New SAR11 isolate genomes and global marine metagenomes resolve ecologically relevant units within the Pelagibacterales

Domain: earth_science
Published: 2025-12-14
Editorial outcome: minor_revision

Abstract
The bacterial order Pelagibacterales (SAR11) is widely distributed across the global surface ocean, where its activities are integral to the marine carbon cycle. High-quality genomes from isolates that can be propagated and phenotyped are needed to unify perspectives on the ecology and evolution of this complex group. Here, we increase the number of complete SAR11 isolate genomes threefold by describing 81 new SAR11 strains from coastal and offshore surface seawater of the tropical Pacific Ocean. Our analyses of the genomes and their spatiotemporal distributions support the existence of 29 monophyletic, discrete Pelagibacterales ecotypes that we define as genera. The spatiotemporal distributions of genomes within genera were correlated at fine scales with variation in ecologically-relevant gene content, supporting generic assignments and providing indications of speciation. We provide a hierarchical system of classification for SAR11 populations that is meaningfully correlated with evolution and ecology, providing a valid and utilitarian systematic nomenclature for this clade.

Introduction
SAR11 marine bacteria, known as the Pelagibacterales, are a genetically diverse, order-level lineage of heterotrophs within the Alphaproteobacteria. They numerically dominate planktonic communities across the global ocean. Correlations between the spatiotemporal distributions of SAR11 lineages, environmental variables, and the subclade structure of Pelagibacterales have long indicated the presence of distinct ecotypes within SAR11. By linking functional traits with phylogenetic and biogeographic patterns, our recent findings support the differentiation of SAR11 into functionally distinct sublineages that segregate over short spatial scales. High-quality SAR11 genomes, available early in the investigation of SAR11 diversity, demonstrated they are a remarkably cohesive genetic assemblage, but were too few to discern complex patterns in the adaptive machinery underlying the stunning success of these minimalist microorganisms.

Since the first observation of SAR11 through environmental 16S rRNA gene fragments over three decades ago, microbiology has benefited from a dramatic increase in microbial sequence data recovered directly from the environment, offering representative genomes for many difficult-to-cultivate microbial lineages. However, even extensive genome-resolved surveys of marine metagenomes have failed to yield high-quality SAR11 genomes, resulting in limited insights into what constitutes ecologically meaningful units within this broad group. The extensive intra-clade diversity of SAR11 confounds the ability to reconstruct environmental genomes from metagenomes, which is why one of the most abundant microbial clades in marine systems suffers from poor representation in genome-resolved metagenomics surveys. By circumventing the need to assemble complex metagenomes to recover genomes, single-cell sorting coupled with whole-genome amplification techniques has been much more effective in sampling environmental SAR11 populations. However, in an extensive effort to characterize surface ocean microbes, the estimated genome completion of single-amplified genomes (SAGs) that could be affiliated with SAR11 remained below 60%, a level that prevents robust phylogenomic insights. Such barriers have led to a reliance on isolated genomes to investigate the evolution of SAR11 populations, yet following this path has been impeded by another formidable challenge: the difficulty of cultivating SAR11 in the laboratory, even with genomic insights regarding its unique growth requirements.

The first successful cultivation of SAR11 in 2002 resulted in the isolation of Pelagibacter strain HTCC1062, followed by the publication of its complete genome. Over the past two decades, additional isolated genomes have been few, with only 28 currently available. Despite their rarity, high-quality genomes from isolated strains not only shed light on SAR11 biology and the origins of this lineage within the Alphaproteobacteria, but also have made it possible to establish key concepts in biology, such as genome streamlining and investigate the evolutionary processes that shape protein evolution.

Here we report 81 high-quality genomes from SAR11 strains, increasing the number available for SAR11 isolates by threefold, and leverage this new collection to build a robust genome phylogeny for the order Pelagibacterales. By incorporating publicly available, high-quality single-cell genomes and surface ocean metagenomes from both a steep, nearshore to open-ocean local environmental gradient and elsewhere from around the globe, we reveal cohesive patterns of genomic and ecotypic diversification. We propose a framework through which to characterize and interpret genome heterogeneity at multiple stages along the evolutionary history of SAR11 marine bacteria, and establish a roadmap for future efforts to organize this globally abundant bacterial clade.

Results
Eighty-one high-quality genomes sequenced from 206 newly isolated SAR11 strains and co-cultures

Four dilution-to-extinction culturing experiments using surface seawater collected from nearshore and adjacent offshore environments of Oʻahu, Hawaiʻi, in the tropical Pacific Ocean resulted in 916 isolates from 2102 inoculated cultures (Table 1 and Supplementary Fig. 1). Using a streamlined isolate-to-genome approach, we identified 206 cultures as either pure SAR11 strains or mixed cultures with at least 50% of the total reads matching a SAR11 strain via 16S rRNA gene amplicon sequencing (Supplementary Data 1), and sequenced draft genomes from 90. Manual curation resulted in 79 high-quality SAR11 isolate genomes. The genomes from two strains (HIMB123 and HIMB109) isolated from a previous culture experiment were also added, resulting in 81 new SAR11 genomes from isolates. The majority of these (n = 60) assembled into ten contigs or less, including 24 closed genomes with an additional 30 containing one to three contigs. They ranged from 1.00 to 1.54 Mbp in size with a GC content of 28.5 to 30.7% (Supplementary Data 2). The median pairwise genome-wide average nucleotide identity (gANI) value across all genomes was 81.8% and none of the 81 new isolate genomes were identical. Having captured a genetically diverse array of SAR11 isolates, we used a phylogenomic approach to characterize evolutionary relationships among these genomes and high-quality single-cell and isolate genomes previously retrieved from seawater.

An extensive genome phylogeny reveals a robust evolutionary backbone populated by clusters of closely related genomes

We first sought to resolve relationships between the strains isolated in this study and other publicly available high-quality Pelagibacterales genomes to precisely establish where the new genomes originate from within the broad spectrum of known SAR11 diversity. For this, we created a database that, in addition to the 81 genomes presented here, included 28 public SAR11 isolate genomes, 8 of which were also isolated from off the windward coast of Oʻahu, Hawaiʻi, and 375 SAR11 SAGs estimated to be ≥85% complete with a redundancy <5% (Supplementary Data 3 and Supplementary Notes). We also included five additional SAR11 SAGs of potentially unique evolutionary origin within the collection of 375. This resulted in a curated collection of 484 SAR11 genomes to assess the evolutionary backbone for SAR11.

Previous studies investigating phylogenomic relationships within the Alphaproteobacteria utilized a curated set of 200 single-copy core genes (SCGs) for this bacterial class. We evaluated the presence of these 200 SCGs across our genome dataset, and excluded genes missing in more than 90% of the 484 SAR11 genomes or redundant in more than 2% of the genomes. This resulted in a SAR11-specific SCG set of 165 genes that were translated to amino acid sequences for downstream phylogenomic analyses, referred to hereafter as the SAR11_165 protein sequence set (Supplementary Data 4).

Our analysis of the 484 genomes using the SAR11_165 protein sequence set revealed that the SAR11 clade consists of four robust, deeply-branching sublineages (Fig. 1 and Supplementary Fig. 2). Three of these branches were the previously characterized subclades Ic, II, and III, while the fourth was a combination of established SAR11 subclades Ia and Ib, which did not form separate monophyletic subclades in this exhaustive genomic dataset and phylogenetic analysis. If the SAR11 clade is assigned to the taxonomic level of a bacterial order, then these four lineages logically resolve to the taxonomic level of families. There is compelling evolutionary and comparative genomic evidence that the AEGEAN-169 lineage, also known previously as SAR11 subclade V, does not share common ancestry with other SAR11 genomes. While less studied than AEGEAN-169, putative SAR11 subgroup IV is also unlikely to share common ancestry with SAR11. Phylogenomic analyses that included genomes from these two groups reinforced their distant relationship to the core set of sublineages recognized as SAR11 (Supplementary Fig. 3).

We further removed genomes from this initial tree in two steps. First, we excluded SAGs that did not fall into a 90% gANI cluster of at least three genomes to focus our analyses on well-resolved regions of the tree. Second, we de-replicated the remaining genomes using a conservative cutoff of 95% gANI (Supplementary Data 5) to minimize subsequent competitive metagenomic recruitment steps, splitting reads among closely related genomes. While the 95% ANI cutoff is broadly recognized in contemporary microbiology as a threshold to identify microbial species, it oversplits ecologically and evolutionarily cohesive units in SAR11 and does not delineate species-like groups. We note that the reason behind our use of the 95% gANI in this step of our analysis was solely to establish a technically robust workflow prior to competitive read recruitment rather than a biologically meaningful partitioning of our genomes, a challenge our study focuses on later.

We then turned our attention to the distal end of the phylogeny, which contained a large number of well-supported clusters of closely related genomes, particularly within the Ia/Ib subgroup that contained 78 of the 81 new isolate genomes. Focusing on well-supported clades within the phylogeny revealed 24 monophyletic clusters within the historical Ia/Ib subgroup that were characterized by a range of gANI values from 84 to 96% (92.1 ± 2.9%; mean ± SD) (Fig. 1). While some of these clusters were recognized previously, we defined an additional 11 here (Fig. 1; Supplementary Data 6). Twelve of the 24 clusters contained an isolated representative, and eight contained at least one isolate from our study area in the tropical Pacific.

In summary, our extensive phylogenomic analysis of SAR11 revealed 24 monophyletic clusters within the historical Ia/Ib subgroup, which included the majority of SAR11 SAGs and the new and previously published isolate genomes. The non-uniform minimum gANI estimates suggest that the application of sequence-based ANI thresholds to demarcate SAR11 diversity may obscure important evolutionary signals. Hypothesized drivers of the maintenance and partitioning of genomic diversity in SAR11 include niche-based processes, where genetically cohesive clusters also display ecological homogeneity and the underlying genetic diversity is maintained by similar forces of selection, recombination, and drift. To understand the potential eco-evolutionary forces that shape SAR11 diversification, we turned to metagenomic read recruitment analysis to recover biogeographical distribution patterns for our genomes across the globe.

Global read recruitment from the surface ocean reveals broadly congruent phylogenetic and ecotypic diversification across SAR11

Our competitive metagenomic read recruitment assessed the distribution of 268 SAR11 genomes (dereplicated at 95% gANI from the initial set of 484) around the globe and relied upon 1345 publicly available marine metagenomes, as well as metagenomes from Kāneʻohe Bay via the Kāneʻohe Bay Time-series (KByT), the location of isolation for the 81 new and 9 of the 28 existing isolate genomes (Supplementary Data 7 and 8). SAR11 genomes collectively recruited a total of 5,074,677,192 reads from the entire collection of global metagenomes that contained a total of 70,533,279,194 reads, or 7.2% of the dataset. The SAR11 genomes accounted for 0.001% to 27.7% of reads in each metagenome, with a median genome coverage up to 12x (Supplementary Data 8 and 9). These data enabled us to investigate whether cohesive genomic and ecological groups, or ecotypes, could be discerned by combining SAR11 phylogeny and biogeography.

Our first priority was to establish whether genome clusters within a given SAR11 clade showed cohesive read recruitment profiles across metagenomes, or, in other words, whether the ecological patterns revealed by a single genome were similar to all genomes within the group to which it belonged. We narrowed our focus here to the historical subgroups Ia/Ib, where the majority of isolated cultures are classified. Detection values (proportion of nucleotides in a genome that are covered at least 1x) for multiple genomes within a genome cluster showed a significant degree of cohesion (PERMANOVA, p < 0.001, Supplementary Fig. 4 and Fig. 2; Supplementary Data 10). For example, patterns of detection for representatives from Ia.3.IV, Ia.3.I, Ia.4.II, Ia.4.N2, Ia.4.N5, Ib.1.III, and Ib.4.N9 show particularly consistent within-cluster patterns (Fig. 2, Supplementary Fig. 4). Overlap in patterns of detection between SAGs and isolate genomes within the same clade demonstrated that both genome types accurately reflect distribution patterns for closely related populations as inferred by phylogeny (Fig. 2 and Supplementary Data 12). A non-metric multidimensional scaling (NMDS) analysis of the overall detection patterns of genomes across metagenomes consistently grouped genomes within a given clade more closely compared to those that belonged to other genome clusters (Supplementary Fig. 4), further supporting a high degree of intra-clade ecological cohesion.

Our second priority was to establish insights into whether SAR11 genome clusters differed in their biogeographical patterns, and whether genome clusters identified SAR11 populations of distinct ecology. Hierarchical clustering of metagenomes based on detection patterns of the SAR11 community revealed four groups: metagenomes that originated from (1) low-latitude samples with low SAR11 diversity, (2) high-latitude samples with low SAR11 diversity, (3) low-latitude samples with high SAR11 diversity, and (4) samples from coastal Kāneʻohe Bay (Fig. 2). The distribution of SAR11 genomes differed significantly across these four community groups (PERMANOVA, p < 2.2e-16; Supplementary Fig. 5). Many SAR11 genome clusters were indeed differentially distributed across these community groups. For example, Ia.4.II and Ia.4.N5 were only consistently found in the low-latitude, low-diversity and low-latitude, high-diversity communities, while Ia.3.IV and Ia.I were found across the high-latitude, low-diversity communities and only in select sites in the other community groups (Fig. 2). However, in multiple cases, the environmental detection patterns of different phylogenomic genome clusters overlapped; while there was some degree of inter-clade ecological differentiation, distinct SAR11 genome clusters frequently co-occurred (Fig. 2 and Supplementary Fig. 6; Supplementary Data 12). This observation suggests that patterns of distribution alone cannot discern the boundaries of cohesive, fundamental units within the ecotypically- and evolutionarily diverse landscape of SAR11, a task that evidently requires the integration of biogeographical patterns identified using metagenomic read recruitment and ancestral relationships discerned by phylogenomics.

Finally, we used the read recruitment analysis to assign ecological patterns to specific SAR11 genome clusters. While multiple broad patterns were clear from the pairing of the phylogenomic relationships and read recruitment data, we focused our investigation on whether the genome clusters within the SAR11 Ia/Ib lineage that appeared to be confined to the coastal end of the KByT environmental gradient (Ia.3.VI, Ia.3.II, and Ia.3.III; Supplementary Fig. 1; also see ref. 11) were similarly constrained to coastal areas globally. Indeed, two of the three genome clusters, Ia.3.II and Ia.3.III, were detected almost exclusively in metagenomes sourced from coastal environments (e.g., Kāneʻohe Bay, the north coast of Panama, Chesapeake Bay, and the Atlantic coast of Portugal). Interestingly, while the clade Ia.3.VI was restricted to nearshore metagenomes across KByT, it was well-detected in both coastal and offshore environments in other oceanic regions (Fig. 2). Genome clusters Ia.3.II and Ia.3.III did not include any SAGs and were only composed of isolates from coastal Kāneʻohe Bay. Yet, we could detect them in rare instances in other oceans, which confirms their global relevance as representatives of SAR11 populations adapted to coastal ecosystems.

Through the combination of global metagenomic read recruitment and phylogenomics, we show that SAR11 genome clusters contain genomes with a high degree of intra-clade ecological cohesion. These genome clusters were often distinguished by their ecological distributions and demonstrated notable inter-clade ecological differentiation. Next, we applied this framework to understand how SAR11 genetic and ecological diversity partitions among ocean biomes, in particular, coastal ocean and open ocean environments. Finally, we compared our delineation of these genome clusters to taxonomic structures proposed elsewhere (Supplementary Notes), and found that clustering of genomes presented in our study more closely reflects the ecological and evolutionary trajectories of this diverse lineage than the strict metric-based approaches implemented by solely relying on relative evolutionary divergence (RED) values or the ranks assigned by Genome Taxonomy Database (GTDB).

The integrated ecological and evolutionary framework here is supported by high-quality genomes that span the known diversity of the Pelagibacterales, providing a critical opportunity to discern distinct ecologically meaningful genome clusters within SAR11. We show that the 24 distinct genome clusters within the combined subgroup Ia/Ib lineage represent groups sharing cohesive ecological patterns and evolutionary relationships, not at the finest tips of the phylogenomic tree, but at relatively deeper branches that encompass gANI values ranging from 84% to 96%. This level of genomic differentiation suggests it is plausible that these genome clusters represent SAR11 diversity at a higher taxonomic level than “species”. The cohesion of these genome clusters is further supported by recent work, which reveals systematic differences in the metabolic potential of SAR11 genome clusters that likely support distinct ecological distributions in immediately adjacent coastal and open ocean surface seawater with habitat-specific metabolic genes that are under higher selective forces. With the combined evidence presented here and in the work by Tucker et al. that unite SAR11 diversity into distinct genome clusters with ecotype properties supported by SAR11 phylogenomics, ecology, metabolic potential, as well as population genetics, we argue that the most conceivable taxonomic rank at which SAR11 genome clusters can be described in a conventional framework emerges as the “genus” level.

This genus-level designation is ideal as it encompasses a degree of diversity previously designated by SAR11 subgroups and has the flexibility to account for subtle variation in ecology recognized between closely related genomes. We identified the highest quality genome representatives (electing for isolates when possible) to assign type genomes for each genus (Supplementary Data 13), which establishes a roadmap to rationally designate new genera as they are identified in the future.

Evidence for ecological speciation within closely related genome clusters

Despite broad ecological cohesion within what we have designated as genera of the order Pelagibacterales, some notable differences highlight underlying complexities in defining the finest scales of divergence. The Ia.3.VI genus includes genomes from strains of Kāneʻohe Bay origin as well as SAGs from other regions of the global ocean, and encompasses significant genomic diversity (minimum gANI 88%) and phylogenomic structure (Fig. 3a). Through read recruitment (Fig. 2), we observed notable differences in the patterns of detection of Ia.3.VI genomes across community groups of (1) coastal Kāneʻohe Bay, (2) low-latitude, low-diversity, and (3) low-latitude, high-diversity. Examining these patterns more closely with a subset of metagenomes from Kāneʻohe Bay, BATS, and Station ALOHA (Fig. 3a, b), we found that isolate genomes from Kāneʻohe Bay harbored the highest detection values of the Ia.3.VI genus from metagenomes also sourced from the bay, while a SAG from the BATS site in the Atlantic Ocean (GCA_902533445.1) had the highest detection values at BATS (Fig. 3b), particularly in the summer and fall (Fig. 2).

Given the underlying genomic divergence between isolate HIMB1556 and BATS SAG GCA_902533445.1 and their distinct biogeographies, we next surveyed the genomes for unique gene content that may be relevant to their distributions. By inspecting the coverage of isolate HIMB1556 and BATS SAG GCA_902533445.1 using metagenomes from Kāneʻohe Bay and the BATS site, we found one genomic region of SAG GCA_902533445.1 that had particularly high coverage at BATS compared to the Kāneʻohe Bay samples (108.9x vs 0.2x ave. coverage; Supplementary Data 14). This region included 29 genes encoding the uptake (phnCDE) and catabolism of phosphonates via the C-P lyase pathway (phnGHIJKLM) (Fig. 3e and Supplementary Data 15). The phnJ phylogeny did not reflect the phylogenomic relationships among genomes (Supplementary Fig. 7), and the pathway for phosphonate catabolism was located on a genomic island similar to a subset of genomes from Ia.3.I and Ia.3.VII. The C-P lyase pathway is known to be enriched in phosphate-depleted systems of the Atlantic Ocean and Mediterranean Sea, so the presence of C-P lyase catabolic genes in a genome sourced from BATS, but missing from a closely related genome sourced from the more phosphate-replete environment of Kāneʻohe Bay in the Pacific, suggest these genes provide an advantage in phosphate-depleted systems and that the BATS SAG GCA_902533445.1 may be locally-adapted to its environment.

While the HIMB1556 genome lacked the C-P lyase pathway, it contained a unique genomic region with particularly high coverage in Kāneʻohe Bay compared to BATS (18.4x vs 0.3x ave. coverage; Supplementary Data 14) that was not found in SAG GCA_902533445.1, and encoded genes for type IV pilus assembly (Supplementary Data 15). The role of type IV pili in SAR11 is unclear, although in other organisms they have been associated with an array of functions including DNA uptake, twitching motility, and aggregation into microcolonies. The presence of type IV pilus assembly genes in the Ia.3.VI genome sourced from the nitrogen-limited Pacific Ocean, but not in genomes from relatively more nitrogen-replete waters of BATS, along with evidence that SAR11 can utilize purine nucleosides and other purine derivatives for nitrogen, suggests that the type IV pilus may be advantageous for DNA uptake in nitrogen-poor environments and that HIMB1556 may be locally-adapted.

Contrary to the hypothesis that SAR11 recombines at a rate sufficient to limit ecotypic differentiation within what we have defined as genus-level clusters of SAR11 genomes, our read mapping instead shows that the HIMB1556 and SAG GCA_902533445.1 genomes within cluster Ia.3.VI have sufficiently diverged at the nucleotide level to reveal clear biogeographic divergence, and that they possess sets of genes that reside in hypervariable genomic regions that are clearly associated with the differences in abundance.

We examined gANI estimates, phylogenetic branching, environmental distributions, and ecologically-relevant gene content to support the characterization of ecological diversification at the finest tips of the SAR11 tree, a process that we theorize to represent speciation. This underscores the complexity of SAR11 ecology, highlights the need to include a diversity of representative genomes within even closely related genera for environmental genomics studies, and indicates that continued efforts to sample SAR11 globally are key to understanding the distribution of this ubiquitous clade.

Proposed Pelagibacterales classification and nomenclature

We leveraged the robust genome phylogeny, gANI metrics, and read recruitment to establish a rational classification and nomenclature system for the bacterial order Pelagibacterales. To provide a framework and vocabulary to discuss groups of SAR11 in a meaningful context, we first defined four family-level monophyletic groups as the Pelagibacteraceae (historical subgroups Ia and Ib), Cosmipelagibacteraceae (historical subgroup II), Fontibacteriaceae (historical subgroup III), and the Mesopelagibacteraceae (historical subgroup Ic) (Fig. 4). We focused our efforts primarily on classification within the Pelagibacteraceae, where the majority of cultured isolates originate. While we did not exhaustively define genera in the three other Pelagibacterales families, we incorporated previously named genera and species, and provided taxonomic names for strains previously cultivated at the Hawaiʻi Institute of Marine Biology (HIMB58 and HIMB114). Within the Pelagibacteraceae, we used phylogenomics and ecological data to characterize 24 genera that represent cohesive genetic and ecological clades, and designate type species for each (Supplementary Data 13). The primary aim of these efforts is to ensure that the taxonomic hierarchy for SAR11 provides a useful and tractable reflection of the ecology and genetic diversity within this globally distributed group and establishes a rational system that future efforts can build upon.

Methods
High-throughput culturing from surface seawater within and adjacent to Kāneʻohe Bay

Growth medium was prepared following previously described methods. Briefly, 20 L of seawater was collected first on 8 July 2017 and again on 20 September 2017 from a depth of 2 m at station SR4 (N 21° 27.699’, W 157° 47.010’) in acid-washed polycarbonate bottles (Supplementary Fig. 1). The seawater was then filtered, autoclaved, and sparged with CO2 and then with air sequentially through three in-line HEPA vent filters (0.3 µm glass fiber, 0.2 µm polytetrafluoroethylene [PTFE], 0.1 µm PTFE; Whatman, GE Healthcare Life Sciences, Chicago, IL, USA), as done previously. After processing, the sterile seawater was stored at 4°C until use.

Two 4 L seawater samples to be used as inoculum were collected on 26 July 2017 in acid-washed polycarbonate bottles from 2 m from stations SB (N 21° 26.181’, W 157° 46.642) and STO1 (N 21° 28.974, W 157° 45.978’) (Supplementary Fig. 1) and immediately returned to the laboratory for further processing. The samples from the KByT samples were previously classified as “nearshore”, “transition”, or “offshore”, with SB and STO1 representing nearshore and offshore sites, respectively. Subsamples of the raw seawater were processed as described previously. Briefly, aliquots were taken for cryopreservation in a final concentration of 10% v/v glycerol and fixed with paraformaldehyde for the enumeration of planktonic microorganisms via flow cytometry. Additionally, 0.96 L from station SB and 1.30 L from station STO1 were filtered through a 25 mm diameter, 0.1 µm pore-sized polyethersulfone membrane (Supor-100; Pall Gelman Inc., Ann Arbor, MI), which was then submerged in 500 µL DNA lysis buffer and stored at -80°C until DNA extraction.

Subsamples of raw seawater from SB and STO1 were enumerated using microscopy, diluted to 2.5 cells mL–1 and plated in 2 mL volumes into a total of 1152 wells (576 wells per site) of custom-fabricated 96-well Teflon microtiter plates. This experiment is referred to here as HTC2017. Plates were then sealed with breathable polypropylene microplate adhesive film and incubated in the dark at 27°C. Plates were monitored for cellular growth at 3.5 and 8 weeks by staining cells with SYBR Green I nucleic acid stain (Invitrogen, Carlsbad, CA, USA) and then counted on a Guava easyCyte 5HT flow cytometer equipped with a 150-mW blue (488-nm) laser (Millipore, Burlington, MA, USA) as done previously. Wells with positive growth (greater than 104 cells mL–1) after 24 or 57 days of incubation were further sub-cultured by transferring approximately 1 mL into 20 mL of sterile seawater media amended following previous studies with 400 µM (NH4)2SO4, 400 µM NH4Cl, 50 µM NaH2PO4, 1 µM glycine, 1 µM methionine, 50 µM pyruvate, 800 nM niacin (B3), 425 nM pantothenic acid (B5), 500 nM pyridoxine (B6), 4 nM biotin (B7), 4 nM folic acid (B9), 6 µM myo-inositol, 60 nM 4-aminobenzoic acid, and 6 µM thiamine hydrochloride (B1). These subcultures were then incubated at 27°C in the dark for an additional 33 days, and then all samples were processed and cataloged. Cultures checked at 33 days that yielded positive growth (>104 cells mL–1) were cryopreserved in duplicate (2 × 500 µL culture and a final concentration of 10% v/v glycerol). Each well with positive growth was assigned an HIMB culture ID, and cells from the approximately 18 mL remaining volume of each culture were collected by filtration through a 13 mm diameter, 0.03 µm pore-sized polyethersulfone membrane (Sterlitech, Kent, WA, USA), which was then submerged in 250 µL DNA lysis buffer and stored at -80°C until DNA extraction. The lysis buffer was prepared by adding the following to Milli-Q water: 8 mL 1 M Tris HCl (pH 8.0), 1.6 ml 0.5 M EDTA (pH 8.0), and 4.8 g Triton X, for a final volume of 400 mL, which was then filter sterilized, with lysozyme added to aliquots immediately before use (at a final concentration of 20 mg mL–1) as done in prior studies.

An additional experiment was performed using cryopreserved samples of seawater collected on July 26, 2017, and described previously. Briefly, the cryopreserved sample was enumerated and then diluted to two cell concentrations (2.5 and 52.5 cells mL–1), and used to plate 480 and 470 2-mL dilution cultures, respectively. This experiment is referred to as HTC2018. Growth was monitored at 2, 3, and 5 weeks after inoculation with positive growth (>104 cells mL–1) from the 2.5 cells mL–1 cultures subcultured into 20 mL of sterile seawater growth medium and monitored for growth for up to 10 weeks at 27°C in the dark. Subcultures were then cryopreserved and cells collected for DNA sequencing as described above. One well from the 52.5 cells mL–1 inoculation was directly collected for DNA sequencing without subculturing.

DNA extraction and 16S rRNA gene amplicon sequencing

Genomic DNA (gDNA) from all filtered cultures, as well as environmental DNA from STO1 and SB, was extracted using the Qiagen DNeasy Blood and Tissue Kit with modified manufacturer’s instructions for bacterial cells (Qiagen, Germantown, Maryland, USA). The modifications included the addition of an initial freeze-thaw step (3 cycles of 10 min at 65°C followed by 10 min at -80°C), the addition of 35 µL Proteinase K and 278 µL buffer AL at the appropriate pretreatment step, and finally, when eluted, the same 200 µL volume was passed through the membrane three times.

For the initial identification of all cultures, gDNA was used as template for the polymerase chain reaction (PCR) amplification (Bio Rad C1000 Touch, Bio Rad, Hercules, CA, USA) using barcoded 515F-Y (5’-GTGYCAGCMGCCGCGGTAA-3’) and 926R (5′-CCGYCAATTYMTTRAGTTT-3′) primers targeting the V4-V5 region of the SSU rRNA gene in a reaction volume of 25 µL composed of: 2 µL gDNA, 0.5 µL each forward and reverse primer, 10 µL 5PRIME HotMasterMix (Quantabio, Beverly, MA, USA), and 12 µL of molecular grade H2O. The reaction was as follows: an initial denaturation step of 3 min at 94°C, 40 cycles of 45 s at 94°C followed by 1 min at 50°C and 1.5 min at 72°C, with a final extension of 10 min at 72°C. The PCR products were quantified, pooled at a concentration of 240 ng per sample, and cleaned (QIAquick PCR purification kit; Qiagen) prior to sequencing following previous methods and sequenced on a MiSeq platform by the Oregon State University Center for Genome Research and Biocomputing.

16S rRNA gene sequence analysis

Amplicon sequence data were processed following previous methods. Briefly, the data were imported into QIIME 2 v2019.4.0 and demultiplexed before being assessed for sequence quality and merged. DADA2 v1.14 was then used for quality control. Taxonomy was assigned to all reads using a Naïve Bayes classifier trained on the Silva rRNA v132 database. Cultures were first classified as defined previously, with “monocultures” consisting of more than 90% of reads from a single amplicon sequence variant (ASV), “mixed cultures” with an ASV that was between 50 and 90% of the reads, and finally cultures with no dominant members. Any samples with less than 1000 reads were not included in further analyses. We aimed to sequence all strains that included monocultures and mixed cultures of SAR11.

Genome sequencing

For all extractions with gDNA concentrations > 0.06 ng µL–1, 10 µL were aliquoted for sequencing. For samples with concentrations < 0.06 ng µL–1, the remaining extraction volume (~180 µL) was concentrated using a SpeedVac (Thermo Fisher) to approximately 30 µL and re-quantified (Qubit 2.0, Invitrogen). Concentrated samples with a minimum of 0.06 ng µL–1 were then aliquoted (10 µL) for sequencing. Samples for sequencing were prepared using a Nextera library kit and sequenced on the NextSeq 500 platform via a 150 bp paired-end run.

Genomes for previously cultured strains HIMB109 and HIMB123 were sequenced by the US Department of Energy’s Joint Genome Institute. Multiple methods were used to sequence the two strains, including directly using 200 µL of cell culture for library prep as well as using multiple volumes (5, 10, or 20 µL) of culture for multiple displacement analysis prior to library preparation (Supplementary Methods). The two genomes were evaluated based on completeness, length, number of reads, and total contigs post-assembly using SPAdes 3.12.0. An additional assembly using all reads generated from the various sequencing attempts from each strain was also constructed using the same assembly method. Based on the metrics described above, the highest quality genomes were manually curated and used for additional analyses.

Genome assembly and assessment

Short reads were trimmed with Trim Galore! (https://github.com/FelixKrueger/TrimGalore) and assembled using Unicycler v0.4.8, which acts as a SPAdes v3.13.0 optimizer with Illumina short read data. Once assembled, reference indexes were built, and read mapping was performed using Bowtie 2 with default parameters. SAMtools was used to convert the SAM file to a sorted and indexed BAM file. These initial assemblies and BAM files were used to visualize genomes in anvi’o v8.0 to check for possible contamination. For genomes with contamination (determined visually as instances where contigs had anomalous GC content or tetranucleotide frequency), suspicious contigs were removed. Redundancy was also used as a way to flag any genomes that needed further curation. After inspection, curated contigs were exported using the program “anvi-summarize”, and reads were re-mapped to the cleaned version of assemblies. The cleaned genomes were processed again for visualization in anvi’o to ensure no erroneous contigs were included. Mapping quality was inspected visually using the Integrative Genomics Viewer v2.8.4 and Tablet v1.21.02.08, and manual curation was undertaken using mapped read data. Manual inspection was used to determine if a circular genome could be considered closed and complete. All contigs shorter than 1000 bp were removed from the genomes that were not closed after final curation, and CheckM v1.1.2 was used to assess final genome completeness and redundancy.

Phylogenomic analyses

To generate an extended phylogeny of the SAR11 clade, a suite of high-quality genomes was curated. Even with an abundance of metagenomes, the high diversity among SAR11 populations makes constructing reliable MAGs currently unfeasible, so to ensure the phylogeny was as robust as possible, only isolate genomes and SAGs were included. The final set of 484 SAR11 genomes for phylogenetic reconstruction included 81 genomes sequenced in this study, 28 previously published reference genomes, and 375 previously published SAGs, in addition to 10 isolate genomes from the family Rhodobacteraceae used as an outgroup (Supplementary Data 3). The majority of SAGs included were equal to or greater than 85% complete according to CheckM 1.1.2. However, genomes of lower quality from subclades of SAR11 with no high-quality representatives were included to produce a phylogeny that best represents established clades, for example, SAR11 Ic genomes that ranged from 56.0 to 93.7% completion were also included (Supplementary Data 3). For comparison, we also constructed a phylogeny including 27 available genomes from what were previously SAR11 subgroups V and IV. Subgroups V and IV are no longer considered to be within SAR11, and the membership of subgroup IV has not been rigorously investigated; thus, its relationship to the Pelagibacterales is questionable, as we further describe in the Supplementary Notes.

We compared two sets of phylogenetic markers to determine the most appropriate genes to use for phylogenetic reconstructions of the SAR11 clade. This included the proteins defined by the bac120 set utilized by GTDB-Tk to determine the bacteria guide tree, and a curated set of markers derived from the 200 genes previously demonstrated to be best fit for the Alphaproteobacteria (Supplementary Data 4). An alignment file of the proteins based on the bac120 set was generated using GTDB-Tk. To curate the second set of phylogenetic markers, we generated a custom HMM profile for the 200 Alphaproteobacteria genes with a noise cutoff term of 1×10−20, ran the HMM profile on all genomes using the anvi’o v8.0 program “anvi-run-hmms”, and generated a presence-absence matrix of genes in this model across genomes using the program “anvi-script-gen-hmm-hits-matrix-across-genomes”. After evaluating the model hits across the genomes matrix, we removed the genes that occurred in less than 90% of the genomes or those that were redundant in more than 2% of the genomes from the Alphaproteobacteria 200-gene collection, which resulted in a new collection with 165 genes, which is referred to as the “SAR11_165” throughout our study (Supplementary Data 4). To generate a concatenated alignment of the proteins of interest for downstream phylogenomic analyses, a custom HMM source was generated that encompassed the SAR11_165 marker protein sequences. The program “anvi-get-sequences-for-hmm-hits” with the custom HMM source was then implemented to extract and align proteins of interest, which uses Muscle (v3.8.1551) to align sequences. For the alignments from each set, the program trimAL 1.3 was then used to remove all positions that were missing in more than 50% of the genomes. Phylogenies were generated with IQ-Tree v2.1.2 with the best-fit model (LG+F+R10) chosen using ModelFinder and 1000 ultrafast bootstraps. Phylogenies were rooted appropriately in FigTree v1.4.4 using the midpoint rooting method, and exported in NEXUS format with the options selected to “Save as currently displayed” and “Include Annotations (NEXUS & JSON only)”. Once exported, phylogenies were then compared using the package phytools v1.2-0 in R v4.2.1.

Once the phylogeny was established, a subset of SAR11 genomes was used for read recruitment. For this, we first used FastANI v1.34 to dereplicate all genomes using 95% gANI as a cutoff, then for genomes within the historical Ia and Ib clades, we excluded SAGs that did not share at least 90% gANI with a neighboring genome. A 95% gANI dereplication cutoff was selected to avoid read splitting during competitive read recruitment, and for any clusters in which isolate genomes were available, they were chosen as preferred representatives.

Classification and nomenclature

The phylogeny was used to define cohesive genetic clusters at the distal end of the SAR11 tree. Single genomes that did not share at least 90% gANI with a neighboring genome in the historical Ia or Ib subclades were not classified into genera.

To determine how taxonomic levels across the SAR11 lineage would compare using relative evolutionary distance (RED) values, we implemented this approach following previous studies (Supplementary Notes). Briefly, a domain-level phylogeny was first constructed using the GTDB-Tk de_novo_workflow with SAR11 isolate genomes and SAGs as well as “p__Chloroflexota” as the outgroup. Marker genes were identified from the input genomes using GTDB-Tk “identify”, and then aligned with GTDB-Tk “align” (using the “–skip_gtdb_refs” flag). Finally, a tree was constructed using FastTree v2.1.10 (model WAG+GAMMA), rooted with the Chloroflexota outgroup. This phylogeny was used as the input for the “scale_tree” program in PhyloRank v0.1.11 (https://github.com/dparks1134/PhyloRank) to convert branch lengths into RED scores. RED values of 0.77 and 0.92 were used to assess how they would align with family and genus-level lineages, respectively. These values were based on the distribution of internal nodes within the SAR11 clade and values used previously for other family and genus-level lineages.

To provide species names that utilize descriptive ʻŌlelo Hawaiʻi (the Hawaiian language) words for species with type material that was cultivated on the island of Mokuo Loʻe located in Kāneʻohe Bay in the watershed of Heʻeia, we collaborated with local community leaders. Guided by collaborative research protocols practiced in Heʻeia, we received input from Kumu Hula (Master Instructor), Frank Kawaikapuokalani Hewett and coordinated with the Indigenous Stewardship Specialist and Stewardship Coordinator at the Heʻeia National Estuarine Research Reserve, Aimee Saito.

Read recruitment

To assess the distribution of the newly described strains described in this study and put them into context with previously sequenced genomes, we used a read recruitment approach with globally distributed metagenomes. The SAR11 genomes included in this study were grouped into clusters that shared 95% gANI or greater (Supplementary Data 5), and representatives from these 95% gANI groups were then used for read recruitment to ensure reads were not split between closely related genomes. Results from read recruitment were extrapolated for the other genomes included in each 95% gANI group.

Metagenomes used for recruitment included those sequenced in Kāneʻohe Bay, the environment from which the genomes were isolated. Only samples from sites previously categorized as “nearshore” and “offshore” were used here. Additionally, globally distributed previously published metagenomes were also used, including those from TARA Oceans expeditions, Station ALOHA, GEOTRACERS cruises, the eastern coast of Japan, Monterey Bay, and the Ocean Sampling Day program (Supplementary Data 7 for a list of appropriate references and details regarding metagenomes included).

Once metagenomes were chosen, raw reads were downloaded using “prefetch” and “fasterq-dump” in the SRA toolkit v2.10.1. We automated the quality filtering of metagenomes, metagenomic read recruitment, and profiling of recruited reads using the program anvi-run-workflow with the “--workflow metagenomics” flag, which implements snakemake recipes for standard analyses in anvi’o. Briefly, this workflow identified and discarded the noisy sequencing reads in metagenomes using the program “iu-filter-quality-minoche”, used SAR11 genomes to competitively recruit short reads from metagenomes using Bowtie 2 SAMtools using the program “anvi-profile”, and finally merged individual profiles into an anvi’o merged profile database using the program “anvi-merge”. The resulting anvi’o merged profile database included essential data, including genome coverages and detection statistics across metagenomes, for our downstream analyses. For coverage, we primarily used the “mean coverage Q2Q3” statistic, which represents the interquartile average of coverage values where, for any given genome, the lowest 25% and the highest 25% of individual coverage values are trimmed prior to calculating the average coverage from the remaining data points, and thus minimizing the impact of biases due to highly conserved or highly variable regions in the final coverage estimates. The detection metric equated to the total proportion of nucleotides in a given contig that are covered at least 1X. We used the previously established threshold of 0.25 to state that a given genome was present in a sample and remove any potential false positives. Visualization of read recruitment data mapped according to the phylogeny constructed was completed using the program “anvi-interactive” with the “-manual” flag.

Metagenome profile clustering

We performed a cluster analysis of metagenomes based on genome detection values from the read recruitment step using the k-means algorithm, where we determined the “k” by identifying the elbow of the curve of within-cluster sum of square values for increasing values of “k” using the R code shared by Delmont et al. at https://merenlab.org/data/sar11-saavs/. The results of the clustering analysis were visualized using anvi’o and the vegan package v2.6-4 in R v4.2.1 was used to perform a permutational multivariate analysis of variance (PERMANOVA) to test the significance of the resulting groupings using the Bray-Curtis method of calculating the dissimilarity matrix and the adonis2 function. This method was also used to test the significance of genomes grouped by subclade. Additionally, the “betadisper” function in vegan was used to conduct a PERMDISP, which was visualized via a principal coordinates analysis. To investigate how similar the detection patterns of genomes within and between genome clusters were, we performed NMDS analysis using the vegan package v2.6-4 in R. Any metagenomes with zero detection across all genomes were removed. The NMDS results were visualized using ggplot2 v2_3.5.1 and plotly v4.10.4, and an interactive plot was generated with ggplotify v0.1.2.

Investigation of C-P lyase pathway

To determine the C-P lyase capacity among high-quality SAR11 genomes, we used the anvi'o program "anvi-search-functions" to search for a key enzyme of the C-P lyase pathway (phnJ) among all SAR11 genomes included in the extended phylogeny (n = 484), and extracted the broader genomic context that included the genes upstream and downstream of phnJ hits from all 57 genomes that encoded phnJ using the program "anvi-export-locus". We then computed a pangenome using the anvi'o pangenomics workflow for all loci with default parameters to compare the presence and absence of other key genes in the C-P lyase pathway and to study the synteny of this region across genomes, and performed a phylogenetic analysis of all PhnJ protein sequences (Supplementary Fig. 7).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "General Comments:\nThe manuscript describes a new phylogeny for the abundant marine bacterium SAR11. This phylogeny is based only on\nhigh quality isolate and single cell-amplified genomes, and is enabled by the addition of 81 new isolates from the ocean\nnear Hawaii, an impressive contribution as only 25 were previously available. The authors additionally map over a thousand\nocean metagenomes from multiple datasets to explore the geographic distribution of their SAR11 clades, and based on a\ncombination of the phylogenetic and ecological groupings suggest new family, genus, and species-level names and type\nstrains. A more clearly delineated SAR11 phylogeny fills a clear need in ocean microbial ecology, and this approach that\ncombines ecology and phylogeny into a manually curated taxonomy calls to mind a widely adopted freshwater taxonomy\nthat has been incredibly valuable to the freshwater community for over a decade\n(https://journals.asm.org/doi/10.1128/mmbr.00028-10). I think this will serve as a similarly valuable, and long overdue,\nresource.\nMy main concern is around the construction of the phylogenetic tree, and although phylogenetics is not my primary expertise\nI did share the public preprint with a colleague whose main focus is phylogenies and they shared my concerns. To create\nthe final tree, the authors excluded genomes that did not have multiple closely related references, as well as excluding\ngroups that are ambiguously part of the SAR11 order. However, all of the clade’s diversity should be included to obtain the\nmost reliable branch structure. Instead of showing both versions in Fig 1 to highlight unstable branches, why not show the\nsingle large tree along with confidence values for the branches? And instead of excluding subgroups V and VI why not\ninclude them and clarify their debated relationship to SAR11? Limiting the downstream mapping analysis to 95ANI clusters\nis required methodologically, but the authors can just clarify which leaves each ANI cluster combines. (In other words, re-\nmaking the tree, which should not take too long, should not require repeating all of the downstream mapping.) Additionally,\nthe authors throughout the paper and in the methods refer to the tree as being constructed from genes, but that is highly\nunusual. Typically trees are constructed from protein sequences.\nOverall the paper is well written and the figures are beautiful. I have a few additional comments and suggestions regarding\nclarity of figures and discussion of results which I detail below.\nSpecific Comments:\n59- I suggest you add the number of proposed genera specifically here\n62- this phrasing makes it sound like you recommend someone do this in the future, not that you do it here in this paper\n141- in presenting your results in table 1, you could consider adding a brief statement as to what your results suggest is the\nbest approach for future culturing attempts\n234- can you add any statistical analysis to support cohesion within groups beyond eyeballing the heat map?\n237- again, can support with any statistics?\n244 and Fig2- clarify how group 1 and 3 are distinguished. How is “low-latitude, high diversity” different than “low-latitude”?\nConfusing because “high-latitude, low-diversity” is explicitly labelled but “low-latitude” does not have any extra description.\n253- Can’t two phylogenetically distinct units form the same ecological unit? I think “ecological unit” is perhaps not the best\nword choice here- if I understand correctly you are trying to say that both ecology and phylogeny, but neither alone,\ndistinguish all the SAR11 “units”, so it’s confusing to call the unit “ecological” when ecology can’t fully resolve it.\n255- typo/grammar\nFig 2- why not make the text at the bottom a little bigger, because you could extend the table to the left edge of the teal\nPelagibacteriaceae label. It’s just pretty hard to read and you have white space there you could fill.\nFig 2- You should clarify how many metagenomes are in each group category, because it’s easy to assume each value is 1\nmetagenome but you mapped almost 1500 (!!). A legend to the heatmap shading could also help clarify how you combined\ncoverage/abundance values from different metagenomes, which isn’t clear to me.\n280- suggest defining RED (rel evol divergence I assume, but this is not super common term and it does not show up on\ngoogle easily)\nFig 3. It seems like some panels are lacking labels\n316- Is there some information on the extent, or significance, of the phylogenomic structure in Fig 3a?\n320- what are these “detection values”? I don’t see units in the Fig 3 barplots either.\nFig 3b- I’m confused about this panel, although it looks very cool I’m not sure what’s on it. At first glance I thought it was the\ncontigs of the two sags along the “x-axis”, and their coverage in the heatmap. But then there are also multiple metagenomes\nfrom each location, so maybe it is the sample on the “x-axis” and the abundance in the metagenome in the heat map?\nAdding a legend with units would help.\nFig 3b- this looks cool but if you stretched it into a straight line along the whole figure width it would be much easier to read.\nIt’s confusing because for example on the left BATS is the bottom row but on the right BATS becomes the top row. In the\nend, interpretability should trump coolness I think, unfortunately. Also, then you could add an x-axis label which would clear\nup my comment above.\nFig 3x- not sure what panel to call this, but can you put station ALOHA on the map as well? Since it is in the panel labelled\nb. How do these stations relate to the metagenome \"groups\" in Fig 2, that could be clarified.\n336- typo/grammar\n339- I’m finding it confusing that in figures the labels are Kane’ohe Bay but in the text the abbreviation KByT is used.\n379- confidence in the tree could help support these group breakdowns\nFig 4. I would swap order of Type material and Subgroup, so Type is on the right\nFig 4- your superscripts are very hard to see/read/distinguish. Consider bold and underline instead? Also, why put the\nsuperscript in the Species column- wouldn’t putting it in the Type column make more sense?\n409- instead of deep/shallow I think fine/coarse is more clear and unambiguous.\n410- a similar combination of phylogeny and biogeography was used to refine taxonomic groups in abundant freshwater\nlakes heterotrophs, albeit for full length 16S, in 2012. I see a lot of parallels here, you could consider pointing to that as an\nexample of the utility of this approach as it has greatly expanded our understanding of freshwater ecology.\n(https://journals.asm.org/doi/10.1128/mmbr.00028-10 , has about a million citations now)\n420- this citation (Zhao et al., 2024) must be unlinked to your reference manager because it is in a different format\n424- I don’t understand how ecotypic differentiation is in conflict with recombination. Couldn’t recombination be the\nmechanism leading to ecological differences? Why does ecotypic differentiation indicate speciation instead?\n432- I don’t see how time series further this. In fact, I would think more spatial resolution would be more clarifying. After all,\nyou have “global” metagenomes but they are just a few sites in total, and wouldn’t spatial span more environmental\nvariables?\n458 – how is your P and N story distinct from the companion paper? May be worth a sentence to mention in the discussion.\n530- primers 515F-926R is V4-V5 not V4. Also, did you use Parada’s 515F-Y or 515F-C, Parada has two 515’s in the paper.\nThis is why you should include the actual primer sequence in parentheses as well, because it gets super confusing with\ncitations alone. Also it’s likely that you used the Walters version of the Parada primers. I would just double check this with\nwhoever performed the amplicon sequencing.\n530- If you went back and did full length 16S of your isolates that could extend the impact of your phylogeny to 16S amplicon\nsurveys as well.\n598- But you are making a comprehensive tree to clarify the SAR11 phylogeny; why would you leave them out? You should\ninclude them, and then tell us with confidence are they part of SAR11 or not.\n602- shouldn’t you be building trees using proteins not genes?\n621- what was your outgroup? This is important, and should further be chosen appropriately to decide if those other groups\nare SAR11 or not (line 598)\n626- Why are you using a pruned phylogeny, why not just include confidence values in the branches and then limit\ndownstream analysis to the major groups only. You shouldn’t exclude genomes from your tree just because you don’t have a\nlot of them, the tree structure should be shaped by the full diversity. Did any 95% ANI clusters disagree with the tree\nstructure? Why not make the tree, then do the whole-genome ANI clusters and show mapping for the branch-level that each\n95ANI maps back to?\n(Remarks on code availability)\nThe NCBI BioProject is a placeholder (line 1108- \"NCBI associated with BioProject XXXXXXX\")\nIt looks like it does exist on Figshare though, but I suggest they use the figshare DOI to cite instead of the in-text link.\nThere is some code and data available on github. It's certainly not all the code used in the analyses but it looks like figure\ncode is there. I guess most stuff was part of the ANVI'O workflow though, so maybe there's not actually a lot of free-standing\ncode with the paper analysis. I did not look in detail, just glanced through.", "reviewer_2": "The manuscript submitted by Freel et al present an original and impactful study investigating the ecology and evolution of\nSAR11 marine bacteria. The researchers have significantly expanded the cultured representation of SAR11 diversity,\ninvestigated biogeography through genomics and metagenomic fragment recruitment, identified ecologically distinct clades\nof SAR11 and then proposed a taxonomic framework and naming convention for SAR11… but I guess we don’t call it that\nanymore!\nThe study is well executed and the paper is well written.\nMy only main concern is the decision to remove genomes from their original phylogenetic tree. The researchers generated a\nphylogeny of all 481 genomes and then removed “SAGs that did not fall into a 90% gANI cluster of at least three genomes…”\nSecond, they dereplicated remaining genomes using a 95% gANI cutoff. This left 268 SAR11 genomes, which are the\ngenomes that are also included in the final taxonomic framework and naming convention. I am okay with this sort of pruning\nand dereplicating in phylogenetic analysis and it will certainly give you a “cleaner” and better resolved tree with nice well\nsupported clades. However, removing this “messiness” might have implications later. So, I would be curious to see what\nhappens to the phylogeny and the taxonomic structure if these genomes are added back in. Does it impact the monophyly of\nthe named genera for example? Also, is it possible to include some bootstrap values on the phylogenies. It would be nice to\nknow how well supported (or not well supported) many of these clades (as well as they deeper relationships) are.", "reviewer_3": "In their study, Freel et al. generated an extensive collection of SAR11 genomes from cultures and combined these with\npublicly available genomic data to reconstruct the evolutionary and ecological units within this lineage. The study is highly\nrelevant given the global distribution of SAR11 and its distinctive evolutionary history, which challenges traditional\ntaxonomic classification methods based n cutoffs. Below, I provide several suggestions on the phylogenomics part of the\nstudy, that I hope will help the authors refine and strengthen their study:\nLines 162–163: The presence/absence of genes is generally not a robust criterion for selecting marker genes within a\nlineage. Since the study is grounded in the vertical evolutionary history of the clade, relying on markers that may have\nexperienced complex horizontal gene transfer events could obscure or distort the phylogenetic relationships. This might\nexplain why subclades Ia and Ib form a monophyletic group in the analysis.\nLines 169–170: The assignment of families appears somewhat arbitrary and is largely based on previous classifications that\ndefine SAR11 as an order. However, the authors have compelling results that could support the development of a more\nconsistent and evolutionarily grounded framework for defining taxonomic levels within SAR11. I particularly appreciate the\napproach of integrating ecological and evolutionary evidence. The same comment applies to the proposed genera shown in\nFigure 4.\nFigure 1: I recommend testing outgroup-independent rooting methods, such as Minimal Ancestor Deviation (MAD), to\nevaluate the influence of the chosen outgroup on the topology and stability of the resulting groups.\nLines 640–652: I find it surprising that Chloroflexota genomes were used as the outgroup, considering the considerable\nphylogenetic distance between this phylum and SAR11. This disparity increases the risk of long-branch attraction artifacts,\nwhich could distort tree topology and affect the RED estimates. In addition, different marker genes (120) are used to get this\nestimates. Why aren't the authors using the marker genes used to build the SAR11 tree?"}, "author_response": "Response to Reviewers\nNature Communications manuscript NCOMMS-25-18926\nFreel, K. C. et al. New isolate genomes and global marine metagenomes resolve\necologically relevant units of SAR11.\nPage, line, figure, and table numbers included here refer to the manuscript\ndocument that includes tracked changes.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nGeneral Comments:\nThe manuscript describes a new phylogeny for the abundant marine bacterium SAR11.\nThis phylogeny is based only on high quality isolate and single cell-amplified genomes,\nand is enabled by the addition of 81 new isolates from the ocean near Hawaii, an\nimpressive contribution as only 25 were previously available. The authors additionally\nmap over a thousand ocean metagenomes from multiple datasets to explore the\ngeographic distribution of their SAR11 clades, and based on a combination of the\nphylogenetic and ecological groupings suggest new family, genus, and species-level\nnames and type strains. A more clearly delineated SAR11 phylogeny fills a clear need\nin ocean microbial ecology, and this approach that combines ecology and phylogeny\ninto a manually curated taxonomy calls to mind a widely adopted freshwater taxonomy\nthat has been incredibly valuable to the freshwater community for over a decade\n(https://journals.asm.org/doi/10.1128/mmbr.00028-10). I think this will serve as a\nsimilarly valuable, and long overdue, resource.\nWe greatly appreciate the positive feedback.\nMy main concern is around the construction of the phylogenetic tree, and although\nphylogenetics is not my primary expertise I did share the public preprint with a colleague\nwhose main focus is phylogenies and they shared my concerns. To create the final tree,\nthe authors excluded genomes that did not have multiple closely related references, as\nwell as excluding groups that are ambiguously part of the SAR11 order. However, all of\nthe clade’s diversity should be included to obtain the most reliable branch structure.\nInstead of showing both versions in Fig 1 to highlight unstable branches, why not show\nthe single large tree along with confidence values for the branches? And instead of\nexcluding subgroups V and VI why not include them and clarify their debated\nrelationship to SAR11? Limiting the downstream mapping analysis to 95ANI clusters is\nrequired methodologically, but the authors can just clarify which leaves each ANI cluster\ncombines. (In other words, re-making the tree, which should not take too long, should\nnot require repeating all of the downstream mapping.) Additionally, the authors\nthroughout the paper and in the methods refer to the tree as being constructed from\ngenes, but that is highly unusual. Typically trees are constructed from protein\nsequences.\nWe thank the reviewer for their careful consideration of our work, and for their\nsuggested improvements. Other reviewers also raised some concerns with our\nphylogenies, which we have addressed with new analyses and clarifications in our\nrevised manuscript. As suggested, we reconstructed a broad, comprehensive\nphylogeny that now includes subgroups IV and V (which were previously classified\nas SAR11) as well as orphan SAGs that were not contained within larger clades. We\nnow include additional text in the manuscript regarding the evolutionary relationships\nbetween subgroups IV and V and other subgroups within the order Pelagibacterales.\nWe highlight that a comprehensive understanding of the relationships between\nsubgroups I-III and IV requires additional, focused analyses that are beyond the\nscope of this work that is largely focused within the family Pelagibacteraceae\n(formerly subgroup I).\nWe have corrected the text throughout the manuscript in order to clarify that protein\nsequences were used to construct the phylogenies. Thank you for catching that.\nOverall the paper is well written and the figures are beautiful. I have a few additional\ncomments and suggestions regarding clarity of figures and discussion of results which I\ndetail below.\nThank you for the kind comments.\nSpecific Comments:\n59- I suggest you add the number of proposed genera specifically here\nDone (page 2, line 41).\n62- this phrasing makes it sound like you recommend someone do this in the future, not\nthat you do it here in this paper\nWe agree with the reviewer and have changed the phrasing to now more clearly\narticulate that this is what was accomplished in this study (page 2, lines 44-45).\n141- in presenting your results in table 1, you could consider adding a brief statement\nas to what your results suggest is the best approach for future culturing attempts\nWhile we understand and appreciate the motivation behind the reviewer’s comment,\noffering appropriate and effective suggestions is complex and requires addressing\nconsiderations that may detract from the focus of the current study. The\ndetermination of favorable culture conditions is notoriously difficult and varies a great\ndeal between different subclades, and final decisions for best approaches will\nultimately depend on the motivation behind the future work (e.g. total strains vs.\n2\ndiversity of strains, etc). After considering this suggestion at length, we finally\nconcluded that a separate study that is dedicated to describing the best practices for\nthe cultivation of SAR11 with lessons from prior efforts and the inclusion of additional\ninvestigators would be a more effective way to realize this important suggestion.\nGiven the space considerations in our already dense manuscript, such a discussion\nwould have unlikely reached its intended utility and impact. We hope that the\nreviewer agrees with our resolution.\n234- can you add any statistical analysis to support cohesion within groups beyond\neyeballing the heat map?\nWe thank the reviewer for this suggestion. Our revision now includes appropriate\nstatistical tests that we describe in Methods (page 14, lines 655-660), Results (page\n5, lines 198-201), and Supplementary Fig. 4.\n237- again, can support with any statistics?\nWe have also addressed this, and revised our Methods (page 14, lines 655-660),\nResults (pages 5, lines 216-217), and Supplementary Fig. 5 to describe the\nanalyses.\n244 and Fig2- clarify how group 1 and 3 are distinguished. How is “low-latitude, high\ndiversity” different than “low-latitude”? Confusing because “high-latitude, low-diversity” is\nexplicitly labelled but “low-latitude” does not have any extra description.\nWe thank the reviewer for catching that. We have now changed the grouping to “low-\nlatitude, low-diversity” in order to distinguish between the two low-latitude groups\n(Figure 2; pages 5, lines 212-217).\n253- Can’t two phylogenetically distinct units form the same ecological unit? I think\n“ecological unit” is perhaps not the best word choice here- if I understand correctly you\nare trying to say that both ecology and phylogeny, but neither alone, distinguish all the\nSAR11 “units”, so it’s confusing to call the unit “ecological” when ecology can’t fully\nresolve it.\nWe agree, and thank the reviewer for pointing this out. We have rephrased this\nsentence in hopes of articulating a more accurate view of our sentiment on this topic\nwhile avoiding circular language (page 5, lines 225-228).\n255- typo/grammar\nNo longer appears in the rephrased sentence (page 5, lines 225-228).\nFig 2- why not make the text at the bottom a little bigger, because you could extend the\ntable to the left edge of the teal Pelagibacteriaceae label. It’s just pretty hard to read and\nyou have white space there you could fill.\nWe appreciate the perspective, and have modified Figure 2 as suggested.\nit’s easy to assume each value is 1 metagenome but you mapped almost 1500 (!!). A\nlegend to the heatmap shading could also help clarify how you combined\ncoverage/abundance values from different metagenomes, which isn’t clear to me.\nWe thank the reviewer for pointing this out and agree that the original presentation\ncould be misinterpreted as representing a single metagenome per column. To clarify\nthe number of metagenomes included in each category, we revised the top section\nof the figure to display a heatmap that functions as a scale bar, indicating the\nnumber of metagenomes per column. We also added Supplementary Table 11,\nwhich lists the specific metagenomes included in each numbered column, along with\nthe total number of metagenomes per column. Additionally, we updated the figure\nlegend to clarify the data shown. The values in the figure represent detection values,\ndefined as the proportion of nucleotides in a given genome covered at least 1x. Each\ncolumn shows the average detection value across all metagenomes grouped into\nthat category (as defined in Supplementary Tables 8 and 11).\n280- suggest defining RED (rel evol divergence I assume, but this is not super common\nterm and it does not show up on google easily)\nWe now define RED (relative evolutionary distance) in the text (page 6, line 252).\nFig 3. It seems like some panels are lacking labels\nWe agree with the reviewer. We have added labels to additional sections of Figure\n3, as well as some other minor modifications to improve its clarity.\n316- Is there some information on the extent, or significance, of the phylogenomic\nstructure in Fig 3a?\nWe have updated the figure caption to indicate that the subtree of Ia.3.VI genomes\nis extracted from the full phylogeny shown in Figure 2 (page 28, lines 969-970) and\nhave now included ultrafast bootstrap values at all nodes.\n320- what are these “detection values”? I don’t see units in the Fig 3 barplots either.\nWe have added the definition of detection values at its first occurrence in the text\n(page 5, lines 198-199), as well as the figure legend. These values refer to the\nproportion of nucleotides with at least 1x coverage from read mapping.\nFig 3b- I’m confused about this panel, although it looks very cool I’m not sure what’s on\nit. At first glance I thought it was the contigs of the two sags along the “x-axis”, and their\ncoverage in the heatmap. But then there are also multiple metagenomes from each\nlocation, so maybe it is the sample on the “x-axis” and the abundance in the\nmetagenome in the heat map? Adding a legend with units would help.\nFig 3b- this looks cool but if you stretched it into a straight line along the whole figure\nwidth it would be much easier to read. It’s confusing because for example on the left\ninterpretability should trump coolness I think, unfortunately. Also, then you could add an\nx-axis label which would clear up my comment above.\nWhile we are fond of the aesthetics of the initial figure, we recognize that, at this\npoint, we are probably over-familiar with this work and thus greatly appreciate the\nfresh input on this. We went ahead and arranged the genomes as two horizontal\nlines as suggested, and added a legend for the coverage values. This is now panel\ne, as we tried to be more thorough in segregating and explaining each of the\nindividual elements of the concatenated figure. We have also included a more\ndescriptive legend for Figure 3 in order to provide additional context and clarity\n(page 28, lines 968-982).\nFig 3x- not sure what panel to call this, but can you put station ALOHA on the map as\nwell? Since it is in the panel labelled b. How do these stations relate to the metagenome\n\"groups\" in Fig 2, that could be clarified.\nWe have revised the maps and added Station ALOHA to clarify its location with\nrespect to Kāneʻohe Bay. This is now specifically identified as panel d. We have also\nadded text to indicate how these stations relate to the metagenome groups in Figure\n2 (page 7, lines 283-291).\n336- typo/grammar\nCorrected.\n339- I’m finding it confusing that in figures the labels are Kane’ohe Bay but in the text\nthe abbreviation KByT is used.\nWe have revised the manuscript text and figures to use “Kāneʻohe Bay” in place of\n'KByT' in all instances except a few cases where referencing the time-series study\nspecifically was more appropriate.\n379- confidence in the tree could help support these group breakdowns\nWe have added bootstrap support values to the main phylogeny in Figure 1 as well\nas the condensed phylogeny of Figure 4.\nFig 4. I would swap order of Type material and Subgroup, so Type is on the right\nDone.\nFig 4- your superscripts are very hard to see/read/distinguish. Consider bold and\nunderline instead? Also, why put the superscript in the Species column- wouldn’t putting\nit in the Type column make more sense?\ndistinguish the relevant species, and have made these annotations in the “Type”\ncolumn as suggested.\n409- instead of deep/shallow I think fine/coarse is more clear and unambiguous.\nDone (page 8, line 358-359).\n410- a similar combination of phylogeny and biogeography was used to refine\ntaxonomic groups in abundant freshwater lakes heterotrophs, albeit for full length 16S,\nin 2012. I see a lot of parallels here, you could consider pointing to that as an example\nof the utility of this approach as it has greatly expanded our understanding of freshwater\necology. (https://journals.asm.org/doi/10.1128/mmbr.00028-10 , has about a million\ncitations now)\nWe thank the reviewer for highlighting this citation, which we have now included in\nthe Discussion (page 8, lines 364-366). There are notable parallels between our\nwork and that of Newton et al. (2011), particularly in the shared emphasis on\nintegrating ecological information into the framework. We also concur with their view\nthat such frameworks should evolve dynamically as new data become available. In\nthat spirit, we hope this publication, like that of Newton et al. (2011), will serve as a\nuseful guide for future efforts in this continually developing field.\n420- this citation (Zhao et al., 2024) must be unlinked to your reference manager\nbecause it is in a different format\nThank you for catching that - it is now fixed (page 8, line 372).\n424- I don’t understand how ecotypic differentiation is in conflict with recombination.\nCouldn’t recombination be the mechanism leading to ecological differences? Why does\necotypic differentiation indicate speciation instead?\nZhao et al. argue that homologous recombination occurs frequently within SAR11,\neven between DNA with relatively low sequence similarity. They suggest that this\nprocess plays a major role in maintaining genetic cohesion, and is responsible for\nthe lower average nucleotide identity (ANI) gap observed between SAR11 species\n(86–91%), compared to the typical 95% threshold used for other bacteria. In\ncontrast, they found that non-homologous recombination, which can lead to\necological diversification, was relatively rare.\nWe offer a different perspective via our study. While Zhao et al. propose that\nhomologous recombination can preserve species-level cohesion even at low\nsequence identity (<91%), our findings suggest that ecological diversification can still\nbe resolved across a continuum of genomic similarity, including above the ANI gap\nof 86-91%. This allowed us to define genus-level groupings as well as detect\ninstances of ecological speciation among closely related SAR11 genomes. In other\nwords, the cohesive effect of homologous recombination does not appear strong\nenough to counteract the forces of environmental selection that drive ecological\n6\ndifferentiation. We also propose that non-homologous recombination, although less\nfrequent, may introduce novel genetic material into populations. If these genetic\nchanges confer a selective advantage, they may spread and contribute to ecological\ndiversification.\n432- I don’t see how time series further this. In fact, I would think more spatial resolution\nwould be more clarifying. After all, you have “global” metagenomes but they are just a\nfew sites in total, and wouldn’t spatial span more environmental variables?\nWe fully agree that increasing the spatial resolution of global ocean sampling for\nmarine microbial metagenomes offers many important benefits. However, the\nspecific point we aim to emphasize here relates to understanding the genetic\ndeterminants that drive either competitive exclusion or co-existence among SAR11\npopulations. In our view, geographically constrained time series studies offer a\npowerful approach to uncover these genetic determinants. Our perspective is\ninformed by our group’s experience working in such a system (Ramfelt et al. 2024;\nTucker et al. 2025; Tucker et al. 2024; Tucker et al. 2021; Ramfelt et al. 2025). In\nthis example, the Kāneʻohe Bay Time-series focuses on the well-defined and\nenvironmentally constrained system of Kāneʻohe Bay, where samples have been\ncollected regularly for over eight years. Within this limited geographic area, we have\nobserved that SAR11 populations separated by only a few nautical miles can exhibit\ndistinct genomic profiles coinciding with sharp environmental gradients (Tucker et\nal., 2024). The limited spatial scale helps constrain environmental variability,\nallowing for more precise investigation of the metabolic and ecological factors that\ndifferentiate co-existing populations of Pelagibacterales.\n458 – how is your P and N story distinct from the companion paper? Maybe a sentence\nto mention in the discussion.\nThis study highlights a unique case in which an isolate genome from Kāneʻohe Bay\nand a single-amplified genome (SAG) from the Bermuda Atlantic Time-series Station\nwithin one genus (Ia.3.VI ) each contain genes that appear to be specific to the\nenvironments in which they were collected. By examining differences in ecologically\nrelevant gene content, we demonstrate potential fine-scale ecological speciation\nbetween these closely related genomes. We interpret regional differences in\nnitrogen and phosphorus availability between the Pacific and Atlantic Oceans as a\nlikely driver of these genetic distinctions, especially in genes involved in phosphorus\nand nitrogen acquisition. Our focus in this analysis is on two specific pathways: the\nC–P lyase pathway and type IV pilus assembly.\nIn parallel work (Tucker et al., 2024), we identified key metabolic traits and selective\npressures that drive the distinct coastal and offshore distributions of co-occurring\nPelagibacteraceae genera in the tropical Pacific. That study presents a broader\nanalysis of gene content related to nutrient acquisition, organic carbon utilization,\nand cellular stress responses. The results reveal consistent differences between\nnearshore and offshore genera, suggesting that biogeochemical variation,\n7\nparticularly in nitrogen and organic carbon availability, has played a role in shaping\necological divergence in gene content at the genus level.\nTogether, these investigations support our proposed taxonomic framework by\nshowing that ecologically meaningful traits and selective pressures align with genus-\nlevel distinctions within the Pelagibacteraceae.\nWe thank the reviewer for their suggestions. To reflect this connection, we have\nadded some text to our Discussion (page 9, lines 410–413) emphasizing how these\ncomplementary studies contribute to a broader understanding of SAR11 niche\npartitioning through a metabolism-focused lens.\n530- primers 515F-926R is V4-V5 not V4. Also, did you use Parada’s 515F-Y or 515F-\nC, Parada has two 515’s in the paper. This is why you should include the actual primer\nsequence in parentheses as well, because it gets super confusing with citations alone.\nAlso it’s likely that you used the Walters version of the Parada primers. I would just\ndouble check this with whoever performed the amplicon sequencing.\nWe are thankful for the attention of our reviewer. We have added the primer\nsequences to the manuscript (page 11, lines 483-487) and clarified that we used the\n515F-Y primer targeting the V4-V5 region. We can confirm that these are the\nprimers from Parada et al., 2016, which is also listed in Table 1 as the reference for\nthese primers in the Walters publication.\n530- If you went back and did full length 16S of your isolates that could extend the\nimpact of your phylogeny to 16S amplicon surveys as well.\nWe added a folder on FigShare with all available full-length 16S rRNA gene\nsequences from these genomes (80 available;\nhttps://doi.org/10.6084/m9.figshare.29983285; page 15, lines 678-683). We\npreviously determined that the amplicon surveys fail to distinguish all of the genera\nwe describe here [e.g. Litoralipelagibacter (Ia.3.III) and Undatipelagibacter (Ia.3.VI)].\nWhile certainly of value, our conclusion is that the 16S rRNA gene cannot be relied\nupon for definitive genus-level resolution in SAR11 bacteria.\n598- But you are making a comprehensive tree to clarify the SAR11 phylogeny; why\nwould you leave them out? You should include them, and then tell us with confidence\nare they part of SAR11 or not.\nWe appreciate the reviewer’s comment and acknowledge that other reviewers also\nraised concerns regarding our phylogenomic analyses. There is a long history of\nstudies attempting to resolve the evolutionary relationship between subgroup V and\nSAR11 subgroups I-III. Based on some of the most rigorous and sophisticated\nanalyses of alphaproteobacterial evolution available (e.g., Muñoz-Gómez et al.,\n2019), we are confident that subgroup V is not specifically related to subgroups I-III.\nAs such, we do not believe that repeating those analyses here would contribute\nmeaningfully to the specific focus of this manuscript.\ndue to a historical lack of representative genomes. While we successfully isolated\nand sequenced the genome of a representative of subgroup V (HIMB59) from\nKāneʻohe Bay nearly two decades ago (Brandon 2006; Thrash et al., 2011; Grote et\nal., 2012), environmental genomes from subgroup IV have only recently become\navailable (Haro-Moreno et al., 2020). Although existing genomic and phylogenetic\ndata suggest that subgroup IV is also not part of the SAR11 lineage (Haro-Moreno et\nal., 2020), the evidence is less definitive than for subgroup V.\nWhile resolving the relationship between subgroup IV and SAR11 subgroups I-III is\ncertainly of scientific interest, the precedent set by subgroup V shows that doing so\nin a definitive manner would require extensive additional analyses beyond the scope\nand resources of our current study. Importantly, we want to emphasize that this\nquestion, though relevant to broader phylogenetic discussions, is tangential to the\ncentral focus of our work and does not alter or diminish the core findings presented\nin our manuscript.\n602- shouldn’t you be building trees using proteins not genes?\nOur apologies; all of these trees were built with proteins and not genes and we have\nmodified the text to clarify this point.\n621- what was your outgroup? This is important, and should further be chosen\nappropriately to decide if those other groups are SAR11 or not (line 598)\nLines 592-593 of the original manuscript describe the outgroup. To investigate the\nrobustness of our family- and genus-level groupings, we first reconstructed four\ndifferent phylogenies that included subgroup IV and V genomes. The phylogenies\nconstructed were: (1) with an outgroup that included isolate genomes from the order\nPseudomonadales; (2) with an outgroup that included ten broadly dispersed isolate\ngenomes from multiple orders within the Gammaproteobacteria; (3) with the original\noutgroup of our submitted manuscript (ten genomes from the Rhodobacteraceae);\nand (4) with no outgroup.\nTo further assess the influence of outgroup selection on tree topology, we applied\nthe Minimal Ancestor Deviation (MAD) method to evaluate rooting stability based on\na suggestion from Reviewer 3. When we ran MAD on the dataset without an\noutgroup, and with prior knowledge that subgroups IV and V are likely basal, we\nfound that these groups were placed on long internal branches, raising concerns\nabout the reliability of this rooting (see additional figures on figshare:\n10.6084/m9.figshare.28127633). To cross-validate, we also applied midpoint rooting,\nwhich placed subgroup V as the outgroup. Importantly, across all phylogenies, the\ngenus- and family-level groupings identified in this study remained consistent\nregardless of the outgroup strategy.\ntaxonomic framework for SAR11. We have updated the Methods section to indicate\nthat we used the midpoint rooting approach (pages 12-13, lines 578–580).\n626- Why are you using a pruned phylogeny, why not just include confidence values in\nthe branches and then limit downstream analysis to the major groups only. You\nshouldn’t exclude genomes from your tree just because you don’t have a lot of them,\nthe tree structure should be shaped by the full diversity. Did any 95% ANI clusters\ndisagree with the tree structure? Why not make the tree, then do the whole-genome ANI\nclusters and show mapping for the branch-level that each 95ANI maps back to?\nHere again, aesthetics may have got in the way of clearly articulating the message\nof this analysis. In our initial phylogeny, we did include all relevant genomes in order\nto create a comprehensive and unpruned phylogeny. Genomes were only excluded\nto create a pruned phylogeny in an attempt to provide a “clean” view of the genus-\nlevel groupings, and to highlight the genomes used in the read recruitment analyses.\nConfidence values were included on all of the supplemental phylogenies, but we\nagree with the reviewer that they should be more apparent. We now include all of\nthe genomes in a revised Figure 1, as well as confidence values. We did not have\nany 95% ANI clusters that disagreed with the tree structure.\nReviewer #1 (Remarks on code availability):\nThe NCBI BioProject is a placeholder (line 1108- \"NCBI associated with BioProject\nXXXXXXX\")\nThe NCBI BioProject has been updated (page 15, line 680). All of the final genome\nsequences and underlying raw data (via the SRA) are now publicly available and the\nrelevant accession numbers have been added to Supplementary Table 3.\nIt looks like it does exist on Figshare though, but I suggest they use the figshare DOI to\ncite instead of the in-text link.\nWe have updated the BioProject number as noted above, and also include the\nFigShare DOI as reference.\nThere is some code and data available on github. It's certainly not all the code used in\nthe analyses but it looks like figure code is there. I guess most stuff was part of the\nANVI'O workflow though, so maybe there's not actually a lot of free-standing code with\nthe paper analysis. I did not look in detail, just glanced through.\nWe can confirm that an extensive amount of the code was from anvi’o. We have\nreviewed the code available on GitHub to ensure that all code used in the analyses\nare included.\nWe thank the reviewer again for the input and constructive criticism.\nThe manuscript submitted by Freel et al present an original and impactful study\ninvestigating the ecology and evolution of SAR11 marine bacteria. The researchers\nhave significantly expanded the cultured representation of SAR11 diversity, investigated\nbiogeography through genomics and metagenomic fragment recruitment, identified\necologically distinct clades of SAR11 and then proposed a taxonomic framework and\nnaming convention for SAR11… but I guess we don’t call it that anymore!\nThe study is well executed and the paper is well written.\nWe thank the reviewer for their positive feedback.\nMy only main concern is the decision to remove genomes from their original\nphylogenetic tree. The researchers generated a phylogeny of all 481 genomes and then\nremoved “SAGs that did not fall into a 90% gANI cluster of at least three genomes…”\nSecond, they dereplicated remaining genomes using a 95% gANI cutoff. This left 268\nSAR11 genomes, which are the genomes that are also included in the final taxonomic\nframework and naming convention. I am okay with this sort of pruning and dereplicating\nin phylogenetic analysis and it will certainly give you a “cleaner” and better resolved tree\nwith nice well supported clades. However, removing this “messiness” might have\nimplications later. So, I would be curious to see what happens to the phylogeny and the\ntaxonomic structure if these genomes are added back in. Does it impact the monophyly\nof the named genera for example? Also, is it possible to include some bootstrap values\non the phylogenies. It would be nice to know how well supported (or not well supported)\nmany of these clades (as well as they deeper relationships) are.\nWe appreciate the reviewer’s comments. Similar perspectives regarding the main\nphylogeny were shared by the other reviewers. The phylogeny shown in the revised\nFigure 1 now includes all 484 genomes and no dereplication. It also includes\nbootstrap support values, which we had previously included only in the supplemental\nfigures for aesthetic reasons. Our original decision to dereplicate the tree was also\ndriven primarily by aesthetic considerations, but informed by analyses that showed\nthat it did not affect the monophyly of the genera we name in this study. It also did\nnot affect the bootstrap support found at the deeper, family-level nodes of the tree.\nReviewer #3 (Remarks to the Author):\nIn their study, Freel et al. generated an extensive collection of SAR11 genomes from\ncultures and combined these with publicly available genomic data to reconstruct the\nevolutionary and ecological units within this lineage. The study is highly relevant given\nthe global distribution of SAR11 and its distinctive evolutionary history, which challenges\ntraditional taxonomic classification methods based n cutoffs. Below, I provide several\n11\nsuggestions on the phylogenomics part of the study, that I hope will help the authors\nrefine and strengthen their study:\nLines 162–163: The presence/absence of genes is generally not a robust criterion for\nselecting marker genes within a lineage. Since the study is grounded in the vertical\nevolutionary history of the clade, relying on markers that may have experienced\ncomplex horizontal gene transfer events could obscure or distort the phylogenetic\nrelationships. This might explain why subclades Ia and Ib form a monophyletic group in\nthe analysis.\nWe appreciate the sentiment of this comment. However, it is important to clarify that\nwe first started with a robust set of genes defined specifically for the phylogenetic\nanalysis of alphaproteobacterial genomes (Wang and Wu 2013; Muñoz-Gómez et\nal., 2019). We then refined this gene set to focus specifically on genes that are\ninformative for high-quality SAR11 genomes. This gene set is predominately single-\ncopy and rarely laterally transferred (Wang and Wu 2013).\nRegarding the monophyly of subclades Ia and lb. We appreciate that the reviewer is\nbringing this up, since it represents one of the strengths of our work. The expectation\nof monophyly within Ia and Ib is an example of historical labels impeding the rational\nand systematic categorization of sub-entities within the SAR11 lineage. When 16S\nrRNA gene databases for environmental marine bacteria began expanding in the\nearly 1990s, it became clear that “strategic” sequence selection was required to\nmaintain Ib as a separate entity from Ia. Even so, it has always been difficult to\nresolve Ib to a monophyletic clade, as opposed to several separate lineages that\nbranch near the base of Ia. The outcomes of genome-based phylogenies of SAR11\nnow have sufficient coverage to mirror those early rRNA-based studies (e.g. Haro-\nMoreno et al. 2020). After extensive analysis of the Ia-Ib relationship, we are\nconfident that (i) the genomes considered to be part of the Ib subgroup do not form a\nmonophyletic lineage on their own, and (ii) the most parsimonious approach, based\non the largest available genome dataset, combines Ia and Ib into a single\nmonophyletic lineage.\nLines 169–170: The assignment of families appears somewhat arbitrary and is largely\nbased on previous classifications that define SAR11 as an order. However, the authors\nhave compelling results that could support the development of a more consistent and\nevolutionarily grounded framework for defining taxonomic levels within SAR11. I\nparticularly appreciate the approach of integrating ecological and evolutionary evidence.\nThe same comment applies to the proposed genera shown in Figure 4.\nWhile we thank the reviewer for their perspective here, we do respectfully disagree\nwith the sentiment that the taxonomic assignments we propose are arbitrary and\nlargely based on previous classifications.\nThe primary impetus of this study is very much focused on resolving the intersection\nbetween ecology and genome evolution at the fine tips of the SAR11 phylogeny.\nWhile a systematics for SAR11 was not our original intention, we have now spent\n12\nconsiderable effort in resolving what we have come to propose as SAR11 genera, as\nnoted in Figure 4. Much of our manuscript illustrates how these genera are both\necologically consistent and firmly grounded in a robust evolutionary framework. Prior\nto our study, there has been broad and pervasive inconsistency in the assignment of\ntaxonomic ranks to different levels of diversity within the SAR11 lineage. Whereas\nthe GTDB has attempted to remove the arbitrary aspect of bacterial taxonomy by\napplying a set of rules based on genome relatedness to assign taxonomic levels,\nwe\nshow that these rules do not fit when one considers ecology as a relevant\nconsideration of the taxonomic outcome. Thus, for a taxonomic hierarchy to be\nmeaningful and useful for SAR11, some human intervention to meld the ecological\nand evolutionary aspects is certainly required, and we are not of the opinion that this\nis an arbitrary endeavor.\nIt is entirely consistent with other orders within the Alphaproteobacteria to consider\nthe SAR11 lineage as the order-level Pelagibacterales. This has been well-\nestablished in robust phylogenies (e.g. Muñoz-Gómez et al., 2019). Ours is the first\nstudy to propose four families within this order, which are wholly consistent with both\nevolutionary history and ecological information.\nFigure 1: I recommend testing outgroup-independent rooting methods, such as Minimal\nAncestor Deviation (MAD), to evaluate the influence of the chosen outgroup on the\ntopology and stability of the resulting groups.\nWe used the Minimal Ancestor Deviation (MAD) method to evaluate the influence of\nthe outgroup on the stability of the overall phylogeny, as suggested. In this figshare\nfolder (https://doi.org/10.6084/m9.figshare.28127633), we include the phylogeny\ngenerated using the MAD method when no outgroup was included; however, the\nclades previously referred to as SAR11 subgroups V and IV were included. We\nknow in this case that SAR11 subgroup V genomes should be identified as the\noutgroup, the relationship between this group and the Pelagibacterales has been\nextensively studied. However, this was not the phylogeny that the MAD method\nyielded. The genomes in the expected outgroup (purple box) were distributed as\nlong internal branches.\nTo further explore outgroup-independent rooting methods, we then implemented a\nmid-point rooting method to root this phylogeny (figshare:\nhttps://doi.org/10.6084/m9.figshare.28127633). While this method can also be\nbiased if an evolutionary rate is miscalculated, it is a simple algorithm and in\ncommon use. In this case, “SAR11 subgroup V” was established as an outgroup as\nexpected from previous literature.\nWhile we are only now becoming familiar with the MAD method, we note that the\nroot identified using the midpoint method aligns with what we know regarding SAR11\nevolution. Most crucially, the midpoint rooted tree demonstrates that the genera we\nidentified in our study, using genomes of the Rhodobacteraceae as an outgroup for\nthe phylogenies, remain stable. (trees available in figshare:\nhttps://doi.org/10.6084/m9.figshare.28127633).\noutgroup, considering the considerable phylogenetic distance between this phylum and\nSAR11. This disparity increases the risk of long-branch attraction artifacts, which could\ndistort tree topology and affect the RED estimates. In addition, different marker genes\n(120) are used to get this estimates. Why aren't the authors using the marker genes\nused to build the SAR11 tree?\nOur study used the default workflow for determining RED scores that is provided by\nthe GTDB-Tk (“de_novo_wf”). As defined in Parks et al., 2018, the aim of this tool is\nto lend objective taxonomic assignments of genomes by placing the genomes in\ndomain-specific protein reference trees, which relies on the bac120 marker gene set.\nWe followed previous guidance provided by the authors of this workflow and chose\n“p__Chloroflexota” as the outgroup (see GitHub issue:\nhttps://github.com/Ecogenomics/GTDBTk/issues/120), this issue was also\nreferenced in a more recent question regarding the election of outgroup (see GitHub\nissue: https://github.com/Ecogenomics/GTDBTk/issues/390). These standardized\nmethods are used for classification through GTDB and our intent was to replicate\nthis method in order to assess how standardized RED scores align with our\necoevolutionary framework.\nIn phylogenies constructed with a variety of different outgroups, we can confirm that\nthe genera identified here remain stable within the Pelagibacterales. No other\ndistortion was evident. While we fully appreciate that the choice of outgroup can\ndistort the phylogeny and impact order of branching, we note that the RED scores\nthemselves are generated independent of the outgroup.\nWe thank the reviewer for their careful consideration of our study and their valuable\nsuggestions.\n14"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I have no additional concerns and appreciate the author's detailed responses. They pushed back on a few of my\nsuggestions/criticisms, but I think in all those cases they supported their decision well.", "reviewer_2": "The authors have addressed my comments sufficiently. Great work and a great paper. Thank you.", "reviewer_3": "Thank you for addressing the suggestions provided."}, "author_response": ""}]

Decision letter