Uncovering the genomic basis of symbiotic interactions and niche adaptations in freshwater picocyanobacteria

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Kavagutti, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4217878/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Aug, 2024 Read the published version in Microbiome → Version 1 posted 4 You are reading this latest preprint version Abstract Background Picocyanobacteria from the genera Prochlorococcus , Synechococcus , and Cyanobium are the most widespread photosynthetic organisms in aquatic ecosystems. However, their freshwater populations remain poorly explored, due to uneven and insufficient sampling across diverse inland waterbodies. Results In this study, we present 170 high-quality genomes of freshwater picocyanobacteria from non-axenic cultures collected across Central Europe. In addition, we recovered 33 genomes of their potential symbiotic partners affiliated with four genera, Pseudomonas , Mesorhizobium , Acidovorax , and Hydrogenophaga . The genomic basis of symbiotic interactions involved heterotrophs benefiting from picocyanobacteria-derived nutrients while providing detoxification of ROS. The global abundance patterns of picocyanobacteriarevealed ecologically significant ecotypes, associated with trophic status, temperature, and pH as key environmental factors. The adaptation of picocyanobacteria in (hyper-)eutrophic waterbodies could be attributed to their colonial lifestyles and CRISPR-Cas systems. The prevailing CRISPR-Cas subtypes in picocyanobacteria were I-G and I-E, which appear to have been acquired through horizontal gene transfer from other bacterial phyla. Conclusions Our findings provide novel insights into the population diversity, ecology, and evolutionary strategies of the most widespread photoautotrophs within freshwater ecosystems. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Picocyanobacteria, with a cell size of less than 3 µm, represent the smallest and most widespread phytoplankton in marine and freshwater ecosystems [ 1 , 2 ]. This group of bacteria is affiliated with three distinct genera: Prochlorococcus , Synechococcus , and Cyanobium [ 1 ]. Currently, Synechococcus and Cyanobium have been further subdivided into three distinct phylogenetic groups referred to as subclusters (SC) 5.1 to 5.3 [ 3 ]. SC5.1 shows a close phylogenetic relationship with Prochlorococcus , collectively forming a marine-specific cluster of picocyanobacterial [ 4 ]. In contrast, strains from SC5.2 and 5.3 can inhabit diverse salinity gradients, including marine, brackish, and freshwater ecosystems [ 5 ]. SC5.3 is predominantly found in temperate and oligotrophic waterbodies [ 4 , 6 ], while SC5.2 demonstrates remarkable adaptability to various environmental conditions [ 5 ]. The​​ evolution of light-harvesting complexes, known as phycobilisomes, plays a crucial role in developing diverse pigmentations in picocyanobacteria and their adaptation to different light regimes [ 7 ]. Several studies of marine Prochlorococcus have unveiled ecologically significant ecotypes associated with growth elements such as iron [ 8 ], nitrogen [ 9 ], and phosphorus [ 10 ]. The recent discovery of novel Synechococcus strains in the deep oxygen-depleted water layers of the Black Sea demonstrated their potential to survive even in extreme conditions [ 11 ]. However, the genetic foundation behind their global success largely remains unresolved. The investigation of freshwater picocyanobacteria has faced even greater challenges, primarily due to the significant undersampling of their populations [ 12 ]. The recent release of a large genome collection for freshwater picocyanobacteria, consisting of 58 isolates, marks a significant advancement in the field [ 5 , 13 ]. However, most of these isolates originated from oligo- and mesotrophic lakes, indicating that their populations in various inland waterbodies might still be overlooked. In the cultures of cyanobacteria where no carbon source is present, heterotrophic bacteria are often co-isolated as their symbiotic partners [ 14 ]. The strong interdependencies between them make acquiring and maintaining axenic cultures of picocyanobacteria a challenging task [ 15 , 16 ]. Conversely, these non-axenic cultures offer a valuable opportunity to explore the unique microbial communities in their environment, as well as the metabolic interactions between hetero- and photoautotrophs. The ‘helper’ heterotrophic bacteria can benefit from phytoplankton-derived organic compounds while supporting enhanced growth or prolonged survival in nutrient-depleted conditions [ 14 , 17 ]. These symbiotic interactions may involve nutrient cycling [ 16 ], vitamin trafficking [ 18 ], and removing reactive oxygen species (ROS) [ 18 ]. However, the mechanisms behind the assembly of heterotrophs in freshwater picocyanobacterial cultures and their symbiotic roles have never been reported. To fill these gaps in our knowledge, we obtained non-axenic picocyanobacterial cultures from diverse freshwater ecosystems. This allowed us to recover the largest genome collection of freshwater picocyanobacteria, along with their abundances and cellular phenotypes under different conditions. Furthermore, we acquired the genomes of co-occurring heterotrophic bacteria present in the cultures. Our investigation into the global phylogeography of freshwater picocyanobacteria revealed ecologically significant ecotypes, associated with diverse environmental settings. Moreover, our genomic data provided insights into the symbiotic interactions of picocyanobacteria with heterotrophic partners and their adaptation strategies across different ecological niches. Materials and Methods Sample collection, isolation, and culture Picocyanobacterial isolates were obtained from water samples collected in 2005, and from additional sampling conducted between 2018 and 2020. During the sampling campaign in 2005, the isolation process comprised the following steps. Initially, water samples underwent filtration through 5 µm polycarbonate membrane filters (Sterlitech, Kent, WA, USA) to remove bigger organisms. The filtered samples were then processed by passing through 0.22 μm polyethersulfone membrane filters (Millipore, Merck, Darmstadt, DE) to enrich the picocyanobacterial biomass. Subsequently, the collected biomass was plated on BG11 medium solidified with 1.5% agar. Finally, individual colonies were picked and cultivated in WC medium, resulting in a total of 47 cultures from 17 different localities. During the years between 2018 and 2020, we employed the dilution-to-extinction cultivation method to obtain isolates. Environmental samples were initially filtered through 1 or 2 µm polycarbonate membrane filters (Sterlitech, Kent, WA, USA), and enumerated using epifluorescence microscopy (Zeiss Imager.Z2, Carl Zeiss, Oberkochen, DE). After a serial dilution, the water samples were inoculated into 96-well plates containing 1.5 mL of WC medium at an estimated 0.5 cell well -1 . Cultures were incubated at room temperature for three weeks and evaluated for growth using epifluorescence microscopy (Zeiss Imager.Z2, Carl Zeiss, Oberkochen, DE). A total of 107 non-axenic cultures from 31 localities were obtained. All isolates were continuously cultivated in liquid WC media, ensuring that the reinoculation timespan did not exceed 2 months. While the majority (92.4%) of our picocyanobacterial cultures were unialgal, they were not axenic. Two or more picocyanobacterial genomes were acquired from 13 cultures (7.6%). Probe design and CARD-FISH analysis To design ecotype-specific probes, 16S rRNA gene sequences obtained in this study and collected from publicly available databases were aligned using the MAFFT algorithm [19] within the Geneious software (https://www.geneious.com). Selected regions (18-25 nucleotides) were examined for hairpins and self-dimerization and evaluated in silico by TestProbe application (https://arb-silva.de/search/testprobe) for hits outside the target group. Competitor oligonucleotides were designed when necessary and tested in a similar manner. The mathFISH software (https://mathfish.cee.wisc.edu) was employed to determine the theoretical best hybridization conditions, and the final formamide concentration was determined in the lab. The list of designed probes and hybridization conditions are presented in Table S9. Environmental samples (2 to 20 mL) were fixed with a final concentration of 2% formaldehyde for at least 2 hours and filtered on 0.2 µm pore-size polycarbonate filters (Millipore, Merck, Darmstadt, DE). A CARD-FISH protocol [20], with some modifications [21]. was used for the labeling of distinct picocyanobacterial groups. Fluorescein-labeled thyramid, whose emission spectrum does not overlap with picocyanobacterial autofluorescence, was used for the amplification step. Filters were counterstained with DAPI and analyzed using epifluorescence microscopy (Zeiss Imager.Z2, Carl Zeiss, Oberkochen, DE) equipped with a Colibri LED light system. The images were captured using an Axiocam 506 (Carl Zeiss, Oberkochen, DE) with the following filter sets: DAPI 49 (Excitation 365; Beamsplitter TFT 395; Emission BP 445/50), fluorescein 38 HE (Excitation BP 470/40; Beamsplitter TFT 495; Emission BP 525/50) and chlorophyll A 62 HE (Excitation BP 370/40, 474/28, 585/35; Beamsplitter TFT 395 + 495 + 610; Emission TBP 425 + 527 + LP615 HE). Genomic DNA extraction For genome sequencing, picocyanobacterial cultures in the stationary phase were centrifuged at 4 ˚C, 8000 rcf for 30 minutes to collect cell pellets, which were subsequently stored at -80 ˚C until further processing. DNA extraction was carried out using the Quick-DNA Microprep Kit (Zymo Research, Irvine, CA, USA), according to the manufacturer’s instructions. Sequencing, preprocessing, and assembly of the sequencing reads Genomic DNA samples were sequenced under the Illumina Novaseq 6000 platform (Novogene, Hong Kong, China), targeting 1 Gbp per sample as output. Low-quality reads were trimmed using reformat.sh and bbduk.sh (Phred score = 18) of the bbmap package (https://sourceforge.net/projects/bbmap/). Any adapters or PhiX contamination were eliminated using the bbduk.sh script. The preprocessed reads were de novo assembledusing MEGAHIT v1.1.4 [22], employing k-mer sizes ranging from 29 to 149 in increments of 10. Finally, all assemblies underwent length-filtering, retaining contigs with a minimum size of 3 kbp. Recovery of the genomes and functional annotations Since all the cultures we obtained were not axenic, genome binning was done for each assembly using MaxBin v2.2.7 [23], Metabat1 v2.15 [24], and Metabat2 [25], with default settings. The contig abundance files were generated by mapping preprocessed reads to the length-filtered assemblies using bbwrap.sh (kfilter=31, subfilter=15, maxindel=80). Contig abundance files were obtained using jgi_summarize_bam_contig_depths [25] for Metabat1 and 2, and CoverM using trimmed_mean mode (https://github.com/wwood/CoverM) for MaxBin. Bins produced by each method were combined, dereplicated, and refined using DASTool v1.1.2 [26]. A taxonomy-based decontamination step was performed in order to produce high-quality bins: protein-coding genes from each contig were predicted using Prodigal v2.6.3 [27] in the metagenomic mode, and the taxonomy of each gene was assigned using MMseqs2 [28] with the GTDB r95 database [29]. Contigs with >30% of genes without hits or hits to eukaryotes or viruses, as well as contigs in which the taxonomy disagreed with the consensus class, were removed. The resulting bins were further evaluated with CheckM v0.8.1 [30], and only those with ≥80% completeness and ≤5% contamination were retained for further analysis. Bins were finally renamed according to the sample of origin, method of binning (mx=Maxbin, m1=Metabat1, m2=Metabat2) and bin number. The taxonomy of the individual bin was assigned using gtdb-tk v1.4.1 [31] and the GTDB r95 database [29]. The assignment of KO (K number) was done using KEGG-Orthology-And-Links-Annotation (KOALA) algorithm against the nonredundant KEGG Genes database [32]. Phylogenomic analysis A maximum-likelihood (ML) phylogenomic tree was constructed for the recovered picocyanobacterial genomes, as well as the references. All the genomes were scanned with hmmsearch for 120 conserved protein HMM markers [33]. Protein sequences for each maker were aligned using MAFFT v7.453 [19] in L-INSI mode and trimmed by trimAl v1.4 [34] with the following parameters: -gt 0.5 -keepheader. Subsequently, the trimmed alignments were concatenated using the catfasta2phyml.pl (https://github.com/nylander/catfasta2phyml), and used as input for tree construction using IQ-TREE2 v2.2.0 with 1000 iterations of ultrafast bootstrapping [35] and SH testing [36]. The best-fitting evolutionary model (Q.pfam+I+G4) was selected based on the BIC score by ModelFinder [37]. The final tree was visualized in the interactive Tree Of Life (iTOL) v6 (https://itol.embl.de/). Metagenomic read recruitment A total of 714 publicly available metagenomes were subsampled to 20 million reads and used for read recruitment [38]. Prior to recruitment, rRNA genes in the individual genomes were masked. MMseqs2 [28] was used with the following parameters to align the sequencing reads to individual genomes and to calculate base coverage per Gb: -minid 0.95 -mincov 0.9 -minlen 50. To examine the presence of heterotrophs in our non-axenic cultures, we conducted the read recruitment of the genomes against the sequencing reads of the cultures using the same parameters. If the genome coverage of the sequencing reads was above 95%, we considered it to be present within the culture. Heatmaps were generated using the R package pheatmap v1.0.12 (https://github.com/raivokolde/pheatmap). Heat trees were generated using the R package metacoder v0.3.6 [39]. Scanning and subtyping of CRISPR-Cas systems We analyzed CRISPR-Cas systems in freshwater picocyanobacterial genomes (n=170) obtained from this study, along with marine picocyanobacterial genomes (n=97) from the Cyanorak database [40]. Additionally, we examined a collection of freshwater prokaryotic genomes (n=9,374) constructed in a previous study [41]. The CRISPRCasTyper v1.6.4 (https://github.com/Russel88/CRISPRCasTyper) was used for the scanning and subtyping of CRISPR-Cas genes. Plots were generated using the R packages ggplot2 v3.3.5 [42]. Phylogenetic tree reconstruction for Cas1 For the phylogenetic reconstruction of Cas1, we used all putative Cas1 sequences from the Uniprot database (https://www.uniprot.org/) and Cas1 predicted from a freshwater genome collection [41]. We manually curated the collected dataset by conducting a scan with hmmsearch [33] to identify significant hits associated with the Cas1 PFAM domain (PF01867). Hits with p -values less than 0.01 and sequence lengths exceeding 80 amino acids were retained. To further narrow down the selection, we opted for the top 10 hits for each picocyanobacterial Cas1 using MMseqs2 [28] (--cov-mode 0). The remaining sequences were clustered using MMseqs2 (easy-cluster workflow) with a minimum sequence identity of 90%. The resulting 416 Cas1 sequences were aligned with MAFFT v7.453 [19]. A Maximum-likelihood (ML) tree was generated with IQ-TREE2 [36] with the following parameters: --perturb 0.2 --nstop 500 -B 1000 -m TEST --alrt 1000. Results Phylogenomic overview and delineating ecologically significant ecotypes We conducted a three-year sampling campaign covering 44 different locations across Central Europe (Fig. S1 and Table S1 ). Our sampling approach was aimed to capture the diversity of freshwater ecosystems, including natural and post-mining lakes, reservoirs, rivers, and fishponds under different trophic statuses (Fig. 1 a). This extensive sampling effort yielded 170 high-quality (≥ 80% completeness and ≤ 5% contamination) picocyanobacterial genomes from 156 non-axenic cultures (Table. S2). Based on the trophic statuses of the isolation sources, these newly obtained genomes were initially classified into oligotrophic (n = 60), mesotrophic (n = 36), eutrophic (n = 16), and hyper-eutrophic (n = 57) groups (Fig. 1 b). To provide a more comprehensive perspective for our study, we analyzed our new dataset alongside 63 previously published genomes of freshwater picocyanobacteria [ 6 , 13 , 43 , 44 ]. A phylogenomic analysis affiliated the newly obtained genomes with either SC5.2 (92.4%; n = 157) or SC5.3 (7.6%; n = 13) (Fig. 1 c and Table S2 ). The strains from SC5.3 showed the smallest average genome sizes (2.3 Mb) and lowest GC contents (51.8%), whereas those in SC5.2 exhibited a degree of flexibility, with genome sizes ranging from 1.9 to 4.2 Mb and GC contents from 55.5 to 72.5% (Fig. S2 ). Potential population boundaries among these genomes were observed based on pairwise average nucleotide identity (ANI) at a cutoff above 85% (Fig. S3). Read recruitment in analyzed metagenomic samples (n = 724) further revealed the global distribution patterns of the isolated picocyanobacteria (Fig. 1 c and Table S3). In line with previous studies [ 3 ], all the strains from SC5.3 were restricted to oligotrophic conditions. Among the members of SC5.2, we delineated ecologically significant ecotypes occupying four major freshwater regimes: oligotrophic, oligotrophic and cold (e.g., Lake Superior), eutrophic or hyper-eutrophic (e.g., Lake Mendota), and humic including environments displaying low pH (e.g., Lake Crystal Bog) (Fig. 1 c). We designated these ecotypes as the low-nutrient (LN), low-nutrient and low-temperature (LNLT), high-nutrient (HN), and low-pH (LP) ecotypes, respectively, and employed these classifications for further analyses. Genome-resolved symbiotic interactions with co-occurring heterotrophs Despite the absence of a major carbon source apart from vitamins and trace elements in the culture media, we additionally acquired 526 genomes of co-occurring heterotrophic bacteria, scoring ≥ 80% completeness and < 5% contamination (Table S4). Among these, we revealed the non-random occurrences of 38 heterotrophs, repeatedly present in more than 5% of the cultures (Table S5). These potential symbionts belonged to taxonomically narrow lineages, affiliated with five different species within four genera, Pseudomonas , Mesorhizobium , Acidovorax , and Hydrogenophaga . The observed co-occurrences of picocyanobacteria and heterotrophs further provided insights into the selective association between them (Fig. 2 ). SC5.3, SC5.2 LN, and LNLT co-occurred with Pseudomonas (sp003033885) as their exclusive heterotrophic partner, while SC5.2 LP exhibited a strong preference for another Pseudomonas species (sp900187495). On the other hand, SC5.2 HN displayed a more complex association with heterotrophic partners, co-occurring primarily with Mesorhizobium , followed by Acidovorax and Pseudomonas . The potential metabolic interactions between these hetero- and photoautotrophs were next explored by analyzing their genomes and the media compositions (Fig. 3 ). The majority of the picocyanobacterial genomes encoded genes for nitrate ( narB ) and nitrite ( nirA ) reductases to convert nitrate, the sole nitrogen source in the growth media, into ammonia. They also encoded nrtABC for the transportation of extracellular nitrate inside the cell. On the contrary, the absence of narB and nirA in all co-occurring heterotrophs, coupled with the presence of ammonia transporters ( amt ), suggested that picocyanobacteria are likely to provide chassis for nitrogen metabolism in the community. It is also noteworthy that urea transporters were detected in both picocyanobacteria and heterotrophs. Heterotrophic bacteria further appeared to rely on picocyanobacteria for sulfur assimilation based on their genome composition. Sulfate was the primary sulfur source in the culture media, and only picocyanobacteria had the complete set of genes required to reduce sulfate into sulfide. Most heterotrophic symbionts lacked these genes, except phosphoadenosine phosphosulfate reductase ( cysH ), which catalyzes the formation of sulfite from phosphoadenosine 5'-phosphosulfate. Heterotrophic bacteria also appeared to provide beneficial functions to picocyanobacteria in return (Fig. 3 ). In line with earlier marine studies [ 45 ], most picocyanobacterial genomes lacked genes necessary for detoxifying ROS (Fig. 3 a). Heterotrophic bacteria showed high potential for hydrogen peroxide metabolism, with the Pseudomonas strains possessing up to five copies of catalase ( katE ) genes (Table S7). The genomes of Acidovorax were further equipped with superoxide dismutase ( sod1 ) genes that control the toxic levels of ROS [ 46 ]. However, the genomes belonging to the genus Mesorhizobium completely lacked these antioxidant systems, leaving their complementary roles unresolved. Genotypic and phenotypic diversifications among distinct ecotypes We examined the genotypic and phenotypic variations among different picocyanobacterial ecotypes to uncover the key factors contributing to their success in a wide range of ecological settings. In accordance with earlier studies [ 3 ], most picocyanobacterial populations adopting an oligotrophic lifestyle (SC5.3, SC5.2 LN, and SC5.2 LNLT) were red-pigmented, whereas the majority of the remaining isolates displayed green color (Fig. 3 a and Table S2 ). The genomes of the red strains were set apart from the others by the presence of cpeA and cpeB genes in their genomes, which are responsible for the phycoerythrin biosynthesis. We also employed the catalyzed reporter deposition-fluorescence in situ hybridization (CARD-FISH) technique to characterize the growth forms of distinct ecotypes within their natural habitats (Fig. 5 a). In (hyper-)eutrophic waterbodies, SC5.2 HN displayed a tendency to form microcolonies or large aggregates, potentially recognized as grazing-resistant forms [ 47 ]. Conversely, the remaining ecotypes predominantly exhibited unicellular phenotypes. A non-metric multidimensional scaling (NMDS) analysis based on the presence or absence of Kyoto Encyclopedia of Genes and Genomes (KEGG) annotated genes revealed distinct clustering of the genomes corresponding to their respective ecotypes (Fig. S4). A notable genomic variation was the selective presence of a two-component system for chemotaxis ( cheBR ) in SC5.2 HN (Fig. 3 a). The presence of pilA encoding type IV pilus further suggests that pili-mediated chemotaxis may facilitate their ability to locate optimal conditions for growth and photosynthesis. The genomes of SC5.2 LNLT, on the other hand, harbored treS , otsA , and otsB genes responsible for trehalose biosynthesis. Trehalose is often recognized for enhancing photosynthesis for cyanobacteria under cold stress [ 48 ], thereby aligning well with their prevalence in cold environments. Finally, SC5.2 LP was distinguished from other ecotypes by possessing an epsilon-lactone hydrolase ( mlhB ), potentially indicating their additional capability to degrade recalcitrant humic compounds containing lactone groups [ 49 ]. CRISPR-Cas systems in picocyanobacteria While there has been a prevailing consensus that CRISPR-Cas systems are rare in picocyanobacteria [ 5 ], we conducted a thorough reassessment of these systems in both marine and our newly acquired freshwater genomes. As expected, CRISPR-Cas systems were absent in nearly all marine genomes (n = 98) examined (Fig. 4 a). Conversely, our examination of the freshwater dataset revealed 15.9% (n = 27) of genomes encoding CRISPR-Cas systems. Further analysis indicated that CRISPR-Cas systems were found in all genomes belonging to SC5.2 HN, which were dominant in eutrophic or hypertrophic environments (Fig. 3 a). On the contrary, these antiviral defense mechanisms were absent in other ecotypes, suggesting a clear segregation in the distribution of CRISPR-Cas systems based on trophic status. CRISPR-Cas systems consist primarily of two essential modules: an adaptation module for acquiring spacers from short segments of foreign DNA, and an interference module that recognizes and cleaves target DNA sequences [ 50 ]. In most cases of our picocyanobacterial genomes, they appeared to possess all the necessary components for interference modules. However, a notable fraction (40.7%) of these genomes lacked cas1 genes required for spacer acquisition (Table S8). Another interesting observation was that the most prevalent CRISPR-Cas systems in picocyanobacteria were the subtypes I-G and I-E (Fig. 4 a). Among Cyanobacteria, subtype I-D is the most common CRISPR-Cas system, followed by I-A, III-A, and III-B [ 51 ]. Therefore, we hypothesized that the evolutionary origin of CRISPR-Cas for picocyanobacteria may involve horizontal gene transfer from different taxonomic groups. Examining a large collection (n = 9,374) of freshwater planktonic bacterial and archaeal genomes [ 41 ] revealed that the subtypes I-C and I-E are the two most common CRISPR-Cas systems within the entire freshwater ecosystems (Fig. 4 b). Among different phyla, Desulfobacterota exhibited the highest enrichment of CRISPR-Cas systems, with 30% of the genomes encoding these antiviral defense mechanisms. We investigated the evolutionary origin of the picocyanobacterial CRISPR-Cas systems through a phylogenetic reconstruction of Cas1 from all available sources (Fig. 4 c). The phylogenetic tree structure of Cas1 showed a considerable agreement with the subtype classification of CRISPR-Cas, as previously described [ 52 ]. Interestingly, the subtype I-G from picocyanobacteria and other Synechococcales genomes appeared to form a clade distinct from other I-G systems, while showing a closer evolutionary proximity to subtype III-B. For the subtype I-E, picocyanobacterial Cas1 clustered with orthologues from many different bacterial phyla such as Acidobacterota, Bdellovibrionota, Myxococcota, and Chloroflexota, making them potential donors. Discussion Our extensive and well-coordinated sampling campaign, combined with non-axenic cultures, substantially augmented the genomic repertoire of freshwater picocyanobacteria. To our knowledge, this dataset represents the largest collection of genomes for freshwater picocyanobacteria to date, expanding the number of available genomes by more than threefold [ 5 , 13 ]. Our dataset further stands out from earlier studies by employing an unbiased sampling approach that targets a variety of freshwater ecosystems (Fig. 1 a). As a result, a significant portion of the genomes were acquired from eutrophic and hyper-eutrophic conditions, where the genomic availability was previously limited (Fig. 1 b). The global phylogeography of freshwater picocyanobacteria in this study highlighted the pivotal roles of trophic status, temperature, and pH as key environmental factors delineating the major ecotypes (Fig. 1 c). The 38 genomes of the co-occurring heterotrophs isolated in this study are affiliated with four genera: Pseudomonas , Mesorhizobium , Acidovorax , and Hydrogenophaga (Fig. 2 ). Although their symbiotic roles for cyanobacteria have rarely been explored, all four genera have been previously observed in the cultures of filamentous cyanobacteria, such as Microcystis and Anabaena [ 14 , 53 , 54 ]. It has been shown that certain Pseudomonas species have an enhancing effect on cyanobacterial growth [ 14 ]. Our dataset further suggested that their inability to use nitrate and sulfate for growth makes them highly dependent on picocyanobacteria, presumably benefiting from carbon compounds, ammonia, and sulfide exported by their symbiotic partners (Fig. 5 b). Previous marine studies on Synechococcus and Prochlorococcus demonstrated that one crucial symbiotic function of heterotrophic bacteria within their immediate environment is detoxifying ROS produced during photosynthesis [ 15 , 18 ]. Within freshwater environments, the contrasting distribution patterns of genes for ROS removal between picocyanobacteria and heterotrophs supports a similar conclusion that the decrease in oxidative stresses is facilitated by these heterotrophs (Fig. 5 b). Yet, the interactions between cyanobacteria and heterotrophs can be far more complex since they might change over time [ 45 ] or under different culture conditions [ 53 ]. The mutualism might also occur beyond the two-species framework [ 55 ], which makes it even more challenging to understand the processes among multiple organisms. The observed genotypic and phenotypic diversifications among defined picocyanobacterial ecotypes in this study hinted at their adaptive strategies in distinct environmental conditions (Fig. 3 a). In shallow and eutrophic waterbodies, bacteria often encounter elevated mortality pressure from grazers such as heterotrophic nanoflagellates, compared to their counterparts in deep and oligotrophic lakes [ 56 ]. As protistan grazing is primarily cell size-dependent, bacteria can develop grazing-resistant forms, such as microcolonies, large aggregates, and filaments [ 47 , 57 ]. Therefore, the colonial lifestyle of SC5.2 HN (Fig. 5 a) could be explained through this perspective. What was also intriguing about this ecotype was the presence of genes encoding type IV pilus ( pilA ) and chemotaxis ( cheBR ) (Fig. 3 a). While picocyanobacteria generally lack flagellar structures for motility, it has been shown that certain Prochlorococcus and Synechococcus strains rely on type IV pili to avoid sinking and predation [ 58 ]. Prior to this study, the rare occurrence of CRISPR-Cas systems in picocyanobacteria could be attributed to their small genome sizes and the likelihood of employing alternative defense mechanisms that impose a lesser genetic load [ 51 ]. However, we demonstrated the strong association of CRISPR-Cas systems for picocyanobacteria thriving in eutrophic and hyper-eutrophic conditions (Fig. 3 a), which poses a fascinating contradiction to their evolutionary trajectory towards genome streamlining and reduced metabolic complexity. This observation can be potentially attributed to elevated viral loads in eutrophic lakes compared to their counterparts in oligotrophic waterbodies [ 59 ]. A strong association between high viral abundance and the increased prevalence of CRISPR-Cas systems has been previously described [ 60 ]. A recent study also demonstrated a much higher virus-to-microbe ratio in freshwater compared to marine environments [ 61 ], which could explain the scarcity of CRISPR-Cas systems in the open ocean. The CRISPR-Cas systems identified in freshwater picocyanobacteria exhibited unique phylogenetic placements and subtype classifications, setting them apart from those observed in other cyanobacteria (Fig. 4 c). This observation implies an extensive evolutionary history of horizontal transfers of CRISPR-Cas loci between various taxonomic groups in freshwater ecosystems. It is important to note that a notable proportion of picocyanobacterial CRISPR-Cas systems were found to lack cas1 genes (Tabls S7). As previously described in other cyanobacteria [ 51 ], the loss of cas1 genes may represent an early stage of losing a CRISPR-Cas system. However, even within the genomes devoid of cas1 genes, spacers were still retained within CRISPR arrays (Table S7). To verify the targets of spacers and understand how certain strains acquired CRISPR arrays without an adaptive module, a thorough investigation of viral agents and other mobile genetic elements targeting these strains will be necessary. Conclusions Together, our findings effectively addressed gaps in understanding population diversity, symbiotic interactions, and adaptation strategies of picocyanobacteria within freshwater environments. Furthermore, this work establishes a genomic foundation for future efforts aimed at the detailed characterization of the genomic landscapes of freshwater picocyanobacteria, in connection with their evolutionary trajectories. Abbreviations ANI Average nucleotide identity LN Low-nutrient LNLT Low-nutrient and low-temperature HN High-nutrient LP low-pH CARD-FISH Catalyzed reporter deposition-fluorescence in situ hybridization NMDS Non-metric multidimensional scaling KEGG Kyoto encyclopedia of genes and genomes Declarations Acknowledgments We thank Hyemin Kim for her valuable contributions regarding the graphic design of the figures. Author contributions Conceptualization: J.J. Methodology: J.J, V.K. H.P. P.B. V.S.K. Investigation: H.P, P.B. J.J. V.K. V.S.K. Visualization: H.P. Writing—original draft: H.P. Writing—review and editing: H.P. T.S. V.K. V.S.K. R.G. Funding This study was supported by the Czech Science Foundation (Grantová Agentura Ceské Republiky - GAČR) grants 20‑12496X (H.P, P.B, and R.G), 23-05081S (T.S), and 19-23261S (V.S.K., V.K and J.J). Data and materials availability The genome sequencing data from this study has been deposited in EBI ENA under the Bioproject PRJEB49198. Additional data, such as alignment files and trees are available in Figshare (https://figshare.com/s/3df321effacfb4fe8835). Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Callieri C, Cronberg Gertrud, John G. Stockner. Freshwater picocyanobacteria: single cells, microcolonies and colonial forms. In Ecology of Cyanobacteria II: Their diversity in space and time. Dordrecht: Springer Netherlands, 2012. 229-269. Scanlan DJ, Ostrowski M, Mazard S, Dufresne A, Garczarek L, Hess WR, et al. Ecological genomics of marine picocyanobacteria. Microbiol Mol Biol Rev. 2009;73(2):249–99. Callieri C, Cabello-Yeves PJ, Bertoni F. The “dark side” of picocyanobacteria: life as we do not know it (yet). Microorganisms. 2022;10(3):1–18. Doré H, Farrant GK, Guyet U, Haguait J, Humily F, Ratin M, et al. 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IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. Mol Biol Evol. 2020;37(5):1530–4. Kalyaanamoorthy S, Minh BQ, Wong TKF, Von Haeseler A, Jermiin LS. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14(6):587–9. Garner RE, Kraemer SA, Onana VE, Fradette M, Varin M, Huot Y, et al. A genome catalogue of lake bacterial diversity and its drivers at continental scale. Nat Microbiol Resour. 2023;8:1920-1934 Foster ZSL, Sharpton TJ, Grünwald NJ. Metacoder: An R package for visualization and manipulation of community taxonomic diversity data. PLoS Comput Biol. 2017;13(2):1–15. Garczarek L, Guyet U, Doré H, Farrant GK, Hoebeke M, Brillet-Guéguen L, et al. Cyanorak v2.1: A scalable information system dedicated to the visualization and expert curation of marine and brackish picocyanobacteria genomes. Nucleic Acids Res. 2021;49(D1):D667–76. Chiriac MC, Haber M, Salcher MM. 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Huber P, Diovisalvi N, Ferraro M, Metz S, Lagomarsino L, Llames ME, et al. Phenotypic plasticity in freshwater picocyanobacteria. Environ Microbiol. 2017;19(3):1120–33. Kandror O, DeLeon A, Goldberg AL. Trehalose synthesis is induced upon exposure of Escherichia coli to cold and is essential for viability at low temperatures. Proc Natl Acad Sci U S A. 2002;99(15):9727–32. Samios S, Lekkas T, Nikolaou A, Golfinopoulos S. Structural investigations of aquatic humic substances from different watersheds. Desalination. 2007;210(1–3):125–37. Koonin E V., Makarova KS. Evolutionary plasticity and functional versatility of CRISPR systems. PLoS Biol. 2022;20(1):1–19. Cai F, Axen SD, Kerfeld CA. Evidence for the widespread distribution of CRISPR-Cas system in the Phylum Cyanobacteria. RNA Biol. 2013;10(5):687–93. Makarova KS, Haft DH, Barrangou R, Brouns SJJ, Charpentier E, Horvath P, et al. Evolution and classification of the CRISPR-Cas systems. Nat Rev Microbiol. 2011;9(6):467–77. Yang T, Lee CS, Cho JY, Bae MJ, Kim EJ. Comparison of Bacterial Assemblages Associated with Harmful Cyanobacteria under Different Light Conditions. Microorganisms. 2022;10(11):1–15. Jung J, Seo YL, Kim KR, Park HY, Jeon CO. Mesorhizobium microcysteis sp. Nov., isolated from a culture of microcystis aeruginosa . Int J Syst Evol Microbiol. 2021;71(7). Kazamia E, Helliwell KE, Purton S, Smith AG. How mutualisms arise in phytoplankton communities: building eco-evolutionary principles for aquatic microbes. Ecol Lett. 2016;19(7):810–22. Berdjeb, L., Pollet, T., Domaizon, I., Jacquet, S. Effect of grazers and viruses on bacterial community structure and production in two contrasting trophic lakes. BMC Microbiol. 2011;11:1-18. Hahn MW, Höfle MG. Grazing of protozoa and its effect on populations of aquatic bacteria. FEMS Microbiol Ecol. 2001;35(2):113–21. Aguilo-Ferretjans M del M, Bosch R, Puxty RJ, Latva M, Zadjelovic V, Chhun A, et al. Pili allow dominant marine cyanobacteria to avoid sinking and evade predation. Nat Commun. 2021;12(1):1–10. Bettarel Y, Sime-Ngando T, Amblard C, Dolan J. Viral activity in two contrasting lake ecosystems. Appl Environ Microbiol. 2004;70(5):2941–51. Meaden S, Biswas A, Arkhipova K, Morales SE, Dutilh BE, Westra ER, et al. High viral abundance and low diversity are associated with increased CRISPR-Cas prevalence across microbial ecosystems. Curr Biol. 2022;32(1):220-227.e5. López-García P, Gutiérrez-Preciado A, Krupovic M, Ciobanu M, Deschamps P, Jardillier L, et al. Metagenome-derived virus-microbe ratios across ecosystems. ISME J. 2023;17:1552-1563 Additional Declarations No competing interests reported. Supplementary Files Supplementarytables.xlsx supplementarydata.pdf Cite Share Download PDF Status: Published Journal Publication published 10 Aug, 2024 Read the published version in Microbiome → Version 1 posted Editorial decision: Revision requested 17 Apr, 2024 Editor assigned by journal 17 Apr, 2024 Submission checks completed at journal 05 Apr, 2024 First submitted to journal 04 Apr, 2024 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4217878","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287906680,"identity":"6e023630-e528-412e-a485-10df67789c5d","order_by":0,"name":"Hongjae Park","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYHACZgbGBhB9gIEhoQLEZ24gQcuHMyA+I9FaGBgYZ7aBKfxazNnPPjZg3GGTJ+94+OFj3nm10fztQC0/Krbh1GLZk26cwHgmrdjwwDFjY95tx3NnHGZsYOw5cxunFoMDacwHGNsOJ25sOGAmzbvtWG4DUAszYxseLeefwbQc//6bd86x3PkEtdxIY04AaZnPcMaMcWZDTe4GQlosZzxjNkhsS0vcwHCmWOLDsQO5G4FaDuLzizl/GrPExzabxPkzjm/8kFBTlzvv/OGDD35U4HEYiEgAu/AAiHkYLHoAp3qYFhCQ728AUXX4FI+CUTAKRsEIBQB/smN7wsdnIgAAAABJRU5ErkJggg==","orcid":"","institution":"Biology Centre of the Czech Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Hongjae","middleName":"","lastName":"Park","suffix":""},{"id":287906681,"identity":"93307648-2b7a-4e68-9286-95ea54dd2c40","order_by":1,"name":"Paul Bulzu","email":"","orcid":"","institution":"Biology Centre of the Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Bulzu","suffix":""},{"id":287906682,"identity":"43480d40-9c6c-4a73-91a3-70b05a727153","order_by":2,"name":"Tanja Shabarova","email":"","orcid":"","institution":"Biology Centre of the Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tanja","middleName":"","lastName":"Shabarova","suffix":""},{"id":287906683,"identity":"8b2058b2-2536-4050-87bc-6a82d2c287b7","order_by":3,"name":"Vinicius S. Kavagutti","email":"","orcid":"","institution":"Biology Centre of the Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Vinicius","middleName":"S.","lastName":"Kavagutti","suffix":""},{"id":287906684,"identity":"cfb3a4ed-32e0-43f6-9585-6c782ccdb424","order_by":4,"name":"Rohit Ghai","email":"","orcid":"","institution":"Biology Centre of the Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Rohit","middleName":"","lastName":"Ghai","suffix":""},{"id":287906685,"identity":"df0582ef-caeb-4bb5-81f0-3075b1e1323f","order_by":5,"name":"Vojtěch Kasalický","email":"","orcid":"","institution":"Biology Centre of the Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Vojtěch","middleName":"","lastName":"Kasalický","suffix":""},{"id":287906686,"identity":"365c3f99-46f7-401a-af70-c1737ac7e802","order_by":6,"name":"Jitka Jezberová","email":"","orcid":"","institution":"Biology Centre of the Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jitka","middleName":"","lastName":"Jezberová","suffix":""}],"badges":[],"createdAt":"2024-04-04 12:27:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4217878/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4217878/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40168-024-01867-0","type":"published","date":"2024-08-10T15:57:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54697979,"identity":"750d99b1-7e61-4ba4-b658-340ff0ef377c","added_by":"auto","created_at":"2024-04-15 11:39:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137884,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIsolation of picocyanobacterial genomes and delineating ecologically significant ecotypes. a\u003c/strong\u003e, Distribution of the picocyanobacterial genomes (n=170) across different freshwater sources (lakes, reservoirs, rivers, and fishponds) and trophic statuses (oligotrophic to hyper-eutrophic). The number of the genomes is given in parentheses. \u003cstrong\u003eb\u003c/strong\u003e, The number of newly obtained genomes compared to a prior study [13]. \u003cstrong\u003ec\u003c/strong\u003e, Maximum likelihood phylogeny of freshwater picocyanobacterial genomes and their global distribution patterns (coverage per Gb). The genomes are collapsed to the species level (95% ANI cutoff), and the genomes within potential population boundaries (85% ANI cutoff) are marked by gray boxes. A complete tree including genome IDs and taxonomy affiliations (GTDB) is also available through the following link: \u003ca href=\"https://itol.embl.de/export/14723125092171591710326046\"\u003ehttps://itol.embl.de/export/14723125092171591710326046\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eA complete dataset for the metagenomic read recruitment can be found in Table S3.\u003c/p\u003e","description":"","filename":"Binder21.png","url":"https://assets-eu.researchsquare.com/files/rs-4217878/v1/231bbee08cf6a540e10aa61f.png"},{"id":54696974,"identity":"ab524418-95aa-45eb-ada1-3c41d87bfdbf","added_by":"auto","created_at":"2024-04-15 11:31:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":18367,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCo-occurrence of heterotrophic bacteria in picocyanobacterial cultures. \u003c/strong\u003eHeat trees for the co-occurrence pattern of the 38 heterotrophic bacterial genomes. The gray tree on the left functions as a key for the smaller unlabeled trees. Node sizes indicate the number of genomes for the respective bacterial taxa. Colors indicate the number of co-occurrences between heterotrophic bacteria and distinct picocyanobacterial ecotypes.\u003c/p\u003e","description":"","filename":"Binder22.png","url":"https://assets-eu.researchsquare.com/files/rs-4217878/v1/339bfc1b754c5f20ec605b29.png"},{"id":54696976,"identity":"8479bbc5-4898-41f7-b9ab-d376ec691954","added_by":"auto","created_at":"2024-04-15 11:31:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":107658,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe distribution of functional genes related to symbiotic interactions and niche adaptations. a\u003c/strong\u003e, Picocyanobacterial genomes selected from different ecotypes. \u003cstrong\u003eb\u003c/strong\u003e, The genomes of 38 heterotrophic bacteria. Empty squares symbolize the lack of the genes, while the filled squares represent the presence of the genes. In terms of pigmentation, green and red represent the cell colors of individual strains. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDetails of these genes are provided in the main text and Table S6-7.\u003c/p\u003e","description":"","filename":"Binder23.png","url":"https://assets-eu.researchsquare.com/files/rs-4217878/v1/716a42651a5f3bbdde965c60.png"},{"id":54696977,"identity":"d5a1d152-f9c3-413e-b714-6c38065c7344","added_by":"auto","created_at":"2024-04-15 11:31:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29239,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCRISPR-Cas systems in picocyanobacteria\u003c/strong\u003e. \u003cstrong\u003ea\u003c/strong\u003e, CRISPR-Cas systems and their subtypes in freshwater and marine picocyanobacteria. \u003cstrong\u003eb\u003c/strong\u003e, Distribution of CRISPR-Cas systems among different freshwater bacterial phyla. \u0026nbsp;\u003cstrong\u003ec\u003c/strong\u003e, Maximum likelihood phylogenetic tree of Cas1. Picocyanobacterial Cas1 sequences are indicated by red circles at node tips. Ultrafast bootstrap values are displayed at selected nodes. Different CRISPR-Cas subtypes are represented by distinct colors.\u003c/p\u003e","description":"","filename":"Binder24.png","url":"https://assets-eu.researchsquare.com/files/rs-4217878/v1/e1b22545d52c1d6823395086.png"},{"id":54696980,"identity":"f8d7afca-be24-4943-bb06-02c7a890425b","added_by":"auto","created_at":"2024-04-15 11:31:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":132572,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA schematic view of symbiotic interactions and niche adaptations in picocyanobacteria. a\u003c/strong\u003e, CARD-FISH images of different picocyanobacterial ecotypes. The panels display the overlap of the probe (green), DAPI (blue), and autofluorescence (red) signals. \u003cstrong\u003eb\u003c/strong\u003e, Schematic diagram of symbiotic interactions and niche adaptations uncovered by genome analysis.\u003c/p\u003e","description":"","filename":"Binder25.png","url":"https://assets-eu.researchsquare.com/files/rs-4217878/v1/59cfe71050d69a2a60694ff8.png"},{"id":62298452,"identity":"c68056b6-943d-4830-a705-b8da0e60e115","added_by":"auto","created_at":"2024-08-12 16:13:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1127704,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4217878/v1/54134ae1-11a3-4ca1-9bab-f35bbe37f447.pdf"},{"id":54696979,"identity":"a782fe4d-fe5a-40d2-af5f-b7e319a65851","added_by":"auto","created_at":"2024-04-15 11:31:32","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2810067,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4217878/v1/a269b8e88886a5026d0689ae.xlsx"},{"id":54697983,"identity":"85fd65e4-c600-4514-bd65-af67f0cb5b7d","added_by":"auto","created_at":"2024-04-15 11:39:32","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":884767,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarydata.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4217878/v1/2762cf9684b0b679744ca646.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Uncovering the genomic basis of symbiotic interactions and niche adaptations in freshwater picocyanobacteria","fulltext":[{"header":"Background","content":"\u003cp\u003ePicocyanobacteria, with a cell size of less than 3 \u0026micro;m, represent the smallest and most widespread phytoplankton in marine and freshwater ecosystems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This group of bacteria is affiliated with three distinct genera: \u003cem\u003eProchlorococcus\u003c/em\u003e, \u003cem\u003eSynechococcus\u003c/em\u003e, and \u003cem\u003eCyanobium\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Currently, \u003cem\u003eSynechococcus\u003c/em\u003e and \u003cem\u003eCyanobium\u003c/em\u003e have been further subdivided into three distinct phylogenetic groups referred to as subclusters (SC) 5.1 to 5.3 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. SC5.1 shows a close phylogenetic relationship with \u003cem\u003eProchlorococcus\u003c/em\u003e, collectively forming a marine-specific cluster of picocyanobacterial [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In contrast, strains from SC5.2 and 5.3 can inhabit diverse salinity gradients, including marine, brackish, and freshwater ecosystems [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. SC5.3 is predominantly found in temperate and oligotrophic waterbodies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], while SC5.2 demonstrates remarkable adaptability to various environmental conditions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe​​ evolution of light-harvesting complexes, known as phycobilisomes, plays a crucial role in developing diverse pigmentations in picocyanobacteria and their adaptation to different light regimes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Several studies of marine \u003cem\u003eProchlorococcus\u003c/em\u003e have unveiled ecologically significant ecotypes associated with growth elements such as iron [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], nitrogen [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and phosphorus [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The recent discovery of novel \u003cem\u003eSynechococcus\u003c/em\u003e strains in the deep oxygen-depleted water layers of the Black Sea demonstrated their potential to survive even in extreme conditions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the genetic foundation behind their global success largely remains unresolved. The investigation of freshwater picocyanobacteria has faced even greater challenges, primarily due to the significant undersampling of their populations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The recent release of a large genome collection for freshwater picocyanobacteria, consisting of 58 isolates, marks a significant advancement in the field [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, most of these isolates originated from oligo- and mesotrophic lakes, indicating that their populations in various inland waterbodies might still be overlooked.\u003c/p\u003e \u003cp\u003eIn the cultures of cyanobacteria where no carbon source is present, heterotrophic bacteria are often co-isolated as their symbiotic partners [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The strong interdependencies between them make acquiring and maintaining axenic cultures of picocyanobacteria a challenging task [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Conversely, these non-axenic cultures offer a valuable opportunity to explore the unique microbial communities in their environment, as well as the metabolic interactions between hetero- and photoautotrophs. The \u0026lsquo;helper\u0026rsquo; heterotrophic bacteria can benefit from phytoplankton-derived organic compounds while supporting enhanced growth or prolonged survival in nutrient-depleted conditions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These symbiotic interactions may involve nutrient cycling [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], vitamin trafficking [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and removing reactive oxygen species (ROS) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, the mechanisms behind the assembly of heterotrophs in freshwater picocyanobacterial cultures and their symbiotic roles have never been reported.\u003c/p\u003e \u003cp\u003eTo fill these gaps in our knowledge, we obtained non-axenic picocyanobacterial cultures from diverse freshwater ecosystems. This allowed us to recover the largest genome collection of freshwater picocyanobacteria, along with their abundances and cellular phenotypes under different conditions. Furthermore, we acquired the genomes of co-occurring heterotrophic bacteria present in the cultures. Our investigation into the global phylogeography of freshwater picocyanobacteria revealed ecologically significant ecotypes, associated with diverse environmental settings. Moreover, our genomic data provided insights into the symbiotic interactions of picocyanobacteria with heterotrophic partners and their adaptation strategies across different ecological niches.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eSample collection, isolation, and culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePicocyanobacterial isolates were obtained from water samples collected in 2005, and from additional sampling conducted between 2018 and 2020. During the sampling campaign in 2005, the isolation process comprised the following steps. Initially, water samples underwent filtration through 5 µm polycarbonate membrane filters (Sterlitech, Kent, WA, USA) to remove bigger organisms. The filtered samples were then processed by passing through 0.22 μm polyethersulfone membrane filters (Millipore, Merck, Darmstadt, DE) to enrich the picocyanobacterial biomass. Subsequently, the collected biomass was plated on BG11 medium solidified with 1.5% agar. Finally, individual colonies were picked and cultivated in WC medium, resulting in a total of 47 cultures from 17 different localities.\u003c/p\u003e\n\u003cp\u003eDuring the years between 2018 and 2020, we employed the dilution-to-extinction cultivation method to obtain isolates. Environmental samples were initially filtered through 1 or 2 µm polycarbonate membrane filters (Sterlitech, Kent, WA, USA), and enumerated using epifluorescence microscopy (Zeiss Imager.Z2, Carl Zeiss, Oberkochen, DE). After a serial dilution, the water samples were inoculated into 96-well plates containing 1.5 mL of WC medium at an estimated 0.5 cell well\u003csup\u003e-1\u003c/sup\u003e. Cultures were incubated at room temperature for three weeks and evaluated for growth using epifluorescence microscopy (Zeiss Imager.Z2, Carl Zeiss, Oberkochen, DE). A total of 107 non-axenic cultures from 31 localities were obtained. All isolates were continuously cultivated in liquid WC media, ensuring that the reinoculation timespan did not exceed 2 months. While the majority (92.4%) of our picocyanobacterial cultures were unialgal, they were not axenic. Two or more picocyanobacterial genomes were acquired from 13 cultures (7.6%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProbe design and CARD-FISH analysis\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eTo design ecotype-specific probes, 16S rRNA gene sequences obtained in this study and collected from publicly available databases were aligned using the MAFFT algorithm [19] within the Geneious software (https://www.geneious.com). Selected regions (18-25 nucleotides) were examined for hairpins and self-dimerization and evaluated \u003cem\u003ein silico\u003c/em\u003e by TestProbe application (https://arb-silva.de/search/testprobe) for hits outside the target group. Competitor oligonucleotides were designed when necessary and tested in a similar manner. The mathFISH software (https://mathfish.cee.wisc.edu) was employed to determine the theoretical best hybridization conditions, and the final formamide concentration was determined in the lab. The list of designed probes and hybridization conditions are presented in Table S9.\u003c/p\u003e\n\u003cp\u003eEnvironmental samples (2 to 20 mL) were fixed with a final concentration of 2% formaldehyde for at least 2 hours and filtered on 0.2 µm pore-size polycarbonate filters (Millipore, Merck, Darmstadt, DE). A CARD-FISH protocol [20], with some modifications [21]. was used for the labeling of distinct picocyanobacterial groups. Fluorescein-labeled thyramid, whose emission spectrum does not overlap with picocyanobacterial autofluorescence, was used for the amplification step. Filters were counterstained with DAPI and analyzed using epifluorescence microscopy (Zeiss Imager.Z2, Carl Zeiss, Oberkochen, DE) equipped with a Colibri LED light system. The images were captured using an Axiocam 506 (Carl Zeiss, Oberkochen, DE) with the following filter sets: DAPI 49 (Excitation 365; Beamsplitter TFT 395; Emission BP 445/50), fluorescein 38 HE (Excitation BP 470/40; Beamsplitter TFT 495; Emission BP 525/50) and chlorophyll A 62 HE (Excitation BP 370/40, 474/28, 585/35; Beamsplitter TFT 395 + 495 + 610; Emission TBP 425 + 527 + LP615 HE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic DNA extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor genome sequencing, picocyanobacterial cultures in the stationary phase were centrifuged at 4 ˚C, 8000 rcf for 30 minutes to collect cell pellets, which were subsequently stored at -80 ˚C until further processing. DNA extraction was carried out using the Quick-DNA Microprep Kit (Zymo Research, Irvine, CA, USA), according to the manufacturer’s instructions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequencing, preprocessing, and assembly of the sequencing reads\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA samples were sequenced under the Illumina Novaseq 6000 platform (Novogene, Hong Kong, China), targeting 1 Gbp per sample as output. Low-quality reads were trimmed using reformat.sh and bbduk.sh (Phred score = 18) of the bbmap package (https://sourceforge.net/projects/bbmap/). Any adapters or PhiX contamination were eliminated using the bbduk.sh script. The preprocessed reads were \u003cem\u003ede novo\u003c/em\u003e assembledusing MEGAHIT v1.1.4 [22], employing k-mer sizes ranging from 29 to 149 in increments of 10. Finally, all assemblies underwent length-filtering, retaining contigs with a minimum size of 3 kbp.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecovery of the genomes and functional annotations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince all the cultures we obtained were not axenic, genome binning was done for each assembly using MaxBin v2.2.7 [23], Metabat1 v2.15 [24], and Metabat2 [25], with default settings. The contig abundance files were generated by mapping preprocessed reads to the length-filtered assemblies using bbwrap.sh (kfilter=31, subfilter=15, maxindel=80). Contig abundance files were obtained using jgi_summarize_bam_contig_depths [25] for Metabat1 and 2, and CoverM using trimmed_mean mode (https://github.com/wwood/CoverM) for MaxBin. Bins produced by each method were combined, dereplicated, and refined using DASTool v1.1.2 [26]. A taxonomy-based decontamination step was performed in order to produce high-quality bins: protein-coding genes from each contig were predicted using Prodigal v2.6.3 [27] in the metagenomic mode, and the taxonomy of each gene was assigned using MMseqs2 [28] with the GTDB r95 database [29]. Contigs with \u0026gt;30% of genes without hits or hits to eukaryotes or viruses, as well as contigs in which the taxonomy disagreed with the consensus class, were removed. The resulting bins were further evaluated with CheckM v0.8.1 [30], and only those with ≥80% completeness and ≤5% contamination were retained for further analysis. Bins were finally renamed according to the sample of origin, method of binning (mx=Maxbin, m1=Metabat1, m2=Metabat2) and bin number. The taxonomy of the individual bin was assigned using gtdb-tk v1.4.1 [31] and the GTDB r95 database [29]. The assignment of KO (K number) was done using KEGG-Orthology-And-Links-Annotation (KOALA) algorithm against the nonredundant KEGG Genes database [32]. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenomic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA maximum-likelihood (ML) phylogenomic tree was constructed for the recovered picocyanobacterial genomes, as well as the references. All the genomes were scanned with hmmsearch for 120 conserved protein HMM markers [33]. Protein sequences for each maker were aligned using MAFFT v7.453 [19] in L-INSI mode and trimmed by trimAl v1.4 [34] with the following parameters: -gt 0.5 -keepheader. Subsequently, the trimmed alignments were concatenated using the catfasta2phyml.pl (https://github.com/nylander/catfasta2phyml), and used as input for tree construction using IQ-TREE2 v2.2.0 with 1000 iterations of ultrafast bootstrapping [35] and SH testing [36]. The best-fitting evolutionary model (Q.pfam+I+G4) was selected based on the BIC score by ModelFinder [37]. The final tree was visualized in the interactive Tree Of Life (iTOL) v6 (https://itol.embl.de/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetagenomic read recruitment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 714 publicly available metagenomes were subsampled to 20 million reads and used for read recruitment [38]. Prior to recruitment, rRNA genes in the individual genomes were masked. MMseqs2 [28] was used with the following parameters to align the sequencing reads to individual genomes and to calculate base coverage per Gb: -minid 0.95 -mincov 0.9 -minlen 50. To examine the presence of heterotrophs in our non-axenic cultures, we conducted the read recruitment of the genomes against the sequencing reads of the cultures using the same parameters. If the genome coverage of the sequencing reads was above 95%, we considered it to be present within the culture. Heatmaps were generated using the R package pheatmap v1.0.12 (https://github.com/raivokolde/pheatmap). Heat trees were generated using the R package metacoder v0.3.6 [39].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScanning and subtyping of CRISPR-Cas systems\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed CRISPR-Cas systems in freshwater picocyanobacterial genomes (n=170) obtained from this study, along with marine picocyanobacterial genomes (n=97) from the Cyanorak database [40]. Additionally, we examined a collection of freshwater prokaryotic genomes (n=9,374) constructed in a previous study [41]. The CRISPRCasTyper v1.6.4 (https://github.com/Russel88/CRISPRCasTyper) was used for the scanning and subtyping of CRISPR-Cas genes. Plots were generated using the R packages ggplot2 v3.3.5 [42].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic tree reconstruction for Cas1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the phylogenetic reconstruction of Cas1, we used all putative Cas1 sequences from the Uniprot database (https://www.uniprot.org/) and Cas1 predicted from a freshwater genome collection [41]. We manually curated the collected dataset by conducting a scan with hmmsearch [33] to identify significant hits associated with the Cas1 PFAM domain (PF01867). Hits with \u003cem\u003ep\u003c/em\u003e-values less than 0.01 and sequence lengths exceeding 80 amino acids were retained. To further narrow down the selection, we opted for the top 10 hits for each picocyanobacterial Cas1 using MMseqs2 [28] (--cov-mode 0). The remaining sequences were clustered using MMseqs2 (easy-cluster workflow) with a minimum sequence identity of 90%. The resulting 416 Cas1 sequences were aligned with MAFFT v7.453 [19]. A Maximum-likelihood (ML) tree was generated with IQ-TREE2 [36] with the following parameters: --perturb 0.2 --nstop 500 -B 1000 -m TEST --alrt 1000.\u003cstrong\u003e\u003cbr\u003e \u003c/strong\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenomic overview and delineating ecologically significant ecotypes\u003c/h2\u003e \u003cp\u003eWe conducted a three-year sampling campaign covering 44 different locations across Central Europe (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Our sampling approach was aimed to capture the diversity of freshwater ecosystems, including natural and post-mining lakes, reservoirs, rivers, and fishponds under different trophic statuses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). This extensive sampling effort yielded 170 high-quality (\u0026ge;\u0026thinsp;80% completeness and \u0026le;\u0026thinsp;5% contamination) picocyanobacterial genomes from 156 non-axenic cultures (Table. S2). Based on the trophic statuses of the isolation sources, these newly obtained genomes were initially classified into oligotrophic (n\u0026thinsp;=\u0026thinsp;60), mesotrophic (n\u0026thinsp;=\u0026thinsp;36), eutrophic (n\u0026thinsp;=\u0026thinsp;16), and hyper-eutrophic (n\u0026thinsp;=\u0026thinsp;57) groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). To provide a more comprehensive perspective for our study, we analyzed our new dataset alongside 63 previously published genomes of freshwater picocyanobacteria [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. A phylogenomic analysis affiliated the newly obtained genomes with either SC5.2 (92.4%; n\u0026thinsp;=\u0026thinsp;157) or SC5.3 (7.6%; n\u0026thinsp;=\u0026thinsp;13) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The strains from SC5.3 showed the smallest average genome sizes (2.3 Mb) and lowest GC contents (51.8%), whereas those in SC5.2 exhibited a degree of flexibility, with genome sizes ranging from 1.9 to 4.2 Mb and GC contents from 55.5 to 72.5% (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePotential population boundaries among these genomes were observed based on pairwise average nucleotide identity (ANI) at a cutoff above 85% (Fig. S3). Read recruitment in analyzed metagenomic samples (n\u0026thinsp;=\u0026thinsp;724) further revealed the global distribution patterns of the isolated picocyanobacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and Table S3). In line with previous studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], all the strains from SC5.3 were restricted to oligotrophic conditions. Among the members of SC5.2, we delineated ecologically significant ecotypes occupying four major freshwater regimes: oligotrophic, oligotrophic and cold (e.g., Lake Superior), eutrophic or hyper-eutrophic (e.g., Lake Mendota), and humic including environments displaying low pH (e.g., Lake Crystal Bog) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). We designated these ecotypes as the low-nutrient (LN), low-nutrient and low-temperature (LNLT), high-nutrient (HN), and low-pH (LP) ecotypes, respectively, and employed these classifications for further analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGenome-resolved symbiotic interactions with co-occurring heterotrophs\u003c/h2\u003e \u003cp\u003eDespite the absence of a major carbon source apart from vitamins and trace elements in the culture media, we additionally acquired 526 genomes of co-occurring heterotrophic bacteria, scoring\u0026thinsp;\u0026ge;\u0026thinsp;80% completeness and \u0026lt;\u0026thinsp;5% contamination (Table S4). Among these, we revealed the non-random occurrences of 38 heterotrophs, repeatedly present in more than 5% of the cultures (Table S5). These potential symbionts belonged to taxonomically narrow lineages, affiliated with five different species within four genera, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eMesorhizobium\u003c/em\u003e, \u003cem\u003eAcidovorax\u003c/em\u003e, and \u003cem\u003eHydrogenophaga\u003c/em\u003e. The observed co-occurrences of picocyanobacteria and heterotrophs further provided insights into the selective association between them (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). SC5.3, SC5.2 LN, and LNLT co-occurred with \u003cem\u003ePseudomonas\u003c/em\u003e (sp003033885) as their exclusive heterotrophic partner, while SC5.2 LP exhibited a strong preference for another \u003cem\u003ePseudomonas\u003c/em\u003e species (sp900187495). On the other hand, SC5.2 HN displayed a more complex association with heterotrophic partners, co-occurring primarily with \u003cem\u003eMesorhizobium\u003c/em\u003e, followed by \u003cem\u003eAcidovorax\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe potential metabolic interactions between these hetero- and photoautotrophs were next explored by analyzing their genomes and the media compositions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The majority of the picocyanobacterial genomes encoded genes for nitrate (\u003cem\u003enarB\u003c/em\u003e) and nitrite (\u003cem\u003enirA\u003c/em\u003e) reductases to convert nitrate, the sole nitrogen source in the growth media, into ammonia. They also encoded \u003cem\u003enrtABC\u003c/em\u003e for the transportation of extracellular nitrate inside the cell. On the contrary, the absence of \u003cem\u003enarB\u003c/em\u003e and \u003cem\u003enirA\u003c/em\u003e in all co-occurring heterotrophs, coupled with the presence of ammonia transporters (\u003cem\u003eamt\u003c/em\u003e), suggested that picocyanobacteria are likely to provide chassis for nitrogen metabolism in the community. It is also noteworthy that urea transporters were detected in both picocyanobacteria and heterotrophs. Heterotrophic bacteria further appeared to rely on picocyanobacteria for sulfur assimilation based on their genome composition. Sulfate was the primary sulfur source in the culture media, and only picocyanobacteria had the complete set of genes required to reduce sulfate into sulfide. Most heterotrophic symbionts lacked these genes, except phosphoadenosine phosphosulfate reductase (\u003cem\u003ecysH\u003c/em\u003e), which catalyzes the formation of sulfite from phosphoadenosine 5'-phosphosulfate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHeterotrophic bacteria also appeared to provide beneficial functions to picocyanobacteria in return (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In line with earlier marine studies [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], most picocyanobacterial genomes lacked genes necessary for detoxifying ROS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Heterotrophic bacteria showed high potential for hydrogen peroxide metabolism, with the \u003cem\u003ePseudomonas\u003c/em\u003e strains possessing up to five copies of catalase (\u003cem\u003ekatE\u003c/em\u003e) genes (Table S7). The genomes of \u003cem\u003eAcidovorax\u003c/em\u003e were further equipped with superoxide dismutase (\u003cem\u003esod1\u003c/em\u003e) genes that control the toxic levels of ROS [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. However, the genomes belonging to the genus \u003cem\u003eMesorhizobium\u003c/em\u003e completely lacked these antioxidant systems, leaving their complementary roles unresolved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGenotypic and phenotypic diversifications among distinct ecotypes\u003c/h2\u003e \u003cp\u003eWe examined the genotypic and phenotypic variations among different picocyanobacterial ecotypes to uncover the key factors contributing to their success in a wide range of ecological settings. In accordance with earlier studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], most picocyanobacterial populations adopting an oligotrophic lifestyle (SC5.3, SC5.2 LN, and SC5.2 LNLT) were red-pigmented, whereas the majority of the remaining isolates displayed green color (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The genomes of the red strains were set apart from the others by the presence of \u003cem\u003ecpeA\u003c/em\u003e and \u003cem\u003ecpeB\u003c/em\u003e genes in their genomes, which are responsible for the phycoerythrin biosynthesis. We also employed the catalyzed reporter deposition-fluorescence \u003cem\u003ein situ\u003c/em\u003e hybridization (CARD-FISH) technique to characterize the growth forms of distinct ecotypes within their natural habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). In (hyper-)eutrophic waterbodies, SC5.2 HN displayed a tendency to form microcolonies or large aggregates, potentially recognized as grazing-resistant forms [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Conversely, the remaining ecotypes predominantly exhibited unicellular phenotypes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA non-metric multidimensional scaling (NMDS) analysis based on the presence or absence of Kyoto Encyclopedia of Genes and Genomes (KEGG) annotated genes revealed distinct clustering of the genomes corresponding to their respective ecotypes (Fig. S4). A notable genomic variation was the selective presence of a two-component system for chemotaxis (\u003cem\u003echeBR\u003c/em\u003e) in SC5.2 HN (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The presence of \u003cem\u003epilA\u003c/em\u003e encoding type IV pilus further suggests that pili-mediated chemotaxis may facilitate their ability to locate optimal conditions for growth and photosynthesis. The genomes of SC5.2 LNLT, on the other hand, harbored \u003cem\u003etreS\u003c/em\u003e, \u003cem\u003eotsA\u003c/em\u003e, and \u003cem\u003eotsB\u003c/em\u003e genes responsible for trehalose biosynthesis. Trehalose is often recognized for enhancing photosynthesis for cyanobacteria under cold stress [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], thereby aligning well with their prevalence in cold environments. Finally, SC5.2 LP was distinguished from other ecotypes by possessing an epsilon-lactone hydrolase (\u003cem\u003emlhB\u003c/em\u003e), potentially indicating their additional capability to degrade recalcitrant humic compounds containing lactone groups [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCRISPR-Cas systems in picocyanobacteria\u003c/h2\u003e \u003cp\u003eWhile there has been a prevailing consensus that CRISPR-Cas systems are rare in picocyanobacteria [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], we conducted a thorough reassessment of these systems in both marine and our newly acquired freshwater genomes. As expected, CRISPR-Cas systems were absent in nearly all marine genomes (n\u0026thinsp;=\u0026thinsp;98) examined (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Conversely, our examination of the freshwater dataset revealed 15.9% (n\u0026thinsp;=\u0026thinsp;27) of genomes encoding CRISPR-Cas systems. Further analysis indicated that CRISPR-Cas systems were found in all genomes belonging to SC5.2 HN, which were dominant in eutrophic or hypertrophic environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). On the contrary, these antiviral defense mechanisms were absent in other ecotypes, suggesting a clear segregation in the distribution of CRISPR-Cas systems based on trophic status. CRISPR-Cas systems consist primarily of two essential modules: an adaptation module for acquiring spacers from short segments of foreign DNA, and an interference module that recognizes and cleaves target DNA sequences [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In most cases of our picocyanobacterial genomes, they appeared to possess all the necessary components for interference modules. However, a notable fraction (40.7%) of these genomes lacked cas1 genes required for spacer acquisition (Table S8).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnother interesting observation was that the most prevalent CRISPR-Cas systems in picocyanobacteria were the subtypes I-G and I-E (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Among Cyanobacteria, subtype I-D is the most common CRISPR-Cas system, followed by I-A, III-A, and III-B [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Therefore, we hypothesized that the evolutionary origin of CRISPR-Cas for picocyanobacteria may involve horizontal gene transfer from different taxonomic groups. Examining a large collection (n\u0026thinsp;=\u0026thinsp;9,374) of freshwater planktonic bacterial and archaeal genomes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] revealed that the subtypes I-C and I-E are the two most common CRISPR-Cas systems within the entire freshwater ecosystems (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Among different phyla, Desulfobacterota exhibited the highest enrichment of CRISPR-Cas systems, with 30% of the genomes encoding these antiviral defense mechanisms. We investigated the evolutionary origin of the picocyanobacterial CRISPR-Cas systems through a phylogenetic reconstruction of Cas1 from all available sources (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). The phylogenetic tree structure of Cas1 showed a considerable agreement with the subtype classification of CRISPR-Cas, as previously described [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Interestingly, the subtype I-G from picocyanobacteria and other Synechococcales genomes appeared to form a clade distinct from other I-G systems, while showing a closer evolutionary proximity to subtype III-B. For the subtype I-E, picocyanobacterial Cas1 clustered with orthologues from many different bacterial phyla such as Acidobacterota, Bdellovibrionota, Myxococcota, and Chloroflexota, making them potential donors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur extensive and well-coordinated sampling campaign, combined with non-axenic cultures, substantially augmented the genomic repertoire of freshwater picocyanobacteria. To our knowledge, this dataset represents the largest collection of genomes for freshwater picocyanobacteria to date, expanding the number of available genomes by more than threefold [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Our dataset further stands out from earlier studies by employing an unbiased sampling approach that targets a variety of freshwater ecosystems (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). As a result, a significant portion of the genomes were acquired from eutrophic and hyper-eutrophic conditions, where the genomic availability was previously limited (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The global phylogeography of freshwater picocyanobacteria in this study highlighted the pivotal roles of trophic status, temperature, and pH as key environmental factors delineating the major ecotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eThe 38 genomes of the co-occurring heterotrophs isolated in this study are affiliated with four genera: \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eMesorhizobium\u003c/em\u003e, \u003cem\u003eAcidovorax\u003c/em\u003e, and \u003cem\u003eHydrogenophaga\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although their symbiotic roles for cyanobacteria have rarely been explored, all four genera have been previously observed in the cultures of filamentous cyanobacteria, such as \u003cem\u003eMicrocystis\u003c/em\u003e and \u003cem\u003eAnabaena\u003c/em\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. It has been shown that certain \u003cem\u003ePseudomonas\u003c/em\u003e species have an enhancing effect on cyanobacterial growth [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Our dataset further suggested that their inability to use nitrate and sulfate for growth makes them highly dependent on picocyanobacteria, presumably benefiting from carbon compounds, ammonia, and sulfide exported by their symbiotic partners (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Previous marine studies on \u003cem\u003eSynechococcus\u003c/em\u003e and \u003cem\u003eProchlorococcus\u003c/em\u003e demonstrated that one crucial symbiotic function of heterotrophic bacteria within their immediate environment is detoxifying ROS produced during photosynthesis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Within freshwater environments, the contrasting distribution patterns of genes for ROS removal between picocyanobacteria and heterotrophs supports a similar conclusion that the decrease in oxidative stresses is facilitated by these heterotrophs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Yet, the interactions between cyanobacteria and heterotrophs can be far more complex since they might change over time [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] or under different culture conditions [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The mutualism might also occur beyond the two-species framework [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], which makes it even more challenging to understand the processes among multiple organisms.\u003c/p\u003e \u003cp\u003eThe observed genotypic and phenotypic diversifications among defined picocyanobacterial ecotypes in this study hinted at their adaptive strategies in distinct environmental conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In shallow and eutrophic waterbodies, bacteria often encounter elevated mortality pressure from grazers such as heterotrophic nanoflagellates, compared to their counterparts in deep and oligotrophic lakes [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. As protistan grazing is primarily cell size-dependent, bacteria can develop grazing-resistant forms, such as microcolonies, large aggregates, and filaments [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Therefore, the colonial lifestyle of SC5.2 HN (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) could be explained through this perspective. What was also intriguing about this ecotype was the presence of genes encoding type IV pilus (\u003cem\u003epilA\u003c/em\u003e) and chemotaxis (\u003cem\u003echeBR\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). While picocyanobacteria generally lack flagellar structures for motility, it has been shown that certain \u003cem\u003eProchlorococcus\u003c/em\u003e and \u003cem\u003eSynechococcus\u003c/em\u003e strains rely on type IV pili to avoid sinking and predation [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrior to this study, the rare occurrence of CRISPR-Cas systems in picocyanobacteria could be attributed to their small genome sizes and the likelihood of employing alternative defense mechanisms that impose a lesser genetic load [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. However, we demonstrated the strong association of CRISPR-Cas systems for picocyanobacteria thriving in eutrophic and hyper-eutrophic conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), which poses a fascinating contradiction to their evolutionary trajectory towards genome streamlining and reduced metabolic complexity. This observation can be potentially attributed to elevated viral loads in eutrophic lakes compared to their counterparts in oligotrophic waterbodies [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. A strong association between high viral abundance and the increased prevalence of CRISPR-Cas systems has been previously described [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. A recent study also demonstrated a much higher virus-to-microbe ratio in freshwater compared to marine environments [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], which could explain the scarcity of CRISPR-Cas systems in the open ocean. The CRISPR-Cas systems identified in freshwater picocyanobacteria exhibited unique phylogenetic placements and subtype classifications, setting them apart from those observed in other cyanobacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). This observation implies an extensive evolutionary history of horizontal transfers of CRISPR-Cas loci between various taxonomic groups in freshwater ecosystems. It is important to note that a notable proportion of picocyanobacterial CRISPR-Cas systems were found to lack cas1 genes (Tabls S7). As previously described in other cyanobacteria [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], the loss of cas1 genes may represent an early stage of losing a CRISPR-Cas system. However, even within the genomes devoid of cas1 genes, spacers were still retained within CRISPR arrays (Table S7). To verify the targets of spacers and understand how certain strains acquired CRISPR arrays without an adaptive module, a thorough investigation of viral agents and other mobile genetic elements targeting these strains will be necessary.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTogether, our findings effectively addressed gaps in understanding population diversity, symbiotic interactions, and adaptation strategies of picocyanobacteria within freshwater environments. Furthermore, this work establishes a genomic foundation for future efforts aimed at the detailed characterization of the genomic landscapes of freshwater picocyanobacteria, in connection with their evolutionary trajectories.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eANI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Average nucleotide identity\u003c/p\u003e\n\u003cp\u003eLN\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Low-nutrient\u003c/p\u003e\n\u003cp\u003eLNLT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Low-nutrient and low-temperature\u003c/p\u003e\n\u003cp\u003eHN\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;High-nutrient\u003c/p\u003e\n\u003cp\u003eLP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;low-pH\u003c/p\u003e\n\u003cp\u003eCARD-FISH\u0026nbsp;\u0026nbsp;Catalyzed reporter deposition-fluorescence in situ hybridization\u003c/p\u003e\n\u003cp\u003eNMDS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Non-metric multidimensional scaling\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Kyoto encyclopedia of genes and genomes\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Hyemin Kim for her valuable contributions regarding the graphic design of the figures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: J.J. Methodology: J.J, V.K. H.P. P.B. V.S.K. Investigation: H.P, P.B. J.J. V.K. V.S.K. Visualization: H.P. Writing—original draft: H.P. Writing—review and editing: H.P. T.S. V.K. V.S.K. R.G.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Czech Science Foundation (Grantová Agentura Ceské Republiky - GAČR) grants 20‑12496X (H.P, P.B, and R.G), 23-05081S (T.S), and 19-23261S (V.S.K., V.K and J.J).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and materials availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genome sequencing data from this study has been deposited in EBI ENA under the Bioproject PRJEB49198. Additional data, such as alignment files and trees are available in Figshare (https://figshare.com/s/3df321effacfb4fe8835).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003cbr\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCallieri C, Cronberg Gertrud, John G. Stockner. Freshwater picocyanobacteria: single cells, microcolonies and colonial forms. In Ecology of Cyanobacteria II: Their diversity in space and time. 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ISME J. 2023;17:1552-1563\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mbio","sideBox":"Learn more about [Microbiome](http://microbiomejournal.biomedcentral.com/)","snPcode":"40168","submissionUrl":"https://submission.nature.com/new-submission/40168/3","title":"Microbiome","twitterHandle":"@MicrobiomeJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4217878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4217878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Picocyanobacteria from the genera \u003cem\u003eProchlorococcus\u003c/em\u003e, \u003cem\u003eSynechococcus\u003c/em\u003e, and \u003cem\u003eCyanobium\u003c/em\u003e are the most widespread photosynthetic organisms in aquatic ecosystems. However, their freshwater populations remain poorly explored, due to uneven and insufficient sampling across diverse inland waterbodies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e In this study, we present 170 high-quality genomes of freshwater picocyanobacteria from non-axenic cultures collected across Central Europe. In addition, we recovered 33 genomes of their potential symbiotic partners affiliated with four genera, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eMesorhizobium\u003c/em\u003e, \u003cem\u003eAcidovorax\u003c/em\u003e, and \u003cem\u003eHydrogenophaga\u003c/em\u003e. The genomic basis of symbiotic interactions involved heterotrophs benefiting from picocyanobacteria-derived nutrients while providing detoxification of ROS. The global abundance patterns of picocyanobacteriarevealed ecologically significant ecotypes, associated with trophic status, temperature, and pH as key environmental factors. The adaptation of picocyanobacteria in (hyper-)eutrophic waterbodies could be attributed to their colonial lifestyles and CRISPR-Cas systems. The prevailing CRISPR-Cas subtypes in picocyanobacteria were I-G and I-E, which appear to have been acquired through horizontal gene transfer from other bacterial phyla.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e Our findings provide novel insights into the population diversity, ecology, and evolutionary strategies of the most widespread photoautotrophs within freshwater ecosystems.\u003c/p\u003e","manuscriptTitle":"Uncovering the genomic basis of symbiotic interactions and niche adaptations in freshwater picocyanobacteria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-15 11:31:27","doi":"10.21203/rs.3.rs-4217878/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-17T16:10:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-17T16:07:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-05T11:41:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbiome","date":"2024-04-04T12:26:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mbio","sideBox":"Learn more about [Microbiome](http://microbiomejournal.biomedcentral.com/)","snPcode":"40168","submissionUrl":"https://submission.nature.com/new-submission/40168/3","title":"Microbiome","twitterHandle":"@MicrobiomeJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f491ecb6-d752-427c-a6ca-e5f5c030b2fb","owner":[],"postedDate":"April 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-12T16:04:18+00:00","versionOfRecord":{"articleIdentity":"rs-4217878","link":"https://doi.org/10.1186/s40168-024-01867-0","journal":{"identity":"microbiome","isVorOnly":false,"title":"Microbiome"},"publishedOn":"2024-08-10 15:57:45","publishedOnDateReadable":"August 10th, 2024"},"versionCreatedAt":"2024-04-15 11:31:27","video":{"identity":"59b7f63c9cbbeed40f308e03db9c83ab"},"vorDoi":"10.1186/s40168-024-01867-0","vorDoiUrl":"https://doi.org/10.1186/s40168-024-01867-0","workflowStages":[]},"version":"v1","identity":"rs-4217878","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4217878","identity":"rs-4217878","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}