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The hornwort genome and early land plant evolution

Hornworts, liverworts and mosses are three early diverging clades of land plants, and together comprise the bryophytes. Here, we report the draft genome sequence of the hornwort Anthoceros angustus. Phylogenomic inferences confirm the monophyly of bryophytes, with hornworts sister to liverworts and mosses. The simple morphology of hornworts correlates with low genetic redundancy in plant body plan, while the basic transcriptional regulation toolkit for plant development has already been established in this early land plant lineage. Although the Anthoceros genome is small and characterized by minimal redundancy, expansions are observed in gene families related to RNA editing, UV protection and desiccation tolerance. The genome of A. angustus bears the signatures of horizontally transferred genes from bacteria and fungi, in particular of genes operating in stress-response and metabolic pathways. Our study provides insight into the unique features of hornworts and their molecular adaptations to live on land.

L and plants (Embryophyta) probably originated in the early Palaeozoic 1 , initiating the colonization of the terrestrial habitat. Because bryophytes (hornworts, liverworts and mosses) emerged from the early split in the diversification of land plants, they are key to the study of early land plant evolution (Supplementary Note 1.1). Unlike other extant land plants, the vegetative body of bryophytes is the haploid gametophyte, the sporophyte is always unbranched and permanently attached to the maternal plant, and both generations lack lignified vascular tissue 2 . Bryophytes occur in nearly all terrestrial habitats on all continents but are absent from marine environments 3 .
With only 200-250 species worldwide, the diversity of hornworts is much lower than that of the other six extant lineages of embryophytes (angiosperms, gymnosperms, ferns, lycophytes, mosses and liverworts) 4 . Long considered sister to all other land plants, or sister to all extant vascular plants, hornworts have recently been resolved as sister to the setaphytes (that is, the mosses and liverworts) within monophyletic bryophytes 1,[5][6][7][8] . Still, hornworts possess a series of distinct features 9 . For instance, most hornworts have chloroplasts with CO 2 -concentrating pyrenoids, which have not been found in any other land plants but are widespread among green algae 10 . Other unusual features of hornworts include the persistent basal meristem in the sporophyte and mucilage-filled cavities for colonial symbionts on the gametophyte 11 . Most hornworts form tight symbiotic relationships with cyanobacteria 12 and fungal endophytes (especially Glomeromycota and Mucoromycotina) 13 .
Here, we present the draft genome of A. angustus Steph. (Anthocerotaceae) (see Methods, Supplementary Figs. 1 and 2, and Supplementary Note 1.2). Completion of this high-quality hornwort genome complements previously sequenced representatives of the mosses (Physcomitrella patens 14 ) and liverworts (Marchantia polymorpha 15 ) and provides a unique opportunity to revisit bryophyte phylogeny, early land plant evolution and the adaptation of plants to live on land.

Genome assembly and annotation
We sequenced the genome of A. angustus (a single individual of unknown sex from the dioecious species) using a combination of Illumina and Oxford Nanopore high-throughput sequencing systems (see Methods). We generated 126.53 Gb raw reads from Illumina and 63.61 Gb raw reads from Nanopore sequencing platforms, and retained 17.  (Table 1  and Supplementary Table 4). Approximately 97.66% of the vegetative gametophyte transcriptome data for A. angustus genome annotation can be mapped to the assembled genome (Supplementary Table 5). Repeat sequences comprise 64.21% of the assembled genome, with transposable elements (TEs) being the major component (Table 1 and Supplementary Tables 6 and 7). Among the TEs, long terminal repeats (LTRs) are the most abundant (Supplementary Table 7). We used a combination of de novo, homology-based and RNA sequence-based predictions to obtain gene models for the A. angustus genome (Supplementary Table 8). In total, we predicted 14,629 protein-coding genes with an average coding-sequence length of 1.31 kb and an average of 4.81 exons per gene (Table 1, Supplementary Fig. 5 and Supplementary Table 8). About 85% of these predicted genes have their best hits on plant sequences from the National Center for Biotechnology Information (NCBI) non-redundant database ( Supplementary Fig. 6), and 78.39% were functionally annotated through Swissprot, TrEMBL, Pfam, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (Supplementary Tables 9 and 32). Our annotation captured 89.64% of the 956 genes in the BUSCO plantae dataset 16 (85.04% complete gene models plus 4.60% fragmented gene models), compared with 93.51% and 92.15% captured in P. patens 14 and M. polymorpha 15 , respectively (Supplementary Table 10). In addition to protein-coding genes, we also identified 30 known mature micro RNAs (miRNAs), 180 novel mature miR-NAs, 347 transfer RNAs, 94 ribosomal RNAs and 83 small nuclear RNAs (snRNAs) in the A. angustus genome (Supplementary  Table 11). Nine mature miRNA sequences that appear conserved among land plants (miR156/157, miR159/319, miR160, miR165/166, miR170/171, miR408, miR477, miR535 and miR536) 17 were also found in A. angustus (Supplementary Table 12).

Comparative genomic analysis
For sequence similarity-based clustering of homologues, we used the predicted proteomes of A. angustus and 18 other green plants with fully-sequenced genomes (that is, 11 other land plants, two charophyte green algae and five chlorophyte green algae; Supplementary Table 13). Genes of A. angustus are distributed among 7,644 gene families that are shared with other plants, and 497 gene families that appear to be unique to A. angustus ( Fig. 1a and Supplementary Table 14). In the shared gene families, most A. angustus genes (that is, 9,680) cluster with land plant genes, and only a very small number (that is, 107) specifically cluster with green algae genes ( Supplementary Fig. 7). The gene families unique to A. angustus are enriched in various biosynthetic categories (for example, terpenoid and zeatin) and various activity categories (for example, nutrient reservoir activity and catechol oxidase activity) (Supplementary  Tables 15 and 16).
Phylogenetic inferences from 85 single-copy nuclear genes sampled for A. angustus and 18 other green plants resolve hornworts (A. angustus), mosses (P. patens) and liverworts (M. polymorpha) as a monophyletic group, with hornworts sister to mosses and liverworts, which agrees with inferences from 852 nuclear genes sampled from 103 plant species 1 (Fig. 1b Fig. 11 and Supplementary Table 20), which is comparable to the crown age of hornworts estimated based on two organellar sequences from 77 hornworts and 11 other land plants 10 . These estimates are thus older than those inferred from the fossil record, considering that the oldest putative hornwort fossil is a spore from the Lower Cretaceous Baqueró Formation, Argentina (from 145 to 100 Ma) that resembles the spores of extant Anthoceros 18 .
Comparative genomics shows that the genome of A. angustus has lost many gene families (that is, 2,145) and comparatively only modest gains (that is, 497) (Fig. 1b). A similar trend characterizes the genome of Marchantia and of the ancestor common to all bryophytes, whereas P. patens has gained more families (that is, 1,334) than it has lost (that is, 1,248; Fig. 1b). Thus, bryophyte genomes may not only harbour a number of genes and gene families comparable to those of vascular plants and in particular seed plants ( Fig. 1b) but may also be highly dynamic through evolutionary time.
Many, if not most, land plants harbour genomic signatures of ancient whole-genome duplication (WGD) 19 . However, like that of Marchantia 15 , the genome of Anthoceros lacks evidence of having undergone a WGD (Fig. 1c, Supplementary Fig. 13 and Supplementary Note 3.1), which confirms the hypothesis drawn previously from the analysis of transcriptomic data 20 . The chromosomal arrangement of genes is not much conserved among the three bryophyte lineages ( Supplementary Fig. 14a,b and Supplementary Note 3.2), which likely reflects the ancient divergence of these different lineages of bryophytes. For example, the longest co-linear block corresponds to a mere five anchor pairs for both A. angustus versus P. patens and A. angustus versus M. polymorpha, whereas within the A. angustus genome, the largest co-linear segment consists of six anchor pairs ( Supplementary Fig. 14).
The A. angustus genome contains a much lower percentage of multi-copy gene families than that of single-copy gene families, implying low genetic redundancy (Supplementary Table 17), which is similar to what has been observed for the liverwort Marchantia 15 .

Transcription factors
The A. angustus genome comprises 333 putative transcription factor (TF) genes covering 61 families, a number that is highly similar  TF compared to P. patens, which experienced at least one WGD in its ancestry, resulting in a substantially larger number of TF genes ( Supplementary Fig. 15). It supports the hypothesis that the WGD is an important mechanism for expansion of TF families 23 .
Phylogenetic analyses of 24 gene families contributing to the development of plant body plans or adaptation to the terrestrial environment, including 16 TF gene families 24,25 (Fig. 2a , confirm that a considerable number of genes, such as genes involved in gametophyte or sporophyte development, haploid-diploid transition, meristem development, filamentous growth, photomorphogenesis and auxin signalling (Fig. 2), composed the genetic toolkit of plants before the conquest of land 26 . In particular, the TF genes for filamentous growth and auxin signalling arose in charophyte green algae 27,28 (Fig. 2b), which are thought to be the closest living relatives to extant land plants, implying the preliminary establishment of relatively more complex body plan in these basal streptophytes for plant terrestrial adaptation 29 . Furthermore, a set of genes underlying key morphological innovations for terrestrial adaptation probably evolved along with the colonization of land 30,31 (Fig. 2b) Supplementary Fig. 38). The sporophyte morphology of bryophytes is relatively simple, and many of the genes involved in the elaborate regulation of embryogenesis 32 , such as FUS3, LEC1, LEC2, NF-YA1/9 and NF-YA3/5/6/8 are absent in A. angustus, Marchantia and Physcomitrella ( Fig. 2a and Supplementary Figs. [39][40][41]. The ABI3 genes that mainly function in embryo maturation and seed desiccation tolerance in flowering plants are present in bryophytes, and have roles in desiccation tolerance in their vegetative tissues 33 . In A. angustus, most genes involved in the development of plant body plans have a single copy, and a few A. angustus TF gene families even lost a subset of duplicates ( Fig. 2a and Supplementary  Figs. . For example, in the bHLH family, the class I RSL gene that controls the development of rhizoids and root hairs, thought to have been important for the colonization of land 34 , is present in the A. angustus genome, whereas the class II RSL genes responsible for regulating protonema differentiation in P. patens or root hair elongation in A. thaliana by auxin 35 are absent (Supplementary Fig. 27 and Supplementary Note 4.2). The lack of class II RSL genes in A. angustus might be related to the morphological simplification of this species with respect to tip-growing filamentous structures 2 . For the KNOX genes from the homeobox gene family, the A. angustus genome retains one class II KNOX gene for haploidto-diploid morphological transition 36 , but lacks class I KNOX genes ( Supplementary Fig. 23), whose activity is necessary for seta extension in the sporophytes in P. patens 37  . Since these auxin-related genes were consistently lost in A. angustus, this hornwort species possesses the simplest auxin molecular toolkit among all investigated land plants so far 38 . Thus, like the liverwort M. polymorpha 15 , A. angustus exhibits low redundancy for genes shaping the plant body plan (Fig. 2b). Such a limited toolkit may be characteristic of the ancestor to bryophytes and hence, perhaps, of the earliest land plants with a dominant thalloid gametophyte, and provide the foundation to explaining the architectural simplicity of these plants. By contrast, the genome of P. patens, which develops a leafy stem, has the most TF genes involved in the development of plant body plans among the compared bryophytes (Fig. 2b). Although the genome of A. angustus seems poor in genes composing the network underlying the development of its body plan, the TF gene families linked to responses to terrestrial environmental stimuli exhibit lineage-specific gene expansions in A. angustus, namely, the LISCL genes for mycorrhizal signalling in the GRAS gene family 39 (Supplementary Fig. 53) and the clade SIP1 for ABA signalling under water stress in the Trihelix gene family 40 ( Supplementary Fig. 54).

Gene-family expansion
Besides two TF gene families, the A. angustus genome harbours a variety of other uniquely expanded gene families ( Supplementary  Fig. 55). The genome comprises an very large number of pentatricopeptide repeat (PPR) genes for plant organellar RNA processing 41 , accounting for approximately 7.90% of the predicted protein-coding genes. The expanded PPR genes are PLS-class PPR genes ( Supplementary Fig. 55, Supplementary Tables 23 and 24 and Supplementary Note 5.1). Most of the PLS-class PPR proteins in A. angustus were predicted to be localized in the mitochondrion or chloroplast (Supplementary Table 24). The expansion of the PLS-class PPR genes correlates with the large number of RNA editing sites estimated in the organellar genomes of A. angustus (Supplementary Table 23). Our findings add further support to the hypothesis that an increase in the number of both RNA editing sites and PPR genes (especially the PLS-class PPR) occurred after the separation of land plants from green algae 41,42 (Supplementary  Table 23). The reduced number of PPR genes and absence of RNA editing in marchantiid liverworts are most probably secondary losses (Supplementary Table 23), as the organellar RNA editing and plant-specific extensions of PPR genes were also found in jungermanniid liverworts 43 . Through RNA editing, the PPR proteins could act as 'repair' factors that alleviate DNA damage caused by increased UV exposure in terrestrial environments 41 . Other stress-response gene families have also expanded in A. angustus, such as cupin and cytochrome P450 (CYP) ( Supplementary Fig. 55). Two groups of cupin (PF00190) proteins-that is, monocupins and bicupinscan be recognized on the basis of the number of cupin domains 44 Tables 26 and 27). The A. angustus CYP genes were assigned to 28 KEGG pathways, of which 'flavonoid 3′-monooxygenase/flavonoid 3′,5′-hydroxylase' and 'abscisic acid 8′-hydroxylase' were the most representative (Supplementary Table 28). Within the CYP71 gene subfamily, genes homologous to flavonoid 3′-hydroxylase (monooxygenase) (F3'H) or flavonoid 3′,5′-hydroxylase (F3′5′H) genes that are involved in flavonoid biosynthesis 46 Charophyte green algae  plants and green algae ( Supplementary Figs. 62, 64, 68 and 72). The A. angustus genome retains the orthologues of both LCIB/C and CAH3 genes, but has no copy of CAH9 (Supplementary Fig. 72).
Besides green algae, the essential CCM components occur in both hornworts and other non-angiosperm land plants that lack pyrenoids ( Supplementary Fig. 72). It implies that the CCM could be an ancestral mechanism of CO 2 fixation by plants, and pyrenoids for CCM are homologous between hornworts and green algae, whereas both CCM components and pyrenoids have undergone multiple losses in land plants in response to atmospheric changes in terrestrial environments 10,48 .

Horizontal gene transfer
Horizontal gene transfer (HGT) from bacteria or fungi has been reported for both the moss P. patens 51   (one gene family). Five families were acquired from fungi, belonging to Ascomycota, Basidiomycota, hornwort-symbiotic Chytridiomycota or Mucoromycota 13 (Fig. 4a, Table 31). The detection of specific HGT in all three fully sequenced bryophytes is remarkable, and is probably related to the fact that these organisms form symbioses with diverse bacteria and fungi, which, together with the weakly protected tissues in the early developmental stages in the life cycle of these plants, provide the possibility for HGT 51 .
In addition, we found that two families originating from HGT from bacteria are shared by the three bryophyte lineages, and one originating from a HGT from fungi is shared between hornworts and liverworts only (Fig. 4c, Fig. 87a), which may be linked to their higher GC content (57.58%) than non-HGT genes (53.26%) (Supplementary Fig. 87b).
The HGT-derived genes in A. angustus mainly contribute to metabolic processes, oxidation-reduction and stress response (Supplementary Table 31). Some transferred genes related to carbohydrate metabolism are predicted to encode glucuronyl (AANG011893) and glycosyl hydrolases (AANG004297) (Fig. 4c, Supplementary Fig. 79 and Supplementary Table 31), which function in cell wall synthesis and modification and might extend the metabolic flexibility of A. angustus in changing environments 52 . The Alphaproteobacteria-derived gene AANG004679 encodes glyoxalase, which is related to drought stress tolerance 53 (Fig. 4a). The Actinobacteria-derived DNA methyltransferase genes that are present only in the three groups of bryophytes are related to DNA repair 54 (Fig. 4d). The hornworts and liverworts share the fungiderived terpene synthase-like (MTPSL) genes ( Supplementary  Fig. 85). Terpene synthases are pivotal enzymes for the biosynthesis of terpenoids, which serve as chemical defences against herbivores and pathogens 55 . Some horizontally transferred genes in A. angustus, such as NAD-binding dehydrogenase (Fig. 4b) and MTPSL genes ( Supplementary Fig. 85), underwent subsequent gene duplications. The results suggest that the acquisition of foreign genes might have provided additional means for environmental adaptation during evolution of the hornwort lineage.

Conclusions
As land pioneers, the three bryophyte groups form a well-supported monophyletic lineage, with hornworts sister to liverworts and mosses. The genome of hornwort A. angustus shows no evidence of WGDs and low genetic redundancy for networks underlying plant body plan, which may be congruent with an overall simple body plan. Hornworts have retained the essential components of CCM found in green algae in response to the atmospheric changes in terrestrial environments. Meanwhile, the gene inventory in A. angustus expanded mainly through tandem duplication and HGT. In particular, the expansion of specific gene families and the acquisition of foreign genes have provided additional metabolic abilities in hornworts that probably facilitated their survival in a terrestrial environment. Together, our results indicate how the draft genome of A. angustus provides a useful model for studying early land plant evolution and the mechanism of plant terrestrial adaptation.

Sample preparation and sequencing. The natural populations of A. angustus
Steph. were collected from Jinping County, Yunnan Province, China. The voucher specimen has been deposited at the herbarium, Institute of Botany, Chinese Academy of Sciences, Beijing, China with collection number W1879-2010-01-18. The sporophytes of A. angustus were detached from the gametophytes, sterilized in 10% sodium hypochlorite and subsequently rinsed with distilled water 56 .
The sporangium was opened and the spores were homogenized and spread onto the 1/2 KnopII agar medium 57 in Petri dishes (Supplementary Fig. 1b). The culture temperature was between 21 °C and 25 °C. Spores germinated within a couple of days, and then the sporelings started to grow. After approximately three to four weeks, the gametophyte started to grow ( Supplementary Fig. 1c,d). Since spores are aposymbiotic, we did not find the phenomenon of mucilage-filled cavities colonization by cyanobacteria on the A. angustus gametophyte during the sterile culture. A gametophyte from a single spore was selected and cultured by asexual propagation. The tissue yielded from subculture was used for genome and RNA sequencing. We tried to induce sexual reproduction by dropping the growth temperature of gametophyte cultures to 10 °C and 16 °C, respectively; however until now they have not yet produced reproductive organs. Therefore, the sequenced A. angustus is indeed a single-sex individual, which is sequenced at the gametophyte phase of its life cycle.
Genomic DNA was isolated using the Plant DNAzol reagent for genomic DNA extraction (Life Technologies) according to the manufacturer's protocols. For whole-genome shotgun sequencing, ten sequencing libraries with insert sizes ranging from 170 bp to 40 kb were generated (Supplementary Table 1). Sequencing libraries were constructed using a library construction kit (Illumina). All libraries were sequenced on the Illumina HiSeq 2000 platform. Raw sequencing reads were trimmed with Trimmomatic (v.0.33) 58 . Only high-quality reads with a total length of 126,532,381,412 bp were used for further analysis (Supplementary Table 1). For Oxford Nanopore sequencing, we constructed a genomic DNA library using the ONT 1D ligation sequencing kit (SQK-LSK108) according to the manufacturer's instructions. The sequencing used a single 1D flow cell on a PromethION sequencer (Oxford Nanopore Technologies). A total of 63,614,292,295 bp raw reads were generated, of which 36,070,452,175 bp were retained for further analysis after filtering and trimming (Supplementary Table 3).
Total RNA was extracted using the PureLink Plant RNA reagent (Life Technologies) and further purified using TRIzol reagent (Invitrogen). For transcriptome sequencing (RNA sequencing), libraries with insert sizes ranging from 200 bp to 500 bp were constructed using the mRNA-Seq Prep Kit (Illumina) and then sequenced using the Illumina HiSeq 2000 platform. For small-RNA sequencing, the library was generated from RNA sample using the Truseq Small RNA Preparation kit (Illumina) and sequenced on the Illumina HiSeq 2500 platform. Table 1) after trimming presented two large groups: one group with a low k-mer frequency (<50) and a wide GC content distribution range (median number at 0.7), and the other group with a high k-mer frequency (60-165) and a concentrated GC content distribution range (median number at 0.5) (Supplementary Fig. 2a). The BLASTN results against the NCBI nucleotide database revealed that the former sequences were mainly from a variety of bacteria and the latter were the real genome sequences of A. angustus. We also investigated the k-mer distributions of the raw reads from the other two published hornwort genomic sequences, A. agrestis (accession: ERX714368) 59 and Anthoceros punctatus (accession: SRX538621) 60 , and found a similar distribution pattern as that of A. angustus, containing two groups, one for the contaminant sequences and the other for sequences of the plant itself ( Supplementary  Fig. 2c,d). Because external bacterial contaminations from the laboratory cause A. angustus to turn yellow and die during culturing, and all three Anthoceros species through axenic cultures still have the same bacterial contamination problems ( Supplementary Fig. 2a,c,d), we infer that these bacterial contaminations are from symbiotic bacteria of Anthoceros that might accompany spores hiding in the sterilized sporangium. Furthermore, we performed the DAPI staining analysis 61 to investigate the distribution of symbiotic bacteria in A. angustus. The gametophytes were stained by 0.2 mg l −1 DAPI (4′,6-diamidino-2-phenylindole dihydrochloride; Sigma, cat. no. D9564) for five minutes. The stained gametophytes were washed three times, and then observed using confocal microscopy. The bacterial microcolonies were observed on the outer surface, as well as in the intercellular space of the gametophytes of A. angustus (Supplementary Fig. 3). Based on the GC content versus k-mer frequency distribution pattern of the Illumina raw reads and the result of the DAPI staining, we could imagine that there is a certain amount of bacterial sequences remaining in the genome sequencing data of A. angustus. In order to isolate them, we performed a series of decontamination steps. After generating the k-mer frequency, we chose the high-abundance k-mer depth (60-165) and retained the corresponding reads for further analysis. This treatment yielded filtered reads with a total length of 17,099,027,576 bp (Supplementary Table 2). The distribution pattern of GC content versus k-mer frequency of the A. angustus filtered reads is depicted in Supplementary Fig. 2b, which shows an entire group with a sequencing depth of approximately 150×. Furthermore, we performed error correction for filtered Nanopore reads using decontaminated Illumina reads by Nextdenovo (v.2.0) 62 , resulting in 9,247,957,448 bp corrected reads (Supplementary Table 3). Through MEGABLAST against the NCBI nucleotide database, we further removed 5,463,972,682 bp prokaryotic sequences or organellar sequences, and finally got 3,783,984,766 clean reads with a sequencing depth of approximately 35× (Supplementary Table 3). A total of approximately 185× coverage was obtained finally.

Decontamination. The GC content versus k-mer frequency distribution pattern of the Illumina raw reads (Supplementary
Genome size estimation. To estimate the genome size of A. angustus, we used clean Illumina reads to calculate the k-mer distribution. According to the Lander-Waterman theory 63 , the genome size can be determined by dividing the total number of k-mers by the peak value of the k-mer distribution. Because we sequenced the haploid gametophyte of A. angustus, only one peak was found in the k-mer distribution. The total number of k-mers was 14,092,039,150, and the position of the peak was at 132 (Supplementary Fig. 4). The peak was used as the expected k-mer depth and substituted into the formula genome size = total k-mer/expected k-mer depth, and the haploid genome size was estimated to be 106,757,872 bp ( Supplementary Fig. 4).
Genome assembly and assessment. The clean Nanopore reads after filtering and decontamination were assembled with wtdbg-1.2.8. After finishing the preassembly (148 Mb), iterative polishing was conducted using Pilon (v.1.22) 64 in which clean Illumina reads were aligned with the pre-assembled contigs. The pre-assembled contig sequences were performed with the MEGABLAST search against the NCBI nucleotide database to further remove prokaryotic sequences or organellar DNA. A total of approximately 29 Mb of data were removed. Further, we combined the final pre-assembled contig sequences from Nanopore sequencing and clean paired-read data from Illumina sequencing into scaffolds using SSPACE (v.3.0) 65 tool (Supplementary Table 4). Genome assembly completeness was assessed using the plantae database of 956 single-copy orthologues using BUSCO (v.3) 16 with a BLAST threshold E-value of 1 × 10 −5 (Supplementary Table 10).
Transcriptome assembly and mapping. We used Trimmomatic 58 to remove adaptors from the raw reads of transcriptome sequences and filter out low-quality reads before assembly. The resulting high-quality reads were de novo assembled and annotated using Trinity (v.2.5.1) 66 . For genes with more than one transcript, the longest transcript was chosen as the unigene and used to predict open reading frames (ORFs) using TransDecoder (v.5.0.2) (https://github.com/TransDecoder/ TransDecoder/wiki). Finally, we obtained 39,044 unigenes, 26,805 of which had predicted ORFs. To extend the validation of genome assembly, the transcriptome was compared to the reference assembly using BLASTN, with an E-value <1 × 10 −5 . Of the 26,805 transcripts (>200 bp), 97.66% were successfully mapped back to the final assembled genome (Supplementary Table 5).
Repeat prediction. Tandem Repeats Finder (v.4.09) 67 was used to search for tandem repeats in the A. angustus genome. Both homology-based and de novo approaches were used to search for TEs. In the homology-based approach, we used RepeatMasker (v.4.1.0) 67 and RepeatProteinMask 68 with the Repbase 69 database of known repeat sequences to search for the TEs in the A. angustus genome. In the de novo approach, we used LTR_FINDER (v.1.0.2) 70 , PILER (v.1.3.4.) 71 and RepeatModeler (v.1.0.3) 72 to construct a de novo repeat sequence database for A. angustus and then used RepeatMasker to search for repeats in the genome. All the repeats identified by different methods were combined into the final repeat annotation after removing the redundant repeats. The predicted repeats covered 64.21% of the genome sequence (Supplementary Table 6). The categories of predicted TEs in the A. angustus genome are summarized in Supplementary Table 7.
Genome annotation. To predict protein-coding genes, three approaches were used: (1) de novo gene prediction, (2) homology-based prediction, and (3) RNA-sequencing annotation. For de novo prediction, AUGUSTUS (v.2.5.5) 73 and GlimmerHMM (v3.0.1) 74 were applied to predict genes. For homologybased prediction, we mapped the protein sequences of five published green plant genomes (Arabidopsis thaliana, Selaginella moellendorffii, P. patens, M. polymorpha and Klebsormidium nitens) onto the A. angustus genome using TBLASTN, with a threshold E-value of 1 × 10 −5 , and then used GeneWise (v.2.4.1) 75 to predict gene structures. The de novo set and five homologue-based results were combined by MAKER (v.1.0) 76 to integrate a consensus gene set (Supplementary Table 8). To supplement and improve the gene set, we aligned the RNA-sequencing data to the genome using TopHat (v2.1.1) 77 , and the alignments were used as input for Cufflinks (v.2.2.1) 78 with default parameters. We manually combined the MAKER gene set and ORFs of transcripts to form the final gene set that contains 14,629 genes (Supplementary Table 8).
The A. angustus predicted genes were aligned against the sequences in NCBI non-redundant protein database using BLASTP 79 (E-value <1 × 10 −5 ). According to the NCBI taxonomy categories of best BLAST hits, the source of A. angustus genes were classified (Supplementary Fig. 6). Functional annotation of these predicted genes was obtained by aligning the protein sequences of these genes against the sequences in public protein databases using BLASTP 79 (E-value <1 × 10 −5 , identity >30% and coverage >70%, excluding annotations only characterized as hypothetical or predicted protein), including, SwissProt 80 , TrEMBL 80 , Pfam 81 , GO 82 and KEGG 83 (Supplementary Tables 9 and 32).
Identification of non-coding RNA genes. To obtain a reliable profile of A. angustus miRNAs, we used mapped reads from small-RNA sequencing with reference to the A. angustus draft genome to search against miRNA sequences in A. thaliana, Oryza sativa, S. moellendorffii, P. patens and C. reinhardtii from miRBase (http://www.mirbase.org/) for predicting the known miRNAs. The mapped reads were also used to identify novel miRNAs using miREvo (v.1.2) 84 software. The tRNA genes were searched by tRNAscan-SE (v.1.3.1) 85 . The rRNA genes were predicted by aligning plant rRNA sequences from NCBI (A. thaliana and Anthoceros agrestis) to the A. angustus genome by BLASTN. The snRNA genes were predicted using INFERNAL (v.1.1) 86 to search from the Rfam database.
Gene-family identification. To construct the dataset for gene-family clustering, the protein-coding genes from the genomes of A. angustus and 18 other green plants were used, including those of seven angiosperms (A. thaliana, Genlisea aurea, Vitis vinifera, O. sativa, Phalaenopsis equestris, Zostera marina and Amborella trichopoda), one gymnosperm (Picea abies), one lycophyte (S. moellendorffii), two bryophytes (moss P. patens and liverwort M. polymorpha), two charophytes (Chara braunii and K. nitens) and five chlorophytes (Volvox carteri, Chlamydomonas reinhardtii, Ulva mutabilis, Coccomyxa subellipsoidea and Chlorella variabilis) (Supplementary Table 13). We chose the longest transcript to represent each gene and removed mitochondrial and chloroplast genes. After performing an all-against-all BLASTP search with a threshold E-value of 1 × 10 −5 , identity >30% and coverage >30%, orthogroups or putative gene families or subfamilies were identified using OrthoMCL (v.2.0) 87 , on the basis of a collection of 397,132 predicted protein-coding genes from the above 19 Viridiplantae genomes.  Fig. 1a. For A. angustus-specific gene families, we conducted GO and KEGG enrichment analyses via an enrichment pipeline (https://sourceforge.net/projects/enrichmentpipeline/).

Phylogenomics.
We extracted 85 single-copy gene families shared by 19 Viridiplantae for phylogenomic analysis (Supplementary Note 2.1). The amino acid alignments of each single-copy gene family were aligned by MAFFT (v.7) 88 , and the nucleotide alignments were generated separately with TranslatorX (v0.9) 89 on the basis of the corresponding amino acid translation. The amino acid data, the complete nucleotide data and the first and second codon positions, as well as the third codon positions, were concatenated as super-matrices. These data matrices were used for maximum likelihood phylogenetic analyses by RAxML (v.7.2.3) 90 with the GTR + Γ and JTT models for nucleotide and amino acid data, respectively. For each analysis, the bootstrap support was estimated based on 300 pseudoreplicates using a GTR + CAT approximation. To estimate the degree of substitutional saturation for the four concatenated datasets mentioned above (Supplementary Note 2.2), we plotted the uncorrected p-distances against the inferred distances using the method described by Forterre and Philippe 91 . The level of saturation was estimated by computing the slope of the regression line in the plot; the shallower the slope, the greater the degree of saturation. The maximum composite likelihood method was used to calculate the inferred distances for nucleotide data and Poisson correction was used to calculate the inferred distances for the amino acid data.
To improve the taxon sampling in bryophytes for divergence time estimation, the transcriptome sequences of 22 other bryophytes were downloaded from the 1KP database 92 (http://www.onekp.com/public_data.html) and used in subsequent analyses (Supplementary Table 18 and Supplementary Note 2.3). The divergence time was estimated using the MCMCTree program in the PAML package (v.4.7) 93 under the nucleotide general time reversible (GTR) substitution model and with the independent rate model as the molecular clock model. The Markov chain Monte Carlo (MCMC) process consists of 500,000 burn-in iterations and 1,500,000 sampling iterations (1 sample per 150 iterations). The same parameters were executed twice to obtain a stable result. We applied nine node constraints in the age estimate ( Supplementary Fig. 10). The minimum and maximum constraints for each node are shown in Supplementary Table 19.
Gene-family sizes were inferred from the gene-family profile obtained by the program OrthoMCL. The minimum ancestral gene families were estimated using DOLLOP program included in the PHYLIP package (v.3.695) 94 to determine genefamily gain or loss evolutions of gene families. There are 8,141 gene families in the A. angustus genome, 8,944 in M. polymorpha and 9,566 in P. patens, and 9,789 ancestral families in the ancestral bryophyte lineage (Fig. 1b).
Phylogenetic analysis of gene families. Generally, HMMER search 100 with a domain profile or BLAST search using known protein sequences from other plants as queries was performed to retrieve the sequences from the A. angustus genome ( Supplementary Notes 4-6). The results of TF prediction by iTAK 98 were used as references. Multiple sequence alignments were performed using the MAFFT 88 program (https://mafft.cbrc.jp/alignment/software/). The maximum-likelihood phylogenetic trees were implemented with RAxML-HPC2 on XSEDE 101 through the CIPRES Science Gateway (v.3.3) (https://www.phylo.org/), estimating branch support values by bootstrap iterations with 1,000 replicates.

Gene-family expansion identification.
To understand gene-family expansion or contraction in A. angustus compared with that in 18 other green plants, the mean gene-family size was calculated for all gene families (excluding orphans and species-specific families). The number of genes per species for each family was transformed into a matrix of z-scores to centre and normalize the data. The first 100 families with the largest gene-family size in A. angustus were selected ( Supplementary Fig. 55). The clustering and visualization were performed using Genesis (v.3.0) 102 . The functional annotation of each family was predicted on the basis of sequence similarity to entries in the Pfam protein domain database, where more than 30% of proteins in the family share the same protein domain. Transposon-derived gene families were removed because the distribution of such families is likely to be a consequence of the gene models derived from a repeatmasked genome sequence and therefore may be artefactual 103 .
Tandem duplication definition. Genes were defined as tandemly arrayed genes if they belonged to the same family, were located within 100 kb each other, and were separated by zero, one or fewer, five or fewer, or ten or fewer non-homologous intervening 'spacer' genes 104 . Therefore, the four sets of tandem gene definitions were analysed.
HGT event identification. In this study, we used two different strategies to identify candidates for A. angustus-specific and bryophyte-specific HGTs. For A. angustusspecific HGTs, we submitted 14,629 predicted coding genes of A. angustus to a BLASTP search against the NCBI protein database (E-value cutoff of 1 × 10 −5 ) (Supplementary Note 7.1). The proteins with the best BLAST hits in bacterial or fungal sequences were extracted. After sequences without support of transcript evidence were excluded, a series of parameters were used to filter the candidates (Supplementary Note 7.1). For the bryophyte-specific HGT, we extracted gene families that are common to at least two of the three members of bryophytes (moss P. patens, liverwort M. polymorpha and hornwort A. angustus). To preliminarily determine whether these clusters are HGT candidates, we submitted the corresponding A. angustus members of each cluster to the NCBI protein database for BLASTP search and checked the taxonomy report of the top 1,000 BLAST hits (Supplementary Note 7.2). The homologues of published HGTs in P. patens 51 and M. polymorpha 15 were also investigated in the A. angustus genome. All candidate HGTs were subjected to phylogenetic analysis for verification. Synonymous codon-usage order values and GC contents of HGT and non-HGT genes were calculated by CodonO 105 .
Reporting Summary. Further information on experimental design is available in the Nature Research Reporting Summary linked to this article.

Data availability
The A. angustus genome project has been deposited at the NCBI under the BioProject number PRJNA543716. The genome sequencing data were deposited in the Sequence Read Archive database under the accession number SRR9696346. The A. angustus transcriptome project has been deposited at the NCBI under BioProject PRJNA543724. The transcriptome sequencing data were deposited in the Sequence Read Archive database under the accession number SRR9662965. The assembled genome sequences, gene models and miRNA data are available via DRYAD (https://doi.org/10.5061/dryad.msbcc2ftv). All data that support the findings of this study are also available from the corresponding authors upon request.

October 2018
Corresponding author(s): Zhi-Duan Chen, Zhong-Jian Liu, Yves Van de Peer, and Shou-Zhou Zhang Last updated by author(s): Dec 17, 2019 Reporting Summary Nature Research wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Research policies, see Authors & Referees and the Editorial Policy Checklist.

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Policy information about availability of computer code Data collection 1.We constructed libraries with insert sizes from 170 bp to 40 kb for whole-genome shotgun sequencing using Illumina HiSeq 2000. 2. We also constructed a genomic DNA library for Oxford Nanopore sequencing.
October 2018 support the findings of this study are also available from the corresponding authors upon request.
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Data exclusions Lines 416-456, 466-472: The prokaryotic sequences and organellar sequences were removed from sequencing data and pre-assembled genome data. There are prokaryotic sequences and organellar sequences that involved in the genome sequencing data. Exclusion of the contamination from foreign DNA sequences and organellar sequences is the prerequisite for accurate genome assembly. Through choose of high-abundance k-mer reads, error-correction and MEGABLAST check, 3,78 Gb high-quality clean reads of Nanopore sequencing remained for A. angustus genome assembly. Lines 521-522: We excluded annotations only characterized as hypothetical/predicted protein, since these proteins could not be treated as really functionally annotated ones. Lines 542-543: During the comparative analysis, we chose the longest transcript to represent each gene and removed mitochondrial and chloroplast genes, since the used genome datasets include multiple transcripts and organellar genes that might complicate the comparative analysis.
Lines 617-618: The mean gene family size was calculated for all gene families, excluding orphans and species-specific families, since these genes are unique to individual species and do not have orthologs in other species for comparison. Lines 624-627: During the gene family expansion identification, transposon-derived gene families were removed, since the distribution of such families is likely to be a consequence of the gene models derived from a repeat-masked genome sequence and therefore may be artefactual. Lines 638-640: The sequences without support of transcript evidence were excluded from the HGT candidates, since these sequences might be contaminated ones but not real HGT genes.

Replication
The spore germination experiment was repeated three times independently. The DAPI staining experiment was repeated three times independently.