Saccharomyces cerevisiae, Candida albicans, Cryptococcus neoformans var. grubii and Aspergillus fumigatus. Between 80 and 90 % of gene models per strain in each of these species are core genes that are highly conserved across all strains of that species, many of which are involved in housekeeping and conserved survival processes. In many of these species, the remaining ‘accessory’ gene models are clustered within subterminal regions and may be involved in pathogenesis and antimicrobial resistance. Analysis of the ancestry of species core and accessory genomes suggests that fungal pan-genomes evolve by strain-level innovations such as gene duplication as opposed to wide-scale horizontal gene transfer. Our findings lend further supporting evidence to the existence of species pan-genomes in eukaryote taxa.The concept of the species ‘pan-genome’, the union of ‘core’ conserved genes and all ‘accessory’ non-conserved genes across all strains of a species, was first proposed in prokaryotes to account for intraspecific variability. Species pan-genomes have been extensively studied in prokaryotes, but evidence of species pan-genomes has also been demonstrated in eukaryotes such as plants and fungi. Using a previously published methodology based on sequence homology and conserved microsynteny, in addition to bespoke pipelines, we have investigated the pan-genomes of four model fungal species:  Gene model prediction and post-processing pan-genome analysis pipelines are available from Saccharomyces cerevisiae, Candida albicans, Cryptococcus neoformans var. grubii and Aspergillus fumigatus. Each species is a model organism for fungal evolutionary biology, genomics and comparative genomics. Our results show that between 80 and 90 % of gene models per strain are core genes that are highly conserved, many of which are involved in housekeeping and conserved survival processes. The remaining accessory gene models are clustered within subterminal regions, and may be involved in pathogenesis and antimicrobial resistance. Analysis of the ancestry of species core and accessory genomes suggests that fungal pan-genomes evolve by strain-level innovations such as gene duplication as opposed to wide-scale horizontal gene transfer. Our findings lend further supporting evidence to the existence of species pan-genomes in eukaryote taxa.Recent prokaryotic genomic studies of multiple individuals from the same species has uncovered large differences in the gene content between individuals. It has become increasingly common to refer to species with multiple genomes sequenced in terms of their ‘pan-genome’. The pan-genome is the union of ‘core’ conserved genes and all ‘accessory’ non-conserved genes across all strains of a species. Species pan-genomes have been analysed in many prokaryotic species, but have been recently demonstrated in eukaryotes such as plants and fungi as well. Here, we have investigated the pan-genomes of four model fungal species namely, Many fields of eukaryote functional and comparative genomics rely on the use of curated reference genomes intended to be broadly representative of a given species. Regardless of their quality, reference genomes do not and cannot contain all genetic information for a species due to genetic and genomic variation between individuals within a species . To accoStreptococcus agalactiae in 2005 [Haemophilus influenzae, Escherichia coli and Sulfolobus islandicus [A species’ pan-genome can evolve as a consequence of lifestyle: sympatric species may have large pan-genomes (and thus a large degree of intraspecific variation), while environmentally isolated or highly specialized species have smaller pan-genomes . The exi in 2005 , and waslandicus . Over 40landicus .Saccharomyces cerevisiae genomes in the mid-2000s [Brassica oleracea found that ~19 % of all genes analysed were part of the B. oleracea accessory genome, with ~2 % of these being cultivar-specific [Glycine soja) strains found approximately the same 80 : 20 proportion of core to accessory gene content within the wild soybean pan-genome, while larger accessory genome sizes have been reported in wheat, maize, grasses and Medicago [Emiliania huxleyi have an accessory complement of up to 30 % of their total gene content, which varies with geographical location [Saccharomyces cerevisiae pan-genome, including a recent large-scale analysis of genome evolution across 1011 strains, have shown evidence for an accessory genome of varying size, as well as large variation in subterminal regions across multiple Saccharomyces cerevisiae strains [Zymoseptoria tritici pan-genome found that up to 40 % of genes in the total Z. tritici pan-genome were either lineage or strain-specific [Although the concept of the species pan-genome is well-established in comparative prokaryote genomics, it has only recently been extended to comparative intraspecific studies of eukaryotes. This is despite repeated observation of intraspecific genomic content variation in eukaryotes dating back to the first intraspecific comparative analyses of id-2000s . The relid-2000s . Additioid-2000s . Despitespecific . A similMedicago . Individlocation . In fung strains , and recspecific .B. oleracea [Emiliania huxleyi pan-genome, with strains containing varying amounts of nutrient acquisition and metabolism as a result of niche specialization [Z. tritici species pan-genome, which is thought to arise from the species’ own genome defence mechanisms inducing polymorphisms as opposed to gene duplication events [et al. [Saccharomyces cerevisiae appear to have arisen via introgression from closely related Saccharomyces species, with a smaller number originating from HGT events with other yeasts [The methods of pan-genome evolution within eukaryotes in the absence of rampant HGT appears to vary among species, and can include genome rearrangement events or more discrete adaptive evolution processes. In plants, accessory genomes may evolve as a result of varying levels of ploidy, heterozygosity and whole-genome duplication within species, as well as adaptive changes and the evolution of phenotypic differences, such as in oleracea . Adaptivlization . High len events . Peter e [et al.  observedr yeasts .https://github.com/chmccarthy/pangenome-pipelines) to construct and characterize the pan-genomes of four exemplar fungal species: Saccharomyces cerevisiae, Candida albicans, Cryptococcus neoformans var. grubii and Aspergillus fumigatus. All four species are model organisms in eukaryotic genomics and play important roles in human health and lifestyles; Saccharomyces cerevisiae is used extensively in biotechnology, Candida albicans is an opportunistic invasive pathogen and the second-most common cause of fungal infection, Cryptococcus neoformans var. grubii is an intracellular pathogen that causes meningitis in immunocompromised hosts, and Aspergillus fumigatus is an opportunistic respiratory pathogen [In this study, we have adapted a method of prokaryotic pan-genome analysis that identifies putative pan-genomic structure within species by accounting for conserved genomic neighbourhoods (CGNs) between strain genomes and applied it to eukaryote analysis   GenBank facility (Table S1). Strains were selected based on geographical and environmental diversity where possible (Table S1). The predicted protein set from each species’ reference genome was also obtained from GenBank. For each strain genome in each species dataset, translated gene model and gene model location prediction was performed using a bespoke prediction pipeline consisting of three parts .(i) Reference proteins were queried against individual strain genomes using Exonerate with a heuristic protein2genome search model . TranslaAb initio hidden Markov model-dependent gene model prediction was carried out using GeneMark-ES, with self-training and a fungal-specific branch point site prediction model enabled [(ii)  enabled . PredictSaccharomyces cerevisiae and Candida albicans strain genomes, these gene models were additionally screened against a dataset of known ‘dubious’ pseudogenes in each species taken from their respective public repositories using blastp with an E value cut-off of 10−4 [blastp hit against a known dubious pseudogene had a sequence coverage of ≥70 % were removed from further processing. All remaining predicted gene models with a length of ≥200 aa and a coding potential score of 100 or greater as assigned by TransDecoder were included in the final strain gene model set. Their corresponding genomic locations were also included in the strain genomic locations dataset.(iii) Finally, position weight matrix-dependent gene model prediction was carried out for all remaining non-coding regions of the genome using TransDecoder . For Sac of 10−4 . Predictab initio prediction approach, followed by a ‘last resort’ approach for predicting gene models in genomic regions for which gene models had not been previously called. We used this approach to ensure consistency in gene models calls between strains and to reduce the potential of poor heterogenous gene model calling within each species dataset, which would in turn reduce the number of false positives/negatives in our analysis. The completeness of each set of predicted gene models was assessed using BUSCO with the appropriate BUSCO dataset for each species [blastp search was carried out for all predicted gene models using an E value cut-off of 10−4. The results of the blastp search were used as input for PanOCT along with the combined genomic location data for each strain genome in a species dataset [Thus, for each strain genome in a species dataset, a gene model set and corresponding genomic location set was constructed using two initial independent prediction methods; a search for gene models orthologous to the reference protein set and an  species   resource for Saccharomyces cerevisiae [ab initio gene model prediction using GeneMark-ES [Saccharomyces cerevisiae strain S288C was also obtained from GenBank [Saccharomyces cerevisiae pan-genome dataset was performed as detailed above, with potentially dubious gene model predictions for each strain genome checked against a dataset of 689 known dubious Saccharomyces cerevisiae gene models obtained from the Saccharomyces Genome Database (SGD) [Saccharomyces cerevisiae BUSCOs from the Saccharomycetales dataset; on average ~1677 BUSCOs (~98 %) were retrieved as complete gene models in each strain (Table S1). In total, 575 940 gene models and corresponding unique genomic locations were predicted for 100 Saccharomyces cerevisiae genomes (Table S1).Genomic data for 100 revisiae . For oureMark-ES . In its eMark-ES . The pro GenBank . Construse (SGD) . The comCandida albicans strains were obtained from the NCBI’s GenBank facility, encompassing predominantly clinical or presumed-clinical strains isolated from North America, Europe and the Middle East (Table S1). The protein set for the reference Candida albicans strain SC5314 was also obtained from GenBank [Candida albicans pan-genome dataset was performed as detailed above, with potentially dubious gene model predictions for each genome checked against a dataset of 152 known dubious gene models from Candida albicans SC5314 obtained from the Candida Genome Database [Saccharomyces cerevisiae BUSCOs from the Saccharomycetales dataset; on average ~1642 BUSCOs (~96 %) were retrieved as complete gene models in each strain (Table S1). In total, 203786 gene models and their corresponding unique genomic locations were predicted for 34 Candida albicans genomes (Table S1).Genomic data for 34  GenBank . ConstruDatabase . The comCryptococcus neoformans var. grubii strains were obtained from the NCBI’s GenBank facility, encompassing both clinical and wild-type strains sampled from North America and Southern African regions (Table S1). The protein set for the reference Cryptococcus neoformans var. grubii strain H99 was also obtained from GenBank [Cryptococcus neoformans var. grubii pan-genome dataset was performed as detailed above, with the exception that a check for known dubious gene models was not carried out as no such data were available for Cryptococcus neoformans var. grubii. The completeness of each strain’s gene model dataset was assessed using the 1335 BUSCOs from the Basidiomycota dataset; on average ~987 BUSCOs (~74 %) were retrieved as complete gene models in each strain (Table S1). In total, 170241 gene models and their corresponding genomic locations were predicted for 25 Cryptococcus neoformans var. grubii genomes (Table S1).Genomic data for 25  GenBank . ConstruAspergillus fumigatus strains were obtained from the NCBI’s GenBank facility, including both clinical and wild-type strains isolated from the Northern and Southern hemispheres, and the International Space Station (Table S1). The protein set for the reference Aspergillus fumigatus strain AF293 was also obtained from GenBank [Aspergillus fumigatus pan-genome dataset was performed as detailed above, with the exception that a check for known dubious gene models was not carried out as no such data was available for Aspergillus fumigatus. The completeness of each strain’s gene model dataset was assessed using 4046 Aspergillus nidulans BUSCOs from the Eurotiomycetes dataset; on average ~3410 BUSCOs (~84 %) were retrieved as complete gene models in each strain (Table S1). In total, 116230 putative proteins and their corresponding unique genomic locations were predicted for 12 Aspergillus fumigatus genomes (Table S1).Genomic data for 12  GenBank . Construblast score ratio [blastp hits in each species dataset (Table S1), the initial core and accessory genomes for each species dataset were constructed using PanOCT with the default parameters.Analysis of the pan-genomes of the four fungal species in our study was performed using the Perl software PanOCT . PanOCT re ratio  and CGN re ratio  approachre ratio . We usedn – 1 (where n is the number of strains in a dataset), parallelized all-vs-all blastp searches of all remaining gene models from accessory clusters (e=10−4) were performed, and this output was parsed to identify instances where two accessory clusters with no overlapping strain representation could be merged into one cluster based on the following criteria. (i) Each member gene model in a ‘query’ cluster of size m had a reciprocal blastp strain top hit with a sufficient number of member gene models in a ‘subject’ cluster of size n – m or smaller. (ii) The size of the resulting ‘merged’ cluster was ≤n.To assess the influence of duplication and microsynteny loss on fungal pan-genomes, we processed the results of the PanOCT analysis using a multi-step Python/R post-processing pipeline. This first step of this pipeline was an iterative search for independent syntenic clusters with the potential to be merged based on reciprocal sequence similarity. Starting with accessory clusters of size n) were recategorized as core clusters, although for this study such recategorizations were a rare occurrence.This approach attempted to account for loss-of-synteny events such as rearrangements or other artefacts arising from different genome sequencing and assembly methods. Merged accessory clusters that now had an orthologous gene model from each strain in a dataset , then that accessory cluster was classified as being a paralogous cluster or a cluster of duplicated core gene models. This approach attempted to account for duplication events followed by subsequent gene loss, rearrangement in strains or strain-/lineage-specific expansions of gene families. Using a sequence-based approach of pan-genome analysis, as opposed to genome alignment or other methods, also facilitated the downstream application of systematic functional analysis of species pan-genomes; e.g. gene ontology (GO)-slim enrichment, which is detailed below. We visualized the distribution of syntenic orthologues within fungal accessory genomes using the UpSet technique, an alternative to Venn or Euler diagrams, which visualizes intersections of sets and their occurrences using a matrix representation [blastp (e=10−4).The second step of our post-processing pipeline assessed the influence of gene duplication on fungal pan-genome evolution by analysing the proportion of accessory gene models that were potentially paralogous to the core genome. Gene models from accessory clusters were assessed for sequence similarity to core gene models from the initial all-vs-all entation . This teentation . Singletmuscle with the default parameters, and for each cluster alignment phylogenetically informative character sites were extracted using paup* [Phylogenomic reconstruction of intraspecific lineages was carried out for all four fungal species using a supermatrix approach. For each fungal pan-genome dataset, all core orthologue clusters whose smallest gene model was at least 90 % the length of the longest gene model were retrieved from the dataset. Each cluster was aligned in ng paup* . Sampledng paup* .Saccharomyces cerevisiae core clusters (431 100 gene models) passed the minimum sequence length criterion and retained alignment data after sampling, and were concatenated into a 100 genome superalignment containing 54 860 aa sites.In total, (i) 4311 Candida albicans core clusters (68 904 gene models) retained alignment data after sampling, and were concatenated into a 34 genome superalignment containing 31 999 aa sites.(ii) 4327 Cryptococcus neoformans var. grubii core clusters (112 800 gene models) retained alignment data after sampling, and were concatenated into a 25 genome superalignment containing 47 811 aa sites.(iii) 4512 Aspergillus fumigatus core clusters (68 904 gene models) retained alignment data after sampling for phylogenetically informative residues, and were concatenated into a 12 genome superalignment containing 20 760 aa sites.(iv) 5 724 Approximate maximum-likelihood phylogenomic reconstruction was performed for each superalignment using FastTree with the default JTT+CAT evolutionary model and Shimodaira–Hasegawa local supports . All phyCryptococcus neoformans var. grubii) and performing a Fischer’s exact test analysis with parent term propagation and false discovery rate correction (P<0.05) for all complements using the Python package GOAtools (Table S2) [P value distribution generated from 500 resampled P values.Pfam, InterPro and GO annotation for all four fungal datasets was carried out using InterProScan . The totable S2) . False dblastp with an E value cut-off of 10−20 [2 tests were carried out to determine the significance of prokaryote and eukaryote origin frequencies within the complements of each species pan-genome [The putative evolutionary history of fungal core and accessory genomes was analysed by querying all gene models per species against a >5 million protein dataset sampled from 1109 bacterial and 488 archaeal genomes obtained from UniProt, using of 10−20 . Gene mon-genome  .The extent of HGT in each fungal accessory genome was assessed by randomly selecting representative gene models from each accessory cluster and searching these using analysis , as well2 tests were carried out for the global frequencies of core and accessory gene models along the subterminal regions of chromosomes, which we defined as approximately the first and last 10 % of each chromosome, in each reference genome (Table S4). Pearson’s χ2 tests were also carried out for the frequencies of core and accessory gene models per chromosome for each reference genome (Table S4) [Pearson’s χable S4) . The chrable S4) .Saccharomyces cerevisiae S288C were obtained from the SGD [blastp search was carried out between all 5815 Saccharomyces cerevisiae S288C gene models from our Saccharomyces cerevisiae pan-genome dataset and the reference protein set for Saccharomyces cerevisiae S288C with an E value cut-off of 10−20 to match predicted proteins to orthologues from the reference protein set. Knockout phenotype viability data, if available, was then inferred for each of our Saccharomyces cerevisiae S288C gene models that had a reciprocal reference orthologue. Pearson’s χ2 tests were carried out for the frequencies of knockout phenotype viability in both the core and accessory genomes of Saccharomyces cerevisiae S288C (Table S5).All available knockout phenotype data for  the SGD . A recipSaccharomyces cerevisiae was taken from a previously published analysis of biotin reacquisition in yeast species [Saccharomyces cerevisiae S288C reference protein set, encompassing 13 of the 14 DP clusters. A reciprocal blastp search was performed between these genes and all 5815 Saccharomyces cerevisiae S288C gene models from the Saccharomyces cerevisiae pan-genome dataset with an E value cut-off of 10−20 to identify DP genes in our predicted gene model set. All 38 DP genes had a unique reciprocal match with a predicted gene model in Saccharomyces cerevisiae S288C. A binary matrix was generated for the presence/absence of syntenic orthologues of DP genes from Saccharomyces cerevisiae S288C in the Saccharomyces cerevisiae pan-genome dataset (Table S5).Data for 14 DP gene clusters containing 41 genes found in  species . A totalAspergillus fumigatus Af293 were obtained from a previous analysis of secondary metabolism in Aspergillus fumigatus [Aspergillus fumigatus Af293 gene models from the Aspergillus fumigatus pan-genome dataset were matched to their homologues from the reference gene data set using a reciprocal blastp search with an E value cut-off of 10−20. A binary matrix was constructed for the presence/absence of syntenic orthologues of the 307 putative BGC genes from Aspergillus fumigatus Af293 within the Aspergillus fumigatus pan-genome dataset (Table S5).Data for 33 known BGCs encompassing 307 genes in umigatus . AspergiSaccharomyces cerevisiae strains with a mean of 5759 gene models predicted per strain . For individual strain genomes, this corresponded to between 83 and 90 % of their total predicted gene model content (Saccharomyces cerevisiae strain (mean size =~859 gene models). Further analysis of the Saccharomyces cerevisiae species accessory genome identified that ~32 % of accessory gene models  were duplicates of core gene models conserved across one or more strains. This corresponded to a mean of 275 gene models per Saccharomyces cerevisiae strain, and 27 511 gene models in total  . Other strains (e.g. YJM1477) lacked singleton gene models altogether  and YJM271 had the largest (967 gene models)  .Saccharomyces cerevisiae strains resolved two major groups; a clade containing strains and mosaics derived from Malaysian, West African, North American and sake populations, and a clade containing strains and mosaics derived from wine/European populations  formed a monophyletic branch within the previously described wine/European clade [Phylogenomic reconstruction of all 100 ulations . Each of Saccharom [et al. ; for exa [et al.  Saccharom [et al.  as havinan clade .Candida albicans strain genomes, with a mean of 5993 gene models predicted per strain, distributed across 7325 unique syntenic orthologue clusters . This corresponded to between 89 and 91 % of the total predicted gene models for each strain genome   were duplicates of core gene models conserved across one or more strains. This corresponded to a mean of 215 gene models per Candida albicans strain, and 7312 gene models in total  were missing a syntenic orthologue in only one other strain, while 928 gene models were singletons. UpSet analysis of the distribution of orthologues within the Candida albicans accessory genome showed that 1056 gene models (32 syntenic clusters) from 33 Candida albicans strains were missing an orthologue in Candida albicans WO-1 and Candida albicans 3153A had 53 putative gene models with no orthologue in any other strain . SC5314 had the smallest number of singleton gene models . Candida albicans A48 had the largest accessory genome (622 gene models) and Candida albicans Ca6 had the smallest (487 gene models)   and S1. in total  and S1.  models) . Phylogen SC5314 .Cryptococcus neoformans var. grubii strain genomes, with a mean of 6809 gene models predicted per strain, distributed across 8193 unique syntenic orthologue clusters . This corresponded to between 76 and 85 % of the total predicted gene models for each strain genome (Cryptococcus neoformans var. grubii strain (mean size =~1334 gene models) (Table S1). Detailed analysis of the Cryptococcus neoformans var. grubii species accessory genome identified that ~29 % of accessory gene models  were duplicates of core gene models conserved across one or more strains. This corresponded to a mean of ~391 gene models per Cryptococcus neoformans var. grubii strain, and 9794 gene models in total (Cryptococcus neoformans var. grubii clusters (encompassing 16 032 accessory gene models) were missing a syntenic orthologue in only one other strain and 668 accessory gene models were singletons. UpSet analysis of the distribution of orthologues within the Cryptococcus neoformans var. grubii accessory genome showed that 3600 gene models (150 syntenic clusters) from 24 Cryptococcus neoformans var. grubii strains were missing an orthologue in Cryptococcus neoformans var. grubii MWRSA852, whereas the Cryptococcus neoformans var. grubii A1358 genome had 49 putative gene models with no orthologue in any other strain . KN99 had no singleton gene models, but it should be noted that that strain is an isogenic derivative of the reference H99 strain. Cryptococcus neoformans var. grubii H99 itself had the largest accessory genome (1590 gene models) and Cryptococcus neoformans var. grubii MW-RSA852 had the smallest (964 gene models)  . The mosmidpoint .Aspergillus fumigatus strain genomes, distributed across 11 075 unique syntenic orthologue clusters, with a mean of 9692 gene models predicted per strain. The core Aspergillus fumigatus genome contained 8073 core gene models that were conserved across 12 Aspergillus fumigatus strains . This corresponded to between 80 and 86 % of the total predicted gene models for each strain genome (Aspergillus fumigatus strain (mean size =~1619 gene models) (Table S1). Detailed analysis of the Aspergillus fumigatus species accessory genome identified that ~41 % of accessory gene models  were duplicates of core gene models that were conserved across one or more strains. This corresponded to a mean of 677 gene models per Aspergillus fumigatus strain, and 8127 gene models in total. Overall, 7953 gene models (from 958 syntenic clusters) were missing a syntenic orthologue in only one other strain, whereas 723 gene models were singletons.A total of 116 311 gene models were predicted across all 12 Aspergillus fumigatus accessory genome found that 2167 gene models (197 syntenic clusters) from 11 Aspergillus fumigatus strains were missing an orthologue in Aspergillus fumigatus IFISWF4 and the reference Aspergillus fumigatus Af293 genome has 150 putative gene models with no orthologue in any other strain . The latter may be due to a lower degree of strain sampling within the Aspergillus fumigatus dataset or the reference genome having a higher-quality assembly than other strains of Aspergillus fumigatus. The Z5 strain has the smallest number of singleton gene models . Aspergillus fumigatus Af293 has the largest accessory genome (1964 gene models) and Aspergillus fumigatus HMRAF706 has the smallest (1294 gene models)  . Phylogend Af210 . The plaublished .P<0.05) (Table S2). Furthermore, molecular function terms for enzymatic and nucleic acid binding activity are also significantly over-represented (Table S2). In fungal accessory genomes, terms relating to transport and localization of proteins, carbohydrate metabolism, as well as protein modification and carboxyl acid metabolism, are significantly over-represented in many species (Table S2). Terms relating to housekeeping processes are significantly under-represented in fungal accessory genomes compared to core genomes. There are no common or synonymous cellular component or molecular function terms that are significantly under-represented across all four fungal accessory genomes in our analysis. However, terms relating to the functions of intracellular membrane-bound organelles are significantly over-represented in the accessory genomes of both Cryptococcus neoformans var. grubii and Aspergillus fumigatus (Table S2).Analysis of the distribution of GO terms in fungal core genomes shows that many housekeeping biological processes, such as translation, nucleic acid metabolism and oligopeptide metabolism, are significantly over-represented in each species (Saccharomyces cerevisiae (Table S2). In addition to transport processes, genes potentially involved in vitamin metabolism and protein dephosphorylation are significantly over-represented within the core genome of Saccharomyces cerevisiae. Similar terms are also significantly over-represented within the core genome of Candida albicans (Table S2). The Cryptococcus neoformans var. grubii core genome is significantly over-represented in some unique terms involved in regulation of homeostasis and biological quality, functional pathways such as the unfolded protein response (UPR) pathway, as well as signal transduction (Table S2). There are fewer terms that are significantly over-represented within the Cryptococcus neoformans var. grubii accessory genome than in the other fungal accessory genomes in this study. Those terms that are significantly over-represented in the Cryptococcus neoformans var. grubii accessory genome are also found elsewhere, e.g. transport. The core Aspergillus fumigatus genome is significantly over-represented in terms related to small molecule biosynthesis and other biosynthetic processes (Table S2). Within the Aspergillus fumigatus core genome, terms relating to vesicle-mediated transport and carboxylic acid metabolism are significantly over-represented, these terms are also significantly over-represented in the Saccharomyces cerevisiae core genome.Many broad and granular housekeeping terms relating to nucleic acid and protein biological processes are significantly over-represented within the core genome of blastp searches (1e−20) of fungal gene models against >5 million prokaryotic sequences from >1500 bacterial and archaeal genomes. Gene models that had hits with prokaryotic sequences exclusively were classified as having originated within the prokaryotes (broken down further by prokaryotic kingdom in Table S3), and gene models that lacked a blastp hit against the prokaryotic database were classified as having originated within the eukaryotes. Using these criteria, for each fungal pan-genome dataset between 69 and 77 % of all gene models were inferred as eukaryotic in origin. Similar proportions of gene models inferred as having originated within eukaryotes were also observed in fungal core genomes. Higher proportions of gene models with a putative origin within eukaryotes was observed in fungal accessory genomes . Statistical analysis of the ancestral history of each fungal species pan-genome found that each fungal accessory genome was significantly enriched for genes of eukaryotic origin and each fungal core genome was significantly enriched for genes of prokaryotic origin (P<0.05) (Table S3).The ancestral origin of fungal core and accessory genomes was inferred via Candida albicans accessory genome to 11 events in the Aspergillus fumigatus accessory genome (Table S3). The distribution of these putative HGT genes in fungal accessory genomes varies from strain-unique singleton genes (particularly in Saccharomyces cerevisiae) to more widely distributed genes (as seen in Cryptococcus neoformans and Aspergillus fumigatus) (Table S3). The majority of potential prokaryote donors are soil-dwelling bacteria, such as Clostridium pasteurianum (a donor to the Aspergillus fumigatus accessory genome) and Acinetobacter pittii (a donor to the Saccharomyces cerevisiae accessory genome). We then applied a similar screen for recent HGT from other fungal species, which suggested up to 8 % of fungal accessory genomes may have arisen via intrakingdom HGT. The largest extent of such intradomain HGT appeared to have occurred into the accessory genomes of Cryptococcus neoformans and Aspergillus fumigatus  (Table S3). In each accessory genome, putative HGT-derived gene models appear to have been transferred mainly from closely related species or species that share similar niches. For example, Aspergillus fumigatus is a potential donor of three Candida albicans accessory gene models (Table S3). However, further comprehensive investigations are required to confidently confirm that these HGT events are bona fide.Systematic screening for interdomain HGT events in each fungal accessory genome revealed small numbers of putative HGT events from prokaryote sources per species, ranging from a single event in the Saccharomyces cerevisiae S288C and Cryptococcus neoformans var. grubii H99 are found in their subterminal regions, whereas this proportion is higher in Candida albicans SC5314 and Aspergillus fumigatus Af293 . Candida albicans SC5314 has a lower proportion of accessory gene models  found in subterminal regions than the other three fungal species, where that proportion is ~28–33 % of their total accessory genomes. There is a statistically significant bias (P<0.05) towards accessory gene models in the subterminal regions of Saccharomyces cerevisiae S288c, Cryptococcus neoformans var. grubii H99 and Aspergillus fumigatus Af293, with a corresponding bias (P<0.05) towards core gene models in the non-subterminal regions of each genome (Table S4). In contrast, there is no significant pattern in the distribution of accessory gene models in Candida albicans SC5314, and instead its subterminal regions are significantly enriched for core gene models (P<0.05) (Table S4). Statistical analysis of core and accessory gene model enrichment per chromosome in each reference genome found that at least one chromosome was significantly enriched for core gene models and another chromosome was significantly enriched for accessory gene models per genome (P<0.05) (Table S4). The number of chromosomes per genome that were significantly biased towards either core or accessory gene models ranged from two in Candida albicans SC5314 (chromosomes 2 and 7) to six in Saccharomyces cerevisiae S288C  (Table S4). Visualizing chromosomal plots showed that clustering of accessory genes mostly occurred in subterminal regions of fungal genomes . There are some exceptions: some chromosomes in Saccharomyces cerevisiae S288c, Cryptococcus neoformans var. grubii H99 and Aspergillus fumigatus Af293 had at least one larger accessory gene cluster closer to the chromosomal midpoint . In contrast, there appeared to be no major clustering of accessory genes in any chromosome in Candida albicans SC5314 .Between 17 and 21 % of all predicted gene models for each fungal reference strain lie in the subterminal regions of that strain’s genome. Approximately 15 % of all core gene models in both Saccharomyces cerevisiae S288C gene models from the species pan-genome dataset, encompassing 4730 core gene models and 613 accessory gene models, were assigned their reference homologue’s corresponding knockout viability phenotype. The remaining 472 predicted gene models from Saccharomyces cerevisiae S288C did not have a knockout viability phenotype assigned to them, either due to the lack of a unique reciprocal blastp hit or a lack of viability data for the reference homologue (Table S5). Those Saccharomyces cerevisiae S288C gene models that had knockout phenotype data were predominantly knockout-viable;~79 % of annotated core gene models and ~88 % of annotated accessory gene models had a reciprocal reference homologue with a viable knockout phenotype (Table S5). There was no significant bias in the distribution of knockout viability within the core Saccharomyces cerevisiae S288C genome, i.e. the core genome was enriched for neither knockout-viable or knockout-inviable gene models (of those which had knockout phenotype data available) (Table S5). The Saccharomyces cerevisiae S288C accessory genome, however, was over-represented for knockout-viable gene models (P<0.05) (Table S5).A total of 5343 predicted Saccharomyces cerevisiae S288C gene models taken from our pan-genome dataset (Table S5). One of the 13 reference DP clusters was syntenically conserved within all strains in the Saccharomyces cerevisiae pan-genome dataset; a three-member GAL cluster involved in galactose utilization. Some clusters are widely conserved within the dataset, but are missing a member gene in a small number of strains; these include a three-member BIO cluster that mediates biotin uptake, a SNO1-SNZ1 vitamin B6 metabolism cluster and a large six-member DAL-DCG cluster that enables utilization of allantoin as a nitrogen source (Table S5). Other clusters had more patchy distribution within the species pan-genome, most notably a three-member ARR gene cluster that confers arsenic resistance was missing a member gene (ARR3) in 49 out of 100 strains (Table S5). Some clusters, such as a four-member FIT/FRE iron uptake cluster, are completely missing in a small number of strains (Table S5).All 38 reference DP genes had a unique reciprocal homologue within the set of predicted Aspergillus fumigatus Af293 had a unique reciprocal homologue within the set of predicted Aspergillus fumigatus Af293 gene models from the Aspergillus fumigatus pan-genome [Aspergillus fumigatus strains, none of which were unique to Aspergillus fumigatus Af293 alone (Table S5). There were 14 Aspergillus fumigatus BGCs that were completely conserved , which included known mycotoxin-producing BGCs such as fumagillin and gliotoxin clusters (Table S5). Other BGCs were found to have one or two genes missing, potentially due to synteny loss or pseudogenization. Some BGCs showed far more variable distribution within the Aspergillus fumigatus pan-genome; for example, a polyketide synthase (PKS) cluster was wholly conserved in four strains  and absent or translocated in the other eight, and a fusarielin-like cluster was completely absent from A1163 and only partially present in some strains but was wholly conserved in others (Table S5).A total of 307 known biosynthetic genes from 33 BGCs in n-genome . A total-genome Ortholog Clustering Tool) [Saccharomyces species, but was limited to assessing syntenic conservation of reference homologues using immediately adjacent genes [To investigate pan-genomic structure within four fungal species, we adapted a method previously used in bacterial pan-genome analysis and implemented in PanOCT (Panng Tool) . Our ratnt genes . To ensunt genes . As our Saccharomyces cerevisiae, Candida albicans, Cryptococcus neoformans var. grubii and Aspergillus fumigatus. In addition to their impact on human lifestyle, each species chosen is a model organism for fungal evolutionary biology, genomics and comparative genomics. Saccharomyces cerevisiae was the first eukaryote to have its genome sequenced, and the other three species each had their genome sequenced during the initial wave of fungal genomics research in the early to mid-2000s [Saccharomyces cerevisiae has undergone ancestral whole-genome duplication and Candida albicans has an alternative genetic code [Cryptococcus neoformans and Aspergillus fumigatus are more intron-dense than either Saccharomyces cerevisiae or Candida albicans and extensive alternative splicing occurs in Cryptococcus species [Saccharomyces cerevisiae, Candida albicans and Aspergillus fumigatus are members of the fungal phylum Ascomycota; the former two are closely related members of the subphylum Saccharomycotina, which includes many typical commensal and pathogenic yeasts that reproduce by budding, while Aspergillus fumigatus is a member of the large subphylum Pezizomycotina of filamentous fungi [Cryptococcus neoformans var. grubii superficially resembles many yeast species and also replicates by budding, but is a member of the phylum Basidiomycota and is more closely related to multicellular fungi within the subphylum Agaricomycotina than other yeast species [Aspergillus fumigatus to >400 for Saccharomyces cerevisiae [We chose to investigate the potential pan-genomic structure of four model fungal species: id-2000s . Our seltic code , whereas species . Our selus fungi . Cryptoc species . Genome revisiae .Saccharomyces cerevisiae was constructed using genomic data from 100 strains, 99 of which were previously included in the 100GS resource (Table S1) [Saccharomyces cerevisiae genomes sequenced prior to 2015 and 93 Saccharomyces cerevisiae genomes sequenced de novo by the 100GS authors, taken from diverse genotypic and phenotypic backgrounds  . The resi et al. ). The re content . The 100 content .Saccharomyces cerevisiae genomes included an analysis of the pan-genome of Saccharomyces cerevisiae in which the authors of that study detected non-reference genomic content by aligning strain genomes to the S288C genome using blastn, and extracting and annotating unique non-reference genes using an integrative multi-method procedure [Saccharomyces cerevisiae genome [Saccharomyces cerevisiae accessory genome; accessory genes were clustered within the subterminal regions of Saccharomyces cerevisiae genomes and some accessory genes may have originated via HGT from divergent yeast species or other fungi [Saccharomyces cerevisiae accessory genome.A more recent study of 1011 rocedure . Notablye genome . The 101e genome , whereaser fungi . We obseSaccharomyces cerevisiae, as well as larger analyses of species pan-genomes in fungi and other taxa. The Candida albicans species pan-genome dataset was constructed using data from 34 strains, predominantly clinical in origin, including both homozygous and heterozygous MTL mating-type strains (Table S1) [Candida albicans comes from strains isolated in hospitals; of the 34 strains in our dataset, 14 strains were clinical isolates from the USA alone (Table S1). A number of other strains were isolated from European and Middle East sources, but for 13 strains no information was available on the isolate source for the genome from GenBank. Perhaps as a consequence of a lower degree of environmental diversity due to sampling primarily clinical strains, the Candida albicans pan-genome has the smallest proportion of accessory gene content of the four species analysed in this study (~9 % of the entire species pan-genome). The Candida albicans pan-genome also has the lowest degree of variation in accessory genome size between individual strains of the four species analysed . Despite this caveat, however, the Candida albicans pan-genome otherwise exhibits many of the same functional and evolutionary trends seen in the other three species we have investigated (as detailed below). With a broader sampling of strains found outside of a clinical context, a more accurate picture of the size of the Candida albicans accessory genome will be attained.For the remaining three species, we constructed species pan-genome datasets based on strain genome assemblies that were available from GenBank at the start of our analyses. For each of these datasets, we attempted to sample strain genomes with as many diverse characteristics  as was possible with the genome assembly data available. Although there are a smaller number of strains sampled for these species pan-genomes, the sizes of these species’ core and accessory genomes are in line with our analysis of able S1) . A substanalysed  and 6b. Candida albicans, both our Cryptococcus neoformans var. grubii and Aspergillus fumigatus pan-genome datasets were constructed using a diverse array of strain genomes taken from both clinical and wild environments. The Cryptococcus neoformans var. grubii pan-genome dataset was constructed using clinical strain genomes isolated predominantly from human immunodeficiency virus positive patients from the USA and Botswana and wild-type strains sampled from Southern Africa sources (Table S1). Cryptococcus neoformans var. grubii has the largest proportion of accessory genes of the four species analysed (~20 % of the entire species pan-genome). As Cryptococcus neoformans is an intracellular pathogen in humans, it has to adapt to extreme variations in environmental stresses in order to survive. This is thought to lead to the high level of genomic rearrangement and instability seen in Cryptococcus neoformans [Cryptococcus neoformans var. grubii. Genomic instability as a result of pathogenic lifestyle fuelling pan-genome evolution has previously been observed in the wheat pathogen Z. tritici [Aspergillus fumigatus pan-genome dataset was constructed using 12 strain genomes sampled from clinical environments in the UK, USA and Canada, wild-type samples taken from China and from South American forest floors, and 2 strains isolated from surfaces within the International Space Station [Aspergillus fumigatus pan-genome is made up of accessory gene content, which is predominantly clustered in the subterminal regions of chromosomes (discussed below). There is a greater degree of variation in the accessory genome sizes of individual Aspergillus fumigatus strains than in the other species analysed, we believe that this is primarily an artefact of the smaller number of genomes in our dataset .In contrast to oformans . It is p tritici . The Asp Station  . In spite of gaps in ontological data for some of our species of interest, there are a number of patterns we can observe across multiple species in our GO analyses of fungal core and accessory genomes, as well as unique patterns of enrichment in some species. Many housekeeping terms such as translation, nucleic acid metabolism and oligopeptide metabolism are statistically over-represented in each fungal core genome we have analysed (P<0.05) (Table S2). There is an over-representation of similar cellular component terms in each of the three ‘yeast’ core genomes  (Table S2). This may reflect the morphological distinctions between these three species and Aspergillus fumigatus; however, the lack of dedicated annotation data for Cryptococcus neoformans var. grubii makes a definitive observation difficult. Terms relating to transport, localization and CAZY processes are statistically over-represented in fungal accessory genomes (Table S2). In part this is to be expected, as many fungi have varying numbers of copies of genes involved in CAZY and transport processes [Cryptococcus neoformans var. grubii and Aspergillus fumigatus may reflect infection or in-host survival processes for both pathogenic species (Table S2). Both the Candida albicans core and accessory species genome share similarly over-represented terms to their Saccharomyces cerevisiae counterparts, a reflection of the two species’ relatively close evolutionary relationship (Table S2).Between 65 and 81 % of gene models per species pan-genome had at least one Pfam domain, while the proportion of gene models with GO data was between 42 and 54 % per species . This varocesses . Terms rCryptococcus neoformans var. grubii core genome may reflect the species’ lifestyle as an intracellular pathogen (Table S2). Such terms include regulation of homeostasis and biological quality (e.g. cell mass), which are vital for Cryptococcus neoformans var. grubii to survive the plethora of environmental stresses it encounters in the host. Similarly, UPR is an over-represented molecular function in the Cryptococcus neoformans var. grubii core genome; the UPR pathway is known to influence thermoregulation in Cryptococcus neoformans var. grubii particularly during the initial infection period [Cryptococcus neoformans var. grubii core genome is signal transduction; many signal transduction pathways in Cryptococcus neoformans var. grubii play an important role in cell differentiation as well as pathogenicity (Table S2) [Aspergillus fumigatus genome is enriched for small molecule biosynthesis and other biosynthetic processes, which concurs with previous comparative studies of Aspergillus species [Aspergillus fumigatus species pan-genome (Table S5). Both transport and localization processes are over-represented within the Aspergillus fumigatus accessory genome, which may have an indirect role in the infection processes of Aspergillus fumigatus. Aspergillus fumigatus strain pathogenesis may, therefore, be influenced by accessory genome evolution, particularly within subterminal regions [Many of the terms that are over-represented in the n period . Anotherable S2) . The cor species  (Table S regions .P<0.05) (Table S3). In other words, genes of prokaryotic origin appear to be more likely to be syntenically conserved and universally retained within these fungal species (Table S3). This appears consistent with the observation that prokaryote-derived genes in Saccharomyces cerevisiae are essential for survival [Our statistical analysis of the ancestral history of each fungal species pan-genome found that gene models of eukaryotic origin are statistically over-represented within fungal accessory genomes, while gene models of prokaryotic origin are statistically over-represented in fungal core genomes  and 379 of the 392 putative HGT events into the Aspergillus fumigatus accessory genome suggest transfer from other species in the subphylum Pezizomycotina  (Table S3). Although there appears to be greater evidence for intrakingdom HGT having a role to play in fungal accessory genome evolution than interdomain HGT, it is our opinion that a dedicated analysis of intrakingdom HGT in fungal accessory genomes using robust phylogenetic methods is required to test the true role of intrakingdom HGT in fungal pan-genome evolution.Given the extent of HGT in prokaryotes and its role in generating novel genetic content and in the evolution of prokaryotic gene families, it is likely that HGT plays a significant role in prokaryote pan-genome evolution. HGT in eukaryotes is known to be far less frequent than in prokaryotes however, so its impact on eukaryotic pan-genome evolution may be limited. We examined the extent of HGT into fungal accessory genomes from two potential sources of novel genetic content: prokaryote species and other species within the fungal kingdom. A screen for interdomain HGT events in each fungal accessory genome following previous methodology  revealedCryptococcus neoformans var. grubii has the smallest proportion of these duplicated core gene models  and Aspergillus fumigatus has the largest (Table S1). This accounts for between 3 and 7 % of the total size of fungal pan-genomes, with the smallest proportion in Candida albicans and the largest in Aspergillus fumigatus , which are usually independently expanded or redistributed within individual fungal genomes [Saccharomyces cerevisiae 100GS dataset, the reduced sizes of both Cryptococcus neoformans var. grubii C45 and MW-RSA852 relative to most other strains)  . There isampling . Saccharto S288C . In totaP<0.05) . The sole exception is Candida albicans SC5314, wherein there is a statistically significant bias for core gene models within subterminal regions (P<0.05) . The subterminal regions of chromosomes are usually areas of genomic instability in eukaryotes, so it is unsurprising that we observe greater breakdown of synteny in these regions [Saccharomyces cerevisiae and Aspergillus fumigatus in particular [Aspergillus fumigatus Af293 large clusters of accessory genome content can be observed outside the subterminal regions, which may reflect instances of strain- or lineage-specific genomic rearrangement events . Such rearrangements are linked to environmental adaptation and reproductive isolation in Saccharomyces cerevisiae genomes [Cryptococcus neoformans var. grubii, the greater degree of accessory genome content found outside subterminal regions may be a reflection of the role that genomic rearrangement plays in shaping the genomes of individual strains within the host [Analysis of the global distribution of core and accessory gene models shows that there is a statistically significant bias towards accessory gene models in the subterminal regions within three of the four reference genomes in our study and a statistically significant bias towards core gene models outside these subterminal regions in the same genomes . This may reflect the fact less than 20 % of the genes encoded in the Saccharomyces cerevisiae S288C genome are thought to be essential for growth and, thus, likely knockout-inviable [Saccharomyces cerevisiae genome (Table S5). In contrast, there is a significant proportion of gene models within the Saccharomyces cerevisiae S288C accessory genome that are associated with a viable knockout phenotype (P<0.05), which appears to reinforce the more variable nature of species accessory genomes relative to core genomes (Table S5).Due to its position as arguably the most complete fungal model organism, there is a wealth of manually annotated functional data available for revisiae . Using rinviable . It is wAspergillus species, many yeasts lack BGCs. Somewhat analogous to BGCs in Saccharomyces cerevisiae are small DP gene clusters of functionally related genes, which have been lost in other Saccharomyces and related species but were later regained in Saccharomyces cerevisiae via HGT or neofunctionalization [Saccharomyces cerevisiae pan-genome found one DP cluster that appears to be completely conserved in the pan-genome; a cluster on chromosome II containing three GAL genes that mediates the degradation of galactose to galactose-1-phosphate within the glycolysis pathway [BIO biotin uptake cluster on chromosome XIV and a SNO1-SNZ1 vitamin B6 metabolism cluster on chromosome XIII (Table S5) [DAL-DCG cluster found on chromosome IX, the largest DP cluster, allows Saccharomyces cerevisiae to use allantoin as its sole nitrogen source through a pathway in which allantoin is converted to urea, which is then converted into ammonium by DUR1-2 [SAM4-SAM3 cluster that enables the usage of S-adenosylmethionine as a sulphur source has one of the two member genes missing in four strains (and is entirely absent in YJM969) (Table S5).Unlike filamentous fungi such as lization . Hall anlization  previouslization . Our ana pathway  (Table Sable S5) . Anothery DUR1-2 . A SAM4-Saccharomyces cerevisiae species pan-genome, particularly those within subterminal regions in Saccharomyces cerevisiae S288C, which may indicate a greater breakdown of synteny or gene loss within these clusters. For some clusters, this may be due to functional redundancy; for example, three DP clusters are involved in vitamin B1 and B6 metabolism, the aforementioned SNO1-SNZ1 cluster is conserved across almost all 100 strains whereas the other two clusters have patchier distribution or are totally missing in some strains  (Table S5). Other potential causes for this varying distribution of DP clusters may include environmental adaptations. One DP cluster that confers arsenic resistance is prevalent in many wine/European strains, but has much patchier conservation in non-European strains or strains with Malaysian or West African ancestry (such as SK1). One member gene of this cluster, ARR3, is absent in 49 out of the 100 strains in our dataset, including many mosaic strains with wine/European and Malaysian ancestry. Increased arsenic resistance has been observed in strains of European ancestry, likely as a result of anthropogenic influence on soil composition, which may explain the ARR cluster’s absence in some non-European strains [ARR cluster is located in the subterminal regions of chromosome XVI in Saccharomyces cerevisiae S228C; this suggests gene loss or chromosomal rearrangements amongst other events may be responsible for the absence of ARR3 in the ARR cluster of many strains [It is possible that some strains may simply be missing a syntenic orthologue of one or more genes in a cluster due to pseudogenization or synteny loss due to chromosomal rearrangement. Other DP clusters have more patchy distribution within the  strains . Additio strains .Aspergillus fumigatus in the human host [Aspergillus fumigatus pan-genome found 14 BGCs that were completely conserved, a number of which are involved in the production of mycotoxins. Other BGCs have one or two syntenic orthologues that are missing in other strains, in these cases the majority of these genes may play more indirect roles in cluster function and, therefore, be less likely to be conserved within clusters, while some are only partially present or completely absent in some strains but are highly conserved in others (Table S5). An analysis of variation of Aspergillus fumigatus BGCs using short-read data by Lind et al. [et al. [Aspergillus fumigatus in both their analysis and ours; for example, a fusarielin-like cluster we identified as missing from A1163 and partially present in other strains has gained pseudogenizing mutations in some strains but not others, whereas variation in other accessory BGCs is due to factors such as transposable elements (as is the case in a 27 member PKS cluster) or lineage-specific gene acquisition/loss events [Within the aspergilli and other fungi, functionally related genes involved in secondary metabolism pathways are often arranged into BGCs within the subterminal regions of chromosomes. These BGCs are involved in a range of infection and survival processes in the aspergilli, and subterminal regions themselves are believed to mediate the infection process of man host . Our anad et al.  found sid et al. . Lind et [et al.  observeds events . This suCryptococcus neoformans var. grubii, which currently lacks a dedicated GO-slim dataset) may have affected our functional characterization of fungal pan-genomes. We attempted to ameliorate this lack of data by using other sources of genomic information (e.g. knockout data from SGD for Saccharomyces cerevisiae), though their efficacy is ultimately dependent on human annotation. One caveat of large-scale pan-genome analysis of this kind may be the usage of genomes assembled via a reference-based approach as opposed to de novo approaches, which may then lead to an underestimation of accessory genome sizes within species pan-genomes due to underestimation of sequence diversity or inheritance of assembly artefacts from the reference genome [de novo approaches, for example, the 100GS dataset is predominantly de novo sequenced strains, so the potential effects of overreliance on reference-based assembly data may have been reduced in our study [Compared to the increasing amount of software designed to construct and characterize bacterial and archaeal pan-genomes, little dedicated pan-genome software exists for eukaryote taxa. Our overall method of analysis, bespoke gene model prediction followed by pan-genome construction using PanOCT as the anchor method, is ad hoc but may point towards a sufficiently optimized syntenic method of pan-genome construction for eukaryotes in the future. On this point, it is worth noting that PanOCT’s current implementation has an exponential memory usage curve per genome added, which makes analysis of prokaryotic or eukaryotic datasets of this scale difficult without dedicated high-performance computational facilities . The rele genome . The majur study .Saccharomyces cerevisiae pan-genome and the 1011 strain analysis by Peter et al. [Candida albicans pan-genome likely underestimates the true size of the Candida albicans accessory genome due to a lack of non-clinical genomic data. The greater variation of accessory genome sizes between individual strains of Cryptococcus neoformans var. grubii and Aspergillus fumigatus may be an artefact of there being fewer strain genomes available for both species, which would in turn affect the sizes of those species’ pan-genomes. There have been attempts to estimate the ‘true’ size of bacterial pan-genomes from existing data using different mathematical models, which vary from inferring almost infinite pan-genomes that increase in size with each strain added to stricter models that infer a more finite structure for most bacterial species [The size of a species pan-genome and its complements are ultimately dependent on the amount and the geographical or phenotypical variety of genomic data sampled. Methodological differences notwithstanding, our 100 strain analysis of the r et al.  predict r et al. . In cont species . Future Saccharomyces cerevisiae, Candida albicans, Cryptococcus neoformans var. grubii and Aspergillus fumigatus. Defining core genomes as containing gene models syntenically conserved throughout species and accessory genomes as containing gene models of varying syntenic conservation and distribution throughout species, we find strong evidence for pan-genomic structure within fungi. Between 80 and 90 % of all potential gene models in fungal species are core gene models, with the remainder being accessory gene models that are strain-specific or specific to individual groups of strains. Fungal core genomes are enriched for genes of ancient origin and facilitate many essential metabolic, regulatory and survival processes in both commensal and pathogenic species. Fungal accessory genomes are enriched for genes of more recent origin, appear to evolve and vary in size by processes like gene duplication and gain/loss events within strains, and are enriched for genes involved in molecule transport and carbohydrate metabolism amongst other functions. Our analysis supports the growing amount of evidence for pan-genomic structure in eukaryotes.Evidence for the existence of pan-genomic structure has been demonstrated in eukaryotic taxa using a variety of methodologies. Using computational methods based on sequence similarity and conserved synteny between strains, we have constructed and characterized species pan-genomes for four model fungi: https://github.com/chmccarthy/pangenome-pipelines (2018).McCarthy CGP, Fitzpatrick DA. Pipelines for eukaryotic pangenome analysis. Github:


Amyotrophic lateral sclerosis (ALS) is an incurable and fatal neurodegenerative disease. Increasing the chances of success for future clinical strategies requires more in-depth knowledge of the molecular basis underlying disease heterogeneity. We recently laid the foundation for a molecular taxonomy of ALS by whole-genome expression profiling of motor cortex from sporadic ALS (SALS) patients. Here, we analyzed copy number variants (CNVs) occurring in the same patients, by using a customized exon-centered comparative genomic hybridization array (aCGH) covering a large panel of ALS-related genes. A large number of novel and known disease-associated CNVs were detected in SALS samples, including several subgroup-specific loci, suggestive of a great divergence of two subgroups at the molecular level. Integrative analysis of copy number profiles with their associated transcriptomic data revealed subtype-specific genomic perturbations and candidate driver genes positively correlated with transcriptional signatures, suggesting a strong interaction between genomic and transcriptomic events in ALS pathogenesis. The functional analysis confirmed our previous pathway-based characterization of SALS subtypes and identified 24 potential candidates for genomic-based patient stratification. To our knowledge, this is the first comprehensive “omics” analysis of molecular events characterizing SALS pathology, providing a road map to facilitate genome-guided personalized diagnosis and treatments for this devastating disease. The disease has an incidence of 2.6 per 100,000 individuals-years and prevalence rates of around 6–7/100,000 in Europe, making it the most common adult-onset motor neuron disease2. About 5–10% of ALS cases show a family history (FALS), while the remainder of cases are classified as sporadic (SALS), and are probably associated to a polygenic and multifactorial etiology5.Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder, characterized by progressive loss and degeneration of motor neurons in both the motor cortex, brainstem and spinal cord, and is usually fatal due to respiratory failure within 3–5 years of onsetSOD1, several candidate-gene or genome-wide association studies (GWAS) have identified multiple single-nucleotide polymorphisms (SNPs) affecting potentially ALS-associated genes, including C9orf72, TDP43, FUS, MATR3, UBQLN2, VCP and OPTN9. In this context, a recent large-scale genome-wide association study identified a common missense variant and several rare loss-of-function (LOF) mutations within the microtubule motor protein-encoding gene, KIF5A, as candidate ALS risk factors, further supporting perturbations in cytoskeletal function play an important role in ALS and offering a potential target for drug development11.The remarkable advances in genome technologies over the last years have led to a huge progress in deciphering the genes and pathways involved in ALS pathogenesis. From the discovery of the first ALS-associated gene 13. In particular, the involvement of CNVs in ALS susceptibility has been clearly highlighted in two ALS genome-wide association studies, where multiple rare CNVs were shown to represent a more important risk factor for SALS than common CNVs15.In addition to the contribution of SNPs, which account for only a limited number of familial and sporadic ALS cases, evidence suggests that other genomic variants, such as copy-number variations (CNVs), that change gene dose rather than gene function, may exert a more pronounced effect on the onset and rate of disease progression16. Developing a robust molecular disease portrait that can explain the heterogeneity of ALS is thus fundamental to improve our understanding of the precise molecular mechanisms underlying disease pathogenesis and develop effective treatments for patients.The complexity of its molecular architecture has completely transformed the way we think about ALS, leading us to reconsider the traditional classification and therapeutic systems. In fact, despite intensive research efforts, the precise causes of ALS remain unknown and there is no cure for this devastating disease. The absence of effective treatments can be due in part by the complex and heterogeneous clinical, biochemical and molecular features of ALS, which is also supported by clinical studies on Edaravone (MCI-186), a free radical scavenger recently approved by FDA for ALS treatment showing effectiveness only in specific sub-cohorts of patients18. In particular, the most representative functional processes deregulated in SALS1 were annotated as involved in the regulation of chemotaxis, immunity, and cell adhesion and communication. Deregulated genes in SALS2, in turn, were selectively associated with cytoskeleton organization, regulation of transport and mitochondrial oxidative phosphorylation17. While these findings are consistent with previous evidence about the crucial role of these pathogenetic mechanisms in ALS24, they suggest for the first time the differential involvement of these mechanisms in specific subsets of ALS patients, offering a useful starting point for the further development of personalized diagnostics and targeted therapies.Our research group has recently characterized the transcriptional profiles of motor cortex samples from control and SALS patients, grouping these on the basis of their similarities measured over the most “hypervariable genes” (9.646 genes with a standard deviation >1.5). Unsupervised hierarchical clustering analysis allowed to discriminate controls from SALS patients and clearly distinguished two greatly divergent SALS subtypes, each associated with differentially expressed genes (DEGs) and biological pathwaysWhile our work lays the foundation for a molecular taxonomy of ALS, very little information is so far available from the single-omic analysis, which makes difficult to discriminate genes critical to ALS pathogenesis (driver genes) from non-relevant genes (passenger genes). An integrated and comprehensive view of multiple genomic data types (such as genome and transcriptome) may provide a powerful potential for defining disease subgroups and their molecular drivers, allowing for an overall understanding of the complex molecular networks that drive ALS pathogenesis at yet another level of systemic complexity.NeuroArray platform, designed to target genes associated with ALS as well as genes associated with other neurological disorders25, to analyze copy number variants (CNVs) in 40 motor cortex samples of control (10) and SALS (30) patients, clinically and transcriptomically characterized in our previous work25. Next, we provided the first comprehensive integrative analysis of genomic aberrations with expression data derived from the same patients to identify specific chromosomal regions and genes with concordant alterations in DNA and RNA profiles that may represent promising key molecular candidates for SALS. Finally, functional pathway and network analyses were carried out to gain further insights into the molecular complexity of ALS and reveal novel and yet unrecognized biomarkers and therapeutic targets, potentially useful for the development of personalized medicine in ALS.In this study, we applied the customized exon-centric comparative genomic hybridization array (aCGH) NeuroArray aCGH platform was used to identify DNA copy number alterations in 30 SALS patients and 10 controls. A total of 1472 CNVs were detected in SALS, including 780 losses and 692 gains  and published PubMed literature. Highly similar genomic altered patterns were observed, supporting the functional importance of these regions in disease etiopathogenesis Table .Table 3CNeuroArray design. A total of 406 significant CNV genes were obtained, including 251 duplications and 161 deletions  that were also differentially expressed in SALS1 patients and 246 CNV-driven DEGs in SALS2 patients (173 duplicated and 76 deleted)  Fig. . Among ted) Fig. . InteresTo demonstrate that the correlation found in our work is meaningful, we also performed a “control experiment” in which we evaluated the overlap between the genomic and transcriptomic data between two random groups within the total SALS samples (different from SALS1 and SALS2). We observed a very low overlap between CNV genes and differentially expressed genes in these randomized disease-related subgroups, confirming the appropriateness/accuracy of our analysis .regulation of cellular component organization , DNA conformation change  and regulation of neuron death , whereas regulation of synaptic transmission  and learning and memory  were the most enriched in SALS2  and ubiquitin protein ligase binding , while the CNV-driven DEGs in SALS2 were enriched in amino acid binding  and transcription factor binding   network analysis of their encoding products was performed, revealing a highly interconnected functional network, also including a greater number of ALS-associated genes Fig. . The PPITIMP2, AKT1, MMP9, CST3, SMN1 and SMN2) were previously associated with susceptibility to ALS while the remaining 18 genes  represent novel candidate mediators for disease progression. The set of CNV-driver genes also included many patient-specific ‘druggable’ genes that may represent good candidates for the development of personalized, molecularly targeted therapies for SALS patients  CNV-driven genes in SALS2. Interestingly, some of these candidate genes , while 60–70% of the SALS2 patients showed simultaneous deletion of precise regions of chromosomes 3q and 18p , a member of the human regulator of complement activation gene cluster34. This gene encodes one of the major immune adherence receptors and plays an important role in immune complex processing and clearance via reducing activation of classical and alternative complement cascade activity. Deletion of CR1 is consistent with the lower mRNA expression level of this receptor in SALS patients, providing support for aberrant complement regulation as a part of ALS process and highlighting the potential use of complement molecules as disease biomarkers36. Another interesting association is represented by a 162.362-kb duplicated region in 1p36.12 spanning several neuronal genes, including the FBXO42 encoding an important member of the F-box protein family involved in the ubiquitin-proteasome system and already known to be associated with ALS  were selectively detected in specific SALS patient subgroups and absent in all control individuals, providing the basis for a CNV-based molecular classification of the disease  is consistent with the observation that high mRNA expression levels of these chemokines may increase activation of the inflammatory system and changes in blood-brain barrier permeability, two key mechanisms implicated in, and possibly aggravating, motor neuron damage41.Besides strengthening previously reported results, we identified new potential susceptibility loci that were overrepresented in SALS patients and absent in controls Table . Among tSYNE1, SDK1, EEF1D, GSDMD, TIMP2, CST3, ALAD, AKT1, EPHA4, SPAST, SMN1, SETX, ATXN2, TBK1, SPG11, TAF15, MMP9) previously reported as potential risk factors in ALS, as well as novel candidates whose association with ALS was previously unappreciated. These genes may represent overlapping genetic signatures among different neurological condition, providing additional features for exploring ALS pathogenesis. To name a few of those detected exclusively in SALS patients  are ALDH4A1, a component of the mitochondrial matrix contributing to protect cells from oxidative stress42; BIN1, the most significant late-onset susceptibility locus for Alzheimer’s disease whose alterations in expression levels and splicing seem to induce muscle weakness and T tubule alterations43, and RABGAP1L, a GTPase-activating protein whose loss-of-function exacerbates neuronal loss44.By integrating the analysis of CNVs and gene expression profiling in the same patients and tissue samples, we found that 71.2% of CNV genes were differentially expressed in SALS patients in comparison to controls, the majority of them were cluster specific, further suggestive of the great divergence of two SALS subgroups at the molecular level Fig. . Among tNIPA1, NAIP, VPS54 and GRIN3B), which do not appear to exert any apparent influence on expression levels, suggesting that the expression of these genes may be not gene-dose dependent and that they are likely to represent secondary ‘passenger’ events in ALS pathogenesis. Genes differently regulated at the transcriptomic and genomic level , in turn, could result from other, non-CNV-related regulatory mechanisms, such as those associated with gene mutation, promoter methylation, and non-coding RNA regulation  involved in inflammatory and immune responses  signaling and Protein folding and processing were among the top canonical pathways for both SALS patient subgroups, supporting the possibility of a causal link between protein aggregation, neurotoxicity and disease severity in ALS and other neurological diseases, like HD48  that were not previously implicated in ALS, representing new candidates for molecularly guided diagnosis and treatments. Within the cluster of CNV-driven genes in SALS2, we found a large number of new candidate ALS genes mainly implicated in oxidative and inflammatory signaling cascades57. Among these, identification of gain and overexpression of TRAF2 was in accordance with previous evidence that correlated elevated expression levels of this gene with inflammatory processes in PD and other neurological disorders59. On the contrary, copy number loss and reduced expression levels HSPA5 in SALS2 may reflect the suppression of neuroprotective role of this molecule against ER stress-associated cell death, leading to oxidative stress and alterations in calcium homeostasis, and rendering neurons vulnerable to degeneration64. Concordantly, pharmacological activation of HSPA5 and its co-chaperones has demonstrated to exert neuroprotective effects on motor neurons of ALS by reducing ER stress-mediated cell death, supporting a translational potential for HSPA5 induction as a therapy against ALS and other neurologic disorders65.It is interesting to note that although partially complementary and convergent, the two SALS patient clusters showed different significantly deregulated processes and mediators. In particular, antigen processing and presentation, and extracellular matrix organization were the most significantly enriched pathways for the CNV-driven genes in SALS1, while the pathways of highest significance in SALS2 were associated with axonal guidance, oxidative stress and inflammatory intracellular signaling cascades Fig. . Therefo67. Moreover, the custom-designed platform used in this study has some disadvantages, including limited use for discovering novel genes or gene features and inability to detect nucleotide repeat expansions or balanced structural chromosomal abnormalities. However, the possibility to simultaneously detect multiple genes involved in neurological disorders may allow for differential diagnosis between common neurological disorders, refine the genotype-phenotype correlations and explore the potential genetic overlapping signatures among different neurological conditions.Overall, our study provided the first comprehensive and integrated map of genomic and transcriptional events characterizing different SALS subtypes, revealing key drivers and etiopathogenic mechanisms that may have been masked by considering SALS pathology as a single entity. Despite the most obvious limitation in this research was that of a small and unbalanced number of samples, our brain tissue samples represent a vanishingly rare resource for investigating molecular mechanisms underlying neurological disorders. The importance of an integrative analysis such as the one presented here, emerges from recent published data that highlight the presence of somatic changes in brain tissue of patients affected by neurological diseasesIn conclusion, the present study proposes the use of a multi-omics analysis as a promising approach for the identification of somatic alteration and candidate drivers in ALS for defining disease subtype and directing molecular targeted clinical trials that more accurately reflect inter-individual differences among patients. Future more in-depth functional and integrative omics studies will be necessary to verify our findings and explore the impact of candidate genes on the outcome of the disease.The analysis workflow is shown in Fig. 17, and whose clinico-pathological parameters are detailed in the Supplementary Table All samples were provided by the Department of Pathology of the Academic Medical Center (University of Amsterdam). This cohort included motor cortex samples from 30 patients with clear SALS diagnosis and 10 control individuals collected as previously describedNeuroArray platform v.2.0 , tailored to detect single/multi-exon deletions and duplications in a large panel of ALS-related genes (n = 154) and to others additional neurological disorders . Briefly, DNA test and a reference of the same sex , both at the concentration of 500 ng, were double digested with RsaI and AluI for 2 hours at 37 °C. After heat inactivation of the enzymes at 65 °C for 20 min, each digested sample was labeled by random priming by using the genomic DNA Enzymatic Labelling Kit  for 2 hours using Cy5-dUTP for patient DNAs and Cy3-dUTP for reference DNAs. Labeled products were column purified by using the SureTag DNA Labeling Kit Purification Columns . After probe denaturation and pre-annealing with Cot-1 DNA, hybridization was performed at 65 °C with rotation for 24 hr. After two washing steps, arrays were scanned at 3 µm resolution using an Agilent G4900DA SureScan Microarray Scanner System and aCGH image data were processed using Agilent’s Feature Extraction software to assess the array spot quality as well as check signal and background intensity statistics in the default setting. Feature extracted raw data was normalized, analyzed and visualized using Agilent CytoGenomics v. 4.0.3.12 and Genomic Workbench v. 7.0.4.0 software . Briefly, after filtering for saturated and non-uniform probes, data were normalized by GC correction with a window size of 2 kb and Diploid Peak Centralization. The Centralization Normalization Algorithm with a threshold of 6.0 and a bin size of 10 was also used for detecting aberrant regions or regions of constant CNVs. Aberrations were detected by the Aberration Detection Method II algorithm (ADM-2), with a sensitivity threshold of 6.0 and moving average window of 2 Mb, which permits to identify all aberrant intervals in a given sample with consistently high or low log ratios based on the statistical score. An aberration filter was applied for identifying copy number alterations; changes were considered as true positive events with a minimum log2 ratio test/control of ±0.5 and a minimum of 3 consecutive probes. Positive statistical score meant amplification, while a negative score indicated deletion. Human reference sequence hg19 assembly was used to define the genomic coordinates of detected CNVs. Raw data of the microarrays are available at NCBI’s Gene Expression Omnibus (GEO) with the accession number GSE107375.High-resolution exon-centered analysis of CNVs was done using an 8 × 60 K custom exon-centric 17. The chromosomal distribution and the frequency of the copy number gains and losses in both SALS subgroups were also investigated. Using ADM-2 generated interval based amplification and deletion data, penetrance analysis was performed to find the percentage of samples that share aberrations in a particular genomic region among multiple samples. A recurrent CNV was called when the gains or losses occurred in at least 10% of the SALS patients, both with and without the assignment into the hierarchically determined two sets. Multiple amplifications and deletions were counted as separate events. Aberrant intervals were also filtered taking into account those occurring in at least 10% of the cases and absent in individual controls. In addition, to assess the effective relations between the detected CNVs and ALS pathogenesis, we compared these aberrant regions with those previously associated with ALS via screening of publicly available databases  and published literature.For statistical analysis, the ALS samples were divided into two groups (SALS1 and SALS2) based on their previously characterized gene expression profilesNeuroArray aCGH design. In addition, significant probe signals were clustered for pathologies according to their location on causative or susceptibility genes through a homemade script on R-platform, in order to search for CNVs in candidate genes of ALS disease68.Frequent amplifications and deletions observed in both SALS patient subgroups were reviewed and annotated to the human hg19 reference genome and then were screened out for only genes included in the Gene expression data set E-MTAB-2325 was downloaded from EBI ArrayExpress database, which was annotated using the platform of GPL6480 (Agilent-014850 Whole Human Genome Microarray 4 × 44 K G4112F). A total of 40 samples were selected out, including 10 normal and 30 SALS motor cortex samples. Raw signal values were thresholded to 1, log2 transformed, normalized to the 50th percentile, and baselined to the median of all samples using GeneSpringGX v.14.5 . Fold change (FC) values were calculated between SALS patients and individual controls. Positive FC meant over-expression, whereas negative FC indicated under-expression. Probes not corresponding to an ENTREZ ID were removed. In cases where several probes corresponded to one ENTREZ ID, the probe showing the highest variance over all samples was chosen for further analysis. Genes that showed a significant P value < 0.05  and FC ≥ ± 1.5 were considered differentially expressed and were taken for further analysis.To assess the contribution of genomic aberrations to global gene expression pattern changes in SALS and identify CNV-associated DEGs, we performed an integrated analysis of differential expression values and the corresponding DNA copy number changes through a gene-by-gene approach. In particular, each gene expression measurement was assigned to the corresponding copy number probe interrogating the same named gene. A CNV-driven gene was defined when the gene expression trend was consistent with the copy number change .69. To interpret the biological significance of CNV-driven genes in the context of known biological pathways, we used QIAGEN’s Ingenuity Pathway Analysis  and MetaCore repository  70. Both these programs identify significantly enriched biological pathways and signaling cascades that are associated with a given list of genes by calculating the hypergeometric distribution. P-value < 0.05 was set as the threshold to filter out significant terms. In addition, to increase the statistical power of our analysis, we compared our results with three other pathway enrichment analysis tools and databases  and selected signaling pathways that were identified as significantly deregulated by two or more platforms.The function of CNV-associated DEGs in SALS patients was annotated and analyzed according to the three organizing principles of Gene Ontology  by using the enrichment analysis tool Enrichr 71 and visualized with the Cytoscape v.3.5.0 software 72. The extended network was constructed by using the CNV-driven genes as seed molecules and setting a high level of confidence between molecular interactions (high confidence score of at least 0.8) and a maximum number of interactions to 100. In order to identify the “Hub” nodes, a network topology analysis was performed by using the Cytoscape plug-in NetworkAnalyzer based on topological parameters 73. The relative importance of the genes in each network, meaning their ability to hold together the communicating nodes in a biological network, was determined based on the node centrality measure setting the topological parameter “node degree” ≥10. Nodes with high degree (hub genes) represented the genes having important biological functions: the higher the value, the higher the relevance of the gene in connecting regulatory molecules. Likewise, values of edge betweenness were mapped with the edge size: high values of this parameter correspond to a large edge size. The final PPI network was visualized based on node degree and edge betweenness parameters.To better understand the interactions of the CNV-driven genes and identify the best candidate genes in SALS, an extended protein-protein interaction (PPI) network of their encoding products was predicted by using the STRING database Finally, target and biomarker assessment tools in both IPA and Metacore were used to screen candidate CNV-driven genes in order to identify potential candidate biomarkers and therapeutic targets for ALS.NeuroArray CGH results performing ad hoc real-time quantitative polymerase chain reaction (RT-qPCR) assays. Briefly, we used DNA extracted from the motor cortex samples of 15 donors assayed by NeuroArray, including 5 controls, 4 SALS1 and 6 SALS2 (Supplementary Table 11). From the list of CNV-driven DEGs genes, we selected 5 candidates  on the basis of their potential clinical relevance as patient-specific biomarkers and therapeutic targets (Supplementary Table 11). The primer sets flanking the putative exonic imbalances were designed using the PrimerBlast tool (http://www.ncbi.nlm.nih.gov/tools/primer-blast/) as described 74 and were available in Supplementary Table 75. 2 − ΔΔCt ≥ 1.4 or ≤0.6 was defined as copy number gain or loss, respectively, whereas 2 − ΔΔCt values included from 0.8 to 1.2 were considered as normal diploid. As calibrator control, we used the same DNA reference hybridized in NeuroArray experiments. The specific PCR products were confirmed by the results of melting curve analysis and agarose gel electrophoresis.To confirm the reliability of our data, we validated the Experiments involving human participants have been approved by an ethical committee  for medical research and have been performed in accordance with ethical standards. Informed consent Informed consent was obtained from all individual participants included in the study.Supplementary Materials