research Initiative (SFARI). First launched in 2008, this database, called the SFARI Gene, has been systematically updated alongside the published scientific literature and became a reliable source of expertly curated information on ASD-associated genes. The database provides a scoring system, based on the presence of de novo likely-gene-disrupting mutations, replication studies, functional effects of the mutations and the inheritance patterns. Reported genes are thus classified based on the confidence levels for ASD risk. There are currently four ranking categories, including S -syndromic, 1 -high-confidence, 2 -strong candidate and 3suggestive evidence  (Abrahams et al.
, 2013). A large-scale project named SPARK -for Simons Foundation Powering Autism Research for Knowledge -aims to collect and analyze clinical and genetic data of up to 50,000 families with a member diagnosed with ASD. SPARK has developed a partnership with not only researchers but also with certified clinical biologists, that allows SPARK to return genetic findings to participants who consented to receive their results. The SPARK gene list, which comprises genomic regions and genes in which de novo pathogenic variants have been identified in at least three affected individuals and reported in at least two independent publications, has been judged to be of sufficient scientific evidence for a clinical utility. This SPARK gene list is updated every 6 months. The ClinVar database and its link with ClinGen project Clinical Genome (ClinGen) is a US state-funded project that has recently taken a step forward especially in the curation of gene-disease associations.
ClinGen seeks to centralize published genomic and phenotypic data related to NDD, among additional disorders, for both scientific and clinical use. After the concertation of a Gene Curation Expert Panel, an annotation is given for each gene of interest, ranging from definitive, strong to moderate or limited evidence in support of its relationship with diseases. Genes falling within the definitive or strong categories are finally determined as disease associated genes (Gene-Disease Validity). ClinVar points at specific genetic variants previously detected in patients with human diseases and provides interpretations of their significance to diseases. To date, more than 500,000 variants (including germline and somatic variants) are listed in ClinVar  (Landrum et al. , 2020). Submitted variants and variant-condition pairs are aggregated in ClinVar, with indications as to whether there is consensus or disagreement among submitters on variant interpretations.
The SysID database SysID, or Systems Biology Approaches to Intellectual Disability, is a database of genes implicated in ID, collected from the literature and the Online Mendelian Inheritance in Man (OMIM) database. The SysID gene catalogue is maintained by the Institute of Human Genetics at Erlangen (Germany) for the diagnosis of isolated and syndromic ID, as well as multisystemic disorders, within which ID is one of the main clinical features. The SysID database includes a list of "primary" ID genes restricted to genes with high level of evidence (criteria are described by Kochinke and colleagues)  (Kochinke et al. , 2016). This list is further classified into 9 categories according to the clinical phenotype (syndromic or non-syndromic ID), its severity (from severe to moderated or very variable cognitive impairment), the penetrance (full or incomplete) and the additional clinical features (with or without malformations of organs, brain or limb).
The DDG2P database Gene2Phenotype (G2P) is a database for many disorders including cancer, eye disorders, skin illness and NDD. This database is available from the DatabasE of genomiC varIation and Phenotype in Humans using Ensembl Resources (DECIPHER) website  (Firth et al. , 2009)  and was first established by a systematic review of the literature, diagnostic tests by clinical laboratories in the UK and in the UniProt database (The UniProt Consortium, 2015). The Developmental Disorder gene panel of G2P (DDG2P) has been regularly updated and enriched using the results of the Deciphering Developmental Disorders (DDD) study. Genetic data of more than 13,400 exomes of individuals with severe NDD were analyzed using bioinformatic filtering and identified variants were reviewed by clinicians and molecular geneticists, all senior experts in the field.
The DBD database The Developmental Brain Disorder (DBD) gene database focuses on six disorders, including ID, ASD, ADHD, bipolar disorder, schizophrenia and epilepsy. A list of disease-causative genes has been generated by manual curation and classified according to the number of reported loss-of-function (LOF) mutations (>3, 2, or 1) and the mode of inheritance (de novo or inherited). DBD also provides a list of high-confidence genes with autosomal recessive inheritance (for more information, see Gonzalez-Mantilla and Colleagues  (Gonzalez-Mantilla et al. , 2016) ). We restricted the DBD database to the most convincing disease-causing genetic variants, with missense variants, deletions encompassing more than one gene, and individuals with multiple LOF variants being excluded, in order to increase the specificity of the genes in the list.
The genetic databases dedicated to epilepsy Regarding epilepsy, a frequent comorbidity of patients with autism, few databases are available. For this review, we used The Lafora Gene Mutation Database  (Ianzano et al. , 2005) , The Epilepsy Genetic Association Database (epiGAD)  (Tan and Berkovic, 2010) , CarpeDB, EpilepsyGene, GenEpi and MeGene (the two last are no longer maintained). Finally, we also included the gene list used by the Mayo Clinic Laboratories in the context of genetic diagnostic of epilepsy.
Specificity and common features of the genetic database for NDD In this section, we first selected genes based on their high-confidence for their association with NDD (Supplementary Table  1 ; see also our website https://genetrek. pasteur. fr/). We then extracted a list of candidate genes for NDD, which are not yet robustly associated with NDD, but which include genes with (i) weak confidence from the NDD-related databases, (ii) high/specific expression in the brain or (iii) under strong selective pressure. These two lists of highly confident and candidate NDD genes can be used for filtering variants in exome/genome of patients and for exploring genes from transcriptomics/proteomics experiments. To centralise the gene set lists from all databases, we developed "GeneTrek" https://genetrek.
High-confidence NDD genes Several genetic databases such as SFARI Gene, SPARK, SysID, DDG2P and DBD provide key information to classify genes into categories that are useful for clinicians. Indeed, for diagnostic purposes, only genes with clear evidence for association can be used. Here, for the high confidence NDD gene list (HC-NDD genes), we selected all genes from the category 1 genes from the SFARI, the SPARK, the primary ID gene list from SysID, the "confirmed" gene list from the DDG2P (with "Brain" or "Cognition" as "organ specificity"), and the Tier 1 or autosomal recessive (AR) inheritance list from the DBD, gathering a total of 1,586 genes (Figure  1  & GeneTrek website).
, 2017)  that were not listed as high confident genes in DDG2P and SysID. In addition, we included in the NDD candidate gene list, the genes displaying high or specific expression (above one standard deviation) in fetal and/or adult human brain and genes encoding synaptic proteins from the Synaptic Gene Ontologies (SYNGO) consortium  (Koopmans et al. , 2019). Finally, we included genes under strong selective pressure using two metrics that were developed from exome and genome sequencing of the Genome aggregation Database (gnomAD) to estimate intolerance to loss of function mutations: the probability of being loss-of-function intolerant score (pLI > 0. 9)  (Lek et al. , 2016)  or/and the observed/expected (oe) number of loss-of-function variants in that genes (LOEUF or upper bound of the oe confidence interval < 0.
Overlap between the databases As illustrated in Figure  1  and table 2, a limited number of HC-NDD genes (n=59; 3. 7%) are shared by the SPARK/SFARI, SysID, DDG2P and DBD gene lists, highlighting shared genetic pathways among NDD phenotypes, but also different selection criteria depending on the database  (Gonzalez-Mantilla et al. , 2016; Myers et al. , 2020). This further points out the importance of considering a broad spectrum of genes for patients with NDD instead of a too restricted list of genes  (Myers et al. , 2020). A number of genes could be more related to a given disorder. For example, 41 genes seem to be more associated to ASD than with other conditions, such as SHANK2, NRXN2/3 and NLGN2. However, their involvement in other NDDs cannot be excluded and requires further investigations.
Within the large list of 6,478 candidate NDD genes, 24 (see Figure  1  and Table  2 ) pass all selection criteria -candidate genes in the NDD database, expressed in the brain and highly intolerant to loss of function mutations -making them strong candidate for being robustly associated with NDD in the future. There is finally a third category that includes 11,768 protein-coding genes in the human genome that are neither in the high-confidence nor candidate NDD gene lists. It is worth mentioning that the lack of scientific evidence supporting the role of such genes in NDD phenotypes is not definitive. Gene curation is still in progress. The biological pathways associated with the HC-NDD genes.
Spatio-temporal expression of the HC-NDD genes in the human brain To further characterize the HC-NDD genes, we performed a Specific Expression Analysis (SEA) on the human Brainspan collection  (Dougherty et al. , 2010). This provides a parallel analysis of transcripts enriched in particular human brain regions, and/or developmental periods. HC-NDD gene list was divided into two non-overlapping set of genes i) HC-NDD genes not listed in SynGO (N= 1,382 genes) and ii) HC-NDD genes listed in SynGO (N=204 genes). As illustrated in the figure  3 , the HC-NDD genes not listed in SynGO are enriched in the very early fetal period (amygdala, cortex, hippocampus, striatum). In contrast, the HC-NDD genes with synaptic functions and listed in SynGO are more expressed after birth in the cerebellum and the cortex.
Interestingly, there are more than 20 genes listed as part of the NLGN-NRXN pathway previously associated with ASD. Within this pathway, there are the genes coding for cell adhesion molecules (e. g. NLGN, NRXN), glutamate pre-and postsynaptic receptors (GRIN2A, GRIN2D, GRIA4), and scaffolding proteins at glutamatergic synapse (SHANK). (A) Pairs of proteins consist of physical interactions (experimental or database) from STRING with the highest confidence score (score > 0. 9). Edge color represents interactions involving proteins that are part of selected enriched gene ontology terms. We merged the gene ontology terms into 5 large groups "Cell cycle", "Mitochondrion", "Chromosome/chromatin", "Nervous system/synapse" and "Transport". (B) Network of interacting proteins annotated in the "Nervous system/synapse" group in (A).
Node colors represent the association of the genes with ASD, ID or both, node border color the association with epilepsy, and node size the number of interactions of each gene. Genes with a SFARI score of 1 and genes from the SPARK gene list were considered as ASD-associated, genes from the SysID database with primary evidence were considered as ID-associated, and genes from any epilepsy-related gene list were considered as epilepsy-associated. 21 Finally, we investigated further the roles of the HC-NDD genes at the synapse. We use the ontology terms provided by the SynGO consortium  (Koopmans et al. , 2019)  (Figure  5 , supplementary Table  2  and Table 3 ). Among the HC-NDD genes, 204 (12,9%) are mapped to  SynGO sunburst plots show nested cellular components (A) and biological processes (B) of the synapse that are statistically enriched in HC-NDD genes.
Perspectives This overview of the different databases highlights the cumulative evidence of a strong genetic contribution to NDD. Regarding the achievements, it is now estimated that more than 1,500 genes are robustly associated with NDD. The use of whole exome/genome sequencing leads to a genetic diagnosis in more than a third of the patients and a recent modelling suggests that more than 1,000 genes associated with developmental disorders have not yet been described, many of which are likely to be less penetrant than the currently known genes  (Kaplanis et al. , 2020). Recent progresses have also been made to develop automated pipelines for filtering pathogenic variants among the large numbers of variants identified through exome/genome sequencing  (Wright et al. , 2020). Regarding the limitations, for the majority of the genes/variants, we have few information about family history and on the impact of the mutations on the clinical trajectory and outcome of the carriers.
Acknowledgements This research has been conducted using public databases ( SFARI, SysID ,  DDG2P , DBD, gnomAD, SYNGO, Lafora Gene Mutation Database, epiGAD, CarpeDB and EpilepsyGene). This work was funded by  Institut Pasteur , the  Bettencourt-Schueller Foundation ,  Université de Paris , the  Conny-Maeva Charitable Foundation , the  Cognacq Jay Foundation , the  Orange Foundation , the  Fondamental Foundation , AIMS-2-TRIALS which received support from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No  777394 , the  Human Brain Project  and the  Inception program  ( Investissement d'Avenir  grant  ANR-16-CONV-0005 ). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Results and Discussion 2. 1. Population Structure, Sequencing and Analysis of Gene Content E. coli were isolated from animals presenting with diarrhoea or mastitis symptoms and subjected to whole-genome sequencing. The original collection consisted of 150 strains that were isolated from 2004 to 2010 and the raw reads from this collection were submitted to the European Nucleotide Archive (ENA) under accession number PRJEB32666. One hundred and forty-seven strains were confirmed as E. coli and 143 strains were included in this study, while four assemblies were discharged due to their poor quality (CFS3241, CFS3306, CFS3348, and CFS3367). The strains represented seven diverse phylogroups (A, B1, B2, C, D, E, and G) as determined by ClermontTyping in EnteroBase  [19]  (Figure  1a ).