Phylogenomics of Asgard archaea reveals a unique blend of prokaryotic-like horizontal transfer and eukaryotic-like gene duplication

Domain: neuroscience
Published: 2026-04-10
Editorial outcome: major_revision

Abstract
Asgard archaea hold a pivotal position in the tree of life as the closest known relatives to eukaryotes and are therefore crucial for understanding eukaryogenesis. Earlier genomic analyses revealed that Asgard genomes are remarkably larger than those of other archaea and contain a significant number of genes seemingly acquired from bacteria. However, the precise contributions of horizontal gene transfer and gene duplication in shaping Asgard genomes remain largely unknown. Here, we present a comprehensive phylogenomic analysis to dissect the evolutionary dynamics of Asgard genomes, quantifying gene duplication, loss, and both inter- and intra-domain gene transfer events. Our findings reveal that gene transfer is widespread throughout Asgard evolution, predominantly affecting metabolic genes at the periphery of interaction networks. However, our analyses demonstrate that gene duplications, rather than horizontal gene transfers, are the primary drivers behind the increased genome sizes observed in Asgard archaea. This unique evolutionary signature in Asgard archaea—a blend of pervasive prokaryotic-like gene transfer alongside significant eukaryotic-like gene duplication—is consistent with their phylogenetic placement and offers novel insights into the genomic transitions that likely underpinned eukaryogenesis.

Introduction
Asgard archaea are the closest prokaryotic relatives of eukaryotes and are therefore key to understanding eukaryogenesis1,2. Asgard genomes encode many of what were previously thought to be eukaryote-specific proteins2, including actin and actin-related proteins3, and an actin cytoskeleton has been found in a cultivated Lokiarchaeum4. The crucial interest of Asgard archaea has promoted sequencing efforts, resulting in a large number of available genomes - 296 assemblies in release 226 of the Genome Taxonomy Database (GTDB)5. These represent 12 class-level lineages sensu GTDB (namely Asgard-, Atabey-, Baldr-, Heimdall-, Hermod-, Jord-, Loki-, Njord-, Odin-, Sif-, Thor-, and Wukongarchaeia). However, most data corresponds to Metagenomic Assembled Genomes (MAGs), which can be contentious due to potential incompleteness or contamination issues6 . Asgard archaea have proven elusive to culture, with two exceptions from Lokiarchaea: Ca. Prometheoarchaeum syntrophicum MK-D17 and Ca. Lokiarchaeum ossiferum B-35 4, which can be cultured with their syntrophic partners and therefore have enabled high-quality genomes.

Asgard archaea were first identified in a deep-sea hydrothermal vent8, but their presence in clone libraries precedes their description by two decades9–11. Recent sampling efforts have uncovered new Asgard lineages, of which most were first described in anoxic marine environments12. However, metagenomic analyses indicate a worldwide cosmopolitan distribution13. This reflects on a broad array of lifestyles and metabolic properties including mixotrophy, homoacetogenesis, facultative anaerobiosis, alkane utilization, and rhodopsin-based phototrophy13–17. However, we have a very limited understanding of how genome variation underlies such diverse adaptations. Earlier analyses of Asgard genomes uncovered a large number of putatively transferred bacterial genes18 and generally larger genome sizes as compared to other archaea19. Previous analyses considering prokaryotes have found weak20 to strong21,22 positive correlations between genome size and horizontal gene transfer (HGT) fraction, but none of them included Asgard genomes. Similarly, Asgard archaeal genomes have been shown to have higher rates of duplication than other archaea19, but the functional implications of these gene duplications, or how these duplication tendencies contrast with HGT (the prevailing form of genome expansion in prokaryotes) have not been comprehensively explored .

Here, we reconstructed the Asgard archaeal pangenome and used phylogenomics on a super-phylum-wide scale to assess the relative contribution of gene duplication, loss and transfer in the evolution of Asgard genomes against the backdrop of its sister clade, Thermoproteota, and investigate possible functional trends. With this we aimed to shed light on the processes that have specifically driven Asgard archaea towards larger genome sizes and other ‘eukaryote-like’ qualities in contrast to Thermoproteota, which remain more ‘prokaryote-like’. Our results indicate that Asgard archaea have high duplication rates that are intermediate between those in their prokaryotic sister clade (despite similar transfer rates) and those observed in unicellular eukaryotes. We uncover widespread but mostly lineage-specific inter-domain HGT involving several donor-acceptor pairs and metabolic genes. Overall, our results indicate that Asgard genomes are uniquely shaped by a blend of prokaryotic horizontal gene transfer and eukaryotic-style gene duplications.

Results
The pangenome and signature proteins of Asgard archaea

We first sought to characterize the Asgard pangenome by performing homology-based clustering of all available genomes from phylum Asgardarchaeota in GTDB5. However, nearly all Asgard genomes are MAGs, resulting in a median CheckM23 completeness of 87.54% (SD 8.21). This lack of genome completeness can lead to an infra-estimation of the core genome and, conversely, an over-estimation of the accessory genome.

For this reason, we compiled a representative subset of 32 genomes covering all family-level lineages of Asgard archaea by choosing the highest-quality available genome (following the GTDB score criterion) per family and prioritizing available isolates and closed genomes (See Methods, Supplementary Data 1). Additionally, we included 163 other prokaryotic genomes as out-groups, including 28 representative species from the Asgard archaea’s sister group, TACK (“Thermoproteota” hereafter, sensu GTDB). We clustered proteins into families (orthogroups, OGs) using a similarity-based approach (See Methods). This yielded a total of 71,402 OGs, including singletons, out of which 17,429 contained at least one Asgard protein. Of these, 12,397 were exclusively present in Asgard archaea, and of these 10,470 were present in a single Asgard genome and no other prokaryote in our dataset (singletons). 79 gene families were found in all Asgard genomes (strict core) and 310 in 90% or more (relaxed core, see Figure 1A for the distribution), with the remaining belonging to the accessory pangenome. We annotated OGs with KEGG Orthologs (KOs, see Methods) and found that proteins in the Asgard relaxed core were enriched in pathways such as ribosome (ko03010), DNA replication (ko03030), and aminoacyl-tRNA biosynthesis (ko00970) (Supplementary Data 2).

We estimated the size of the total Asgard pangenome both by Chao’s lower bound24 (yielding a lower bound estimate of 61,815 OGs) and by the best-fitting binomial mixture model (resulting in a pangenome of 75,659 OGs and a strict core of 18 OGs). We additionally calculated rarefaction curves (Figure 1B) which, along with the Heaps estimate (alpha = 0.26), indicated an open pangenome.

To assess the impact of MAG incompleteness on pan-genome size estimates, we repeated the analyses replacing the 32 best-quality representatives by 32 randomly-chosen, taxonomically-equivalent genomes. In this case, we obtained 72,179 OGs, with 18,135 including at least one Asgard representative, and a strict and relaxed observed core of 57 and 269, respectively. We inferred a similarly open pangenome estimate with identical Heaps alpha (0.26) and strict core inferred by the binomial mixture model (18), but a higher Chao’s lower bound (72,712) and total estimated size by the binomial mixture model (94,783). Hence, both genomic subsets provided similar estimates in terms of openness, OGs and total pan-genome size but not in terms of observed core genome or lower bound estimates, which were more affected by genome incompleteness.

As the core size estimate is particularly sensitive to genome incompleteness25, we ran mOTUpan26 for a more robust estimation of core size and fluidity, as this approach intrinsically considers genome incompleteness (as provided by CheckM completeness). This approach estimated a larger core set for both of the high-quality (473) and random (509) datasets. Hence, even when considering the most conservative estimates, these results underscore how incomplete is our knowledge of the breadth of Asgard archaeal protein diversity, implying the intriguing possibility that new protein families with potential implications for eukaryogenesis may await discovery.

We defined Asgard signature proteins (ASPs) as those OGs common (present in ≥ 86% of the proteomes, see Methods for the choice of threshold) in Asgard archaea but rare (present in ≤15% of the proteomes, following the ‘cloud’ pangenome definition, see ref.27) in other archaea or bacteria. This rendered 14 ASPs, for which we obtained a consensus of the annotations across Asgard proteomes and found them to be enriched in processes such as endocytosis (KEGG pathway ko04144) or motor proteins (ko04814), see Supplementary Data 2). We cross-mapped a set of 83 InterPro domains associated with 79 Eukaryotic Signature proteins (ESPs) recently identified in Asgard archaea19 and found 44 out of 79 of them in our dataset. We found all ASPs within the set of OGs comprising ESPs, suggesting that these eukaryotic-like proteins contributed to the differentiation of Asgard archaea from other prokaryotes and that some of these differentiating gene families ended up into the eukaryotic lineage. ASPs include proteins previously considered ESPs involved in typical eukaryotic processes such as Ras-related proteins (K07877), ESSCRT-II complex subunit VPS22 (K12188), actin (K10355) and actin-related proteins (K10369), as well as other motor proteins (K10398). In addition to these ESPs, ASPs include other relevant proteins such as motility proteins (K25153) which are putative adaptations to a mat environment28.

All 14 ASP OGs are found in Ca. Prometheoarchaeum syntrophicum MK-D1, where they comprise 90 proteins. We used this organism’s protein-protein interaction network (PPI) in STRING29 as a template to assess the connectivity of these ASPs (Figure 1C), which indicated that they are more connected than would be expected by chance (PPI enrichment p-value of 5.32e-09).

To identify lineage-level signature proteins that may underlie lineage-specific adaptations, we selected OG that are part of the lineage’s core and rare outside (cloud-level, ≤15%). These lineage-signature families were enriched in functions such as “two-component system” (ko02020) in Sifarchaeia, and “ABC transporters” and “Various types of N-glycan biosynthesis” (ko02010, ko00513) in Lokiarchaeia (Supplementary Data 2).

Genome dynamics across Asgard archaea

To assess dynamic changes and their underlying mechanisms in Asgard genomes, we performed a comprehensive phylogenomic analysis. For this, we ran a species overlap30 and tree reconciliation31 pipelines (see Methods).

For the species overlap pipeline, we selected ten Asgard and ten Thermoproteota representatives and used them as seeds for the reconstruction of the evolutionary histories of all their proteins in the context of the 60 Asgard+Thermoproteota genomes, such that each phylum was balanced in the dataset. These 20 phylomes comprised a total of 38,889 gene phylogenies (available in PhylomeDB30 as phylomes 1532-1551).

For the reconciliation pipeline, we reconstructed the gene trees of the amino acid sequences of the orthogroups from OrthoFinder that had sequences from at least four species, and included at least one Asgard/Thermoproteota sequence, totaling 4,699 families and 344,709 proteins.

We then performed reconciliation and species-overlap analyses using two alternative species tree topologies for the Asgard clade due to ongoing debates (see Methods) to i) detect and date gene duplication events, including gene family expansions ii) infer orthology and paralogy relationships, and iii) infer HGT. Although the reconciliation analysis was performed for the full, 195-genomes dataset, we only show results for the clades of interest, Asgard archaea and Thermoproteota.

The average number of duplications per gene and lineage (duplication rate hereafter, measured as the total number of duplications at a give node divided by the number of gene families present at that node), as computed with either the species-overlap or reconciliation algorithms were significantly correlated (p-value 4E-14, R=0.82), and we show reconciliation-based duplication rates, together with transfer and gene loss rates in Figure 2A for the Eme et al. topology (results for the alternative topology are shown in Supplementary Figure 1, which are by and large congruent).

Our results for both topologies confirmed previously described high levels of duplications at the base of Heimdallarchaeia and Lokiarchaeia19, but extended this observation throughout the Asgard phylogeny. Focusing on the main topology, which we will discuss hereafter except explicitly mentioned, duplication rates were particularly high at the base of the Asgard group and at the branch separating the majority of Asgard lineages from the earliest-diverging clades (that is, excluding Baldr-, Odin-, Jordi- and Atabeyarchaeia), with additional hotspots at the base of the above-mentioned Lokiarchaeia, as well as Heimdallarchaeia, a key lineage to eukaryogenesis as current reconstructions suggest eukaryotes either stemmed from within19 or as sister to32 this clade. Some of the bursts of duplications coincide with the origin of clades with larger genomes, such as Lokiarchaeia and Heimdallarchaeia, hinting towards gene duplication driving increased genome sizes in Asgard archaea. Conversely, lineages with smaller genome sizes are associated with high rates of gene loss. In many cases, gene transfers add to the increase in the genome size, uncompensated by gene loss, such as the base of Asgard, and in many other early nodes. The exceptions to this are at the Heimdall-Njord-Wukong-Sif clade, where most nodes (to the exception of the MRCA of the sampled Heimdallarchaeia) are dominated by gene loss. This result was consistent with the alternate topology.

Importantly, the high duplication rates in Asgards were the result of large expansions in a few gene families (for the OGs for which there is duplication information, 23.87% of the genes have duplicated at least once within the Asgard lineage, see Figure 2B and Supplementary Figure 1B, where the result is overall similar), which were functionally diverse. Assessing by COG category, the categories with more than 10 OGs and the highest mean duplications across Asgard were Intracellular trafficking, secretion, and vesicular transport (U, mean 11.12, sd 59.87), mobilome (X, mean 6.68, sd 18.90), and transcription (K, mean 4.08, SD 12.68). The high values of duplications for OGs associated with the mobilome indicate the genome size increase associated with gene duplication may be driven, at least partially, by these mobile genetic elements. Despite high numbers of transfers throughout the tree, and despite high variability, compared with Thermoproteota, Asgards displayed significantly higher levels of gene duplication (as mentioned in30, Figure 2C also true for Supplementary Figure 1C), suggesting that they might display duplication levels more akin to those in eukaryotes. To test this, we calculated the gene phylogenies of all the genes in a set of 4 eukaryotic seed genomes against a dataset of 30 Free-Living Unicellular Heterotrophic eukaryotes (FLUHs) and calculated duplication rates for FLUHs, Asgards and Thermoproteota with the species-overlap algorithm (Figure 2D, Supplementary Figure 1D). Asgard archaea displayed duplication rates that were intermediate between those in Thermoproteota and free-living unicellular eukaryotes for both topologies. In this regard, it is also notable that proteins of Asgard origin are the ones that duplicated the most in the proto-eukaryotic lineage33.

We wondered then if the evolutionary regime governing the evolution of Asgard genome size was more ‘prokaryote-like’ (deletion-driven) or ‘eukaryote-like’ (neutral) (Supplementary Figure 2, Methods).

Compared to eukaryotes, prokaryotes have a narrower size range for their genomes, and genome size increases correlate with increases in protein number34, whereas in eukaryotes such increases seem to be driven by ncDNA, especially repetitive elements35,36. To maintain a streamlined genome, prokaryotes couple gene transfer with extensive gene loss34. In both Asgard and Thermoproteota (but more so in Asgard), the number of duplications is positively correlated with the number of gene family copies (Supplementary Figure 2A), suggesting a role of gene duplication in increasing genome size, whereas we detect no statistically significant correlation between gene gains (duplications and transfers) and gene losses in either group (Supplementary Figure 2B), nor any statistically significant change in coding density in their genomes (Supplementary Figure 2C). However, when analyzing the copy number of gene families that contained integrase domains (see Methods), indicative of mobile genetic elements, we saw that, on average, Asgard nodes contain a higher copy number of these elements both in terminal and ancestral nodes (Supplementary Figure 2D), which may be indicative of their accumulation and therefore of a more neutral regime. Asgard archaea are low-abundance2 and with doubling times in the range of weeks7, which suggests a low effective population size (Ne) that may weaken selection towards keeping a streamlined genome.

Results for the duplication rate analysis with the species-overlap algorithm can be seen in Supplementary Figure 3. These results corroborate the aforementioned bursts of gene duplication in the Lokiarchaeia and Heimdallarchaeia clade, as well as in the deeper nodes of the Asgard clade.

Next, for clades with more than 2 representative genomes (to have more than 3 nodes of reference), we looked at the set of “lineage signature proteins” described above to understand the relative contribution of gene duplication to lineage diversification. We compared the duplication rates of these OGs within and outside the lineage, normalized by the overall duplication rate of that node, and found that, these signature OGs were more duplicated within than outside the lineage, although it was only significant for Lokiarchaeia (Supplementary Figure 4), hinting to a possible role of gene duplication in lineage-specific adaptations in these lineages

We searched for tandem duplications (e.g. resulting in sets of paralogous genes that are contiguous in the genome), and found a highly variable number across Asgard genomes (Supplementary Figure 5A). Closer inspection of tandem duplications, revealed interesting cases of the same gene, or block of genes, being repeatedly duplicated in tandem (which in some cases were preserved across several genomes), as well as cases of different tandem duplications occurring contiguously in the genome (Supplementary Data 3). We performed a KEGG pathway enrichment for genes within tandem duplications and found them to be enriched in several metabolic functions including ABC transporters, butanoate metabolism, carbon metabolism, endocytosis, Microbial metabolism in diverse environments, and quorum sensing (Supplementary Figure 5B, Supplementary Data 4). Paralogs within tandem duplications had signs of purifying selection (median dn/ds of 0.367, sd 0.137, n = 8694), suggesting both copies remain functional.

Horizontal Gene Transfer in Asgard archaea

We then assessed HGT, both intra- and inter-domain, across Asgard archaeal genomes. We first analyzed HGT using gene tree reconciliation algorithms in the above described phylogenetic dataset (see Methods). On average, and despite high variability, Asgard archaea have significantly lower transfer rates than its sister clade (Figure 2E, Supplementary Figure 1E). This difference is mostly driven by lower rates of acquisition of genes in Asgards from other Archaeal donors, including Thermoproteota, whereas acquisitions from Bacterial donors were similar for the main topology (Figure 2F) but slightly higher for Asgard archaea in the alternate topology (Supplementary Figure 1F). Among Asgard archaea, for lineages with more than 1 representative, all lineages had similar levels of HGT (Kruskal-Wallis chi-squared =10.082, df = 5, p-value =0.07295), while the duplication rates were lineage-dependent (Kruskal-Wallis chi-squared =19.472, df = 5, p-value = 0.001569).

We focused on intra-Asgard HGT exchange partners. HGT patterns depict a dense network, in which most partners have exchanged a comparatively low number of genes, but where certain key pairs stand out (Figure 2A and Supplementary Figure 6), such as highways between Loki and Heimdall clades. For the transfer pairs beyond percentile 95, we ran a KEGG pathway enrichment of the orthogroups transferred with probability ≥ 0.7 and obtained an enrichment in pathways related to metabolic functions (“Glycolysis / Gluconeogenesis”, “Propanoate metabolism”, “Porphyrin metabolism”, “Degradation of aromatic compounds”, “Pyruvate metabolism”, “Butanoate metabolism”, “Microbial metabolism in diverse environments”, “Benzoate degradation”, and “ABC transporters” were the pathways enriched in at least 2 pairs). Overall, we interpret these results as evidence of a high degree of phylogenetic incongruence in the context of a strong phylogenetic backbone, with frequent and dispersed HGT. Importantly, intra-Asgard HGT has been proposed as a potential factor explaining the absence of ESPs in Heimdallarchaeia, the closest Asgard relatives of Eukaryotes37. In this regard, we observed that, for OGs that are transferred within Asgard nodes, ESPs had a significantly higher number of transfers as compared to other genes (Figure 2G).

To assess inter-domain HGT we performed an exhaustive search restricted to fourteen seed Asgard genomes, by using a combined BLAST- and phylogeny-based approach. as well as using a broader database comprising 491,850 sequenced genomes from prokaryotes, eukaryotes and viruses (see Methods).

Blast-based approaches suggested a large amount of HGT, involving between 11.37-19.36% (SD 2.37) of the total gene repertoire (Supplementary Data 5). As many blast-based HGT predictions are likely to be false positives, we reconstructed phylogenies for each candidate and ran two different phylogeny-based HGT detection algorithms (see Methods). This approach predicted 3,000 gene transfer events in the 14 investigated genomes, involving 1.94-13.10% (SD 3.56) of the gene repertoire. These HGT events involved 980 OGs, out of which 922 were in the AleRax dataset, that, on average, had both a significantly higher number of duplications (Wilcoxon Rank Sum Test, two-tailed, W(922,3752)=2336359 p-value 1.04E-67) and transfers (permutation test, p-value 2.2E-16) within Asgard nodes, highlighting the complex history of these gene families.

The overall good agreement between the two phylogeny-based HGT detection approaches, the generally high phylogenetic support (Supplementary Figure 7A), the confirmation of a previously described HGT event38, and the lack of correlation with genomic contamination estimates (Supplementary Figure 7B), suggested that our pipeline is rather conservative and that our phylogeny-based inter-domain HGT inferences are likely to be correct. We additionally kept a subset of transfer events for which the bootstrap was well-supported (≥70), which we labeled as the “high-confidence” set, to test that the qualitative conclusions held at higher stringency.

Based on the gene tree topologies, we inferred the directionality of the transfers (Supplementary Figure 7C), which now allows us to discern between Asgard-to-Bacteria or Bacteria-to-Asgard transfers. Although our pipeline was designed to primarily search for Bacteria-to-Asgard HGT, we captured some Asgard-to-Bacteria transfers. We used the last common ancestor of the genomes (see Methods) carrying the transferred genes as a proxy for the relative time of the event , and mapped this onto the Asgard species tree (Figure 3A).

We assessed the relative contribution of the different bacterial clades as exchange partners (irrespective of HGT directionality), Our results (Figure 3C) show many different partners, with varying levels of taxonomic width. Interestingly, some of the identified partners belong to taxonomic groups that are known to co-occur or interact with extant Asgards. For example, Lokiarchaeota co-occurs with Deltaproteobacteria (phylum Proteobacteria) and Anaerolineaeae (phylum Chloroflexota), both of which appear as significant transfer partners (>5% of trees). Desulfobacterota (sensu GTDB) is particularly interesting in the Lokiarchaeia lineages, as both Ca. P. syntrophicum and Ca. L. ossiferum have known syntrophic partnerships with such bacteria, particularly of the Desulfovibrio and Halodesulfovibrio genera4,7. Importantly, most of the Desulfobacterota HGT partners identified by our pipeline belong to lineages different from the co-cultured symbionts, discarding contamination as a potential source.

To understand the impact of HGT in Asgard evolution, we focused on bacteria-to-Asgard transfers in subsequent analyses. A functional assessment of the transferred genes further supports the lineage-specificity of the transfers, with some trends in the broad sense (see COG categories in Figure 3D) but with all enriched GO terms displaying a patchy distribution (Figure 3E). Similar results were obtained when using the high-confidence set, and the same applies for KEGG functional categories (Figure 3E), despite the general KEGG category “Metabolic pathways” being a common enrichment. This is in line with previous observations in prokaryotes as a whole, where ancient transfers have been found to be enriched for genes involved in amino acid, carbohydrate and energy metabolism20. As for COG categories, which allow a broader overview, some key categories stand out such as Z (cytoskeleton) for Njordarchaeia, T (signal transduction mechanisms) and Q (secondary metabolites biosynthesis, transport and metabolism) and I (lipid transport and metabolism) across several lineages.

ThyX, previously identified as an HGT event in Lokiarchaeia38, is confirmed in this analysis, as “one carbon pool by folate” is 4.31 fold enriched in Ca. P. syntrophicum and is also enriched in the Hodarchaeales (GCA016839515.1), Heimdallarchaeia (GCA021513695.1), Thorarchaeia (GCA_004524565.1) and Jordiarchaeia (GCA037305845.1) representatives as well as in Ca. L. ossiferum (GCA025839675.1), despite none of these remaining significant after FDR correction.

We next asked whether HGT-derived proteins were integrated in the recipient organism interactome. To do so, we calculated the proteome-wide protein-protein interactions using STRING29 and assessed the topological properties of the resulting interactome. We found that, within this network, HGT-derived proteins have a smaller degree, that is, they were less connected than proteins of vertical descent, and were less central in the network (Figure 3F). This was true for all lineages, and for other centrality measures (Supplementary Figure 8A,B).

We found that HGT-derived proteins tend to be more connected to proteins of vertical origin than to other HGT proteins, when normalizing by their respective abundance (Figure 3E). In fact, we found that the KOs brought about by the HGT events are to some extent functionally redundant, as on average, 40.93% (SD 11.66) of the HGT-derived KOs are also found in a gene that is vertically inherited in the same genome. We also estimated the metabolic coherence of these HGT proteins by estimating the metabolism that could be completed with just these HGT KOs with anvi’o (anvi-estimate-metabolism). On average, per genome, the modules that can be assembled from these HGT KOs have a mean pathwise completeness of 0.1955 (SD 0.0438). Therefore, we conclude that HGT does not generally involve the addition of whole modules, but rather a stepwise addition of enzymes to native pathways. Although this was the overall trend, in some cases HGT-derived genes formed a stepwise complete module, thereby expanding the metabolic potential of the recipient organism: it is the case for M00046 in GCA016839515.1, M00632 in L. ossiferum and M00086/M00045 in P. syntrophicum. Nevertheless, given the overlap of KOs across pathways and the uncertainty of functional inferences, claims for contributed metabolic potential should be considered with caution.

We searched for “HGT islands”, that is, segments of the genome in which more than one HGT gene is located contiguous to another HGT gene or interspaced by a single vertically inherited gene. Most of the HGT genes are interspaced with vertically inherited genes, diminishing the likelihood that they are due to errors in the metagenome assembly. However, we found 340 HGT “islands”, comprising 797 Bacteria-to-Archaea HGT genes (on average, 31.84% of the Bacteria-to-Archaea HGT genes, SD=11.63). These islands contained up to 8 genes (mean 2.77, SD 1.09). 48 of these islands contained genes belonging to the same COG category, suggesting that they may be operons. We studied these cases in detail. Some of these islands were composed of the same transferred gene duplicated in tandem, such as GCA016840465.1_JPJMNJDP_02194 and GCA016840465.1_JPJMNJDP_02195 from Desulfobacterota to Baldrarchaeum (bcrC) or GCA037305845.1_JPIBEIIO_00281 and GCA037305845.1_JPIBEIIO_00282 (CRYZ) from Patescibateria to Jordarchaeia. An illustrative example of putative operons is GCF008000775.2_JEDDKEKE_02754 and GCF008000775.2_JEDDKEKE_02755, which are excinuclease ABC subunit B and A transferred from Firmicutes to the CR-4 lineage, while protein C is GCF008000775.2_JEDDKEKE_00387, or GECA016840425.1_IAHJMLLN_00393, GCA016840425.1_IAHJMLLN_00394 and GCA016840425.1_IAHJMLLN_00395, transfers to the Heimdall-Loki clade, likely from Nitrospirota that are zinc-transport system znuA/B/C.

Last, we set to assess which HGT proteins may have reached the eukaryotic lineage via the Asgard ancestor of the Last Eukaryotic Common Ancestor (LECA). We assessed all 331 trees flagged as HGT (both Asgard-to-Bacteria and Bacteria-to-Asgard, as we were also interested in assessing whether proteins in eukaryotes may be falsely identified as of bacterial origin) which contained at least one eukaryotic sequence, and 28 contained sequences from at least 3 eukaryotic supergroups suggesting they might have been present in LECA39. Upon manual inspection of these candidate phylogenies, we did not find any clear case where a gene of bacterial origin was horizontally transferred to Asgard archaea and passed on through the eukaryotic lineage by vertical inheritance. This has interesting implications for eukaryogenesis, as it implies that the proteins of bacterial origin beyond Alphaproteobacteria that are detected in LECA are unlikely to have come from Bacteria-to-Asgard HGT events in the Asgard lineage prior to eukaryogenesis, strengthening the credibility of the implication of more bacterial partners during eukaryogenesis40. Interestingly, however, we do find instances where viruses (mainly Nucleocytoviricota) seem to act as mediators for the transfer of genes from prokaryotes to eukaryotes, supporting and expanding previous observations39. These viruses, of Algavirales, Imitervirales, Pimascovirales clades, mainly infect microbial eukaryotes of varied phylogenetic affiliations (amoebozoa, and other protists).

Methods
Data retrieval and annotation
From GTDB r226 we compiled a set of 32 genomes, covering all Asgard family-level lineages sensu GTDB and chosen for their best assembly quality parameters (applying the score of CheckM completeness - 5*CheckM contamination), except in the cases in which closed genomes were available, which were prioritized over metagenomes as they served as a baseline that the findings of HGT were not due to contaminating sequences in the metagenomes. We generated a second dataset of Asgard archaea, which was a random set of equal size and same lineage-based representation for quality control of the pangenomic measures. Taxonomies were assigned sensu GTDB.

We additionally retrieved representatives of the Thermoproteota superphylum (“p__Thermoproteota” sensu GTDB), one per order-level lineage, as well as 1 representative per phylum for the rest of the prokaryotic phyla. This generates a dataset of 195 genomes in total.

To avoid biases in genome annotation, we re-annotated all genomes using Prokka v.1.14.643 with the flags --kingdom Archaea (or --kingdom Bacteria, when necessary) and --metagenomes, as Prokka is a reliable and widely used prokaryotic genome annotator. We adapted the headers of the proteome files, changed the NCBI taxID when the one in the GTDB metadata table was too broad or conflicting, and uploaded them to PhylomeDB30 via the Phylomizer.

We annotated the Gene Ontology (GO) terms and domains associated with each protein using InterProScan v5.39-77.044 (filtering for score ≤ 1E-3) , and the COG categories and KEGG KOs with the COG HMMs45 and KoFamScan (https://github.com/takaram/kofam_scan), respectively. To perform functional assessments across the Asgard pangenome, we also annotated the orthogroups by compiling the annotations of their member proteins following the pipeline in39. In brief, we followed the strategy followed in eggNOG, which takes into account the proportion of the KO and COG within the set of sequences and its overrepresentation with respect to the background KOs distribution (the whole set of annotated proteins).

When querying for ESPs, we performed a manual curation of InterProScan results. To assign an ESP to a given OG (for example, for the results in Figure 1) we took the OG where the majority of the proteins from that ESP were grouped (or several in case of ties).

We took the annotations from the Asgard proteins for the consensus, to keep the inferred function as close as possible to the actual one performed in Asgard cells, except for when looking for integrase domains, where we took into account Thermoproteota annotations as well.

Last, we taxonomically downsampled the list of Free-Living Unicellular Heterotroph (FLUH) proteomes from Bernabeu et al.39, by keeping a proportion of proteomes per supergroup to a total of 30.

The list of genomes, along with associated metadata, can be found in Supplementary Data 1.

Selected thresholds for Asgard Signature Proteins
We defined signature proteins of the Asgard clade (which we termed Asgard Signature Proteins or ASPs) as those common within Asgard genomes and rare in the outgroups. We determined the abundance thresholds as follows. First, we defined the outgroup abundance threshold at 15% which is the upper limit defined for the ‘cloud’ component of a pangenome as defined in27. For the presence within Asgard archaea, we considered the lower limit of the extended core (90%) as too strict, given the high genome incompleteness within the set (see Results). Therefore, we analyzed the prevalence of housekeeping protein families that can be thought to be core, in the sense that they perform essential functions for the cell. For this, we annotated the orthogroups by KEGG KOs and COG categories and assessed the prevalence within the Asgard group of protein families annotated as the COG category J (Translation, ribosomal structure and biogenesis) and of the orthogroups annotated as the KOs for the ribosomal proteins in archaea (BRITE for ko03011). We selected a threshold of prevalence of 86%, as this is the median prevalence for the COG category J and similar to the median for ribosomal proteins (87%). This resulted in 14 orthogroups selected as Asgard Signature Proteins.

Alternative species tree reconstruction
We inferred branch lengths of the Asgard species tree by retrieving the markers for all archaeal species from GTDB, aligning them with MAFFT-linsi v7.40746, trimming with trimAl v1.4.rev1547, and reconstructing the species tree with IQ-TREE v1.6.948 (--models DCmut,JTTDCMut,LG,WAG,VT), both constraining the topology to the current consensus (see ref.19) for the main lineages or to the GTDB topology, resulting in an alternative topology to benchmark results.

Asgard archaeal pangenome assessment
We performed an orthogroup clustering with OrthoFinder v.2.5.449 using BLAST50 as an aligner. To assess pangenome metrics, such as Chao’s lower bound, Heaps estimate, and the binomial mixture models we employed the R package micropan51.

We defined Asgard signature proteins (ASPs) as those OG common (present in ≥ 86% of the proteomes) in Asgard archaea but rare (present in ≤15% of the proteomes) in other prokaryotes (see Methods). We mapped ASPs onto the Protein-Protein Interaction Network (PPI), obtained with STRING v12.029, of the seed genome with the highest number of ASPs (Ca. P. syntrophicum) using STRING web server.

To perform functional enrichments, we collapsed the annotations at orthogroup level following the aforementioned pipeline.

Genome dynamics across Asgard archaea
We constructed a metaphylome consisting of a collection of phylomes employing one seed per major lineage. For the phylome analysis, we took the 60 representative genomes of Asgard archaea and Thermoproteota as the dataset and selected 10 seed genomes per phylum that covered the diversity of the phyla. For the set of FLUHs, we selected 4 seed genomes with 30 the aforementioned 30 representatives.

The phylome reconstruction pipeline was applied as defined elsewhere (see ref.30 for full details): In brief, for each of the genes contained within each of the seed genomes, we performed a protein BLAST50 search against all proteomic data to retrieve proteins with significant similarity (defined as e-value < 1e-05, continuous overlap over 50% of the query sequence). We then took the top 150 hits per protein and generated an MSA with three different methods (MUSCLE V5.252, MAFFT v7.52553 and KALIGN v3.5.054), in forward and reverse, and combined the resulting six alignments into one consensus alignment with Mergealign 355. This alignment was then trimmed with trimAl v1.4.rev1547, with –gappyout. These alignments were then used to reconstruct the phylogenetic tree of each protein family with IQ-Tree v3.0.148, for which we selected the final Maximum Likelihood (ML) tree using the best model selected based on the Bayesian Information Criterion (BIC) and support was calculated with ultrafast bootstrap under 1000 repetitions.

We then ran the species overlap algorithm following the PhylomeDB pipeline30. We employed both topologies for the prokaryotic dataset and the species tree as inferred by Duptree56 for the eukaryotic set.

For the reconciliation algorithm, which we ran for the prokaryotic set as we infer HGT to play a significant role in its genome dynamics, we built gene trees of the OGs from OrthoFinder that had sequences from more than 4 species following the tree reconstruction pipeline in (LECA-TIME) ran AleRax31 v1.3.0 using as input the distribution of bootstrap trees (as supplied by IQ-TREE) and the generated species tree as backbone, estimating rates per family.

Evidence of selection
Within tandem duplications, we looked for signs of positive selection by assessing the dn/ds ratio. For this, we built codon alignments and assessed the dn/ds ratio employing the python library biopython v1.86. Cases where the sequence had “N” or where there were no synonymous mutations were discarded from the analysis.

Mobile Genetic Elements
We took the InterProScan annotation of the Asgard and Thermoproteota proteomes and assessed the prevalence of signatures related to Mobile Genetic Elements (MGEs). For this we took as reference the list of InterPro domains employed by proMGE57. We took the orthogroups for which at least 50% of the proteins with annotations were annotated (score <= 1E-3) with a given domain within this list. We then assessed the number of copies per node of these orthogroups by doing the sum of copies of each orthogroup.

Inter-domain HGT assessment
We performed a similarity search with BLAST 2.11.050 of the proteomes against a custom-made database (BROADDB_V2), which comprises all species representatives from GTDB r2075, viral sequences from RVDB 25.058, and eukaryotes from UniRef v2023_0159 and EukProtV160. We chose not to employ NCBI NR as it does not provide a sufficient representation of the Asgard genomes. We employed GTDB taxonomy for prokaryotes as it allows a more comprehensive, robust and fine-grained taxonomic analysis as compared to NCBI taxonomy.

We analyzed the BLAST results with HGTector v2.0b361, which systematically looks for hit distribution patterns incongruent with vertical evolution, given a series of hierarchically defined evolutionary categories. We ran the algorithm setting the “self” group as Asgard archaea and the “close” as Archaea, therefore allowing us to focus on detection of inter-domain HGT events. The output statistics can be found in Supplementary Data 5 and the output of HGTector in the data repository.

For candidate transfers selected by HGTector, we retrieved the best 300 BLAST hits with an e-value cut-off of 0.001 and used them to reconstruct a gene tree following the pipeline in39.

When the selected set of homologous sequences was saturated with eukaryotic sequences (that is, if they comprised >=80% of the non-Asgard sequences) or Asgard sequences, we performed a subsampling of these clades by selecting the first hit per major eukaryotic group as defined in EukProt taxonomical framework60, then retrieved all hits from those organisms that had a lower e-value than the lowest e-value for a non-Asgard prokaryotic sequence for that query. We kept the non-eukaryotic sequences from the BLAST that passed the threshold, the subsampled eukaryotic sequences, and up to 250 non-eukaryotic sequences passing the threshold performed in a prokaryotic-only BLAST. This allows us to have taxonomically-informative insight into the distribution of the sequences across eukaryotes whilst allowing us to analyze the distribution of more distant prokaryotic homologues.

We further analyzed the resulting gene trees our custom script (HGT_detection.py) (see below for the pseudo-code) and with Abaccus v1.262, which identifies taxonomical “jumps” within gene trees that do not follow the species tree and therefore further helps discern putative HGT events. For this, we used the taxonomy sensu GTDB for prokaryotes.

There are several methods to root a phylogenetic tree in absence of a clear outgroup. We tested the number of HGT events called by rooting the trees by midpoint and Minimal Ancestral Deviation (MAD), which resulted in a 94% overlap in the genes detected as HGT (2,946), 2% MAD-only (54) and 5% midpoint-only (244). Given the small difference, we decided to proceed with MAD rooting as implemented in toytree63.

Additionally, we retrieved the support of the branch of the transfer (e.g. the branch subtending the acceptor Asgard clade and the putative non-archaeal donor clade). We only kept cases when this support was ≥50%, or, if the node was poorly supported, cases where the sister of the transfer branch contained ≤ 10% of archaeal sequences were also considered, even if the specific donor clade could not be ascertained. To ensure that branch support was not affecting the conclusions of the analysis, we built a subset of well-supported (≥ 70% support) HGT cases in which all analyses were also run.

Last, we manually curated the HGT results by filtering out incongruencies and false positives resulting from trees saturated with eukaryotic sequences. We performed this step for all proteins flagged by HGTector, whether they were picked up by Abaccus or not.

To assess the directionality of the transfer (Asgard-to-bacteria or bacteria-to-Asgard), we assessed whether there were Asgard sequences in sister2, if there were, Asgard-to-bacteria was assumed.

HGT detection pipeline
To do so, we first numbered the internal nodes in the Asgard tree . Then, we parsed the gene trees with an in-house script (HGT_detection.py) based on ete364, using the following criteria:
1. Trees were rooted with Minimum Ancestor Deviation (MAD)65.
2. The start node was established as the one subtending the largest Asgard monophyletic clade containing the seed protein. In assessing monophyly, we considered eukaryotes as a de facto Asgard lineage stemming from Heimdallarchaea. The most recent common ancestor (MRCA) of the Asgard lineages present in the clade is assumed as the acceptor Asgard lineage.
3. Then, the sister clade of the start node was analyzed:
a. If the fraction of non-Asgard Archaeal sequences in the sister clade was ≥ 10%, the tree was labeled as a False Positive. This means that either vertical inheritance cannot be ruled out, or that an Archaeal-to-Asgard HGT cannot be ruled out, which is also beyond the scope of this analysis since we are focusing on inter-domain HGT.
b. The donor was set as the MRCA of the non-Asgard sequences of the sister clade.
In cases where the MRCA is a broad category (e.g “Bacteria”), we established the predominant phyla as the putative donor lineage. For cases with low support (<= 50) we marked the donor as “uncertain”.
4. Additionally, taxonomy information of the sister clade to the (start+sister) branch (sister2) was retrieved.
5. We assessed the percentage of non-Asgard sequences in the tree that were of eukaryotic origin. In cases where this percentage ≥ 80% were labeled eukaryote-saturated and submitted to the subsampling mentioned above and the subsampled gene tree was re-analyzed.
After this analysis, we additionally flagged as FP events for which support was low (≤ 50) and sister2 contained ≥ 10% non-Asgard archaeal sequences. These criteria are very restrictive and likely result in an underestimation of the true number of HGT events: namely, we will miss inter-domain HGT and the 10% of Archaeal sequences in the sister needed to label a tree as a false positive will lead to true HGTs being missed due to secondary transfers of the gene family from bacteria to other archaea. However, this ensures that the trees that are picked up as HGT are true HGT events.

One of the limitations of the analysis of acceptor nodes of horizontally transferred genes is the use of Maximum Parsimony. There are methods better suited for this (e.g reconciliation, as used for the estimation of duplication, transfer and loss rates), but these become computationally prohibitive given that running time scales cubic to the number of species, and given that the search is done against a broad database of almost 500.000 genomes. Moreover, for the purpose of analyzing the tempo of transfers across Asgard phylogeny (that is, whether there are ‘hotspots’ or it is more widespread) we believe a more simplistic assumption like Maximum Parsimony is sufficient for this purpose.

Tandem duplications and HGT islands
To infer tandem duplications we used a window-based approach to query the GFF files where we looked for 2 or more genes belonging to the same OG being either directly adjacent or interspaced by another gene, which we applied with a custom R script (Tandem_Duplications.R). For HGT islands, we also queried the GFFs to find HGT genes gene immediately adjacent or interspaced by a vertically inherited gene to reconstruct “islands”. We visualized it with the R package gggenes 0.5.1 (https://github.com/wilkox/gggenes/tree/master).

Functional enrichment analysis
We performed GO term enrichment analyses using the Bioconductor package topGO (https://git.bioconductor.org/packages/topGO), with the method “weight01” and using the Fisher’s exact test, correcting for multiple testing and keeping results with a False Discovery Rate (FDR ≤ 0.15). For KEGG pathway enrichment, we employed the package ClusterProfiler66 with the same statistical criteria except in the tandem duplications, where we cut to FDR ≤ 0.1 for visualization purposes. We visualized the results with the R package ggpubr (https://github.com/kassambara/ggpubr/) and ggtree/ggtreeExtra67,68.

Protein-protein interaction networks
We further analyzed the functional relationship between transferred genes by assessing the network of protein-protein interactions (PPI). Since none of the analyzed Asgard genomes are available in the database, we first submitted them to be added to STRING29 v.12.0 by making use of the tool developed in the latest version69. We then uploaded the sequences of the transferred genes to the web server and ran the analysis using the newly uploaded proteome as target organism. We downloaded the results, performed analysis with the R implementation of iGraph (https://github.com/igraph/rigraph) and visualized the networks with the R package ggnetwork (https://journal.r-project.org/archive/2017/RJ-2017-023/index.html).

Data availability
BioProject IDs for the genomes are available in Supplementary Data 1. For the representative set of Asgard genomes, the publications (if any) associated with the assemblies are as follows: GCA_001940655.12; GCA_005191415.1 and GCA_005191425.170; GCA_014729935.171; GCA_015523565.172; GCA_024720975.173; GCA_021513695.118; GCA_001940665.274; GCA_027061945.1, GCA_027068385.1, and GCA_026993975.175, GCA_016292335.176; GCA_029856635.119; GCA_040225955.1 and GCA_029838215.177, GCA_030638685.1 and GCA_030671945.178; GCA_038858525.179; GCA_037305845.1 and GCA_037308085.180; GCF_008000775.27,81 All input proteomes and output files for all steps can be found in Zenodo (10.5281/zenodo.15789115)

Code availability
All described scripts are available on Github (https://github.com/Gabaldonlab/Asgard)82.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this article, the authors performed phylogenetic analyses centered around the gene repertoire of Asgard archaea, and suggested that the Asgard archaeal genomes contain a substantial amount of duplication, which is more similar to eukaryotes than prokaryotes. The authors also examined the rates of HGT from other prokaryotic lineages and found that metabolic genes are enriched with such events. I find the topic of interest as a detailed gene content profiling of Asgard archaea is likely to provide some missing information to further our understanding in eukaryogenesis. The authors are relatively careful about conclusions on the second part of the paper regarding HGT, which I appreciate.\nHowever, I do have major concerns regarding the use of small number of taxonomically biased genomes to infer duplication events and make the major conclusion of enriched duplication in Asgard archaea: The authors used only 61 prokaryotic genome set, where from what I can find in the texts - 31 is from Asgard, 17 from TACK, 10 other Archaea, and 5 Bacteria – which by btw add up to 63, not 61 as stated in the paper. In addition, given that a selected gene may not exist in some of the 63 genomes examined, an actual gene tree in this study very likely included genes from less than 63 genomes. This has a number of consequences:\n1. This provides little, if not completely no, chance for the discovery of HGT events from outside Asgard/TACK, and hence a strong over-estimate of duplication events. For example, if a Lokiarchaeal genome encode two genes that belong to the same gene family, each were HGTed from a different bacterial lineage (or from archaea outside TACK), but this gene family does not exist in TACK. These two gene copies would have been reported as a duplication within Asgard using the authors’ approach.\n2. Such a selection restricted the paper to make comparisons only with TACK for the extent of duplication. The comparison itself has two main issues: 1) The authors selected 17 TACK vs 31 Asgard, and while the gene family selection was based on 12 Asgard without considering TACK-specific genes. 2) The selected TACK lineages mostly have small genomes averaging 1.5Mb, almost half of the average smaller than most Asgard. These factors can lead to strong statistical bias. More importantly, even without considering the statistical bias, the main conclusion of the paper was made based on a comparison with TACK without considering other bacteria and archaea with larger genomes. I feel that it is far from sufficient.\nTo make the conclusions written in this paper, I think it is important that the authors make a broader and more even taxonomic sampling, as well as providing sufficient evidence to exclude potential HGT that disguise as duplication from outside the selected lineages. Please do pay attention to potential biases in genome sizes and gene counts when making quantitative comparisons.\nOther major concerns:\nNovelty of the paper: 1) On the topic of gene duplication in Asgard archaea, Vosseberg et al 2020 Nature Eco & Evo, where the last authors of this article T. Gabaldon was a corresponding author, has specifically highlighted such a case. It is strange to me that the authors did not refer to this paper. In this 2020 aper, it was already concluded that Asgard archaea exhibited most duplications, so I suggest that the authors discuss the current work in the context of their previous paper, and state clearly what is new and different. 2) The main conclusion for the HGT side seems to be that the metabolic functions are more prominently involved, which does not seem too surprising. Beyond that, the authors seem to follow with various detailed descriptions, which I do appreciate but unfortunately show little implications for eukaryogenesis. Can the authors perhaps provide more insights into the functional consequences of those HGTs and potential link to eukaryogenesis?\nThe taxonomic sampling is also a little strange: Even with as few as 61 or 63 genomes, the authors selected MAGs that belong to the same species or genus: two Baldr from g__DXJG01, two Heimdall from g__UBA460, 3 Hermod from g__Hermodarchaeum, 3 Hod from g__Hodarchaeum, two Jord from g__DTBI01, two Odin from g__LCB-4, 2 Borr from the same species s__Borrarchaeum weybense, two Wokong from species s__Wukongarchaeum yapensis. Same goes for two g__Nitrosopumilus. In addition, is there a reason not to use circularized Freya/Atabey/Heimdall genomes available for analyses? In the SI table file, I can only find the information for the Asgard and TACK genomes but not the remaining 15, so I cannot judge for the rest.\nThe authors suggest that there is higher rate of intra-Asgard HGT of ESPs than other gene families. Given that most of the ESPs only exist in the Asgard archaea, could this also be statistically biased?\nOther minor and general issues:\nThe two parts of the paper used different approaches. Have the authors cross-compared the duplication events concluded from the method 1 with the HGT events concluded from method 2?\nLine 321, PMIDs, replace with real reference.\nLine 269-271, please consistently use Desulfoproteobacteriota for SRBs.\nLine 340-343, This is interesting. who are the hosts of those viruses? Can you provide some clear examples?\nLine 254: (D)->(E)", "reviewer_2": "In this work, Manzano-Morales and Gabaldon performed a comparative phylogenomic analysis of Asgard archaea and their sister group TACK and found that genome evolution in Asgard archaea is characterized by high rates of gene duplication and horizontal gene transfer (HGT). Such blend of prokaryotic-like HGT rates and eukaryotic-like gene duplication is a remarkable finding that, in my opinion, will be of interest to many potential readers of Nature Communications.\nThe analysis is comprehensive and thorough, and the authors made a great effort to ensure that their findings are not due to contamination or genome incompleteness by applying rigorous filters and combining multiple lines of evidence. There are some details that require clarification or could be better explained (see below), but I do not think that these compromise the integrity of the manuscript.\nSome general comments and questions:\n1. I wonder if the authors could go one step further and provide some insight into the evolutionary regime that characterizes genome evolution in Asgard archaea. To contextualize this comment: Most bacterial genomes evolve in a regime of deletion-driven genome reduction, such that useful genes are maintained by selection. Some manifestations of that regime are a positive association between genome size and effective population size, and a scarcity of psedogenes in bacterial genomes. Instead, eukaryotic genomes evolve in a non-adaptive regime driven by gene duplication and the proliferation of transposons and other mobile genetic elements. Such non-adaptive regime results in a negative association between genome size and effective population size, as well as accumulation of noncoding DNA. Estimating the effective population size is complicated, possibly unfeasible if only one high-quality genome per species is available. However, it would be interesting to use the ancestral reconstructions to investigate how genome size has changed along the evolution of Asgard archaea and compare it with the TACK group. Have high duplication rates resulted in a net increase in genome size? Were duplications compensated for by higher rates of gene loss? If there has been a net increase in genome size, did it result from higher numbers of functional genes or from pseudogenes and mobile genetic elements? In that regard, it may be worth assessing the abundance of pseudogenes and the rates of pseudogenization and pseudogene decay.\n2. I found it surprising that there were no signs of transposons, proviruses, and other integrated mobile genetic elements among the duplicated and horizontally transferred genes. Is the lack of MGE duplication and transfer a consequence of the conservative filters applied to identify HGT and duplications or is it a genuine finding?\nSome points that need to be clarified or better explained:\n3. Lines 69-73. After reading this paragraph it is not clear if those MAGs were used or not.\n4. Lines 85-86: How is it that only 17K out of 31K ***Asgard archaeal*** OGs contain at least 1 Asgard representative? What about the other 14K? Perhaps I am missing something: what do you exactly mean by Asgard representative?\n5. Lines 91-92: Did you only include KEGG pathways or also KEGG ontologies?\n6. Lines 108-109: The finding that Chao's lower bound and core genome size are sensitive to genome incompleteness is not surprising. Chao's lower bound depends on the number of singletons and OGs with 2 members, hence it is a not quite robust estimator. As for the core genome, the reasons are trivial (but see for example PMID:37904249 for a quantitative assessment). In the light of these results, it may be worth running mOTUpan on both sets of genomes to get a more robust estimate of the core genome size and pangenome fluidity.\n7. Line 110: I am not sure of what you mean by the \"full set\". Do you mean all available MAGs? It has been shown that orthofinder performs quite poorly when applied to incomplete MAGs. I also wonder if the analysis of the full dataset is really necessary: given the openness of the pangenome and looking at the rarefaction curve in Fig 1 it is sufficiently clear that there are a lot of Asgard archaeal genes yet to discover and that non-HQ MAGs will contain genes that are not present in the representative set.\n8. Figure 1A: This plot would be more informative if it used a logarithmic scale for the y-axis.\n9. Lines 120-121: Here \"entire genome set\" means the representative HQ genomes, right? Which is not the same as the \"full genome set\" mentioned in line 110. That is confusing.\n10. Figure 1C: What is the hub of the PPI network (K12158)? What are the proteins that form the 2 triangles?\n11. Line 129: In the absence of a null model, to say that the network is highly connected is quite subjective. Less than half of the proteins have interactions and, if we only look at those, more than half of the interactions have a low score (around 200).\n12. Line 187: I am not sure of what you mean by \"a value of 1 would indicate a whole genome duplication\". In the figure there are cases of 10: I do not suppose that those represent 10 whole genome duplications, right? Adding to the confusion, the caption says \"mean number of duplications per node\" but the label of the x-axis says \"mean number of duplications per OG\". That requires clarification.\n13. Figure 2: What are the rate units for the duplication and transfer rates? Events per unit of time (what unit of time)? Per gene? Per genome?\n14. Figure 2E: Do the distributions correspond to nodes or OGs? Equivalently, do the tails of the distribution correspond to OGs that duplicate a lot or genomes with a lot of duplications?\n15. Figure 2F: What is the definition of verticality?\n16. Figure 2G (the caption says F): Something looks wrong with the boxplot and overlapping scatter plots. The \"other\" category is clearly not normally distributed, so it would probably be better to use logarithmically transformed data. The same applies to the statistical test: did you use a robust test or transformed data? What are the effect size and p-value? These are important details that must be disclosed (not only for panel G, but also for panels C, D, F and Fig 3E). The biological implications (higher transfer of ESP than other genes), if confirmed by statistical analyses, are certainly relevant.\n17. Lines 193-196: When comparing the duplication rate of lineage signature proteins within and outside the lineage, did you control for the lineage-wise duplication rates? Did lineage signature proteins also duplicate more than other (non-signature) OGs within the same lineage?\n18. Line 232 and below: Do the methods used to infer inter-domain HGT account for the fact that proteins can evolve at very different rates in bacteria, archaea and eukaryotes? Would it be possible to obtain reliable estimates of the fraction of false positives?\n19. Figure 3: Do all panels in Fig 3 refer to Bacteria-to-Asgard HGT?\n20. Fig 3A: what does \"contribution of different seeds\" mean?\n21. Figure 3D: Besides the KEGG and KO terms, it would be helpful to see a higher-order classification of the transferred genes, for example in terms of COG categories.\n22. Fig 3E, bottom panel: Why are the degrees always <1? Does it mean that most proteins have degree 0 (no interactions)? But then, how is it that in the top panel the Q25 is already well above 0?\n23. Line 266: Figure 3C would be more informative if instead of the number of taxa it showed which taxa acted as donors. That information is not currently shown here or the supplement, although some donor taxa are mentioned in the main text. If adding that to Fig 3 is not feasible, a summary table in the supplement would work too.\n24. Line 294: The degree is only one of several possible measurements of centrality. Have you checked other centrality indices, such as the eigenvector centrality?\n25. Line 303: Based on phylogenomic reconstructions, did the transfer of these enzymes lead to the replacement of a preexisting enzyme carrying out the same function? Otherwise, did HGT lead to qualitatively new or broader metabolic potential?\nOnline Methods\n26. Line 26, regarding the lowest CheckM contamination criterion: Was it only applied to break ties? Did you apply any hard threshold on the contamination score? Contamination would result in apparent duplication and HGT, so, for these purposes, minimizing contamination seems more important than maximizing completeness.\n27. Line 41: How did you harmonize the need to work with homogeneous genome annotations (to control for biases and annotation-related confounding factors) while keeping the original genome annotations?\n28. Line 51: Please, provide a brief summary of the annotation workflow (Ref 33).\n29. Line 53: FLUH has not been defined. Furthermore, this paragraph is quite cryptic. What does it refer to?\n30. Line 59 and below: Were the alignments and trees based on nucleotide or amino acid sequences?\n31. Line 176: Did you check that putative instances of tandem duplications are not due to fragments of the same gene interrupted by a stop codon (either real or resulting from a sequencing error)?\n32. Extended Data Figure 3: The distribution of the number of transfers (upper left) would be more informative if it was plotted in log-log or log-linear scale.\n33. Line 311: This caption refers to folate metabolism, not carbon metabolism.\nTypos\n- Line 264: Data Figure 4A (instead of 3C).\n- Line 345: High (instead of highly).\n- Online methods, lines 68-69: the sentence in parentheses is repeated.", "reviewer_3": "The paper presents the results of a phylogenomic study focused on Asgard archaea. The main findings relate to patterns of gene duplication and HGT and their functional and genomic impacts. The findings are interesting and shed light on some unique aspects of Asgard evolution with implications for understanding eukaryogenesis. My comments primarily have to do with thoroughness of the analysis and appropriateness of the methods used.\nMajor comments:\n- There are several cutoff thresholds used throughout the paper but these seem to have been chosen somewhat arbitrarily. As an example, Asgard signature proteins are defined using thresholds of at least 70% presence in Asgard and at most 15% presence in other archaea or bacteria. Why were these specific thresholds chosen and how do results/findings change if other reasonable thresholds, such as 80% and 10%, are used instead? The same applies to the thresholds used on lines 139-140.\n- It is mentioned that two different species phylogenies, constrained and unconstrained, were generated and used for HGT and gene duplication analyses. However, it seems that results are only presented for one of those phylogenies. Which phylogeny was used for the reported results? And how do findings change when the alternative phylogeny is used? Results should be shown for both phylogenies, and the phylogenies themselves should be provided as well.\n- It is mentioned that two methods were used for duplication analysis, but results are reported only for the reconciliation-based method. Results should also be shown for the other (species overlap) method. Are all reported results supported by both methods?\n- Same as above, there also appear to be multiple methods used for HGT inference. Are all reported results consistent between these different analyses (e.g., reconciliation-based and non-reconciliation-based)?\n- The AleRax tool is used for reconciliation-based analysis. It appears that the species tree generated by AleRax was used to perform this analysis. Do we now have a third species tree that is different from the constrained and unconstrained species phylogenies used for the other analyses? If so, this needs to be clearly stated in the paper. And how do results change if the two previous species phylogenies are used for this reconciliation analysis? The use of multiple species trees makes it all the more important to clear report how findings change as different reasonable species trees are used for the analysis.\n- Some of the results, such as elevated duplication rates at the base of the Asgards, may be artifacts of errors in the gene trees. Paying further attention to gene tree quality may help resolve some of these artifacts.\n- In the time of acquisition analysis of Lines 259-264, using last common ancestors of all genomes carrying the transferred gene is almost guaranteed to provide incorrect time estimates since such genes may have been acquired multiple times or may have been transferred again after initial acquisition. A much more accurate way would be to directly use the reconciliation to estimate when a gene was first acquired.\n- Line 87: where are the 8365 OGs found only in a single Asgard genome coming from? A BLAST search against a larger collection of prokaryotic and eukaryotic genomes could help resolve this question.\n- Line 83 of online methods supplementary text: why are only the top 150 hits chosen per protein? Artificial cutoffs such as this can result in under-clustering, leading to significant errors in downstream analyses and especially in reconciliation. This analysis should be repeated with a more biologically grounded approach for identifying gene families.\n- Lines 95-101 of online methods supplementary text: The description of how and on what data AleRax was run is unclear. What is meant by “we selected one sequence per OG present in the Asgard clade”?\nMinor comments:\n- Increase font size of node annotations in Figure 1.\n- Typo on line 345, highly dynamism.\n- Lines 133-134: why were only 39 out of the 83 previously identified proteins found? Is it because only a subset of available Asgard genomes was used in the analysis?\n- Lines 146-147: Why not use all 31 Asgard genomes for this analysis? How were the 12 genomes chosen?\n- In the analysis of tandem duplications (Lines 198-208), were phylogenetic relationships between the genomes considered? Ancestral tandem duplications would be inherited by multiple genomes and may be counted multiple times, possibly impacting the results shown in Extended Data Figure 2.\n- Line 59 of online methods supplementary text. Which markers were used?\n- Line 142 of online methods supplementary text. There are better rooting methods than midpoint rooting. The authors could consider using Minimum Ancestor Deviation (MAD) rooting.", "reviewer_1_attached": "In this article, the authors performed phylogenetic analyses centered around the gene repertoire of Asgard archaea, and suggested that the Asgard archaeal genomes contain a substantial amount of duplication, which is more similar to eukaryotes than prokaryotes. The authors also examined the rates of HGT from other prokaryotic lineages and found that metabolic genes are enriched with such events. I find the topic of interest as a detailed gene content profiling of Asgard archaea is likely to provide some missing information to further our understanding in eukaryogenesis. The authors are relatively careful about conclusions on the second part of the paper regarding HGT, which I appreciate."}, "author_response": "Response to Reviewers\n\nReviewer #1 (Remarks to the Author):\nIn this article, the authors performed phylogenetic analyses centered around the gene\nrepertoire of Asgard archaea, and suggested that the Asgard archaeal genomes contain a\nsubstantial amount of duplication, which is more similar to eukaryotes than prokaryotes. The\nauthors also examined the rates of HGT from other prokaryotic lineages and found that\nmetabolic genes are enriched with such events. I find the topic of interest as a detailed gene\ncontent profiling of Asgard archaea is likely to provide some missing information to further our\nunderstanding in eukaryogenesis. The authors are relatively careful about conclusions on the\nsecond part of the paper regarding HGT, which I appreciate.\nResponse: We thank the reviewer for appreciating the interest of our contribution.\nHowever, I do have major concerns regarding the use of small number of taxonomically biased\ngenomes to infer duplication events and make the major conclusion of enriched duplication in\nAsgard archaea: The authors used only 61 prokaryotic genome set, where from what I can find\nin the texts - 31 is from Asgard, 17 from TACK, 10 other Archaea, and 5 Bacteria – which by\nbtw add up to 63, not 61 as stated in the paper. In addition, given that a selected gene may not\nexist in some of the 63 genomes examined, an actual gene tree in this study very likely\nincluded genes from less than 63 genomes. This has a number of consequences:\n1. This provides little, if not completely no, chance for the discovery of HGT events\nfrom outside Asgard/TACK, and hence a strong over-estimate of duplication events. For\nexample, if a Lokiarchaeal genome encode two genes that belong to the same gene family,\neach were HGTed from a different bacterial lineage (or from archaea outside TACK), but this\ngene family does not exist in TACK. These two gene copies would have been reported as a\nduplication within Asgard using the authors’ approach.\n2. Such a selection restricted the paper to make comparisons only with TACK for\nthe extent of duplication. The comparison itself has two main issues: 1) The authors selected\nconsidering TACK-specific genes. 2) The selected TACK lineages mostly have small\ngenomes averaging 1.5Mb, almost half of the average smaller than most Asgard. These\nfactors can lead to strong statistical bias. More importantly, even without considering the\nstatistical bias, the main conclusion of the paper was made based on a comparison with TACK\nwithout considering other bacteria and archaea with larger genomes. I feel that it is far from\nsufficient.\nTo make the conclusions written in this paper, I think it is important that the authors make a\nbroader and more even taxonomic sampling, as well as providing sufficient evidence to\nexclude potential HGT that disguise as duplication from outside the selected lineages.\nPlease do pay attention to potential biases in genome sizes and gene counts when making\nquantitative comparisons.\nResponse: We thank the reviewer for their comments. We would like to emphasize that the\npurpose of our study is not to provide an exhaustive catalogue of HGT in Asgards, but rather\nprovide a comparative assessment of the relative impact of HGT with respect to duplication, in\nthis group. The reviewer rightly points that the taxonomic scope of the phylogenetic analysis\nlimits the range of HGT that could be detected. We agree with that. However, we note that the\nlong-range (inter-domain) HGT is covered by a different, multi-step analysis that starts with\nblast searches and ends with phylogenetic assessment. In this analysis there is no limit other\nthan the entire sequence database.\nNevertheless the reviewer is also right in that the imbalance between the selected Bacteria,\nAsgard and TACK genomes in the phylogenetic dataset may affect our results. We also agree\non this point. Our choice was the result of the focal interest in Asgards and the computational\nconstraints to reconstruct large phylogenies of sufficient quality. We have now re-assessed\nthis choice considering the reviewer’s concerns, as explained below.\nWe have now crafted a new dataset of 195 genomes, comprising 32 Asgard archaea (one per\nfamily, except for the Lokiarchaeia MK-D1 family that contains both cultured isolates) and 28\nTACK (one per order, as TACK is a superphylum-level clade and selecting one genome per\nfamily would become unfeasible with 108 genomes). We complement this with one\nrepresentative per phylum for the rest of prokaryotes. In this way we significantly increase the\ndiversity of other prokaryotes, decreasing the odds of HGT being misdirected as a duplication\nThis dataset is also more balanced in terms of the Asgard-to-TACK ratio, both for the\nreconciliation approach (which uses the gene trees computed from the complete dataset) and\nthe phylomes (for which we select the 60 proteomes belonging to Asgard and TACK, and\nselect 10 seeds for Asgard and 10 for TACK to perform 20 phylomes). .\nWe are aware that Asgard archaea have significantly larger genome sizes than TACK (in fact,\nthey have significantly larger genome sizes than any other archaeal phylum), as this fact\ntriggered our question about their genome evolution. We want to note that when comparing\nduplications, we normalize by using the duplication rate (that is, number of duplications divided\nby number of gene families present), not the absolute number of duplication events, as we do\nfor the transfer rate, to account for differences in genome size. We tested normalizing the\nduplications and transfers by speciations, as performed in\n(https://doi.org/10.1101/2025.11.11.687807), with no effect in the conclusions.\nAdditionally, to minimise potential HGT that disguise as duplication, we now remove the basal\nnodes for each clade (Asgard and TACK) from the nodes we consider for the duplication rate\nassessment.\nFinally, the reviewer mentions that we base our conclusions on the TACK-versus-Asgard\ncomparison, irrespective of other prokaryotes with large genomes. This is correct and this was\nexactly our purpose. In this work we are not addressing what mechanisms render large\ngenomes across prokaryotes (an interesting question but out of our scope), but rather\nspecifically on how Asgards specifically enlarged their genome. That is why we consider that\ncomparison to the closest sister group is the best approach here. More specifically we address\nthis question in relation to how this change in genome evolution may relate to the origin of\nEukaryotes. We hope the reviewer understands our focus and agrees that a global comparison\nof duplication rates across prokaryotes is not necessary here. We now rewrote part of our\nintroduction to better stress the purpose of our analyses and the adequacy of the datasets\nused.\nOther major concerns:\nNovelty of the paper: 1) On the topic of gene duplication in Asgard archaea, Vosseberg et al\nauthor, has specifically highlighted such a case. It is strange to me that the authors did not refer\nto this paper. In this 2020 aper, it was already concluded that Asgard archaea exhibited\nmost duplications, so I suggest that the authors discuss the current work in the context of their\nprevious paper, and state clearly what is new and different.\nResponse: The focus of the Vosseberg et. al. paper and this one are totally different. In that\npaper only duplications within the Last Eukaryotic Common Ancestor (LECA) lineage are\nconsidered. So the reference to Asgard in that paper relates to the origin of these eukaryotic\nlineages, and all quantified duplications occur within eukaryotes, not in Asgard. This work, in\ncontrast, focuses on Asgard evolution and the duplications tracked are within the Asgard\nlineage and therefore distinct from the ones reported in Vosseberg et. al. This is why that\narticle was not mentioned, and we do not think that study detracts any novelty from this one.\nNevertheless, we now comment on that finding, as we think is interesting to discuss that the\nduplication trend in Asgard genes is maintained in their descendants within eukaryotes.\n2) The main conclusion for the HGT side seems to be that the metabolic functions are more\nprominently involved, which does not seem too surprising. Beyond that, the authors seem to\nfollow with various detailed descriptions, which I do appreciate but unfortunately show little\nimplications for eukaryogenesis. Can the authors perhaps provide more insights into the\nfunctional consequences of those HGTs and potential link to eukaryogenesis?\nResponse: We believe that the conclusion that HGT predominantly involves metabolic\nfunctions is, on itself, a result with implications for eukaryogenesis. Given that, for the\nreconstructions of the Last Eukaryotic Common Ancestor, the gene families with metabolic\nfunctions seem to be predominantly from bacteria, whereas the archaeal contribution is\nenriched in informational genes (see https://doi.org/10.1101/2024.10.14.618062), the fact that\nmost HGT in Asgard archaea is metabolic, combined with the notion that we did not detect\namong these HGT genes any that we could confidently trace to LECA leads us to believe that,\nfor the families in LECA whose origin can be traced to bacteria outside alphaproteobacteria,\nwe have to imagine scenarios outside HGT to Asgard and subsequent vertical inheritance in\neukaryotes through the Asgard ancestor, and therefore lend likelihood to direct transfers to the\nproto-eukaryote. We now better stress this in the revised manuscript.\nThe taxonomic sampling is also a little strange: Even with as few as 61 or 63 genomes, the\nauthors selected MAGs that belong to the same species or genus: two Baldr from\ng__DXJG01, two Heimdall from g__UBA460, 3 Hermod from g__Hermodarchaeum, 3 Hod\nfrom g__Hodarchaeum, two Jord from g__DTBI01, two Odin from g__LCB-4, 2 Borr from the\nsame species s__Borrarchaeum weybense, two Wokong from species s__Wukongarchaeum\nyapensis. Same goes for two g__Nitrosopumilus. In addition, is there a reason not to use\ncircularized Freya/Atabey/Heimdall genomes available for analyses? In the SI table file, I\ncan only find the information for the Asgard and TACK genomes but not the remaining 15, so I\ncannot judge for the rest.\nResponse: We agree with this criticism. The selection was made purely following genome\nquality criteria, without assessing the final taxonomic choice within a lineage. To solve this, we\nhave now performed a more taxonomically balanced sampling of Asgard genomes (one per\nfamily-level clade sensu GTDB), in addition to the increased sampling mentioned above. We\nassigned each assembly a quality score (CheckM completeness - 5*CheckM contamination)\nand selected one genome per family, with the exception of the replacement of f__MK-D1\nrepresentative with the isolates Ca. Prometheoarchaeum syntrophicum and Ca.\nLokiarchaeum ossiferum. We have additionally taken note of the reviewer’s comment and\nadded the highest quality circularized Heimdall genomes from the two available at (PMID:\n35027677), the highest quality Atabey and the Freya from (PMID: 39406503).\nWe now include the associated GTDB metadata for all the selected genomes (bacterial and\narchaeal), along with their quality score, in Supplementary Table 1.\nThe authors suggest that there is higher rate of intra-Asgard HGT of ESPs than other gene\nfamilies. Given that most of the ESPs only exist in the Asgard archaea, could this also be\nstatistically biased?\nResponse: When comparing the transfers of ESPs vs the general background of transfers, we\nonly take into account the transfer events that have occurred within Asgard nodes. Therefore\nthat they are Asgard-specific should not be a bias as we only compare with proteins present,\nand sum the number of events across all Asgard nodes per OG. Additionally, given that the\nnumber of OGs per category (ESPs vs non-ESPs) is very different, we apply a permutation test\nrather than a t-test or Wilcoxon rank sum test, which we also hope alleviates the potential\nstatistical biases.\nOther minor and general issues:\nThe two parts of the paper used different approaches. Have the authors cross-compared the\nduplication events concluded from the method 1 with the HGT events concluded from method\n2?\nResponse: The different HGT-detection approaches have different target scopes and are\nnecessarily different as explained above. One of the methods (reconciliation) jointly predicts\nboth HGT and duplications. When two alternative approaches can be used in the same dataset\n(as species-overlap and reconciliation based duplication inference) we compare results. The\nreviewer seems to ask for a comparison of results from a duplication-inference and HGT\ninference. We have now done this comparison. The HGT events from method 2 affect 980\nOrthogroups that, on average, have a significantly higher number of duplications (p = 1.0413e-\n67) as well as transfers (p = 2.2000e-16). We now report these results.\nLine 321, PMIDs, replace with real reference.\nResponse: We apologize, this is now corrected.\nLine 269-271, please consistently use Desulfoproteobacteriota for SRBs.\nResponse: We have clarified this in the manuscript.\nLine 340-343, This is interesting. who are the hosts of those viruses? Can you provide some\nclear examples?\nResponse: They belong to phylum Nucleocytoviricota (Algavirales, Imitervirales,\nPimascovirales) which mainly infect microbial eukaryotes of varied phylogenetic affiliations\n(amoebozoa, and other protists). We now discuss this finding and provide the trees and the\ntaxonomies of the viruses in the data repository.\nLine 254: (D)->(E)\nResponse: This has now been corrected.\nReviewer #2 (Remarks to the Author):\nIn this work, Manzano-Morales and Gabaldon performed a comparative phylogenomic\nanalysis of Asgard archaea and their sister group TACK and found that genome evolution in\nAsgard archaea is characterized by high rates of gene duplication and horizontal gene transfer\n(HGT). Such blend of prokaryotic-like HGT rates and eukaryotic-like gene duplication is a\nremarkable finding that, in my opinion, will be of interest to many potential readers of\nNature Communications.\nThe analysis is comprehensive and thorough, and the authors made a great effort to\nensure that their findings are not due to contamination or genome incompleteness by\napplying rigorous filters and combining multiple lines of evidence. There are some details\nthat require clarification or could be better explained (see below), but I do not think that\nthese compromise the integrity of the manuscript.\nResponse: We thank the reviewer for the appreciation of our work, we now made an effort to\nclarify the points raised by the reviewer.\nSome general comments and questions:\n1. I wonder if the authors could go one step further and provide some insight into the\nevolutionary regime that characterizes genome evolution in Asgard archaea. To\ncontextualize this comment: Most bacterial genomes evolve in a regime of deletion-driven\ngenome reduction, such that useful genes are maintained by selection. Some\nmanifestations of that regime are a positive association between genome size and\neffective population size, and a scarcity of psedogenes in bacterial genomes. Instead,\neukaryotic genomes evolve in a non-adaptive regime driven by gene duplication and the\nproliferation of transposons and other mobile genetic elements. Such non-adaptive\nregime results in a negative association between genome size and effective population\nsize, as well as accumulation of noncoding DNA. Estimating the effective population size is\ncomplicated, possibly unfeasible if only one high-quality genome per species is available.\nHowever, it would be interesting to use the ancestral reconstructions to investigate how\ngenome size has changed along the evolution of Asgard archaea and compare it with\nthe TACK group. Have high duplication rates resulted in a net increase in genome size?\nWere duplications compensated for by higher rates of gene loss? If there has been a net\nincrease in genome size, did it result from higher numbers of functional genes or from\npseudogenes and mobile genetic elements? In that regard, it may be worth assessing the\nabundance of pseudogenes and the rates of pseudogenization and pseudogene decay.\nResponse: The reviewer points out a very interesting biological insight that can be inferred\nfrom the reconciliation analysis, and we thank them for the suggestion. As the reviewer\nmentions, it is unfeasible to estimate population sizes in the Asgard clade, given the scarcity of\ndata. We can, however, assess the genome size in relation to the duplication rate across both\nthe TACK group and Asgard group. We now show these results in Supplementary Figure 2,\nand discuss them in a new section in the Supplementary Discussion. In brief, we see that high\nduplication rates tend to result in a net increase in genome size. Number of duplications and\nnumber of orthogroups present tend to linearly correlate both for TACK and for Asgards (but\nless so for TACK), but the number of duplications is not correlated with the number losses for\nboth cases.\nAs for the comment on Mobile Genetic Elements (MGEs), we analyzed the copy number of\northogroups associated with MGEs (see Supplementary Methods) and found that Asgard\narchaea tend to have more copies than TACK. We have now added this information to the\nmanuscript.\n2. I found it surprising that there were no signs of transposons, proviruses, and other\nintegrated mobile genetic elements among the duplicated and horizontally transferred\ngenes. Is the lack of MGE duplication and transfer a consequence of the conservative filters\napplied to identify HGT and duplications or is it a genuine finding?\nResponse: For the functional analysis of duplicated genes, we have based our conclusions in\nthe KEGG Pathways. These unfortunately do not have a particular pathway for MGEs and\ntherefore they would go undetected in our enrichments. When analyzing the COG categories\nof the Orthogroups, however, we see that X (mobilome) is the highest category after U\n(Intracellular trafficking, secretion and vesicular transport) with a mean of 6.68 duplications\nacross Asgard nodes for these orthogroups. We now comment on this in the article. Also,\nrelated to the previous question, these MGEs seem to accumulate more in Asgard than in its\nsister clade Thermoproteota.\nFor the HGT genes, we could add GO term enrichment, which does include terms for\nintegrases and trasposons, some of which are enriched in respect to the background of their\nrespective lineagesdoes not pass FDR correction. We also see an increase in the proportion of\nthe COG category X in HGT genes related to the genomic background for some lineages.\nSome points that need to be clarified or better explained:\n3. Lines 69-73. After reading this paragraph it is not clear if those MAGs were used or not.\nResponse: We mention that MAGs can suffer from genome incompleteness and this might\naffect the downstream conclusions obtained from their analysis. However, there is a scarcity of\ngenomes available in the Asgard clade that come from cultured isolates, which forces\nanalyses on Asgard archaea to use MAGs, therefore, we do use them. However, being aware\nof this possible caveat, we performed the same analyses on alternative datasets (chosen at\nrandom, instead of following quality criteria) to ensure robustness of the results.\n4. Lines 85-86: How is it that only 17K out of 31K ***Asgard archaeal*** OGs contain at least 1\nAsgard representative? What about the other 14K? Perhaps I am missing something: what do\nyou exactly mean by Asgard representative?\nResponse: The reviewer is right, this was an error on our part in the writing of the manuscript.\nThere are 31K orthogroups total, out of which 17K contain at least 1 Asgard representative, the\nrest of them being orthogroups formed by representatives of the other bacteria and archaea in\nthe dataset. This has been corrected in the new version of the manuscript.\n5. Lines 91-92: Did you only include KEGG pathways or also KEGG ontologies?\nResponse: We annotated each protein with the KEGG Ortholog (KO) assigned by eggNOG-\nmapper, calculated the majority KO per orthogroup (see Methods) and performed a KEGG\nPathway enrichment analysis of these KOs. So, in this sense, for the functional enrichment we\ntook into account KEGG pathways.\n6. Lines 108-109: The finding that Chao's lower bound and core genome size are sensitive to\ngenome incompleteness is not surprising. Chao's lower bound depends on the number of\nsingletons and OGs with 2 members, hence it is a not quite robust estimator. As for the core\ngenome, the reasons are trivial (but see for example PMID:37904249 for a quantitative\nassessment). In the light of these results, it may be worth running mOTUpan on both sets of\ngenomes to get a more robust estimate of the core genome size and pangenome fluidity.\nResponse: We thank the reviewer for the suggestion. We have now included the analysis of\nmOTUpan in the paper.\n7. Line 110: I am not sure of what you mean by the \"full set\". Do you mean all available MAGs?\nIt has been shown that orthofinder performs quite poorly when applied to incomplete MAGs. I\nalso wonder if the analysis of the full dataset is really necessary: given the openness of the\npangenome and looking at the rarefaction curve in Fig 1 it is sufficiently clear that there are a\nlot of Asgard archaeal genes yet to discover and that non-HQ MAGs will contain genes that are\nnot present in the representative set.\nResponse: The ‘full set’ refers to the full collection of species representatives available in this\nversion of GTDB for Asgard archaea. We agree that MAG incompleteness could cause biases\nin the estimates, that is why we mainly considered the set of high-quality representatives and\nonly used this ‘full set’ as a complementary analysis. We have removed this part from the\nmanuscript.\n8. Figure 1A: This plot would be more informative if it used a logarithmic scale for the y-axis.\nResponse: Thank you for the suggestion, we have now modified the figure accordingly\n9. Lines 120-121: Here \"entire genome set\" means the representative HQ genomes, right?\nWhich is not the same as the \"full genome set\" mentioned in line 110. That is confusing.\nResponse: The reviewer is correct, it means the set of HQ genomes. We apologize for the\nconfusion and have re-written this in the manuscript for clarity.\n10. Figure 1C: What is the hub of the PPI network (K12158)? What are the proteins that form\nthe 2 triangles?\nResponse: K12158 is an ubiquitin-like protein (Nedd8). For clarity, we have now labeled the\nnodes with their protein name, rather than the KO ID. As we have increased the dataset, the\nnetwork has changed slightly.\n11. Line 129: In the absence of a null model, to say that the network is highly connected is quite\nsubjective. Less than half of the proteins have interactions and, if we only look at those, more\nthan half of the interactions have a low score (around 200).\nResponse: We agree with the reviewer that the statement can come across as subjective. We\nhave now performed a PPI enrichment score of the protein set against the full set of PPIs in the\nproteome as implemented in STRING.\n12. Line 187: I am not sure of what you mean by \"a value of 1 would indicate a whole genome\nduplication\". In the figure there are cases of 10: I do not suppose that those represent 10 whole\ngenome duplications, right? Adding to the confusion, the caption says \"mean number of\nduplications per node\" but the label of the x-axis says \"mean number of duplications per OG\".\nThat requires clarification.\nResponse: Figure 2B represents the frequency distribution across orthogroups of the number\nof duplication events per orthogroup. A value of 1 implies that the given orthogroup has\nduplicated on average once in the Asgard clade, whereas 10 means a mean number of 10\nduplication events. The reviewer is right that a value of 1 does not mean a whole genome\nduplication: this was a mistake on our part from previous versions of the figure when Figure 2B\nrepresented the mean number of duplication events per node (not per orthogroup).We\napologize for this mistake and have now corrected the text.\n13. Figure 2: What are the rate units for the duplication and transfer rates? Events per unit of\ntime (what unit of time)? Per gene? Per genome?\nResponse: The duplication and transfer rates are calculated as the total number of\nduplications, or transfers, relative to the number of OGs present at the given node. We also\ntested normalizing by speciations, as in https://doi.org/10.1101/2025.11.11.687807, with no\neffect on the comparisons versus its sister clade. This is now clarified in the text.\n14. Figure 2E: Do the distributions correspond to nodes or OGs? Equivalently, do the tails of\nthe distribution correspond to OGs that duplicate a lot or genomes with a lot of duplications?\nResponse: The distributions correspond to duplication rates per node, that is, number of\nduplication events relative to the total number of orthogroups present at that given node. The\ntails represent nodes in which a lot of duplication events have occurred. This is now better\nclarified in the text.\n15. Figure 2F: What is the definition of verticality?\nResponse: We have now removed this panel, as the information it provides was redundant\nwith the duplication rate and transfer rate (Fig 1C,D) and created confusion.\n16. Figure 2G (the caption says F): Something looks wrong with the boxplot and overlapping\nscatter plots. The \"other\" category is clearly not normally distributed, so it would probably be\nbetter to use logarithmically transformed data. The same applies to the statistical test: did you\nuse a robust test or transformed data? What are the effect size and p-value? These are\nimportant details that must be disclosed (not only for panel G, but also for panels C, D, F and\nFig 3E). The biological implications (higher transfer of ESP than other genes), if confirmed by\nstatistical analyses, are certainly relevant.\nResponse: We were using Wilcoxon rank sum test as the data was not normally distributed.\nTo ensure that we are performing the correct tests for statistical significance of the data, we\nhave now selected the appropriate test using the R package “groupcompare”. We have now\nincluded the information about p-values and effect sizes in the text. We agree that this result is\nrelevant and we highlight this more now in the manuscript.\n17. Lines 193-196: When comparing the duplication rate of lineage signature proteins within\nand outside the lineage, did you control for the lineage-wise duplication rates? Did lineage\nsignature proteins also duplicate more than other (non-signature) OGs within the same\nlineage?\nResponse: We did not. We appreciate this point. In the updated version, we normalize the\nduplications each of these OGs suffers per node by the duplication rate of that given node. We\nthank the reviewer for noting this.\n18. Line 232 and below: Do the methods used to infer inter-domain HGT account for the fact\nthat proteins can evolve at very different rates in bacteria, archaea and eukaryotes? Would it\nbe possible to obtain reliable estimates of the fraction of false positives?\nResponse: Our method for the inference of inter-domain HGT events is based on the topology\nof the gene trees. For the inference of the trees, we incorporate into the model selection\nmixture models that take into account site heterogeneity, with which we expect to alleviate\nLong Branch Attraction, which could be an effect of the differential evolutionary rates. We\ncannot think of a way to estimate the fraction of FPs, and the analysis we apply are the state of\nthe art in the field.\n19. Figure 3: Do all panels in Fig 3 refer to Bacteria-to-Asgard HGT?\nResponse: All panels correspond to inter-domain HGT. Figures A, B and C do not differentiate\ndirectionality (both Bacteria-to-Asgard and Asgard-to-Bacteria are considered) whereas D and\nE, as they involve analysis within the Asgard proteome, deal only with Bacteria-to-Asgard\ntransfer events. We use ‘partner’ throughout the text when directionality is not considered, and\n‘donor/acceptor’ when it is. This is now better clarified in the text.\n20. Fig 3A: what does \"contribution of different seeds\" mean?\nResponse: We analyze these HGT events starting from a set of ‘seed’ genomes, from which\nwe pre-select candidate proteins of HGT origin and then build a gene tree based on a BLAST\nsearch of that protein. Therefore, the gene trees are seed based. The pie chart represents the\nproportion of proteins from each given ‘seed’ proteome whose gene trees point to HGT events\noccurring at that given node. We wanted to confirm that the HGT signal for the internal nodes\ndoes not come from only one lineage, but rather, that it is backed by the signal in several\nproteins independently.\n21. Figure 3D: Besides the KEGG and KO terms, it would be helpful to see a higher-order\nclassification of the transferred genes, for example in terms of COG categories.\nResponse: Thank you for the suggestion. We have added this in the new version of the\nmanuscript (Figure 3)\n22. Fig 3E, bottom panel: Why are the degrees always <1? Does it mean that most proteins\nhave degree 0 (no interactions)? But then, how is it that in the top panel the Q25 is already well\nabove 0?\nResponse: Given that there are less HGT proteins than proteins of vertical inheritance, we\nnormalized the degrees by the total number of each category. We have clarified this in the\nlegend and apologize for the inconvenience.\n23. Line 266: Figure 3C would be more informative if instead of the number of taxa it showed\nwhich taxa acted as donors. That information is not currently shown here or the supplement,\nalthough some donor taxa are mentioned in the main text. If adding that to Fig 3 is not feasible,\na summary table in the supplement would work too.\nResponse: Thank you again, we have now modified it to show the main contributor phyla\n24. Line 294: The degree is only one of several possible measurements of centrality. Have you\nchecked other centrality indices, such as the eigenvector centrality?\nResponse: We have added eigenvector centrality and betweenness to the analysis. They\nyield qualitatively the same results. We add this to Supplementary Figure 7.\n25. Line 303: Based on phylogenomic reconstructions, did the transfer of these enzymes lead\nto the replacement of a preexisting enzyme carrying out the same function? Otherwise, did\nHGT lead to qualitatively new or broader metabolic potential?\nResponse: Based on manual analysis of the KEGG pathways with KEGG mapping, we saw a\nmixture of new metabolic potential and functional redundancy.\nTo perform this analysis in a more systematic way, we have now assessed the KOs brought by\nthe HGT events (Bacteria-to-Asgard only) into the genome and found that, on average, around\n40% of the KOs found in HGT genes are also found in a copy with vertical inheritance in the\nsame genome, suggesting at least a degree of functional redundancy. Despite this, the\nmajority of them still suppose novel KOs.\nTo go deeper into the extent to which these novel KOs constitute novel metabolic capabilities,\nwe took the KOs and estimated the metabolic pathways they might form with anvi’o (anvi-\nestimate-metabolism). The resulting modules have a mean pathwise completeness of 0.17(of\n1), reinforcing the view of an overall addition to parts of existing metabolic pathways rather\nthan the addition of whole new metabolic pathways to the organism. Nevertheless, we found,\nsome modules in P. syntrophicum and L. ossiferumthat could be considered stepwise\ncomplete and are comprised entirely of KOs of HGT origin. These new results are now\ncommented on in the text.\nOnline Methods\n26. Line 26, regarding the lowest CheckM contamination criterion: Was it only applied to break\nties? Did you apply any hard threshold on the contamination score? Contamination would\nresult in apparent duplication and HGT, so, for these purposes, minimizing contamination\nseems more important than maximizing completeness.\nResponse: We agree with the reviewer and we now used an index that better balances\ncompleteness and contamination, based on the quality score of the GTDB (PMID: 38512750)\nwhich is CheckM completeness - 5*(CheckM contamination).\n27. Line 41: How did you harmonize the need to work with homogeneous genome annotations\n(to control for biases and annotation-related confounding factors) while keeping the original\ngenome annotations?\nResponse: We apologize for the confusion this may have caused. As for ‘genome annotation’\nwe mean the detection of protein-coding genes within a genome, for which Prokka is a widely\nused tool. As for the functional annotation of these protein-coding genes, we re-annotated\nthem from scratch by mapping them to eggNOG OGs with eggNOG-mapper and to GO terms\nand Pfam domains with InterProScan, disregarding any annotations performed by Prokka or\nother tools so that all of them were annotated by the same method. As for the gene calling, we\nhave harmonized this by annotating all the genomes with Prokka. We would have liked to take\nthe protein files from GTDB directly, but unfortunately they do not provide the GFFs we need\nfor the genomic context analysis.\nWe compared the number of called proteins with the number of proteins in the FASTA files\nfrom GTDB, resulting in a median difference of -47.00 proteins, or around -1.94% (relative to\nthe number in the GTDB file). Given that the GTDB annotation is done by Prodigal v2.6.3 and\nProkka uses Prodigal under the hood, we expect the differences to stem from the fact that\nProkka applies various filtering to its Prodigal results, and has a mode more tailored to\nmetagenomes, and thus we believe the Prokka annotation to be more accurate.\n28. Line 51: Please, provide a brief summary of the annotation workflow (Ref 33).\nResponse: We now provide a brief explanation in the text, after the citation.\n29. Line 53: FLUH has not been defined. Furthermore, this paragraph is quite cryptic. What\ndoes it refer to?\nResponse: We have now clarified that FLUH refers to “Free-Living Unicellular Heterotroph”.\nWe thank the reviewer for pointing this out as it may be confusing.\n30. Line 59 and below: Were the alignments and trees based on nucleotide or amino acid\nsequences?\nResponse: We only employed amino acid sequences for all phylogenies.\n31. Line 176: Did you check that putative instances of tandem duplications are not due to\nfragments of the same gene interrupted by a stop codon (either real or resulting from a\nsequencing error)?\nResponse: That is theoretically possible, although we expect it to be rare. For this to happen,\nboth fragments should have hits in BLAST to the same protein from the seed genome A which\nare in the top 150 hits that pass e-value < 1e–05 and a continuous overlap over 50% of the\nquery sequence. To check for this, we assessed whether the two proteins in the tandem have\ntheir hit with e-value < 1e-5 and continuous overlap >= 50% in the same range of the query\nprotein. For the cases with overlap, we assessed what percentage of the total alignment with\nthe query protein (with respect to the shortest alignment of the two) they represent. We found\nthat there’s only 4.28% where there’s no overlap, and when there’s overlap, the median\noverlap is 94.93% (sd 10.57). We filter out cases where this overlap is <= 50%, to err on the\nside of caution.\n32. Extended Data Figure 3: The distribution of the number of transfers (upper left) would be\nmore informative if it was plotted in log-log or log-linear scale.\nResponse: Thank you for this suggestion. We have modified the figure accordingly\n33. Line 311: This caption refers to folate metabolism, not carbon metabolism.\nResponse: Corrected\nTypos\n- Line 264: Data Figure 4A (instead of 3C).\nResponse: Corrected\n- Line 345: High (instead of highly).\nResponse: Corrected\n- Online methods, lines 68-69: the sentence in parentheses is repeated.\nResponse: Corrected\nReviewer #3 (Remarks to the Author):\nThe paper presents the results of a phylogenomic study focused on Asgard archaea. The main\nfindings relate to patterns of gene duplication and HGT and their functional and genomic\nimpacts. The findings are interesting and shed light on some unique aspects of Asgard\nevolution with implications for understanding eukaryogenesis. My comments primarily have\nto do with thoroughness of the analysis and appropriateness of the methods used.\nResponse: We thank the reviewer for the insightful comments on our work.\nMajor comments:\n- There are several cutoff thresholds used throughout the paper but these seem to have\nbeen chosen somewhat arbitrarily. As an example, Asgard signature proteins are defined\nusing thresholds of at least 70% presence in Asgard and at most 15% presence in other\narchaea or bacteria. Why were these specific thresholds chosen and how do results/findings\nchange if other reasonable thresholds, such as 80% and 10%, are used instead? The same\napplies to the thresholds used on lines 139-140.\nResponse: We agree that the cut-off thresholds may come across as arbitrary, we now justify\nthe choice better and have changed one of the thresholds. Pangenomes tend to have an ‘U’\nshape, where most gene families are either very rare or very common. The threshold for the\npresence ‘outside’ (15%) corresponds to that typically defined as the cloud component of a\npangenome. For the presence ‘inside’, since the estimation of the core component of the\npangenome is sensitive to genome incompleteness, we assessed the prevalence of\northogroups annotated as the COG category “J” (Translation, ribosomal structure and\nbiogenesis) and OGs annotated as KOs from archaeal ribosomal proteins. We used the\nprevalence of these categories, which we assume to be housekeeping genes, to select the\nthreshold of 86 instead of 70% for what we consider ‘widespread’ in the Asgard clade (see\nSupplementary Methods).\n- It is mentioned that two different species phylogenies, constrained and unconstrained,\nwere generated and used for HGT and gene duplication analyses. However, it seems that\nresults are only presented for one of those phylogenies. Which phylogeny was used for\nthe reported results? And how do findings change when the alternative phylogeny is used?\nResults should be shown for both phylogenies, and the phylogenies themselves should be\nprovided as well.\nResponse: The results shown are the ones for the topology that follows the species tree for\nEme et al. (2023). However, since the Asgard species tree is a highly debated and still\nunresolved topic, we tested the robustness of the conclusions by employing an alternative\ntopology for the Asgard clade, for which we used the GTDB archaeal tree topology. In this\nversion of the manuscript, we could not let the topology fully unconstrained as it broke the\nmonophyly of Thermoproteota. The results for the alternative topology are qualitatively similar.\nWe have added them as Supplementary Figure 1.\n- It is mentioned that two methods were used for duplication analysis, but results are\nreported only for the reconciliation-based method. Results should also be shown for the\nother (species overlap) method. Are all reported results supported by both methods?\nResponse: We assessed the correlation between duplication rates for the reconciliation and\nspecies-overlap approach and found them to be correlated, and the results to be qualitatively\nthe same. We now add the resulting plot for the species-overlap method as a supplementary\nimage (Supplementary Figure 3) so readers can compare.\n- Same as above, there also appear to be multiple methods used for HGT inference. Are all\nreported results consistent between these different analyses (e.g., reconciliation-based and\nnon-reconciliation-based)?\nResponse: We believe the results for the two approaches for HGT inference are\ncomplementary, yet not directly comparable. For example, when we apply the search against\na broad-DB and posterior phylogenetic pipeline, we are exclusively looking for inter-domain\n(Bacteria-Asgard) HGT events, and therefore we consciously overlook intra-Archaea or intra-\nAsgard HGT. On the contrary, due to the relatively scarce sampling of bacteria for the\nreconciliation approach, we focus on intra-Asgard events, as Bacteria-Asgard transfers are\nlikely underestimated due to scarcity of bacterial genomes. However, we did see that the OGs\nto which the proteins flagged as HGT belong have a significantly higher number of transfer\nevents than the rest of OGs.\n- The AleRax tool is used for reconciliation-based analysis. It appears that the species tree\ngenerated by AleRax was used to perform this analysis. Do we now have a third species\ntree that is different from the constrained and unconstrained species phylogenies used for the\nother analyses? If so, this needs to be clearly stated in the paper. And how do results change if\nthe two previous species phylogenies are used for this reconciliation analysis? The use of\nmultiple species trees makes it all the more important to clear report how findings change as\ndifferent reasonable species trees are used for the analysis.\nResponse: We apologize for the misunderstanding. The AleRax analysis was performed with\nthe Eme et al. (2023) topology, and with the ‘alternative’ topology built from the GTDB archaeal\ntree. There is no third species tree topology. We now better clarify this in the manuscript.\n- Some of the results, such as elevated duplication rates at the base of the Asgards, may be\nartifacts of errors in the gene trees. Paying further attention to gene tree quality may help\nresolve some of these artifacts.\nResponse: We agree that the duplication rates at the base of Asgard may be inflated. To\nminimize the impact of these artefacts, we now have removed the MRCA of Asgard and\nThermoproteota, respectively, from the duplication rate analysis. Moreover, we expect that\nwith the increased size in the dataset these errors are diminished, as we increase the sampling\noutside the Asgard group.\nAs for the tree quality comment. Given that AleRax considers the breadth of bootstrap trees in\nits analysis, we think that the variability in gene tree quality is inherently taken into account (the\nmore noisy trees the lower bootstrap and variability across bootstrap trees).\n- In the time of acquisition analysis of Lines 259-264, using last common ancestors of all\ngenomes carrying the transferred gene is almost guaranteed to provide incorrect time\nestimates since such genes may have been acquired multiple times or may have been\ntransferred again after initial acquisition. A much more accurate way would be to directly use\nthe reconciliation to estimate when a gene was first acquired.\nResponse: The computation time for AleRax is roughly cubic to the number of species. Given\nthat the database we employ for the homolog search of putative HGTs in Asgard contains all\nspecies representatives of GTDB v202, as well as eukaryotic species, this becomes\ncomputationally prohibitive. Moreover, our aim when analyzing the HGT events in Asgard is to\nassess how much of their genome is comprised of genes of HGT origin and the transfer\npartners. We agree that the last common ancestor of all transfers (i.e. maximum parsimony)\nmight not be totally accurate. However, given the computational barriers of using reconciliation\nat this scale we think it is a good proxy. Moreover, the only conclusion we infer from this\ntemporal analysis is that transfers have occurred as a continuum rather than in a single wave.\nWe do not think a reconciliation analysis would change that qualitative conclusion in any way.\nNevertheless, we now discuss the limitation of the Maximum Parsimony assumption.\n- Line 87: where are the 8365 OGs found only in a single Asgard genome coming from? A\nBLAST search against a larger collection of prokaryotic and eukaryotic genomes could help\nresolve this question.\nResponse: This is an interesting question, but as we performed this analysis for a broad\ncharacterization of the Asgard gene repertoire, we felt that an in-depth analysis of the\nsingletons was beyond the scope of the paper.\n- Line 83 of online methods supplementary text: why are only the top 150 hits chosen per\nprotein? Artificial cutoffs such as this can result in under-clustering, leading to significant\nerrors in downstream analyses and especially in reconciliation. This analysis should be\nrepeated with a more biologically grounded approach for identifying gene families.\nResponse: This cut-off is due to constraints of the phylome approach and the computational\nload of the metaphylome, this threshold is justified elsewhere (PMID: 34718760). We agree\nthat it is not ideal. In this version of the manuscript, while we maintain this cut-off as part of the\nphylome pipeline, the trees for the reconciliation step are built from OrthoFinder orthogroups,\nproviding a complementary source of information that lessens these biases.\n- Lines 95-101 of online methods supplementary text: The description of how and on what data\nAleRax was run is unclear. What is meant by “we selected one sequence per OG present in the\nAsgard clade”?\nResponse: Initially, the gene trees employed by the reconciliation were chosen among the\ngene trees calculated by the phylome. We have now changed this and calculated the gene\ntrees directly from the OrthoFinder OGs. We have improved this section of the methods so it is\nclearer.\nMinor comments:\n- Increase font size of node annotations in Figure 1.\nResponse: We have corrected this in the manuscript\n- Typo on line 345, highly dynamism.\nResponse: We appreciate the reviewer for pointing out this typo. It has been corrected in the\nmanuscript\n- Lines 133-134: why were only 39 out of the 83 previously identified proteins found? Is it\nbecause only a subset of available Asgard genomes was used in the analysis?\nResponse: Our intuition is that the rest of the proteins were not found due to the search being\nconstrained to a subset of genomes, yes. With the new set of genomes, we have found more\n(44). We have additionally relaxed our score criteria to encompass those that may have been\npriorly overlooked due to poor scores.\n- Lines 146-147: Why not use all 31 Asgard genomes for this analysis? How were the 12\ngenomes chosen?\nResponse: We restricted the set of genomes out of computational constraints. We selected\nthe highest-quality genomes while maintaining a taxonomic balance.\n- In the analysis of tandem duplications (Lines 198-208), were phylogenetic relationships\nbetween the genomes considered? Ancestral tandem duplications would be inherited by\nmultiple genomes and may be counted multiple times, possibly impacting the results shown in\nExtended Data Figure 2.\nResponse: Extended Data Figure 2 (now Supplementary Figure 5) represents the percentage\nof proteins in an organism’s proteome that have been flagged as a tandem duplication,\nby dividing the proteins in the GFF into two categories. Therefore each protein is counted only\nonce per genome, irrespective of the number of duplication nodes it appears in. Out of this\npercentage per proteome, a given proportion will be shared with other proteomes and the rest\nwould be specific, but as we are seeing the proportion per proteome, each protein is counted\nonce per genome, irrespective of the number of duplication nodes it appears in. Out of this\npercentage per proteome, a given proportion will be shared with other proteomes and the rest\nwould be specific, but as we are seeing the proportion per proteome, each protein is counted\nonly once\n- Line 59 of online methods supplementary text. Which markers were used?\nResponse: We used the markers employed by GTDB to build their archaeal taxonomy.\n- Line 142 of online methods supplementary text. There are better rooting methods than\nmidpoint rooting. The authors could consider using Minimum Ancestor Deviation (MAD)\nrooting.\nResponse: We have taken this into account. We tested both rooting methods and found an\noverlap of 94% in the genes we detect as HGT. In any case, we take note of the reviewer’s\nadvice and use MAD rooting in the resubmitted version. We have added a section in the\nSupplementary Methods to discuss this."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed the previous comments and made substantial improvement to the analyses and clarification of the paper. I have only a few minor comments on the revised manuscript:\n1. I recommend that the authors refer to the original articles reporting the 32 representative Asgard archaeal genomes.\n2. The authors used Em et al 2023 tree for reconciliation, does it indeed include all the species used in this study?\n3. Note that Deltaproteria is now designated into various Desulfobacterota clades in GTDB, Desulfoproteobacterota-> Desulfobacterota. Please be consistent throughout the paper.\n4. line 431, 'high dynamism in Asgard genome'. It is unclear what it means, please rephrase.\n5. Figure 3A. What do all the colors of the pie charts stand for? what is Njordarchaeia.1?\n6. Please properly label the species names and accession numbers on the tips of phylogenomic trees in Fig. S1, S3 and S6.\n(Remarks on code availability)\nThe page does not seem to be accessible at the moment.", "reviewer_2": "Most of my previous comments have been addressed in the revised version. I only have a minor request:\nCould you please comment on the (putative) functionality of duplicated genes? Is there any evidence of pseudogenization (e.g. truncating mutations, high variance of ORF lengths)? Are they subject to positive, relaxed, or negative selection? With bacteria in mind, one would expect that, if duplicated genes have persisted for a long time, they are probably functional (simply because pseudogenes are quickly eroded in bacteria). But these are not bacteria and it seems that gene loss is less frequent than gene duplication/gain in Asgard, so it might be that persistence does not imply functionality.\nOther than that, I detected some typos and would suggest some edits for clarity:\nl. 77: I would suggest to add some connector in the beginning of this paragraph to stress that restricting the pangenome to 32 high-quality Asgard genomes was a necessary compromise to circumvent the potential problems of working with a larger set of incomplete MAGs. One can figure it out from the context, but a more explicit statement would be helpful.\nl. 84: \"17,429 contained at least one Asgard protein, 12,397 were exclusively present in Asgard archaea, and 10,470 were present in a single Asgard genome and no other prokaryote in our dataset.\" Are these numbers nested within each other? I interpret that the 10,470 singletons are included within the 12,397 Asgard-only OGs, and those are included within the 17,429 Asgard OGs. Just in case, it may be a good idea to rephrase a little bit (perhaps adding \"of those\") to make that point totally clear.\nl. 86: \"figAsgard\" -> possible typo?\nl. 103: \"not in terms of core genome\" -> I find that statement confusing because the estimated strict core is 18 in both cases. Perhaps the statement refers to the observed strict and relaxed cores (but those are not reported for the random dataset) of for the mOTUpan estimates (but those are not presented until the following paragraph).\nl. 141: \"Figure 1D\" -> Figure 1C in the updated version.\nl. 188: missing space before 23.87%\nl. 202: \"Figure 2E\" -> Figure 2D?\nl. 239: \"(F)\" -> (G)\nl. 312: The pie charts appear below (not above) the nodes. The legend linking colors to seeds is missing.\n(Remarks on code availability)", "reviewer_3": "The authors have made many changes to the manuscript and have addressed most of my concerns. But there is a related issue that has not been adequately addressed: While the authors have performed additional analysis and reported their findings, some of these new findings are not well integrated into the manuscript. For example, the authors now provide the results of the reconciliation analysis using the alternative topology in Supplementary Figure 1. However, there is no discussion of these results elsewhere in the manuscript.\nThe same applies to results presented in Supplementary Figure 3.\nIt would be great if the authors could better integrate the results of these alternative analyses into the manuscript, clearly explaining which findings are supported and not supported by the alternative analyses.\nMinor comment:\nThe two topologies shown in Supplementary Figure 3 are missing leaf labels.\n(Remarks on code availability)"}, "author_response": "REVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have addressed the previous comments and made substantial improvement to the\nanalyses and clarification of the paper. I have only a few minor comments on the revised\nmanuscript:\n1. I recommend that the authors refer to the original articles reporting the 32 representative Asgard\narchaeal genomes.\nResponse: We appreciate the reviewer's comment. We note that some of the assemblies do not\nhave associated publications. For these, the BioProject is supplied in ST1. For the genomes that\nhave publications we have added the corresponding citations to the “Data Availability” section.\n2. The authors used Em et al 2023 tree for reconciliation, does it indeed include all the species\nused in this study?\nResponse: We used the Eme et al. tree for the class-level topology of the lineages, as it indeed\ndoes not contain all the species used in this study. We took the GTDB archaeal tree and re-\narranged the class-level branches to fit the Eme et al. topology, as the topology of the GTDB\narchaeal tree was somewhat discordant. We have clarified this in the text.\n3. Note that Deltaproteria is now designated into various Desulfobacterota clades in GTDB,\nDesulfoproteobacterota-> Desulfobacterota. Please be consistent throughout the paper.\nResponse: We changed “Desulfobacterota” to “Desulfoproteobacterota” upon a previous request\nfor consistent use of “Desulfoproteobacterota” for SRBs. However, we are strictly employing the\nGTDB taxonomy framework, in which the phylum name is “Desulfobacterota”. We are now using\n“Desulfobacterota” throughout to comply with the taxonomy sensu GTDB.\n4. line 431, 'high dynamism in Asgard genome'. It is unclear what it means, please rephrase.\nResponse: We have modified the text to clarify this respect.\n5. Figure 3A. What do all the colors of the pie charts stand for? what is Njordarchaeia.1?\nResponse: The colors in the pie chart represent the different seed genomes. We have colored the\ntip labels as a legend. We apologize for the .1 in Njordarchaeia, it is indeed a typo.\n6. Please properly label the species names and accession numbers on the tips of phylogenomic\ntrees in Fig. S1, S3 and S6.\nResponse: SF1 contains the clade names, we feel that adding the species names and accession\nnumbers would make the figure too overcrowded. In any case, we have added the requested\nlabels to SF3, which contains both the Eme et al (2023) and the GTDB topologies. Given that the\ntree is the same, it should not be difficult for a reader to find the label(s) of their interest in SF3 and\nextrapolate that to SF1. We have added accession numbers to SF6 to not make the figure too\ncrowded as well.\nReviewer #1 (Remarks on code availability):\nThe page does not seem to be accessible at the moment.\nResponse: We apologize for the inconvenience. The GitHub repository is currently set to private.\nWe have left a copy of the code in the Zenodo for review. https://zenodo.org/records/15789115?\npreview=1&token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6Ijc5OWY3YjZkLWJlNWYtNGY0My1hYmViLT\nQyM2VlNmExYzRjMCIsImRhdGEiOnt9LCJyYW5kb20iOiI5YjQzYjUyMzU5NTI3NWFjNWZiZGEyY\nzU0OGQ1ZTFjYyJ9.MVQ7lZimHu4O9lK2xWsxnzDqwd_SbiAzPKp_WKO2E_GGkAFFtlKEdk_M72\np_5leISqCqfN6ZuH-TMWs8aOnb2Q\nReviewer #2 (Remarks to the Author):\nMost of my previous comments have been addressed in the revised version. I only have a minor\nrequest:\nCould you please comment on the (putative) functionality of duplicated genes? Is there any\nevidence of pseudogenization (e.g. truncating mutations, high variance of ORF lengths)? Are they\nsubject to positive, relaxed, or negative selection? With bacteria in mind, one would expect that, if\nduplicated genes have persisted for a long time, they are probably functional (simply because\npseudogenes are quickly eroded in bacteria). But these are not bacteria and it seems that gene\nloss is less frequent than gene duplication/gain in Asgard, so it might be that persistence does not\nimply functionality.\nResponse: To gain insight into the putative functionality of the duplicated genes, we have\nassessed the variance in ORF lengths (the ratio between the CDS, measured as longest/shortest)\nand their dn/ds ratio. For this, we have taken the tandem duplications as a representative set. For\nthe dn/ds ratio, we see that for Asgard duplications for which we could obtain this ratio (n = 8694),\nthe dn/ds ratio is of median 0.367 (sd 0.137)\nOther than that, I detected some typos and would suggest some edits for clarity:\nl. 77: I would suggest to add some connector in the beginning of this paragraph to stress that\nrestricting the pangenome to 32 high-quality Asgard genomes was a necessary compromise to\ncircumvent the potential problems of working with a larger set of incomplete MAGs. One can figure\nit out from the context, but a more explicit statement would be helpful.\nResponse: We have modified line 77 to improve readability as suggested by the reviewer.\nl. 84: \"17,429 contained at least one Asgard protein, 12,397 were exclusively present in Asgard\narchaea, and 10,470 were present in a single Asgard genome and no other prokaryote in our\ndataset.\" Are these numbers nested within each other? I interpret that the 10,470 singletons are\nincluded within the 12,397 Asgard-only OGs, and those are included within the 17,429 Asgard\nOGs. Just in case, it may be a good idea to rephrase a little bit (perhaps adding \"of those\") to make\nthat point totally clear.\nResponse: We have modified this sentence to make it easier to understand.\nl. 86: \"figAsgard\" -> possible typo?\nResponse: The reviewer is right to point it out as a typo, we have corrected this in the new version\nof the manuscript.\nl. 103: \"not in terms of core genome\" -> I find that statement confusing because the estimated strict\ncore is 18 in both cases. Perhaps the statement refers to the observed strict and relaxed cores (but\nthose are not reported for the random dataset) of for the mOTUpan estimates (but those are not\npresented until the following paragraph).\nResponse: Yes, the statement referred to the observed strict and relaxed cores, which are 57 and\n269 for the random set, respectively. The reviewer is right to point out that this wasn’t discussed in\nthe text. We apologize for this. We have added this data and clarified this comment in the text.\nl. 141: \"Figure 1D\" -> Figure 1C in the updated version.\nResponse: We have modified the text accordingly.\nl. 188: missing space before 23.87%\nResponse: We have corrected this.\nl. 202: \"Figure 2E\" -> Figure 2D?\nResponse: We have corrected this.\nl. 239: \"(F)\" -> (G)\nResponse: We have corrected this.\nl. 312: The pie charts appear below (not above) the nodes. The legend linking colors to seeds is\nmissing.\nResponse: We have colored the tip labels with the corresponding color to act as a legend, as\nadding a legend for the color would make the figure too crowded.\nReviewer #3 (Remarks to the Author):\nThe authors have made many changes to the manuscript and have addressed most of my\nconcerns. But there is a related issue that has not been adequately addressed: While the authors\nhave performed additional analysis and reported their findings, some of these new findings are not\nwell integrated into the manuscript. For example, the authors now provide the results of the\nreconciliation analysis using the alternative topology in Supplementary Figure 1. However, there is\nno discussion of these results elsewhere in the manuscript.\nThe same applies to results presented in Supplementary Figure 3.\nIt would be great if the authors could better integrate the results of these alternative analyses into\nthe manuscript, clearly explaining which findings are supported and not supported by the\nalternative analyses.\nResponse: We have now made more effort to comment on the alternative analyses.\nMinor comment:\nThe two topologies shown in Supplementary Figure 3 are missing leaf labels.\nResponse: We have now added the corresponding leaf labels"}]

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