Ecophysiology and global dispersal of the freshwater SAR11-IIIb clade

Ecophysiology and global dispersal of the freshwater SAR11-IIIb clade | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Ecophysiology and global dispersal of the freshwater SAR11-IIIb clade Michaela Salcher, Clafy Fernandes, Markus Haber, Paul Layoun, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6457240/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The freshwater SAR11-IIIb genus Allofontibacter (initially described as ‘Ca. Fonsibacter’ ) within the order Pelagibacterales is recognised for its ubiquitous presence in freshwater environments. However, it remains poorly understood due to cultivation limitations, with only one cultured genome published to data. As a result, its genetic diversity, metabolic capabilities and ecological roles remain largely unexplored, with most available data limited to lakes in the Northern Hemisphere. Here, we present seven new isolates representing two novel species, along with 93 high-quality metagenome-assembled genomes (MAGs) derived from a global survey across five continents. Phylogenomic analysis revealed 16 species forming nine distinct biogeographic clusters, indicating speciation patterns linked to water temperature and latitude. Notably, we observed phylogeographic partitioning, including endemic species restricted to African lakes, quasi-endemic species confined to either the Northern or Southern Hemisphere, and the co-existence of cosmopolitan species alongside regionally constrained lineages. Furthermore, metabolic profiling and growth experiments uncovered species- and strain-specific adaptations for nutrient uptake, along with unique pathways for sulfur metabolism. These findings provide the first global-scale genomic and ecological overview for this lineage, raising key questions about dispersal barriers, priority effects, evolutionary trajectories, and mechanisms of niche adaptation in freshwater SAR11. Biological sciences/Microbiology/Environmental microbiology/Water microbiology Biological sciences/Microbiology/Bacteria/Bacterial genomics Earth and environmental sciences/Ecology/Biogeography Earth and environmental sciences/Ecology/Microbial ecology Biological sciences/Ecology/Freshwater ecology Pelagibacterales Allofontibacter Bacterial cultures Long-read metagenomic sequencing MAGs Metabolism Ecogenomics Biogeography Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The SAR11 order, known as Pelagibacterales, is known for its ubiquitous presence in the plankton of both marine and freshwater systems 1 – 4 , its heterotrophic nature, and small cell sizes (~ 0.04 µm 3 ). Since its discovery as a dominant bacterial clade, SAR11 has been extensively studied as a model for streamlined genomes 1 , 5 , 6 . Subsequent genomic investigations have outlined genus-like lineages representing different SAR11 subclades with specific spatial and temporal distributions in oceans (SAR11-I, II), brackish or coastal waters (SAR11-IIIa) and brackish/freshwater habitats (SAR11-IIIb) 7 . In addition to being abundant, SAR11 exhibits a high genomic diversity that is persistent and characterized by closely related ecotypes as opposed to distinct species clusters 1 , 5 , 6 . Although SAR11 is one of the most abundant bacterial groups in aquatic systems, most subclades still lack a comprehensive characterization due to a low number of stable cultures. One such group is the SAR11-IIIb (LD12), which is highly abundant in freshwater lakes worldwide 4 , 8 , 9 . Just like their marine ancestors, freshwater SAR11-IIIb are streamlined bacteria with reduced genome sizes and incomplete or missing metabolic pathways leading to multiple auxotrophies and unusual nutrient requirements 1 , 5 . This makes this group reliant on co-occurring microbes for crucial metabolites (Black Queen Hypothesis) 10 such as reduced sulfur, vitamins, or their precursors 11 , 12 . Notably their lower recombination frequencies compared to the marine clades suggest a bottleneck during their transition from marine to freshwaters 13 . Genomic islands, present across all SAR11 genomes, act as hot spots for horizontal gene transfer (HGT) events and contribute to microdiversity 14 . Certain genes in hypervariable region 2 (HVR2), for instance, are thought to be related to membrane modifications and synthesis of extracellular structures for potential phage receptors 14 , 15 or strain-specific metabolic abilities 7 . The generally high population numbers and microdiversity of SAR11 are mutually reinforcing; large populations can maintain diversity through genetic variation, while this diversity supports high abundances by mitigating environmental pressures 6 , 16 . Thus, this interplay, along with environmentally-mediated selection, may contribute to a distinct global biogeography of SAR11 17 While niche separation among marine SAR11-clades is influenced by factors like water temperature, ocean currents and inorganic nutrients 18 , 19 , freshwater lakes present a contrasting scenario. Due to physical isolation and distinct physico-chemical parameters, microbial dispersal between lakes is limited 20 . Although different lakes can be inhabited by distinct species of the same ubiquitous genus 21 – 24 , microbes of the same species (average nucleotide identity ANI > 95%) have been reported from lakes located hundreds of km apart or even from different continents 21 , 24 – 26 . However, large-scale analyses indicative of phylogeographic partitioning of lake microbes remain rare 27 , 28 , mainly because global sampling has been uneven and focused mainly on temperate regions in the northern hemisphere 29 , 30 . Adding to these challenges, the study of SAR11-IIIb is further constrained by a lack of cultured representatives. To date, only one isolate from the freshwater SAR11 lineage has been successfully cultivated (‘ Ca . Fonsibacter ubiquis’, later reclassified to Allofontibacter communis 31 ) from the brackish Lake Borgene, USA 2 . Consequently, most insights into SAR11-IIIb’s metabolic and ecological adaptations rely on cultivation-independent approaches such as metagenome-assembled genomes (MAGs) 32 and single amplified genomes (SAGs) 13 . While these approaches have significantly advanced our knowledge, they have inherent disadvantages, including fragmented and incomplete genomes, the inability to conform functional roles experimentally, and the lack of information on phenotypic traits such as growth rates, metabolic fluxes, and physiological response to environmental stress, which can only be studied through cultures. This study aims to advance current gaps in understanding the ecology, diversity, and biogeographic distribution of freshwater SAR11-IIIb by leveraging a combination of global sampling, culture isolation, and long-read metagenomics. We report seven new isolates alongside 93 high-quality MAGs, including data from the underrepresented Southern Hemisphere (Fig. S1 ). These isolates provide experimental validation, such as temperature and nutrient preferences, and complement genomic analysis. The integration of culture-based and cultivation-independent approaches in this study offers insights into their ecological roles by linking biogeographic patterns to metabolic and physiological adaptations. Results In this study we isolated and genome-sequenced seven new SAR11-IIIb strains from three oligo-mesotrophic freshwater habitats in the Czech Republic (Klíčava Reservoir, Lake Medard and Lake Milada). The new genomes are complete, with one circular chromosome and have been classified as “Fonsibacter” by GTDB-tk 33 . Long-and short-read sequencing of water samples from five continents (Fig. S1 , Table S1 ) yielded 93 new high-quality MAGs of SAR11-IIIb, along with 36 from SAR11-I, II, IIIa subgroups (Table S2). All new SAR11 cultures and MAGs have common features like small genome sizes (1.05–1.11 Mbp), low GC contents (~ 29.5%), minimal amount of non-coding DNA (< 5%), and short median intergenic spacers (5–6 bps), similar to other genome-streamlined bacterial groups 5 , 34 , 24 , 22 . Phylogenomic analyses recover 16 distinct species of SAR11-IIIb Phylogenetic analysis based on the core genome of the family (751 protein sequences; Fig. 1 , Table S3), confirmed the subdivision of the SAR11 clade into SAR11-I, II, IIIa and IIIb subgroups 19 , 35 – 37 . Interestingly, 12 MAGs from Lake Malawi grouped within marine SAR11-I, and three MAGs retrieved from deep alpine lakes grouped within marine SAR11-II, each bordered by genomes gained from brackish systems. Based on average nucleotide and amino acid identities (ANI and AAI) and phylogenetic tree branching patterns (Fig. 1 , Tables S4, S5), we propose that the SAR11-IIIb clade contains 16 species. However, our analysis revealed that some of these do not conform to the commonly suggested ANI and AAI threshold of 95% for bacterial species delineation 38 , 39 (Fig. S2, Tables S4-S5), aligning with studies highlighting the unique intraspecific microdiversity within the SAR11 group, which often defies the concept of discrete species boundaries 40 . Still, all species formed separate ANI clusters with higher within-species ANI values compared to the closest relative (Fig. S2, S3) and had unique geographic distribution patterns (see below). The seven new isolated strains isolated here represent two distinct species in the SAR11-IIIb clade that we tentatively named and registered at SeqCode 41 as Allofontibacter medardicus (species cluster IIIb.8, representative strain ME-17) and Allofontibacter abundans (IIIb.9, MiE-29). We further named 11 SAR11-IIIb, one SAR11-I species, and one SAR11-II genus based on high-quality MAGs (Fig. 1 ). A detailed description of the newly proposed species can be found in the Supplementary text. Global and seasonal distribution of SAR11-IIIb In the phylogenomic tree (Fig. 1 ), SAR-IIIb genomes grouped based on the climatic zone of isolation habitat and biogeographic occurrence. The global distribution of individual SAR11-IIIb species was investigated using a total of 620 metagenomes, encompassing both publicly available and newly sequenced metagenomes (Fig. 2 , Table S6). The deeply branching species A. africanus (IIIb.1, Fig. 1 ), consisting of a previously reported MAG from Lake Tanganyika 42 along with nine new MAGs from Lake Malawi assembled in this study, was restricted to tropical lakes in Africa (up to 68x coverage per Gb). Four species (IIIb.2 to IIIb.5) contained MAGs from lakes in boreal and subarctic regions in Scandinavia and North America. While A. scandinavicus (IIIb.2) was rare and detectable (> 1x coverage per Gb; max. 6.25x coverage per Gb) in only 7 metagenomes from Scandinavian lakes during the summer-stratified period when water temperatures exceeded 10°C 29 , the other three taxa, including A. borealis (IIIb.4), also mostly recruited in boreal lakes in North America and Europe, but with higher abundances than the former (max. 32.6x coverage per Gb). A. baikalensis (IIIb.6), consisting of a single MAG recovered from Lake Baikal, Russia 43 , is mainly present in its isolation source (Lake Baikal, up to 6x coverage per Gb). Species containing newly obtained cultures A. medardicus (IIIb.8) and A. abundans (IIIb.9) included mainly genomes from the temperate zone, while A. temperatus (IIIb.10) and A. universalis (IIIb.11) were retrieved from a variety of climatic regions ranging from tropical to temperate. These taxa (SAR11-IIIb.8 to IIIb.11), cluster closely in the tree, partly also reflecting similar distribution patterns. Fragment recruitment from metagenomes shows a ubiquitous distribution in temperate and boreal lakes of different trophic states, generally being more abundant in epilimnetic layers. While IIIb.8, 9, and 11 exemplified low coverages in deeper zones of lakes, A. temperatus (IIIb.10) also had discernible presence in the deep hypolimnion (up to 6.7 x coverage per Gb in samples gained from 100–300 m depth) and was even recorded in > 1000 m depth in Lake Baikal (1.7-2x coverage per Gb, Fig. 2 ). A. meridianamericanus (IIIb.12), A. lacus (IIIb.13) 32 and A. subtropicus (IIIb.14) contained MAGs obtained from subtropical regions and could be detected in only a few metagenomes (18–23), albeit with slight differences in global distribution. While A. lacus and A. meridianamericanus seemed to be more relevant in freshwater lakes and rivers in South America and Australia, with highest coverage in a coastal freshwater lagoon in Uruguay (80x and 60x coverage per Gb, respectively, in Laguna de Briozzo), A. subtropicus (IIIb.14) was not detected in rivers and was, besides subtropical lakes, also present in temperate and boreal regions (up to 14x coverage per Gb in Lake Simoncouche, Canada). A. oligotrophicus (IIIb.15, containing MAGs from Central Europe), prevailed in temperate to subtropical oligotrophic lakes, with highest densities in Central European Lake Most (126x coverage per Gb). Finally, A. communis (IIIb.16) represented by the LSUCC0530 culture genome 2 and MAGs from tropical, sub-tropical and temperate regions, had a global presence in high abundances (up to 142x coverage per Gb), mainly in warm surface water layers of subtropical and tropical lakes. We identified nine major distribution groups by clustering and non-multidimensional scaling (NMDS) of metagenomic recruitment data (Fig. 2 b, S4-S7), which concur with the above observations. The divergent distribution patterns of different lineages indicate that biogeography (latitude) is a key factor in the distribution of different Allofontibacter species (Fig. 2 b). We further explored the seasonal distribution of SAR11-IIIb in metagenomic time series datasets from six lakes on three continents (Lake Biwa in Asia, Římov Reservoir, Lakes Stechlin, Tiefwaren, and Breiter Luzin in Europe, and Lake Mendota in North America; Fig. 3 , Table S7). These meso- to eutrophic lakes are dimictic, except for Lake Biwa (monomictic) with maximum depths ranging from 23 to 104 m. Up to six SAR11-IIIb species coexisted in the same environment, with notable differences among the lakes. Lake Biwa was dominated by A. communis (SAR11-IIIb.16) and to a lesser extent by A. temperatus (IIIb.10). On the other hand, A. temperatus was the dominant species in Lakes Stechlin and Breiter Luzin. Lake Tiefwaren had equally high densities of A. abundans and A. temperatus (IIIb.9, 10), along with lower proportions of four additional species (IIIb.7, 8, 11, and 15), while the Římov Reservoir was dominated by A. medardicus (IIIb.8) followed by A. universalis (IIIb.11), and minor occurrences of six more species (IIIb.7, 9, 10, 12–14). Lake Mendota was co-dominated by A. medardicus, A. abundans , and A. temperatus , (IIIb.8–10), along with minor population densities of other SAR11-IIIb species (IIIb.7, 11). These observations confirm that A. abundans, A. temperatus and A. medardicus are dominant and co-exist in temperate lakes, while A. communis is dominant in subtropical lakes, which aligns well with the spatial distribution described above. Generally, the dimictic lakes exhibited discernible seasonal population maxima of freshwater SAR11-IIIb related to water temperature and stratification status (Fig. 3 , Table S7). An initial surge in abundance was observed in the clear water phase in late spring to early summer (May-June), followed by a maximum during the warmest period in summer (July-August) and a third peak in the late autumn period at the onset of partial mixis (October-November). In monomictic Lake Biwa, A. communis dominated throughout the year in the surface layers with distinct peaks during spring and summer. A. temperatus was consistently present in low proportions in the hypolimnion and was introduced to the surface during holomixis in February to March. All available physico-chemical data was used to explain the driving environmental factors using RDA and Spearman’s correlations ( p < 0.001, Table S8). Overall, water temperature was the most consistent and influential environmental factor shaping the seasonal SAR11-IIIb distribution in all analyzed lakes (Fig. S8, Table S9); except for Lake Mendota where total chlorophyll a content was the main factor that negatively correlated with SAR11-IIIb abundances ( p < 0.05). However, a potential temperature bias should be acknowledged as the Mendota dataset (2008–2012) consists of surface samples collected predominantly during the summer stratification period. Ecophysiology and metabolism of Allofontibacter strains All Allofontibacter strains were isolated and maintained at 16°C in light: dark (12:12 h) conditions. The cells of A. medardicus ME-17 were of small size (0.52 ± 0.05 µm length, 0.17 ± 0.02 µm width) with a slightly curved morphology (Fig. 1 b), similar to the only other culture A. communis LSUCC0530 2 and marine SAR11 44 . Strains ME-17 and MiE-29, affiliated with A. medardicus and A. abundans , respectively, were further characterized in experiments to identify optimal growth conditions. Both strains grew to maximal densities of 10 7 cells ml − 1 with maximum growth rates of 0.6 to 0.7 day − 1 and one strain ( A. medardicus ME-17) grew faster in med2 (see Table S10 for media composition), whereas the other exhibited similar growth rates across three tested media (Figs. S9-S10, Tables S11-S13). Temperature range (8–30°C) and salinity tolerance (0-5.9 PSU) assays revealed that both strains grew best between 16–24°C, with temperature significantly affecting maximal abundances of individual strains ( p < 0.0001), and tolerated salinities up to 2.9 PSU (10 6 cells ml − 1 , Fig. 4 , Fig. S11, S12). Three carbon and sulfur sources (sodium pyruvate, oxaloacetate and sulfur-rich amino acids cysteine and methionine) were added at incremental concentrations (0.1-5 µM) to identify optimum growth concentrations. Optimal growth based on maximum cell density and growth rate was achieved on 0.1–0.5 µM of pyruvate and oxaloacetate and 0.5-1 µM of sulfur-rich amino acids. Cell densities of both strains were significantly lower at 5 µM concentrations of any carbon or sulfur source ( p < 0.001 compared to other concentrations, Figs. S9-S10, Tables S11-13), which may be an indicator for oligotrophy. Metabolically, the new isolates ( A. medardicus and A. abundans ) and all species-representative MAGs share commonalities with the marine SAR11, including the presence of the glycolysis pathway, the TCA cycle, and transporters for C4-dicarboxylate compounds 44 , 45 (Fig. 5 , Supplementary text, Tables S14, S15). Further, all freshwater SAR11 representatives possess genes for propionate metabolism via methylcitrate pathway, recently described in marine SAR11 46 . Some SAR11-IIIb species representatives also harbor additional secondary solute transporters with broader substrate range, such as the TctCBA in A. africanus (IIIb.1), potentially liked to methylcitrate pathway substrate transport. The glyoxylate shunt common to marine SAR11-Ia subclade 7 has a patchy distribution in the freshwater IIIb group as described previously 2 and the isocitrate lyase gene ( aceA ) was not present in any of our culture genomes, but present in some MAGs (Fig. S13). Several strain-specific distinctions were identified in our culture genomes such as the presence of a cytochrome bd oxidase gene in one A. medardicus strain (ME-18) and the presence of a tetrahydrofolate-ligase (FtfL) for formate oxidation along with the synthesis of molybdenum cofactor in one A. abundans strain (MKE-138; Fig. 5 , Tables S14, S15). Notable, this strain was the only one that lacked the glycine cleavage pathway. The absence of assimilatory sulfur reduction is typical for the SAR11 clade, with the freshwater IIIb group relying on sulfur-rich amino acids such as cysteine and methionine 2 . Surprisingly, one strain ( A. medardicus ME-20), contained genes cysNC and cysD , which convert sulfate to PAPS (3-phosphoadenosine-5-phosphosulfate), in the hypervariable region between 23S rRNA and 5S rRNA genes (HVR2, Fig. S14). However, phylogenetic analysis of the cys NC and cys D genes did not hint at horizontal gene transfer but at selective retention, as the closest relatives were marine SAR11 species (Fig. S15). Further, all culture genomes contained a transmembrane protein (YeeE) and a putative thiosulfate/3-mercaptopyruvate sulfurtransferase enzyme (SseA) which was recently demonstrated to facilitate uptake of thiosulfate, an inorganic sulfur source 47 . We tested the use of thiosulfate and sulfur-rich amino acids (cysteine and methionine) as sole inorganic and organic sulfur sources, respectively. Both strains, A. medardicus ME-17 and A. abundans MiE-29, showed no growth in the absence of sulfur in media ( p < 0.0001; Fig. 4 ). However, growth was restored upon the addition of sulfur sources in a strain-specific manner, A. medardicus ME-17 showed higher growth with sulfur rich amino acids, while A. abundans MiE-29 exhibited significantly higher growth rates with thiosulfate (Table. S13c). Genome analysis further revealed complete biosynthetic pathways for 16 amino acids, and partial pathways for synthesis of cysteine in one A. medardicus strain (ME-20; Fig. 5 , Tables S14-S15). A. medardicus ME-17 and A. abundans MiE-29 did not grow if no amino acids were supplemented in the medium (Fig. 4 , Tables S11-13). Both strains were affected in a similar manner by the absence of certain amino acids but still could attain maximum cell densities of 10 6 cells ml − 1 (Fig. 4 , Fig. S11). Significant decrease in growth was associated with absence of nitrogen-rich amino acids (arginine, asparagine, lysine, tryptophan and glutamine) and sulfur amino acids (cysteine and methionine) ( p < 0.001). Discussion In this study, we advance the current metabolomic and genomic understanding of SAR11-IIIb, a relatively little explored subgroup within the SAR11 lineage, by isolating and characterizing seven new cultures along with 93 high-quality MAGs from freshwater and brackish environments from five continents (Fig. S1 , Table S2). Consistent with the previously described culture isolate LSUCC0530 ( Allofontibacter communis ) and other SAGs and MAGs, our new genomes exhibit features of genome streamlining such as low GC content, genome sizes in the range of 1.05–1.1 Mbp, and characteristic small curved cell morphology 2 , 32 . We also confirm the conservation of a large hypervariable region (~ 5000 bps, Fig. S1 4) in the new isolates which is present across the SAR11 clade 2 , 7 , 13 , 15 , 48 . We identified 16 distinct species within the freshwater IIIb subclade (Fig. 1 ), with the newly cultured isolates from our study belonging to two species ( A. medardicus and A. abundans , IIIb.8 and 9) out of 11 new Allofontibacter species proposed here (Supplementary text). Physiological characterization of these two species revealed optimal growth temperatures and salinity tolerances different from what has been reported for A. communis strain LSUCC0530 2 . A marked reduction in growth yield was observed in the absence of nitrogen-rich amino acids supply in both species (Fig. 4 ). This growth decline aligns with the strategic optimization of metabolic processes in genome streamlined microbes 5 , especially the use of cellular nitrogen where absence of readily assimilable sources can trigger down-regulation of growth to conserve energy. Bacteria are known to have varied ability to utilize amino acids due to genetic, adaptive or/and environmental factors; for example E. coli uses different amino acids as its sole carbon and nitrogen source during aerobic vs. anaerobic growth conditions 49 , while genome-streamlined freshwater Planktophila spp. show species-specific increased growth with the supply of sulfur-rich amino acids 50 . Common metabolic pathways and inter- and intraspecific traits in freshwater SAR11. The conservation of gene content within the SAR11 clade is evident 7 , 17 , 19 , 51 – 53 , yet discernible variations exist in specific genes or pathways, indicative of lineage-specific adaptations or ecotypes 17 , 51 – 53 . The marine SAR11 clade has been shown to exhibit high genomic diversity in adaptations to nutrient-deficient waters, displaying greater affinity for certain micronutrients 54 . For instance, the metabolic functionalities of mesopelagic-adapted clades compared to their surface-dwelling counterparts have evolved to thrive in respective niches, due to variations in sulfur assimilation pathways or capacity for nitrate reduction 55 . Comparable patterns exist also in freshwater SAR11-IIIb. The presence of unique or expanded transporters (e.g., TctCBA in A. africanus ) may reflect niche adaptations based on substrate availability. Similarly, strains affiliated to A. medardicus , isolated from Lake Medard (Czech Republic), contained an additional nitrogen metabolism gene, encoding glutamate dehydrogenase thus providing an alternate route for nitrogen assimilation (Fig. 5 , Tables S14, S15, Supplementary text). A similar strategy was seen in marine cyanobacteria 56 , with a switch from ATP-dependent glutamine synthetase-glutamate synthase complex (which aminates glutamate) towards the reduced form of NADP+-dependent glutamate dehydrogenase (which aminates 2-oxoglutarate) and can be energy efficient in nitrogen rich environments. Sulfur auxotrophy is widespread across the SAR11 clade, and while marine SAR11 rely on exogenous sulfur sources like DMSP (3-dimethylsulphoniopropionate), the freshwater IIIb group was until now believed to depend on sulfur-rich amino acids 2 , 12 , 32 . Our genome analyses identified a potential uptake route for thiosulfate via the YeeE membrane protein 47 . Still, most strains lacked genes for assimilatory sulfur reduction, except for one A. medardicus strain (ME-20) that contained genes involved in sulfate reduction in HVR2 (Fig. S14). However, growth experiments clearly showed that sodium thiosulfate supports growth of two strains (Figs. 4 , 5 , S11), particularly in a species-specific fashion. These species-specific responses to different sulfur sources provide additional evidence for potential differences in sulfur metabolism between A. medardicus and A. abundans and suggest that a so far unknown route or unannotated enzymes might be responsible for sulfur reduction in freshwater SAR11. While some metabolic strategies can be generalized at the ecotype level, even closely related strains with only subtle differences in their metabolic networks can occupy different niches in the physical and chemical space, coined as nano-niche 57 . Strain-specific distinctions were noted in one of our five A. medardicus strains (ME-18) which had a cytochrome bd oxidase gene which is known to be beneficial under oxygen limiting conditions 58 , while another strain (ME-20) contained genes involved in sulfate reduction and cysteine metabolism (Fig. 5 , Tables S14, S15). Likewise, the A. abundans isolate MKE-138 had the ability to oxidize formate along with the synthesis of molybdenum cofactor, which can be linked to the tetrahydofolate (THF)-linked oxidation pathway, making it capable of utilizing C1 compounds. All these genes were lacking from the other A. medardicus and A. abundans strains and are thus likely a result of horizontal gene transfer. Conversely, the widespread distribution of isocitrate lyase AceA in marine SAR11-Ia subclade and its irregular presence in brackish/freshwater SAR11 genomes (Fig. S13) suggest gene loss events likely driven by environmental selection 59 . The ecological importance of the isocitrate lyase gene in the marine SAR11 is underscored by its upregulation under iron scarcity; a common phenomenon when iron limits phytoplankton productivity in oceans 60 . Iron limitation is less pronounced in freshwater systems compared to marine habitats and specific to lakes with limited terrestrial inputs 61 , which may account for the gene’s sporadic presence. Global and seasonal distribution is mainly driven by water temperature Building on these physiological traits, we examined how environmental variables, particularly water temperature driven by climate, shape the distribution of SAR11-IIIb lineages on a global scale. Fragment recruitment analysis of 620 metagenomic datasets revealed nine distinct global distribution clusters (Fig. 2 , S4-S7) that provide insight into the speciation, environmental adaptation, and colonization patterns of the SAR11-IIIb group. For instance, the newly isolated cultures ( A. medardicus and A. abundans ) were mainly present in temperate lakes in Europe, North America and Japan and were demonstrated to have a temperature preference between 16–24°C in growth experiments (Fig. 4 ). In contrast, A. communis (IIIb.16) displays a more universal distribution pattern with a preference for warmer lakes in Asia, Australia, Africa, South America, and the Caribbean, which aligns with its description and an optimal growth at 30°C 2 . Four more species ( A. meridianamericanus , A. africanus, A. subtropicus, A. lacus ) were primarily present in subtropical lakes, while four others ( A. borealis, A. scandinavicus , and undescribed species IIIb.3 and 5) were limited to boreal lakes (Fig. 2 ). Our data thus highlight the role of temperature and latitude in diversification of SAR11-IIIb, which is in accordance with studies involving marine SAR11 and other marine planktonic bacteria 19 , 17 , 62 and consistent with broader patterns observed in microbial biogeography 63 . Geographical isolation, on the other hand, seemed to be less important, as most species were detected on multiple continents (with notable exceptions described below), in accordance with previous observations for Allofontibacter 27 and other freshwater bacteria 64 . In this study, we recovered many high quality MAGs from Lake Malawi in Africa, revealing a deeply branching species ( A. africanus , IIIb.1, Fig. 1 ) that was previously represented by only one MAG from Lake Tanganyika 42 . The tropical African Rift Valley lakes are meromictic, and while the MAG from Lake Tanganyika was assembled from an anoxic sample (200 m) 42 , Lake Malawi is oxygenated down to > 200 m depth, where our samples were gained from. A. africanus was also present in deep, anoxic samples from Lake Tanganyika (7.3x coverage per Gb in 1200 m), and while no hints for anaerobic growth were found in any of the genomes, A. africanus encodes large subunits of cytochrome b6f ( petABCD ) which is typically found in photosynthetic microorganisms and can contribute to the generation of a proton gradient for ATP synthesis through diverse electron donors 65 . A. africanus was restricted to Africa (Lakes Malawi, Kivu and Tanganyika), which highlights regional endemism, contrasting to the cosmopolitan distribution of A. communis , that also includes 15 MAGs assembled from the African Great Lakes, which form a separate branch in the tree (Fig. 1 , 2 ). Another potentially endemic species might be A. baikalensis (IIIb.6), represented by one MAG that was mainly present in Lake Baikal. Lake Baikal as well as the African Great Lakes are among the oldest and largest lakes on earth and well known for their endemic fauna and flora 66 , a trait that seems to stretch out also to a distinct microbiome containing deeply branching and very specific prokaryotic lineages 42 , 43 , 67 . We also observed quasi-endemism in lakes of the Southern Hemisphere, primarily dominated by A. meridianamericanus (IIIb.12) and A.lacus (IIIb.13). Besides endemic or quasi-endemic Allofontibacter species, these lakes also hosted other ubiquitous Allofontibacter species ( A. temperatus in Lake Baikal, A. communis in the African Great Lakes and South American water-bodies), suggesting that neither dispersal nor ecological selection are limiting factors, while the ancient history of these lakes might have led to evolutionary priority effects favoring distinct species 68 . Interestingly, 12 additional MAGs from Lake Malawi were affiliated with the marine SAR11-I clade ( Pelagibacter malawensis ), closely related to genomes obtained from Lake Baikal, that were proven to be of freshwater origin 43 . These freshwater genomes, as well as another group of MAGs obtained from deep alpine lakes affiliated with marine SAR11-II ( Pelagilacustribacter hypolimneticus ) were most closely related to genomes from brackish systems (Caspian Sea, Baltic Sea, San Francisco Bay, brackish coastal lagoons in Uruguay, Fig. 1 ), and hint at multiple marine-freshwater transitions in the evolutionary history of the SAR11 clade and a refuge of ancient lineages in deep lakes. Numerous time series observations of the marine SAR11 clade and its subgroups have revealed varied responses to environmental factors, with distinct peaks associated with water temperature and mixing events 51 – 53 , 69 , 70 . Our time series analysis of freshwater Allofontibacter species across six temperate and subtropical lakes showed that during seasonal successions, multiple species co-occurred in the same water samples, some being more prevalent than others (Fig. 3 , Table S7). Overall, the patterns of seasonal succession with maxima during warmer months 4 , 9 , 71 were similar between the lakes with a distinctive species dominance which reaffirms the high diversity of freshwater SAR11 each occupying a distinct niche 32 . The seasonal and spatial partitioning of Allofontibacter species points to an ecological strategy, where even closely related species exhibit differential responses to the same or similar environmental cues. This diversity within SAR11 reflects a broader trend in planktonic microbial systems, where minute genetic variations translate into niche specialization, allowing both coexistence and competitive advantages. Our results endorse the ‘ubiquity-by-diversification’ principle 72 resulting in the expansion of functional capacities within different species of the same genus or ecotypes within the same species. Thus, the ‘everything is everywhere but the environment selects’ hypothesis 73 should be deliberated due to significant capability of microorganisms to disperse and colonize new locations. By combined cultivation, genomics, metagenomics and sampling of underrepresented regions in the Global South, this study is an important step towards understanding the distribution of one of the ecologically most significant genera in freshwaters on a global scale. Yet, several questions remain open which were beyond the scope of this study. There are likely additional niche-based species and/or ecotypes present within SAR11-IIIb to be identified due to still limited representative cultures. In this study, we addressed a crucial gap by cultivating the freshwater SAR11-IIIb group; as stable cultures are essential for physiological niche characterization and provide insights into their functional roles. While we also expanded the geographic scope of available data through new high-quality MAGs from previously undersampled regions in Africa, Asia, Australia and South America (Fig. S1 , Table S1 ), public datasets remain skewed toward temperate regions. The distribution of different SAR11-IIIb species appears to be largely driven by temperature and/or latitude. It is thus interesting to ask if ongoing climate warming with increasing global temperatures could result in a redistribution of these species, with warmer-adapted species expanding into temperate zones? This question underscores broader ecological implications of our findings, linking microbial biogeography to global climate dynamics. Materials and Methods Sample collection and strain isolation Samples for long- and short-read metagenomic sequencing were collected from Vistula River, Vistula Lagoon and the coastal Baltic Sea in Poland, four lakes in Japan, eight lakes in Australia, five coastal lagoons and reservoirs in Uruguay and four stations along Lake Malawi (Fig. S1 , Table S1 ). A submersible multiparameter probe (YSI EXO2, Yellow Springs Instruments, Yellow Springs, OH, USA) was deployed to measure profiles of temperature, oxygen, pH, chlorophyll a , salinity, and conductivity. Water from different depths was prefiltered through a 20 µm mesh and then sequentially filtered through 5 µm (STERILITECH PES membrane filters, USA) and 0.22 µm (Millipore express PLUS, Germany) polysulfone filters until clogged (1–11 l depending upon the lake) 74 , 75 . Filters were stored at -80°C until DNA extraction and subsamples of the 0.22 µm filtered water were taken for nutrient (phosphorus, nitrogen, ions, and dissolved organic carbon) analyses 76 . Additionally, we used metagenomes obtained from the epi- and hypolimnion of multiple Central European lakes (Table S1 ) 74 , 75 . All SAR11-IIIb strains were isolated from the epilimnion of oligo-mesotrophic lakes and reservoirs (Medard, Milada and Klíčava) in the Czech Republic, using high-throughput dilution-to-extinction cultivation 22 , 77 . Firstly, water samples were prefiltered through 20 or 0.4 µm filters to eliminate larger microbes and total prokaryote numbers were counted with a flow cytometer (CytoFLEX S, Beckman Coulter; Brea, CA, USA) equipped with a blue (488 nm) laser (bandpass filters 525/40 and 690/50) after staining with SYBR Green (0.5 x final concentration; Lonza, Rockland, ME, USA). For dilution-to-extinction cultivation, the filtrate was diluted in artificial lake water media 78 amended with different carbon, sulfur and nitrogen sources (Table S10) and dispensed into 96-deep-well plates to theoretically obtain 0.5, 1 or 5 cells per well. After incubation at 16°C in light:dark conditions (12:12 h) for 6 weeks, a subsample of each well was stained with SYBR Green and examined by flow cytometry. Wells with growth (> 10 4 cells ml − 1 ) were transferred to 10 ml cultivation tubes containing the same isolation media and a subsample was stored at -80°C after adding glycerol (final concentration 20%, v/v). Screening through 16S rRNA gene PCR amplification and SANGER sequencing was done as previously described 22 , 77 . All cultures identified as SAR11-IIIb were routinely maintained in the same isolation conditions for further analyses and experiments. Genome sequencing and assembly Each of the SAR11-IIIb strains were grown in 500 ml glass Erlenmeyer flasks or plastic tubes until late stationary phase. Cells were harvested at 10 6 cells ml − 1 densities by filtration onto 0.2 µm PES filters (Millipore express PLUS) in sterile conditions. DNA was extracted using MagAttract® HMW DNA Kit (Qiagen, Venlo, NL). Thereafter, paired end libraries (PE150) were sequenced on an Illumina NovaSeq 6000 instrument (Novogene HK). Raw reads underwent quality trimming with BBMap v36.x and were subsequently assembled using Spades v3.12.0 with multiple K-mers (29, 49, 59, 69, 79, 89, 99, 109, 119, and 127). Most assemblies resulted in 1–3 contigs, which were then manually curated into circular chromosomes through iterative rounds of read mapping to contigs using Geneious 10 (default mapper, high sensitivity; www.geneious.com ). Contigs were extended bidirectional by identifying overlapping ends followed by de novo assembly with Geneious 10 assembler (high sensitivity settings). Metagenomic sequencing, assembly, and binning DNA extraction of the 0.22 µm filters, metagenomic short-read sequencing (Illumina NovaSeq 6000, PE150) and assembly of samples obtained from lakes in Central Europe was done as previously described 74 . Long-read sequencing of DNA extracted with a Quick-DNA HMW MagBead Kit (Zymo Research; Irvine, CA, USA) was carried out on a promethION platform (Oxford Nanopore) by an external sequencing provider (Novogene HK). Quality-controlled and trimmed short-read sequences 74 were used to polish noisy long-read sequences by generating a Burrows Wheeler Transform (BWT), according to the ropebwt2 construction approach 79 . Nanopore basecalled long-reads with an average Q score ≥ 8 were subjected to adapter and barcode trimming by Porechop 80 and were further polished using the generated Illumina BWT with FMLRC2 v0.1.8 with default parameters 81 . Polished long-reads were assembled using Flye v2.9.1-b1780 (--nano-corr –meta –no-alt-contigs) 82 . Assemblies were binned to metagenome-assembled genomes (MAGs) using DAS tool 83 by cross-mapping of reads gained from the same lake (up to 12 samples in the case of Lake Malawi). Genome annotation and phylogenomic analyses All genomes obtained in this study, along with genomes of all GTDB representative species of SAR11-I, II and IIIa and all SAR11-IIIb genomes retrieved from public databases (ENA, NCBI), were classified with GTDB-Tk v2.4.0 toolkit 33 based on GTDB r220. CheckM v1.0.18 84 , CheckM2 v1.0.1 (“predict”) 85 and an in-house script were used to evaluate genome characteristics (completeness, contamination, genome size, coding density, GC%, predicted genes). Genes were predicted with PROKKA 86 , tRNA and rRNA genes were identified with barrnap 0.9 ( https://github.com/tseemann/barrnap ) and tRNAscan-SE 87 , and proteins were annotated using hmmsearch 88 against collections of COG 89 , TIGRFAM 90 , and KEGG 91 , 92 . Metabolic pathways were inferred from the KEGG database and were subsequently manually assessed for completeness in all culture genomes. A subset of 36 high quality genomes (> 95% completeness and 97.2% of this collection were considered to be the core genome of the family (751 proteins, Table S3). All SAR11 genomes were subjected to hmmsearch 88 against the 751 marker proteins and only genomes that containing at least 55% of the markers (n = 295) were used for phylogenetic tree reconstruction. Proteins were individually aligned using PRANK v.150803 (-protein + F) 93 , concatenated, and a maximum-likelihood tree was constructed using IQ-TREE 94 (-bb 1000, -alrt 1000) with ultrafast bootstrapping and the model VT + F + I + G4, chosen by ModelFinder 95 . Genomes affiliated with SAR11-IV and V clades, that exhibit distinct phylogenetic placements outside of Pelagibacterales 96 , were used as outgroup to root the tree. Average amino acid and nucleotide identities (AAI and ANI) were computed following previously established methods using a 95% cut-off for different species and 65% AAI for different genera 97 , 98 . The resulting matrices (Tables S4, S5) were compared to the phylogenomic tree to validate the classification of the new genomes within the SAR11 clade. One representative genome per species was selected with dRep 99 at ANI > 95%. Whole-genome alignments and BLAST comparisons of complete genomes (tBLASTx) and representatives of the 16 species (BLASTn) were done as previously described 22 . MAGs containing multiple contigs were first aligned to the closest complete relative to reorder and concatenate contigs with the Mauve Contig Mover algorithm 100 and the residue numbering was changed to start with DnaA ATPase in all genomes to enable easier visual comparisons. Phylogenetic analysis of the isocitrate lyase (aceA) and Cys ( Cys E, NC, D) genes All SAR11 genomes used in this study were scanned for the presence of the isocitrate lyase gene ( aceA ) using Prodigal v2.6.3 101 and publicly available proteins from all bacterial phyla were searched against AceA HMMs for significant hits using hmmsearch 88 . Hits were filtered based on protein coverage (> 80%) and dereplicated using MMseqs2 102 (easy-cluster workflow) with a minimum sequence identity of 95%. The filtered protein sequences (n = 1367) were aligned with MAFFT 103 (-local pair –max iterate 1000) and a tree was constructed using IQ-TREE 94 with 1000 ultrafast bootstrap replicates and the LG + F + G4 evolutionary model 95 . A SAR11 subtree was generated after a local blast search to identify the closest 200 sequences ( p < 1e − 2) of AceA protein sequences from SAR11 genomes used in this study. Similarly, trees were also generated for three genes identified in HVR2 in A. medardicus ME-20, i.e., cysD (O-acetylhomoserine (thiol)-lyase), cysNC (sulfate adenylyltransferase), and cysE (serine O-acetyltransferase). The 100 closest relatives were identified by BLASTx, protein sequences were aligned as before, and the phylogenetic tree was inferred using the LG + I + G3 model (AceA) and LG + I + G4 (CysE, NC, D) as chosen by Modelfinder 95 . Metagenomic fragment recruitment Additionally to the here sequenced metagenomes, seasonally resolved samples from the Římov Reservoir (124 samples, 2015–2022) 75 , 104 , Lake Biwa (24 samples along 1 year) 105 , the TYMEFLIES dataset from Lake Mendota (94 samples, 2008–2012) 106 , a part of the LIMNOS dataset (Lake Stechlin, Lake Breiter-Luzin, and Tiefwaren, 317 samples collected over 10 years), and 559 metagenomic freshwater datasets from public domain were used for fragment recruitment (Tables S6, S7). Metagenomes were subsampled to 20 million reads and rRNA genes in genomes were masked prior to recruitment. MMseqs2 102 was used to map metagenomic reads to the 16 representative Allofontibacter species genomes, to obtain base coverage per Gb (-minid 0.95 -mincov 0.9 -minlen 50). Representatives with less than 40% of their total genome aligned to the query metagenome were filtered out and assigned a coverage value of zero. False positive hits caused by cross-mapping of reads can be ruled out because of strict mapping cutoffs (> 95% nucleotide identity covering > 40% of a genome) and low nucleotide identities between different Allofontibacter species (Fig. S2, S3, Table S4). Scanning Electron Microscopy Strain ME-17 was grown as above until late stationary phase and fixed with glutaraldehyde (2.5% final concentration) over night at 4°C. Four ml were filtered onto white polycarbonate filters (0.2 µm pore size, Millipore) and stepwise dehydrated in 30, 50, 70, and 100% ethanol for 10 mins each. Dried filters were stored at 4°C until further processing. For imaging, the filters were cut with a blade and mounted onto a metal knob using conductive copper tape. The samples were sputter coated (10 nm layer) with gold in a Sputter Coater Polaron chamber (Polaron Ltd., Watford, UK). Scanning electron microscopy (SEM) was performed using JEOL 7401-F field emission SEM (JEOL Europe, Prague, Czech Republic). Growth experiments Two axenic strains ( A. medardicus ME-17 and A. abundans MiE-29), each representing a different species, were used in short-term growth assays to identify optimal growth conditions and species-specific differences. We tested three different artificial media that were also used for dilution-to extinction cultivation (Table S10). Further growth experiments included four temperatures (8, 16, 24, and 30°C), salinity tolerances (0, 2.89, and 5.89 PSU), different concentrations (0.1, 0.5, 1, 5 µM) of carbon (pyruvate, oxaloacetate) and sulfur sources (thiosulphate and sulfur rich amino acids cysteine and methionine), and selective removal of individual amino acids (Table S11). All experiments were carried out in triplicates in 96 deep-well plates at 16°C (except for the temperature treatments) in light:dark conditions (12:12 h) with med2 as control. Cell abundances were enumerated every two to four days until stationary phase by flow cytometry as described above. Maximum growth rates were calculated for each treatment and strain as previously described 107 and statistically compared to control treatments using ANOVA and post-hoc tests (Tukey HSD). Statistical Analysis All analyses were performed using R 108 packages vegan v2.5-7 109 and cluster v2.0.7–1 110 . To identify distribution patterns of SAR11-IIIb species, hierarchical clustering was performed. A species x species dissimilarity matrix was first calculated using the Bray-Curtis index on the relative abundance of species across all samples. Hierarchical clustering was then applied to this matrix using Ward's minimum variance method (hclust function, method = ward.D2). The optimal number of species clusters was determined by evaluating the dendrogram structure alongside the average silhouette width (cluster::silhouette function). The relationships between species based on the distribution patterns were visualized using Non-Metric Multidimensional Scaling (NMDS) (vegan::metaMDS function). Further to detect patterns in SAR11-IIIb species composition among the different samples, a separate NMDS was performed on a sample x sample Bray-Curtis dissimilarity matrix, calculated from the relative abundances of all species within each sample. The influence of latitude and longitude on the sample community structure was assessed by fitting them as linear vectors onto the ordination using the vegan::envfit function. Significant correlations (p < 0.05) were assessed via permutation tests, and significant vectors were displayed on the sample NMDS plot. Potential non-linear spatial gradients across the sample community ordination were also visualized by fitting latitude and longitude individually as smooth surfaces onto the sample NMDS plot using the vegan::ordisurf function. Differences in the geographic locations (latitude and longitude values) associated with the previously identified species clusters were statistically tested using Kruskal-Wallis tests 111 . Physico-chemical data available from time series metagenomic datasets was used to perform multivariate analyses. This involved RDA (redundancy analysis, model testing using ANOVA with all available parameters for each lake with permutations tests (999 permutations, < 0.001)) and biplots to visualize the relationship between species, season and environmental variables. Further, to identify the dominant species in each lake, histograms were plotted to show distribution of regression coefficients (loadings), for each axis and all the species. Pairwise correlations between the abundant species and environmental factors were also calculated using the cor function in the package psych (version 2.2.9) 112 . Plots and heatmaps were generated using the R 108 package ggplot2 (v3.3.5) 113 and ComplexHeatmap (v1.10.2r) 114 . Declarations Data availability Genomes from cultures have been submitted to ENA under project accession number PRJEB77526, Illumina and Oxford Nanopore reads, and MAGs under ENA project accession numbers PRJEB35770, PRJEB86000-PRJEB86004. Novel species were registered at SeqCode 41 . Acknowledgements We thank local fisherman, captains and technicians for their help during sampling. Sample collection in Lake Biwa was supported by Center for Ecological Research, Kyoto University, a Joint Usage / Research Center. Sampling in Lake Toya was supported by a joint usage with Toya Lake Station of Field Science Center for Northern Biosphere, Hokkaido University. F. Kostanjšek, M. Okrouhliková, A. Férová and I. Lebeda are acknowledged for excellent laboratory support. We thank the Biology Centre CAS core facility LEM supported by MEYS CR (LM2023050 Czech-BioImaging and OP VVV CZ.02.1.01/0.0/0.0/18_046/0016045). This study was supported by Czech Science Foundation (GAČR) grants 22-03662S (MMS, CF, PL, M-CC), 25-15813S (MMS, CF, MH), 20-12496X (RG, P-AB, VK) and 21-21990S (MH, M-CC). CF was also supported by grant 017/2022/P and PL by grant 022/2019/P (Grant Agency of the University of South Bohemia in České Budějovice). JW and H-PG were funded by the Leibniz foundation and thank the entire LIMNOS team. YO was funded by JST FOREST program (JPMJFR2273) and JSPS KAKENHI grants (16H06279, 18J00300, 22K15182). Author contributions MMS conceived the study. Sampling was performed by CF, MH, PL, M-CC, RG, VK, TS, H-PG, JW, KP, CA, JZ, DH, MN, S-iN, YO and MMS. CF, MH, PL and MMS isolated and maintained the strains. CF, M-CC, P-AB and MMS assembled, binned and annotated the metagenomes with pipelines designed by P-AB and RG. CF analyzed the data and prepared the figures supervised by MMS. CF and MMS wrote the manuscript with input from all authors. 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Long-read-resolved, ecosystem-wide exploration of nucleotide and structural microdiversity of lake bacterioplankton genomes. mSystems 7 , e00433-00422 (2022). https://doi.org:doi:10.1128/msystems.00433-22 Linz, A. M. et al. Freshwater carbon and nutrient cycles revealed through reconstructed population genomes. PeerJ 6 , e6075 (2018). Cheng, C. & Thrash, J. C. Sparse-growth-curve: a computational pipeline for parsing cellular growth curves with low temporal resolution. Microbiology Resource Announcements 10 , 10.1128/mra. 00296-00221 (2021). Giorgi, F. M., Ceraolo, C. & Mercatelli, D. The R language: an engine for bioinformatics and data science. Life 12 , 648 (2022). Oksanen, J. et al. Community ecology package. R package version 2 , 321-326 (2013). Maechler, M. Cluster: cluster analysis basics and extensions. R package version 2.0. 7–1 (2018). Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. Journal of the American statistical Association 47 , 583-621 (1952). Revelle, W. & Revelle, M. W. Package ‘psych’. The comprehensive R archive network 337 , 161-165 (2015). Wickham, H. ggplot2. Wiley interdisciplinary reviews: computational statistics 3 , 180-185 (2011). Gu, Z. Complex heatmap visualization. Imeta 1 , e43 (2022). Additional Declarations There is NO Competing Interest. Supplementary Files FernandesSupplementaryinformation.pdf Supplementary Information FernandesSupplTables.xlsx Tables S1-S15 Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6457240","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":467649468,"identity":"b4f8afdb-0ba3-4d68-9f08-2ad359317cc5","order_by":0,"name":"Michaela 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18:05:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6457240/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6457240/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85175322,"identity":"d842ffe2-1a24-4faa-b05d-b57494315531","added_by":"auto","created_at":"2025-06-23 06:24:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":433474,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogeny of the SAR11 order.\u003c/strong\u003e \u003cstrong\u003ea:\u003c/strong\u003ePhylogenomic tree with ultrafast bootstrapping using 751 single copy marker protein sequences and representatives of SAR11-IV and SAR11-V as outgroup. Different clades of SAR11-I, II, IIIa and species of SAR11-IIIb are displayed in different colors. Annotations of the rings from inside to outside: 2: Genome type; genomes sequenced in this study are highlighted by filled symbols; 3: Habitat of origin; 4: Climatic zone of origin. \u003cstrong\u003eb: \u003c/strong\u003eRepresentative scanning electron microscopic image of \u003cem\u003eA. medardicus\u003c/em\u003e strain ME-17.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6457240/v1/0418425232402a490ba422f7.png"},{"id":85175316,"identity":"aff19302-48f1-4594-84f3-1e82e6e63f94","added_by":"auto","created_at":"2025-06-23 06:24:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131395,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal occurrence of different \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAllofontibacter\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003especies.\u003c/strong\u003e \u003cstrong\u003ea: \u003c/strong\u003eMetagenomic fragment recruitment of 16 \u003cem\u003eAllofontibacter\u003c/em\u003e species in 307 freshwater metagenomes with coverages exceeding 1 per Gb. Samples are sorted by continents and water depths, which are indicated in different colors at the top of the heatmaps, climatic zones are shown below. \u003cstrong\u003eb:\u003c/strong\u003e Non-metric multidimensional scaling (NMDS) of SAR11-IIIb species based on Bray-Curtis dissimilarity. Points represent sites grouped into nine distinct clusters. Environmental fitting of vectors (Latitude and Longitude) indicates the direction and strength of gradients in the ordination space, with contour lines illustrating the latitude gradient.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6457240/v1/c40e00f3a2a0c608c451200e.png"},{"id":85175302,"identity":"515b0c97-9744-40bc-8fb1-ee90b37b531a","added_by":"auto","created_at":"2025-06-23 06:24:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1116621,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSeasonal distribution of SAR11-IIIb species in six lakes.\u003c/strong\u003e Shown are heatmaps of coverage per Gb values for Lake Biwa (Japan), Římov Reservoir (Czechia), Lakes Stechlin, Breiter Luzin and Tiefwaren (Germany); and Lake Mendota (USA). Samples are sorted by water depths and sampling date and color coded by season.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6457240/v1/5a35c3c247a9ddb3aedf424d.png"},{"id":85175290,"identity":"52c5912c-b3af-421b-b7f7-1c373865b5a7","added_by":"auto","created_at":"2025-06-23 06:24:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":39766,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaximum abundances of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA. medardicus\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e ME-17 and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA. abundans\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e MiE-29 grown under different experimental conditions.\u003c/strong\u003e \u003cstrong\u003ea:\u003c/strong\u003e Temperature gradients (8, 16, 24, and 30°C); \u003cstrong\u003eb:\u003c/strong\u003e Salinity gradients (0, 2.89, and 5.89 PSU); \u003cstrong\u003ec:\u003c/strong\u003e Different sulfur sources (sulfur-rich amino acids cysteine and methionine and sodium thiosulfate); \u003cstrong\u003ed:\u003c/strong\u003e Selective removal of individual amino acids. Asterisks or letters indicate significant differences between treatments.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6457240/v1/19ee2b55bb055bd63ba4f815.png"},{"id":85175300,"identity":"930e0f98-30c0-47da-b04f-12a3653897ae","added_by":"auto","created_at":"2025-06-23 06:24:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":286237,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolic pathways of seven novel \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAllofontibacter\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003estrains.\u003c/strong\u003e Red arrows indicate strain-specific pathways, while black arrows represent common pathways; dashed lines denote multiple reactions; question marks indicate partial or incomplete pathways; and a red X indicates the absence of a pathway.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6457240/v1/a4d34a5695fb2d35b4a09ca4.png"},{"id":85175962,"identity":"4f655aba-b64d-46b4-b3a1-bb78ea753977","added_by":"auto","created_at":"2025-06-23 06:32:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3448833,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6457240/v1/dcea171f-23ea-48a6-9cce-f58f7071fcdb.pdf"},{"id":85175306,"identity":"c02aec8f-7a51-46fe-b945-d70c6f8faaf4","added_by":"auto","created_at":"2025-06-23 06:24:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2504344,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"FernandesSupplementaryinformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6457240/v1/2d842f018f1fd30e307d4f77.pdf"},{"id":85175313,"identity":"b93ffa2f-b089-451d-8af1-c7c8166abd77","added_by":"auto","created_at":"2025-06-23 06:24:17","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2804048,"visible":true,"origin":"","legend":"Tables S1-S15","description":"","filename":"FernandesSupplTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6457240/v1/dcfd75f5962c6050df3580c2.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Ecophysiology and global dispersal of the freshwater SAR11-IIIb clade","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe SAR11 order, known as Pelagibacterales, is known for its ubiquitous presence in the plankton of both marine and freshwater systems\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, its heterotrophic nature, and small cell sizes (~\u0026thinsp;0.04 \u0026micro;m\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e). Since its discovery as a dominant bacterial clade, SAR11 has been extensively studied as a model for streamlined genomes\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Subsequent genomic investigations have outlined genus-like lineages representing different SAR11 subclades with specific spatial and temporal distributions in oceans (SAR11-I, II), brackish or coastal waters (SAR11-IIIa) and brackish/freshwater habitats (SAR11-IIIb)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In addition to being abundant, SAR11 exhibits a high genomic diversity that is persistent and characterized by closely related ecotypes as opposed to distinct species clusters\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Although SAR11 is one of the most abundant bacterial groups in aquatic systems, most subclades still lack a comprehensive characterization due to a low number of stable cultures.\u003c/p\u003e \u003cp\u003eOne such group is the SAR11-IIIb (LD12), which is highly abundant in freshwater lakes worldwide\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Just like their marine ancestors, freshwater SAR11-IIIb are streamlined bacteria with reduced genome sizes and incomplete or missing metabolic pathways leading to multiple auxotrophies and unusual nutrient requirements\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This makes this group reliant on co-occurring microbes for crucial metabolites (Black Queen Hypothesis)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e such as reduced sulfur, vitamins, or their precursors\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Notably their lower recombination frequencies compared to the marine clades suggest a bottleneck during their transition from marine to freshwaters\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Genomic islands, present across all SAR11 genomes, act as hot spots for horizontal gene transfer (HGT) events and contribute to microdiversity\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Certain genes in hypervariable region 2 (HVR2), for instance, are thought to be related to membrane modifications and synthesis of extracellular structures for potential phage receptors\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e or strain-specific metabolic abilities\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The generally high population numbers and microdiversity of SAR11 are mutually reinforcing; large populations can maintain diversity through genetic variation, while this diversity supports high abundances by mitigating environmental pressures\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Thus, this interplay, along with environmentally-mediated selection, may contribute to a distinct global biogeography of SAR11\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWhile niche separation among marine SAR11-clades is influenced by factors like water temperature, ocean currents and inorganic nutrients\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, freshwater lakes present a contrasting scenario. Due to physical isolation and distinct physico-chemical parameters, microbial dispersal between lakes is limited\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Although different lakes can be inhabited by distinct species of the same ubiquitous genus\u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, microbes of the same species (average nucleotide identity ANI\u0026thinsp;\u0026gt;\u0026thinsp;95%) have been reported from lakes located hundreds of km apart or even from different continents\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. However, large-scale analyses indicative of phylogeographic partitioning of lake microbes remain rare\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, mainly because global sampling has been uneven and focused mainly on temperate regions in the northern hemisphere\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdding to these challenges, the study of SAR11-IIIb is further constrained by a lack of cultured representatives. To date, only one isolate from the freshwater SAR11 lineage has been successfully cultivated (\u0026lsquo;\u003cem\u003eCa\u003c/em\u003e. Fonsibacter ubiquis\u0026rsquo;, later reclassified to \u003cem\u003eAllofontibacter communis\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e) from the brackish Lake Borgene, USA\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Consequently, most insights into SAR11-IIIb\u0026rsquo;s metabolic and ecological adaptations rely on cultivation-independent approaches such as metagenome-assembled genomes (MAGs)\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e and single amplified genomes (SAGs)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. While these approaches have significantly advanced our knowledge, they have inherent disadvantages, including fragmented and incomplete genomes, the inability to conform functional roles experimentally, and the lack of information on phenotypic traits such as growth rates, metabolic fluxes, and physiological response to environmental stress, which can only be studied through cultures.\u003c/p\u003e \u003cp\u003eThis study aims to advance current gaps in understanding the ecology, diversity, and biogeographic distribution of freshwater SAR11-IIIb by leveraging a combination of global sampling, culture isolation, and long-read metagenomics. We report seven new isolates alongside 93 high-quality MAGs, including data from the underrepresented Southern Hemisphere (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These isolates provide experimental validation, such as temperature and nutrient preferences, and complement genomic analysis. The integration of culture-based and cultivation-independent approaches in this study offers insights into their ecological roles by linking biogeographic patterns to metabolic and physiological adaptations.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn this study we isolated and genome-sequenced seven new SAR11-IIIb strains from three oligo-mesotrophic freshwater habitats in the Czech Republic (Kl\u0026iacute;čava Reservoir, Lake Medard and Lake Milada). The new genomes are complete, with one circular chromosome and have been classified as \u0026ldquo;Fonsibacter\u0026rdquo; by GTDB-tk\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Long-and short-read sequencing of water samples from five continents (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) yielded 93 new high-quality MAGs of SAR11-IIIb, along with 36 from SAR11-I, II, IIIa subgroups (Table S2). All new SAR11 cultures and MAGs have common features like small genome sizes (1.05\u0026ndash;1.11 Mbp), low GC contents (~\u0026thinsp;29.5%), minimal amount of non-coding DNA (\u0026lt;\u0026thinsp;5%), and short median intergenic spacers (5\u0026ndash;6 bps), similar to other genome-streamlined bacterial groups\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenomic analyses recover 16 distinct species of SAR11-IIIb\u003c/h2\u003e \u003cp\u003ePhylogenetic analysis based on the core genome of the family (751 protein sequences; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table S3), confirmed the subdivision of the SAR11 clade into SAR11-I, II, IIIa and IIIb subgroups\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Interestingly, 12 MAGs from Lake Malawi grouped within marine SAR11-I, and three MAGs retrieved from deep alpine lakes grouped within marine SAR11-II, each bordered by genomes gained from brackish systems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on average nucleotide and amino acid identities (ANI and AAI) and phylogenetic tree branching patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Tables S4, S5), we propose that the SAR11-IIIb clade contains 16 species. However, our analysis revealed that some of these do not conform to the commonly suggested ANI and AAI threshold of 95% for bacterial species delineation\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e(Fig. S2, Tables S4-S5), aligning with studies highlighting the unique intraspecific microdiversity within the SAR11 group, which often defies the concept of discrete species boundaries\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Still, all species formed separate ANI clusters with higher within-species ANI values compared to the closest relative (Fig. S2, S3) and had unique geographic distribution patterns (see below).\u003c/p\u003e \u003cp\u003eThe seven new isolated strains isolated here represent two distinct species in the SAR11-IIIb clade that we tentatively named and registered at SeqCode\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e as \u003cem\u003eAllofontibacter medardicus\u003c/em\u003e (species cluster IIIb.8, representative strain ME-17) and \u003cem\u003eAllofontibacter abundans\u003c/em\u003e (IIIb.9, MiE-29). We further named 11 SAR11-IIIb, one SAR11-I species, and one SAR11-II genus based on high-quality MAGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A detailed description of the newly proposed species can be found in the Supplementary text.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGlobal and seasonal distribution of SAR11-IIIb\u003c/h3\u003e\n\u003cp\u003eIn the phylogenomic tree (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), SAR-IIIb genomes grouped based on the climatic zone of isolation habitat and biogeographic occurrence. The global distribution of individual SAR11-IIIb species was investigated using a total of 620 metagenomes, encompassing both publicly available and newly sequenced metagenomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S6). The deeply branching species \u003cem\u003eA. africanus\u003c/em\u003e (IIIb.1, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), consisting of a previously reported MAG from Lake Tanganyika\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e along with nine new MAGs from Lake Malawi assembled in this study, was restricted to tropical lakes in Africa (up to 68x coverage per Gb). Four species (IIIb.2 to IIIb.5) contained MAGs from lakes in boreal and subarctic regions in Scandinavia and North America. While \u003cem\u003eA. scandinavicus\u003c/em\u003e (IIIb.2) was rare and detectable (\u0026gt;\u0026thinsp;1x coverage per Gb; max. 6.25x coverage per Gb) in only 7 metagenomes from Scandinavian lakes during the summer-stratified period when water temperatures exceeded 10\u0026deg;C\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, the other three taxa, including \u003cem\u003eA. borealis\u003c/em\u003e (IIIb.4), also mostly recruited in boreal lakes in North America and Europe, but with higher abundances than the former (max. 32.6x coverage per Gb). \u003cem\u003eA. baikalensis\u003c/em\u003e (IIIb.6), consisting of a single MAG recovered from Lake Baikal, Russia\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, is mainly present in its isolation source (Lake Baikal, up to 6x coverage per Gb). Species containing newly obtained cultures \u003cem\u003eA. medardicus\u003c/em\u003e (IIIb.8) and \u003cem\u003eA. abundans\u003c/em\u003e (IIIb.9) included mainly genomes from the temperate zone, while \u003cem\u003eA. temperatus\u003c/em\u003e (IIIb.10) and \u003cem\u003eA. universalis\u003c/em\u003e (IIIb.11) were retrieved from a variety of climatic regions ranging from tropical to temperate. These taxa (SAR11-IIIb.8 to IIIb.11), cluster closely in the tree, partly also reflecting similar distribution patterns. Fragment recruitment from metagenomes shows a ubiquitous distribution in temperate and boreal lakes of different trophic states, generally being more abundant in epilimnetic layers. While IIIb.8, 9, and 11 exemplified low coverages in deeper zones of lakes, \u003cem\u003eA. temperatus\u003c/em\u003e (IIIb.10) also had discernible presence in the deep hypolimnion (up to 6.7 x coverage per Gb in samples gained from 100\u0026ndash;300 m depth) and was even recorded in \u0026gt;\u0026thinsp;1000 m depth in Lake Baikal (1.7-2x coverage per Gb, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). \u003cem\u003eA. meridianamericanus\u003c/em\u003e (IIIb.12), \u003cem\u003eA. lacus\u003c/em\u003e (IIIb.13)\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eA. subtropicus\u003c/em\u003e (IIIb.14) contained MAGs obtained from subtropical regions and could be detected in only a few metagenomes (18\u0026ndash;23), albeit with slight differences in global distribution. While \u003cem\u003eA. lacus\u003c/em\u003e and \u003cem\u003eA. meridianamericanus\u003c/em\u003e seemed to be more relevant in freshwater lakes and rivers in South America and Australia, with highest coverage in a coastal freshwater lagoon in Uruguay (80x and 60x coverage per Gb, respectively, in Laguna de Briozzo), \u003cem\u003eA. subtropicus\u003c/em\u003e (IIIb.14) was not detected in rivers and was, besides subtropical lakes, also present in temperate and boreal regions (up to 14x coverage per Gb in Lake Simoncouche, Canada). \u003cem\u003eA. oligotrophicus\u003c/em\u003e (IIIb.15, containing MAGs from Central Europe), prevailed in temperate to subtropical oligotrophic lakes, with highest densities in Central European Lake Most (126x coverage per Gb). Finally, \u003cem\u003eA. communis\u003c/em\u003e (IIIb.16) represented by the LSUCC0530 culture genome\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and MAGs from tropical, sub-tropical and temperate regions, had a global presence in high abundances (up to 142x coverage per Gb), mainly in warm surface water layers of subtropical and tropical lakes. We identified nine major distribution groups by clustering and non-multidimensional scaling (NMDS) of metagenomic recruitment data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, S4-S7), which concur with the above observations. The divergent distribution patterns of different lineages indicate that biogeography (latitude) is a key factor in the distribution of different \u003cem\u003eAllofontibacter\u003c/em\u003e species (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further explored the seasonal distribution of SAR11-IIIb in metagenomic time series datasets from six lakes on three continents (Lake Biwa in Asia, Ř\u0026iacute;mov Reservoir, Lakes Stechlin, Tiefwaren, and Breiter Luzin in Europe, and Lake Mendota in North America; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table S7). These meso- to eutrophic lakes are dimictic, except for Lake Biwa (monomictic) with maximum depths ranging from 23 to 104 m. Up to six SAR11-IIIb species coexisted in the same environment, with notable differences among the lakes. Lake Biwa was dominated by \u003cem\u003eA. communis\u003c/em\u003e (SAR11-IIIb.16) and to a lesser extent by \u003cem\u003eA. temperatus\u003c/em\u003e (IIIb.10). On the other hand, \u003cem\u003eA. temperatus\u003c/em\u003e was the dominant species in Lakes Stechlin and Breiter Luzin. Lake Tiefwaren had equally high densities of \u003cem\u003eA. abundans\u003c/em\u003e and \u003cem\u003eA. temperatus\u003c/em\u003e (IIIb.9, 10), along with lower proportions of four additional species (IIIb.7, 8, 11, and 15), while the Ř\u0026iacute;mov Reservoir was dominated by \u003cem\u003eA. medardicus\u003c/em\u003e (IIIb.8) followed by \u003cem\u003eA. universalis\u003c/em\u003e (IIIb.11), and minor occurrences of six more species (IIIb.7, 9, 10, 12\u0026ndash;14). Lake Mendota was co-dominated by \u003cem\u003eA. medardicus, A. abundans\u003c/em\u003e, and \u003cem\u003eA. temperatus\u003c/em\u003e, (IIIb.8\u0026ndash;10), along with minor population densities of other SAR11-IIIb species (IIIb.7, 11). These observations confirm that \u003cem\u003eA. abundans, A. temperatus\u003c/em\u003e and \u003cem\u003eA. medardicus\u003c/em\u003e are dominant and co-exist in temperate lakes, while \u003cem\u003eA. communis\u003c/em\u003e is dominant in subtropical lakes, which aligns well with the spatial distribution described above. Generally, the dimictic lakes exhibited discernible seasonal population maxima of freshwater SAR11-IIIb related to water temperature and stratification status (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table S7). An initial surge in abundance was observed in the clear water phase in late spring to early summer (May-June), followed by a maximum during the warmest period in summer (July-August) and a third peak in the late autumn period at the onset of partial mixis (October-November). In monomictic Lake Biwa, \u003cem\u003eA. communis\u003c/em\u003e dominated throughout the year in the surface layers with distinct peaks during spring and summer. \u003cem\u003eA. temperatus\u003c/em\u003e was consistently present in low proportions in the hypolimnion and was introduced to the surface during holomixis in February to March. All available physico-chemical data was used to explain the driving environmental factors using RDA and Spearman\u0026rsquo;s correlations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table S8). Overall, water temperature was the most consistent and influential environmental factor shaping the seasonal SAR11-IIIb distribution in all analyzed lakes (Fig. S8, Table S9); except for Lake Mendota where total chlorophyll \u003cem\u003ea\u003c/em\u003e content was the main factor that negatively correlated with SAR11-IIIb abundances (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, a potential temperature bias should be acknowledged as the Mendota dataset (2008\u0026ndash;2012) consists of surface samples collected predominantly during the summer stratification period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEcophysiology and metabolism of\u003c/b\u003e \u003cb\u003eAllofontibacter\u003c/b\u003e \u003cb\u003estrains\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAll \u003cem\u003eAllofontibacter\u003c/em\u003e strains were isolated and maintained at 16\u0026deg;C in light: dark (12:12 h) conditions. The cells of \u003cem\u003eA. medardicus\u003c/em\u003e ME-17 were of small size (0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 \u0026micro;m length, 0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 \u0026micro;m width) with a slightly curved morphology (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), similar to the only other culture \u003cem\u003eA. communis\u003c/em\u003e LSUCC0530\u003csup\u003e2\u003c/sup\u003e and marine SAR11\u003csup\u003e44\u003c/sup\u003e. Strains ME-17 and MiE-29, affiliated with \u003cem\u003eA. medardicus\u003c/em\u003e and \u003cem\u003eA. abundans\u003c/em\u003e, respectively, were further characterized in experiments to identify optimal growth conditions. Both strains grew to maximal densities of 10\u003csup\u003e7\u003c/sup\u003e cells ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e with maximum growth rates of 0.6 to 0.7 day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and one strain (\u003cem\u003eA. medardicus\u003c/em\u003e ME-17) grew faster in med2 (see Table S10 for media composition), whereas the other exhibited similar growth rates across three tested media (Figs. S9-S10, Tables S11-S13). Temperature range (8\u0026ndash;30\u0026deg;C) and salinity tolerance (0-5.9 PSU) assays revealed that both strains grew best between 16\u0026ndash;24\u0026deg;C, with temperature significantly affecting maximal abundances of individual strains (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and tolerated salinities up to 2.9 PSU (10\u003csup\u003e6\u003c/sup\u003e cells ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig. S11, S12). Three carbon and sulfur sources (sodium pyruvate, oxaloacetate and sulfur-rich amino acids cysteine and methionine) were added at incremental concentrations (0.1-5 \u0026micro;M) to identify optimum growth concentrations. Optimal growth based on maximum cell density and growth rate was achieved on 0.1\u0026ndash;0.5 \u0026micro;M of pyruvate and oxaloacetate and 0.5-1 \u0026micro;M of sulfur-rich amino acids. Cell densities of both strains were significantly lower at 5 \u0026micro;M concentrations of any carbon or sulfur source (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 compared to other concentrations, Figs. S9-S10, Tables S11-13), which may be an indicator for oligotrophy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMetabolically, the new isolates (\u003cem\u003eA. medardicus\u003c/em\u003e and \u003cem\u003eA. abundans\u003c/em\u003e) and all species-representative MAGs share commonalities with the marine SAR11, including the presence of the glycolysis pathway, the TCA cycle, and transporters for C4-dicarboxylate compounds\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Supplementary text, Tables S14, S15). Further, all freshwater SAR11 representatives possess genes for propionate metabolism via methylcitrate pathway, recently described in marine SAR11\u003csup\u003e46\u003c/sup\u003e. Some SAR11-IIIb species representatives also harbor additional secondary solute transporters with broader substrate range, such as the TctCBA in \u003cem\u003eA. africanus\u003c/em\u003e (IIIb.1), potentially liked to methylcitrate pathway substrate transport. The glyoxylate shunt common to marine SAR11-Ia subclade\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e has a patchy distribution in the freshwater IIIb group as described previously\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and the isocitrate lyase gene (\u003cem\u003eaceA\u003c/em\u003e) was not present in any of our culture genomes, but present in some MAGs (Fig. S13). Several strain-specific distinctions were identified in our culture genomes such as the presence of a cytochrome bd oxidase gene in one \u003cem\u003eA. medardicus\u003c/em\u003e strain (ME-18) and the presence of a tetrahydrofolate-ligase (FtfL) for formate oxidation along with the synthesis of molybdenum cofactor in one \u003cem\u003eA. abundans\u003c/em\u003e strain (MKE-138; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Tables S14, S15). Notable, this strain was the only one that lacked the glycine cleavage pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe absence of assimilatory sulfur reduction is typical for the SAR11 clade, with the freshwater IIIb group relying on sulfur-rich amino acids such as cysteine and methionine\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Surprisingly, one strain (\u003cem\u003eA. medardicus\u003c/em\u003e ME-20), contained genes \u003cem\u003ecysNC\u003c/em\u003e and \u003cem\u003ecysD\u003c/em\u003e, which convert sulfate to PAPS (3-phosphoadenosine-5-phosphosulfate), in the hypervariable region between 23S rRNA and 5S rRNA genes (HVR2, Fig. S14). However, phylogenetic analysis of the \u003cem\u003ecys\u003c/em\u003eNC and \u003cem\u003ecys\u003c/em\u003eD genes did not hint at horizontal gene transfer but at selective retention, as the closest relatives were marine SAR11 species (Fig. S15). Further, all culture genomes contained a transmembrane protein (YeeE) and a putative thiosulfate/3-mercaptopyruvate sulfurtransferase enzyme (SseA) which was recently demonstrated to facilitate uptake of thiosulfate, an inorganic sulfur source\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. We tested the use of thiosulfate and sulfur-rich amino acids (cysteine and methionine) as sole inorganic and organic sulfur sources, respectively. Both strains, \u003cem\u003eA. medardicus\u003c/em\u003e ME-17 and \u003cem\u003eA. abundans\u003c/em\u003e MiE-29, showed no growth in the absence of sulfur in media (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, growth was restored upon the addition of sulfur sources in a strain-specific manner, \u003cem\u003eA. medardicus\u003c/em\u003e ME-17 showed higher growth with sulfur rich amino acids, while \u003cem\u003eA. abundans\u003c/em\u003e MiE-29 exhibited significantly higher growth rates with thiosulfate (Table. S13c).\u003c/p\u003e \u003cp\u003eGenome analysis further revealed complete biosynthetic pathways for 16 amino acids, and partial pathways for synthesis of cysteine in one \u003cem\u003eA. medardicus\u003c/em\u003e strain (ME-20; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Tables S14-S15). \u003cem\u003eA. medardicus\u003c/em\u003e ME-17 and \u003cem\u003eA. abundans\u003c/em\u003e MiE-29 did not grow if no amino acids were supplemented in the medium (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Tables S11-13). Both strains were affected in a similar manner by the absence of certain amino acids but still could attain maximum cell densities of 10\u003csup\u003e6\u003c/sup\u003e cells ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig. S11). Significant decrease in growth was associated with absence of nitrogen-rich amino acids (arginine, asparagine, lysine, tryptophan and glutamine) and sulfur amino acids (cysteine and methionine) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we advance the current metabolomic and genomic understanding of SAR11-IIIb, a relatively little explored subgroup within the SAR11 lineage, by isolating and characterizing seven new cultures along with 93 high-quality MAGs from freshwater and brackish environments from five continents (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Table S2). Consistent with the previously described culture isolate LSUCC0530 (\u003cem\u003eAllofontibacter communis\u003c/em\u003e) and other SAGs and MAGs, our new genomes exhibit features of genome streamlining such as low GC content, genome sizes in the range of 1.05\u0026ndash;1.1 Mbp, and characteristic small curved cell morphology\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. We also confirm the conservation of a large hypervariable region (~\u0026thinsp;5000 bps, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e4) in the new isolates which is present across the SAR11 clade\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe identified 16 distinct species within the freshwater IIIb subclade (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with the newly cultured isolates from our study belonging to two species (\u003cem\u003eA. medardicus\u003c/em\u003e and \u003cem\u003eA. abundans\u003c/em\u003e, IIIb.8 and 9) out of 11 new \u003cem\u003eAllofontibacter\u003c/em\u003e species proposed here (Supplementary text). Physiological characterization of these two species revealed optimal growth temperatures and salinity tolerances different from what has been reported for \u003cem\u003eA. communis\u003c/em\u003e strain LSUCC0530\u003csup\u003e2\u003c/sup\u003e. A marked reduction in growth yield was observed in the absence of nitrogen-rich amino acids supply in both species (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This growth decline aligns with the strategic optimization of metabolic processes in genome streamlined microbes\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, especially the use of cellular nitrogen where absence of readily assimilable sources can trigger down-regulation of growth to conserve energy. Bacteria are known to have varied ability to utilize amino acids due to genetic, adaptive or/and environmental factors; for example \u003cem\u003eE. coli\u003c/em\u003e uses different amino acids as its sole carbon and nitrogen source during aerobic vs. anaerobic growth conditions\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, while genome-streamlined freshwater \u003cem\u003ePlanktophila\u003c/em\u003e spp. show species-specific increased growth with the supply of sulfur-rich amino acids\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCommon metabolic pathways and inter- and intraspecific traits in freshwater SAR11.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe conservation of gene content within the SAR11 clade is evident \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, yet discernible variations exist in specific genes or pathways, indicative of lineage-specific adaptations or ecotypes\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. The marine SAR11 clade has been shown to exhibit high genomic diversity in adaptations to nutrient-deficient waters, displaying greater affinity for certain micronutrients\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. For instance, the metabolic functionalities of mesopelagic-adapted clades compared to their surface-dwelling counterparts have evolved to thrive in respective niches, due to variations in sulfur assimilation pathways or capacity for nitrate reduction\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eComparable patterns exist also in freshwater SAR11-IIIb. The presence of unique or expanded transporters (e.g., TctCBA in \u003cem\u003eA. africanus\u003c/em\u003e) may reflect niche adaptations based on substrate availability. Similarly, strains affiliated to \u003cem\u003eA. medardicus\u003c/em\u003e, isolated from Lake Medard (Czech Republic), contained an additional nitrogen metabolism gene, encoding glutamate dehydrogenase thus providing an alternate route for nitrogen assimilation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Tables S14, S15, Supplementary text). A similar strategy was seen in marine cyanobacteria\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, with a switch from ATP-dependent glutamine synthetase-glutamate synthase complex (which aminates glutamate) towards the reduced form of NADP+-dependent glutamate dehydrogenase (which aminates 2-oxoglutarate) and can be energy efficient in nitrogen rich environments. Sulfur auxotrophy is widespread across the SAR11 clade, and while marine SAR11 rely on exogenous sulfur sources like DMSP (3-dimethylsulphoniopropionate), the freshwater IIIb group was until now believed to depend on sulfur-rich amino acids\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Our genome analyses identified a potential uptake route for thiosulfate via the YeeE membrane protein\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Still, most strains lacked genes for assimilatory sulfur reduction, except for one \u003cem\u003eA. medardicus\u003c/em\u003e strain (ME-20) that contained genes involved in sulfate reduction in HVR2 (Fig. S14). However, growth experiments clearly showed that sodium thiosulfate supports growth of two strains (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, S11), particularly in a species-specific fashion. These species-specific responses to different sulfur sources provide additional evidence for potential differences in sulfur metabolism between \u003cem\u003eA. medardicus\u003c/em\u003e and \u003cem\u003eA. abundans\u003c/em\u003e and suggest that a so far unknown route or unannotated enzymes might be responsible for sulfur reduction in freshwater SAR11.\u003c/p\u003e \u003cp\u003eWhile some metabolic strategies can be generalized at the ecotype level, even closely related strains with only subtle differences in their metabolic networks can occupy different niches in the physical and chemical space, coined as nano-niche\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Strain-specific distinctions were noted in one of our five \u003cem\u003eA. medardicus\u003c/em\u003e strains (ME-18) which had a cytochrome bd oxidase gene which is known to be beneficial under oxygen limiting conditions\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, while another strain (ME-20) contained genes involved in sulfate reduction and cysteine metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Tables S14, S15). Likewise, the \u003cem\u003eA. abundans\u003c/em\u003e isolate MKE-138 had the ability to oxidize formate along with the synthesis of molybdenum cofactor, which can be linked to the tetrahydofolate (THF)-linked oxidation pathway, making it capable of utilizing C1 compounds. All these genes were lacking from the other \u003cem\u003eA. medardicus\u003c/em\u003e and \u003cem\u003eA. abundans\u003c/em\u003e strains and are thus likely a result of horizontal gene transfer. Conversely, the widespread distribution of isocitrate lyase AceA in marine SAR11-Ia subclade and its irregular presence in brackish/freshwater SAR11 genomes (Fig. S13) suggest gene loss events likely driven by environmental selection\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. The ecological importance of the isocitrate lyase gene in the marine SAR11 is underscored by its upregulation under iron scarcity; a common phenomenon when iron limits phytoplankton productivity in oceans\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Iron limitation is less pronounced in freshwater systems compared to marine habitats and specific to lakes with limited terrestrial inputs\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e, which may account for the gene\u0026rsquo;s sporadic presence.\u003c/p\u003e\n\u003ch3\u003eGlobal and seasonal distribution is mainly driven by water temperature\u003c/h3\u003e\n\u003cp\u003eBuilding on these physiological traits, we examined how environmental variables, particularly water temperature driven by climate, shape the distribution of SAR11-IIIb lineages on a global scale. Fragment recruitment analysis of 620 metagenomic datasets revealed nine distinct global distribution clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, S4-S7) that provide insight into the speciation, environmental adaptation, and colonization patterns of the SAR11-IIIb group. For instance, the newly isolated cultures (\u003cem\u003eA. medardicus\u003c/em\u003e and \u003cem\u003eA. abundans\u003c/em\u003e) were mainly present in temperate lakes in Europe, North America and Japan and were demonstrated to have a temperature preference between 16\u0026ndash;24\u0026deg;C in growth experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, \u003cem\u003eA. communis\u003c/em\u003e (IIIb.16) displays a more universal distribution pattern with a preference for warmer lakes in Asia, Australia, Africa, South America, and the Caribbean, which aligns with its description and an optimal growth at 30\u0026deg;C \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Four more species (\u003cem\u003eA. meridianamericanus\u003c/em\u003e, \u003cem\u003eA. africanus, A. subtropicus, A. lacus\u003c/em\u003e) were primarily present in subtropical lakes, while four others (\u003cem\u003eA. borealis, A. scandinavicus\u003c/em\u003e, and undescribed species IIIb.3 and 5) were limited to boreal lakes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Our data thus highlight the role of temperature and latitude in diversification of SAR11-IIIb, which is in accordance with studies involving marine SAR11 and other marine planktonic bacteria\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e and consistent with broader patterns observed in microbial biogeography\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Geographical isolation, on the other hand, seemed to be less important, as most species were detected on multiple continents (with notable exceptions described below), in accordance with previous observations for \u003cem\u003eAllofontibacter\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and other freshwater bacteria\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we recovered many high quality MAGs from Lake Malawi in Africa, revealing a deeply branching species (\u003cem\u003eA. africanus\u003c/em\u003e, IIIb.1, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) that was previously represented by only one MAG from Lake Tanganyika\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The tropical African Rift Valley lakes are meromictic, and while the MAG from Lake Tanganyika was assembled from an anoxic sample (200 m)\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, Lake Malawi is oxygenated down to \u0026gt;\u0026thinsp;200 m depth, where our samples were gained from. \u003cem\u003eA. africanus\u003c/em\u003e was also present in deep, anoxic samples from Lake Tanganyika (7.3x coverage per Gb in 1200 m), and while no hints for anaerobic growth were found in any of the genomes, \u003cem\u003eA. africanus\u003c/em\u003e encodes large subunits of cytochrome b6f (\u003cem\u003epetABCD\u003c/em\u003e) which is typically found in photosynthetic microorganisms and can contribute to the generation of a proton gradient for ATP synthesis through diverse electron donors\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eA. africanus\u003c/em\u003e was restricted to Africa (Lakes Malawi, Kivu and Tanganyika), which highlights regional endemism, contrasting to the cosmopolitan distribution of \u003cem\u003eA. communis\u003c/em\u003e, that also includes 15 MAGs assembled from the African Great Lakes, which form a separate branch in the tree (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Another potentially endemic species might be \u003cem\u003eA. baikalensis\u003c/em\u003e (IIIb.6), represented by one MAG that was mainly present in Lake Baikal. Lake Baikal as well as the African Great Lakes are among the oldest and largest lakes on earth and well known for their endemic fauna and flora\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, a trait that seems to stretch out also to a distinct microbiome containing deeply branching and very specific prokaryotic lineages\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. We also observed quasi-endemism in lakes of the Southern Hemisphere, primarily dominated by \u003cem\u003eA. meridianamericanus\u003c/em\u003e (IIIb.12) and \u003cem\u003eA.lacus\u003c/em\u003e (IIIb.13). Besides endemic or quasi-endemic \u003cem\u003eAllofontibacter\u003c/em\u003e species, these lakes also hosted other ubiquitous \u003cem\u003eAllofontibacter\u003c/em\u003e species (\u003cem\u003eA. temperatus\u003c/em\u003e in Lake Baikal, \u003cem\u003eA. communis\u003c/em\u003e in the African Great Lakes and South American water-bodies), suggesting that neither dispersal nor ecological selection are limiting factors, while the ancient history of these lakes might have led to evolutionary priority effects favoring distinct species\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Interestingly, 12 additional MAGs from Lake Malawi were affiliated with the marine SAR11-I clade (\u003cem\u003ePelagibacter malawensis\u003c/em\u003e), closely related to genomes obtained from Lake Baikal, that were proven to be of freshwater origin\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. These freshwater genomes, as well as another group of MAGs obtained from deep alpine lakes affiliated with marine SAR11-II (\u003cem\u003ePelagilacustribacter hypolimneticus\u003c/em\u003e) were most closely related to genomes from brackish systems (Caspian Sea, Baltic Sea, San Francisco Bay, brackish coastal lagoons in Uruguay, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and hint at multiple marine-freshwater transitions in the evolutionary history of the SAR11 clade and a refuge of ancient lineages in deep lakes.\u003c/p\u003e \u003cp\u003eNumerous time series observations of the marine SAR11 clade and its subgroups have revealed varied responses to environmental factors, with distinct peaks associated with water temperature and mixing events\u003csup\u003e\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Our time series analysis of freshwater \u003cem\u003eAllofontibacter\u003c/em\u003e species across six temperate and subtropical lakes showed that during seasonal successions, multiple species co-occurred in the same water samples, some being more prevalent than others (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table S7). Overall, the patterns of seasonal succession with maxima during warmer months\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e were similar between the lakes with a distinctive species dominance which reaffirms the high diversity of freshwater SAR11 each occupying a distinct niche\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe seasonal and spatial partitioning of \u003cem\u003eAllofontibacter\u003c/em\u003e species points to an ecological strategy, where even closely related species exhibit differential responses to the same or similar environmental cues. This diversity within SAR11 reflects a broader trend in planktonic microbial systems, where minute genetic variations translate into niche specialization, allowing both coexistence and competitive advantages. Our results endorse the \u0026lsquo;ubiquity-by-diversification\u0026rsquo; principle\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e resulting in the expansion of functional capacities within different species of the same genus or ecotypes within the same species. Thus, the \u0026lsquo;everything is everywhere but the environment selects\u0026rsquo; hypothesis\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e should be deliberated due to significant capability of microorganisms to disperse and colonize new locations. By combined cultivation, genomics, metagenomics and sampling of underrepresented regions in the Global South, this study is an important step towards understanding the distribution of one of the ecologically most significant genera in freshwaters on a global scale. Yet, several questions remain open which were beyond the scope of this study. There are likely additional niche-based species and/or ecotypes present within SAR11-IIIb to be identified due to still limited representative cultures.\u003c/p\u003e \u003cp\u003eIn this study, we addressed a crucial gap by cultivating the freshwater SAR11-IIIb group; as stable cultures are essential for physiological niche characterization and provide insights into their functional roles. While we also expanded the geographic scope of available data through new high-quality MAGs from previously undersampled regions in Africa, Asia, Australia and South America (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), public datasets remain skewed toward temperate regions. The distribution of different SAR11-IIIb species appears to be largely driven by temperature and/or latitude. It is thus interesting to ask if ongoing climate warming with increasing global temperatures could result in a redistribution of these species, with warmer-adapted species expanding into temperate zones? This question underscores broader ecological implications of our findings, linking microbial biogeography to global climate dynamics.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSample collection and strain isolation\u003c/h2\u003e \u003cp\u003eSamples for long- and short-read metagenomic sequencing were collected from Vistula River, Vistula Lagoon and the coastal Baltic Sea in Poland, four lakes in Japan, eight lakes in Australia, five coastal lagoons and reservoirs in Uruguay and four stations along Lake Malawi (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A submersible multiparameter probe (YSI EXO2, Yellow Springs Instruments, Yellow Springs, OH, USA) was deployed to measure profiles of temperature, oxygen, pH, chlorophyll \u003cem\u003ea\u003c/em\u003e, salinity, and conductivity. Water from different depths was prefiltered through a 20 \u0026micro;m mesh and then sequentially filtered through 5 \u0026micro;m (STERILITECH PES membrane filters, USA) and 0.22 \u0026micro;m (Millipore express PLUS, Germany) polysulfone filters until clogged (1\u0026ndash;11 l depending upon the lake)\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Filters were stored at -80\u0026deg;C until DNA extraction and subsamples of the 0.22 \u0026micro;m filtered water were taken for nutrient (phosphorus, nitrogen, ions, and dissolved organic carbon) analyses\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Additionally, we used metagenomes obtained from the epi- and hypolimnion of multiple Central European lakes (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAll SAR11-IIIb strains were isolated from the epilimnion of oligo-mesotrophic lakes and reservoirs (Medard, Milada and Kl\u0026iacute;čava) in the Czech Republic, using high-throughput dilution-to-extinction cultivation \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Firstly, water samples were prefiltered through 20 or 0.4 \u0026micro;m filters to eliminate larger microbes and total prokaryote numbers were counted with a flow cytometer (CytoFLEX S, Beckman Coulter; Brea, CA, USA) equipped with a blue (488 nm) laser (bandpass filters 525/40 and 690/50) after staining with SYBR Green (0.5 x final concentration; Lonza, Rockland, ME, USA). For dilution-to-extinction cultivation, the filtrate was diluted in artificial lake water media\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e amended with different carbon, sulfur and nitrogen sources (Table S10) and dispensed into 96-deep-well plates to theoretically obtain 0.5, 1 or 5 cells per well. After incubation at 16\u0026deg;C in light:dark conditions (12:12 h) for 6 weeks, a subsample of each well was stained with SYBR Green and examined by flow cytometry. Wells with growth (\u0026gt;\u0026thinsp;10\u003csup\u003e4\u003c/sup\u003e cells ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were transferred to 10 ml cultivation tubes containing the same isolation media and a subsample was stored at -80\u0026deg;C after adding glycerol (final concentration 20%, v/v). Screening through 16S rRNA gene PCR amplification and SANGER sequencing was done as previously described\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. All cultures identified as SAR11-IIIb were routinely maintained in the same isolation conditions for further analyses and experiments.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenome sequencing and assembly\u003c/h3\u003e\n\u003cp\u003eEach of the SAR11-IIIb strains were grown in 500 ml glass Erlenmeyer flasks or plastic tubes until late stationary phase. Cells were harvested at 10\u003csup\u003e6\u003c/sup\u003e cells ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e densities by filtration onto 0.2 \u0026micro;m PES filters (Millipore express PLUS) in sterile conditions. DNA was extracted using MagAttract\u0026reg; HMW DNA Kit (Qiagen, Venlo, NL). Thereafter, paired end libraries (PE150) were sequenced on an Illumina NovaSeq 6000 instrument (Novogene HK). Raw reads underwent quality trimming with BBMap v36.x and were subsequently assembled using Spades v3.12.0 with multiple K-mers (29, 49, 59, 69, 79, 89, 99, 109, 119, and 127). Most assemblies resulted in 1\u0026ndash;3 contigs, which were then manually curated into circular chromosomes through iterative rounds of read mapping to contigs using Geneious 10 (default mapper, high sensitivity; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.geneious.com\u003c/span\u003e\u003c/span\u003e). Contigs were extended bidirectional by identifying overlapping ends followed by \u003cem\u003ede novo\u003c/em\u003e assembly with Geneious 10 assembler (high sensitivity settings).\u003c/p\u003e\n\u003ch3\u003eMetagenomic sequencing, assembly, and binning\u003c/h3\u003e\n\u003cp\u003eDNA extraction of the 0.22 \u0026micro;m filters, metagenomic short-read sequencing (Illumina NovaSeq 6000, PE150) and assembly of samples obtained from lakes in Central Europe was done as previously described\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Long-read sequencing of DNA extracted with a Quick-DNA HMW MagBead Kit (Zymo Research; Irvine, CA, USA) was carried out on a promethION platform (Oxford Nanopore) by an external sequencing provider (Novogene HK). Quality-controlled and trimmed short-read sequences\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e were used to polish noisy long-read sequences by generating a Burrows Wheeler Transform (BWT), according to the ropebwt2 construction approach\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. Nanopore basecalled long-reads with an average Q score\u0026thinsp;\u0026ge;\u0026thinsp;8 were subjected to adapter and barcode trimming by Porechop\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e and were further polished using the generated Illumina BWT with FMLRC2 v0.1.8 with default parameters\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Polished long-reads were assembled using Flye v2.9.1-b1780 (--nano-corr \u0026ndash;meta \u0026ndash;no-alt-contigs)\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. Assemblies were binned to metagenome-assembled genomes (MAGs) using DAS tool\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e by cross-mapping of reads gained from the same lake (up to 12 samples in the case of Lake Malawi).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenome annotation and phylogenomic analyses\u003c/h2\u003e \u003cp\u003eAll genomes obtained in this study, along with genomes of all GTDB representative species of SAR11-I, II and IIIa and all SAR11-IIIb genomes retrieved from public databases (ENA, NCBI), were classified with GTDB-Tk v2.4.0 toolkit\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e based on GTDB r220. CheckM v1.0.18\u003csup\u003e84\u003c/sup\u003e, CheckM2 v1.0.1 (\u0026ldquo;predict\u0026rdquo;)\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e and an in-house script were used to evaluate genome characteristics (completeness, contamination, genome size, coding density, GC%, predicted genes). Genes were predicted with PROKKA\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e, tRNA and rRNA genes were identified with barrnap 0.9 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/barrnap\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/barrnap\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and tRNAscan-SE\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e, and proteins were annotated using hmmsearch\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e against collections of COG\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e, TIGRFAM\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e, and KEGG\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e,\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. Metabolic pathways were inferred from the KEGG database and were subsequently manually assessed for completeness in all culture genomes.\u003c/p\u003e \u003cp\u003eA subset of 36 high quality genomes (\u0026gt;\u0026thinsp;95% completeness and \u0026lt;\u0026thinsp;1% contamination, mainly culture genomes) were subjected to hmmsearch\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e against TIGRFAMs\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e and proteins present in \u0026gt;\u0026thinsp;97.2% of this collection were considered to be the core genome of the family (751 proteins, Table S3). All SAR11 genomes were subjected to hmmsearch\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e against the 751 marker proteins and only genomes that containing at least 55% of the markers (n\u0026thinsp;=\u0026thinsp;295) were used for phylogenetic tree reconstruction. Proteins were individually aligned using PRANK v.150803 (-protein\u0026thinsp;+\u0026thinsp;F)\u003csup\u003e93\u003c/sup\u003e, concatenated, and a maximum-likelihood tree was constructed using IQ-TREE\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e (-bb 1000, -alrt 1000) with ultrafast bootstrapping and the model VT\u0026thinsp;+\u0026thinsp;F\u0026thinsp;+\u0026thinsp;I\u0026thinsp;+\u0026thinsp;G4, chosen by ModelFinder\u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. Genomes affiliated with SAR11-IV and V clades, that exhibit distinct phylogenetic placements outside of Pelagibacterales\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e, were used as outgroup to root the tree.\u003c/p\u003e \u003cp\u003eAverage amino acid and nucleotide identities (AAI and ANI) were computed following previously established methods using a 95% cut-off for different species and 65% AAI for different genera\u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e,\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e. The resulting matrices (Tables S4, S5) were compared to the phylogenomic tree to validate the classification of the new genomes within the SAR11 clade. One representative genome per species was selected with dRep\u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e at ANI\u0026thinsp;\u0026gt;\u0026thinsp;95%.\u003c/p\u003e \u003cp\u003eWhole-genome alignments and BLAST comparisons of complete genomes (tBLASTx) and representatives of the 16 species (BLASTn) were done as previously described\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. MAGs containing multiple contigs were first aligned to the closest complete relative to reorder and concatenate contigs with the Mauve Contig Mover algorithm\u003csup\u003e\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e and the residue numbering was changed to start with DnaA ATPase in all genomes to enable easier visual comparisons.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePhylogenetic analysis of the isocitrate lyase (aceA) and Cys (\u003c/b\u003e \u003cb\u003eCys\u003c/b\u003e\u003cb\u003eE, NC, D) genes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll SAR11 genomes used in this study were scanned for the presence of the isocitrate lyase gene (\u003cem\u003eaceA\u003c/em\u003e) using Prodigal v2.6.3\u003csup\u003e101\u003c/sup\u003e and publicly available proteins from all bacterial phyla were searched against AceA HMMs for significant hits using hmmsearch\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. Hits were filtered based on protein coverage (\u0026gt;\u0026thinsp;80%) and dereplicated using MMseqs2\u003csup\u003e102\u003c/sup\u003e (easy-cluster workflow) with a minimum sequence identity of 95%. The filtered protein sequences (n\u0026thinsp;=\u0026thinsp;1367) were aligned with MAFFT\u003csup\u003e\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e (-local pair \u0026ndash;max iterate 1000) and a tree was constructed using IQ-TREE\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e with 1000 ultrafast bootstrap replicates and the LG\u0026thinsp;+\u0026thinsp;F\u0026thinsp;+\u0026thinsp;G4 evolutionary model\u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. A SAR11 subtree was generated after a local blast search to identify the closest 200 sequences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1e\u0026thinsp;\u0026minus;\u0026thinsp;2) of AceA protein sequences from SAR11 genomes used in this study. Similarly, trees were also generated for three genes identified in HVR2 in \u003cem\u003eA. medardicus\u003c/em\u003e ME-20, i.e., \u003cem\u003ecysD\u003c/em\u003e (O-acetylhomoserine (thiol)-lyase), \u003cem\u003ecysNC\u003c/em\u003e (sulfate adenylyltransferase), and \u003cem\u003ecysE\u003c/em\u003e (serine O-acetyltransferase). The 100 closest relatives were identified by BLASTx, protein sequences were aligned as before, and the phylogenetic tree was inferred using the LG\u0026thinsp;+\u0026thinsp;I\u0026thinsp;+\u0026thinsp;G3 model (AceA) and LG\u0026thinsp;+\u0026thinsp;I\u0026thinsp;+\u0026thinsp;G4 (CysE, NC, D) as chosen by Modelfinder\u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMetagenomic fragment recruitment\u003c/h2\u003e \u003cp\u003eAdditionally to the here sequenced metagenomes, seasonally resolved samples from the Ř\u0026iacute;mov Reservoir (124 samples, 2015\u0026ndash;2022)\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e, Lake Biwa (24 samples along 1 year)\u003csup\u003e\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u003c/sup\u003e, the TYMEFLIES dataset from Lake Mendota (94 samples, 2008\u0026ndash;2012)\u003csup\u003e\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e, a part of the LIMNOS dataset (Lake Stechlin, Lake Breiter-Luzin, and Tiefwaren, 317 samples collected over 10 years), and 559 metagenomic freshwater datasets from public domain were used for fragment recruitment (Tables S6, S7). Metagenomes were subsampled to 20\u0026nbsp;million reads and rRNA genes in genomes were masked prior to recruitment. MMseqs2\u003csup\u003e102\u003c/sup\u003e was used to map metagenomic reads to the 16 representative \u003cem\u003eAllofontibacter\u003c/em\u003e species genomes, to obtain base coverage per Gb (-minid 0.95 -mincov 0.9 -minlen 50). Representatives with less than 40% of their total genome aligned to the query metagenome were filtered out and assigned a coverage value of zero. False positive hits caused by cross-mapping of reads can be ruled out because of strict mapping cutoffs (\u0026gt;\u0026thinsp;95% nucleotide identity covering\u0026thinsp;\u0026gt;\u0026thinsp;40% of a genome) and low nucleotide identities between different \u003cem\u003eAllofontibacter\u003c/em\u003e species (Fig. S2, S3, Table S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eScanning Electron Microscopy\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eStrain ME-17 was grown as above until late stationary phase and fixed with glutaraldehyde (2.5% final concentration) over night at 4\u0026deg;C. Four ml were filtered onto white polycarbonate filters (0.2 \u0026micro;m pore size, Millipore) and stepwise dehydrated in 30, 50, 70, and 100% ethanol for 10 mins each. Dried filters were stored at 4\u0026deg;C until further processing. For imaging, the filters were cut with a blade and mounted onto a metal knob using conductive copper tape. The samples were sputter coated (10 nm layer) with gold in a Sputter Coater Polaron chamber (Polaron Ltd., Watford, UK). Scanning electron microscopy (SEM) was performed using JEOL 7401-F field emission SEM (JEOL Europe, Prague, Czech Republic).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGrowth experiments\u003c/h2\u003e \u003cp\u003eTwo axenic strains (\u003cem\u003eA. medardicus\u003c/em\u003e ME-17 and \u003cem\u003eA. abundans\u003c/em\u003e MiE-29), each representing a different species, were used in short-term growth assays to identify optimal growth conditions and species-specific differences. We tested three different artificial media that were also used for dilution-to extinction cultivation (Table S10). Further growth experiments included four temperatures (8, 16, 24, and 30\u0026deg;C), salinity tolerances (0, 2.89, and 5.89 PSU), different concentrations (0.1, 0.5, 1, 5 \u0026micro;M) of carbon (pyruvate, oxaloacetate) and sulfur sources (thiosulphate and sulfur rich amino acids cysteine and methionine), and selective removal of individual amino acids (Table S11). All experiments were carried out in triplicates in 96 deep-well plates at 16\u0026deg;C (except for the temperature treatments) in light:dark conditions (12:12 h) with med2 as control. Cell abundances were enumerated every two to four days until stationary phase by flow cytometry as described above. Maximum growth rates were calculated for each treatment and strain as previously described\u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e and statistically compared to control treatments using ANOVA and post-hoc tests (Tukey HSD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed using R\u003csup\u003e\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e packages vegan v2.5-7\u003csup\u003e109\u003c/sup\u003e and cluster v2.0.7\u0026ndash;1\u003csup\u003e110\u003c/sup\u003e. To identify distribution patterns of SAR11-IIIb species, hierarchical clustering was performed. A species x species dissimilarity matrix was first calculated using the Bray-Curtis index on the relative abundance of species across all samples. Hierarchical clustering was then applied to this matrix using Ward's minimum variance method (hclust function, method\u0026thinsp;=\u0026thinsp;ward.D2). The optimal number of species clusters was determined by evaluating the dendrogram structure alongside the average silhouette width (cluster::silhouette function). The relationships between species based on the distribution patterns were visualized using Non-Metric Multidimensional Scaling (NMDS) (vegan::metaMDS function). Further to detect patterns in SAR11-IIIb species composition among the different samples, a separate NMDS was performed on a sample x sample Bray-Curtis dissimilarity matrix, calculated from the relative abundances of all species within each sample. The influence of latitude and longitude on the sample community structure was assessed by fitting them as linear vectors onto the ordination using the vegan::envfit function. Significant correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were assessed via permutation tests, and significant vectors were displayed on the sample NMDS plot. Potential non-linear spatial gradients across the sample community ordination were also visualized by fitting latitude and longitude individually as smooth surfaces onto the sample NMDS plot using the vegan::ordisurf function. Differences in the geographic locations (latitude and longitude values) associated with the previously identified species clusters were statistically tested using Kruskal-Wallis tests \u003csup\u003e\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePhysico-chemical data available from time series metagenomic datasets was used to perform multivariate analyses. This involved RDA (redundancy analysis, model testing using ANOVA with all available parameters for each lake with permutations tests (999 permutations, \u0026lt;\u0026thinsp;0.001)) and biplots to visualize the relationship between species, season and environmental variables. Further, to identify the dominant species in each lake, histograms were plotted to show distribution of regression coefficients (loadings), for each axis and all the species. Pairwise correlations between the abundant species and environmental factors were also calculated using the cor function in the package psych (version 2.2.9)\u003csup\u003e\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e. Plots and heatmaps were generated using the R\u003csup\u003e\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e package ggplot2 (v3.3.5)\u003csup\u003e\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e and ComplexHeatmap (v1.10.2r) \u003csup\u003e\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomes from cultures have been submitted to ENA under project accession number PRJEB77526, Illumina and Oxford Nanopore reads, and MAGs under ENA project accession numbers PRJEB35770, PRJEB86000-PRJEB86004. Novel species were registered at SeqCode\u003csup\u003e41\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank local fisherman, captains and technicians for their help during sampling. Sample collection in Lake Biwa\u0026nbsp;was supported by Center for Ecological Research, Kyoto University, a Joint Usage / Research Center.\u0026nbsp;Sampling in Lake Toya was supported by a joint usage with Toya Lake Station of Field Science Center for Northern Biosphere, Hokkaido University.\u0026nbsp;F. Kostanj\u0026scaron;ek, M. Okrouhlikov\u0026aacute;, A. F\u0026eacute;rov\u0026aacute; and I. Lebeda are acknowledged for excellent laboratory support.\u0026nbsp;We thank the Biology Centre CAS core facility LEM supported by\u0026nbsp;MEYS CR (LM2023050 Czech-BioImaging and OP VVV\u0026nbsp;CZ.02.1.01/0.0/0.0/18_046/0016045).\u0026nbsp;This study was supported by Czech Science Foundation (GAČR) grants 22-03662S (MMS, CF, PL, M-CC), 25-15813S (MMS, CF, MH), 20-12496X (RG, P-AB, VK) and 21-21990S (MH, M-CC). CF was also supported by grant 017/2022/P and PL by grant 022/2019/P (Grant Agency of the University of South Bohemia in Česk\u0026eacute; Budějovice). JW and H-PG were funded by the Leibniz foundation and thank the entire LIMNOS team.\u0026nbsp;YO was funded by JST FOREST program (JPMJFR2273) and\u0026nbsp;JSPS KAKENHI grants (16H06279, 18J00300, 22K15182).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMMS conceived the study. Sampling was performed by CF, MH, PL, M-CC, RG, VK, TS, H-PG, JW, KP, CA, JZ, DH, MN, S-iN, YO and MMS. CF, MH, PL and MMS isolated and maintained the strains. CF, M-CC, P-AB and MMS assembled, binned and annotated the metagenomes with pipelines designed by P-AB and RG. CF analyzed the data and prepared the figures supervised by MMS. CF and MMS wrote the manuscript with input from all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary information is available for this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGiovannoni, S. J. 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Complex heatmap visualization. \u003cem\u003eImeta\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, e43 (2022). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pelagibacterales, Allofontibacter, Bacterial cultures, Long-read metagenomic sequencing, MAGs, Metabolism, Ecogenomics, Biogeography","lastPublishedDoi":"10.21203/rs.3.rs-6457240/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6457240/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe freshwater SAR11-IIIb genus \u003cem\u003eAllofontibacter\u003c/em\u003e (initially described as \u0026lsquo;Ca. \u003cem\u003eFonsibacter\u0026rsquo;\u003c/em\u003e) within the order \u003cem\u003ePelagibacterales\u003c/em\u003e is recognised for its ubiquitous presence in freshwater environments. However, it remains poorly understood due to cultivation limitations, with only one cultured genome published to data. As a result, its genetic diversity, metabolic capabilities and ecological roles remain largely unexplored, with most available data limited to lakes in the Northern Hemisphere. Here, we present seven new isolates representing two novel species, along with 93 high-quality metagenome-assembled genomes (MAGs) derived from a global survey across five continents. Phylogenomic analysis revealed 16 species forming nine distinct biogeographic clusters, indicating speciation patterns linked to water temperature and latitude. Notably, we observed phylogeographic partitioning, including endemic species restricted to African lakes, quasi-endemic species confined to either the Northern or Southern Hemisphere, and the co-existence of cosmopolitan species alongside regionally constrained lineages. Furthermore, metabolic profiling and growth experiments uncovered species- and strain-specific adaptations for nutrient uptake, along with unique pathways for sulfur metabolism. These findings provide the first global-scale genomic and ecological overview for this lineage, raising key questions about dispersal barriers, priority effects, evolutionary trajectories, and mechanisms of niche adaptation in freshwater SAR11.\u003c/p\u003e","manuscriptTitle":"Ecophysiology and global dispersal of the freshwater SAR11-IIIb clade","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-23 06:24:05","doi":"10.21203/rs.3.rs-6457240/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-microbiology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nmicrobiol","sideBox":"Learn more about [Nature Microbiology](http://www.nature.com/nmicrobiol/)","snPcode":"","submissionUrl":"","title":"Nature Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6e5d4e5d-6bd3-4521-a9be-547f84829327","owner":[],"postedDate":"June 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":49657140,"name":"Biological sciences/Microbiology/Environmental microbiology/Water microbiology"},{"id":49657141,"name":"Biological sciences/Microbiology/Bacteria/Bacterial genomics"},{"id":49657142,"name":"Earth and environmental sciences/Ecology/Biogeography"},{"id":49657143,"name":"Earth and environmental sciences/Ecology/Microbial ecology"},{"id":49657144,"name":"Biological sciences/Ecology/Freshwater ecology"}],"tags":[],"updatedAt":"2025-07-15T17:26:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-23 06:24:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6457240","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6457240","identity":"rs-6457240","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}