Two worlds beneath: Distinct microbial strategies of the rock-attached and planktonic subsurface biosphere

Two worlds beneath: Distinct microbial strategies of the rock-attached and planktonic subsurface biosphere | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Two worlds beneath: Distinct microbial strategies of the rock-attached and planktonic subsurface biosphere Alisha Sharma, Kirsten Küsel, Carl-Eric Wegner, Olga Maria Pérez-Carrascal, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7131340/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Jan, 2026 Read the published version in Microbiome → Version 1 posted 11 You are reading this latest preprint version Abstract Background Microorganisms in groundwater ecosystems exist either as planktonic cells or as attached communities on aquifer rock surfaces. Attached cells outnumber planktonic ones by at least three orders of magnitude, suggesting a critical role in aquifer ecosystem function. However, particularly in consolidated carbonate aquifers, where research has predominantly focused on planktonic microbes, the metabolic potential and ecological roles of attached communities remain poorly understood. Results To investigate the differences between attached and planktonic communities, we sampled the attached microbiome from passive samplers filled with crushed carbonate rock exposed to oxic and anoxic groundwater in the Hainich Critical Zone Exploratory and compared it to a previously published, extensive dataset of planktonic communities. Microbial lifestyle (attached vs. planktonic) emerged as the strongest determinant of community composition, explaining more variance than redox conditions. Metagenomic analysis revealed a striking taxonomic and functional segregation: the 605 metagenome-assembled genomes (MAGs) from attached communities were dominated by Proteobacteria (358 MAGs) and were enriched in genes for biofilm formation, chemolithoautotrophy, and redox cycling (e.g., iron and sulfur metabolism). In contrast, the 891 MAGs from planktonic communities were dominated by Cand. Patescibacteria (464 MAGs) and Nitrospirota (60 MAGs) and showed lower functional versatility. Only 7% of genera were shared, and even closely related MAGs (> 90% ANI) differed in genome size and metabolic traits, demonstrating lifestyle-specific functional adaptation. Analysis of active replication indicated that the active fraction of the attached community was primarily shaped by the most abundant MAGs. Planktonic communities featured more active MAGs, but overall with lower abundances. Conclusions The high abundance, metabolic specialization, and carbon fixation potential of attached microbes suggest that they are key drivers of subsurface biogeochemical processes. Carbonate aquifers may act as much larger inorganic carbon sinks than previously estimated based on CO 2 fixation rates of the planktonic communities alone. Our findings underscore the need to incorporate attached microbial communities into models of subsurface ecosystem function. Biofilms Chemolithoautotrophy Carbonate rock Groundwater Metagenomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Earth’s ecosystems host diverse microbial communities that exist in either planktonic or attached lifestyles (Wolters & Schwartz, 1956 ; Griebler et al., 2002 ; Flemming & Wuertz, 2019 ). Planktonic microbes are free-floating in the water phase, while attached microbes adhere to solid surfaces like rocks, mineral grains or organic particles (Dang et al., 2016; Battin et al., 2016 ; Esterhues et al., 2014; Karwautz, 2015 ). Research often focuses on planktonic communities due to ease of access and handling. However, biofilms actually represent the major lifestyle of prokaryotes on Earth (Flemming and Wuertz, 2019 ). Despite this universality, the mechanisms behind biofilm formation can vary depending on the environmental conditions present. Different energy sources like organic matter, light, or inorganic compounds can drive the costly biofilm formation process (Dang et al., 2016; Battin et al., 2016 ; Flemming & Wingender 2010 ). Especially under the oligotrophic conditions in pristine groundwater, the mechanisms of biofilm formation and the functional traits of the attached community are not well understood. Microbial attachment to the aquifer rock surfaces may offer several benefits. It provides access to rock-derived minerals, which can serve as electron donors for energy generation (Karwautz, 2015 ; Guo et al., 2023 ). This process is not only sustaining microbial growth but also drives essential biogeochemical processes like rock dissolution and mineral precipitation (Griebler & Lueders, 2009 ; Flynn et al., 2013 ). In addition, biofilms formed on the rock surfaces can provide protection from environmental stressors and predators (Flemming & Wingender, 2010 ; Stewart & Franklin, 2008 ). Following an initial attachment and coordinated by intercellular communication via quorum sensing, microbes produce extracellular polymeric substances (EPS) that form the biofilm matrix. After establishment of the biofilm, dispersal can occur to colonize new surfaces, perpetuating the biofilm cycle (Kapellos et al., 2015 ; Flemming and Wuertz, 2019 ). In the nutrient-limited groundwater, EPS furthermore represents a source of organic carbon for growth of heterotrophic microbes (Flemming & Wuertz, 2019 ; Griebler et al., 2002 ), providing them an advantage compared to a planktonic lifestyle. These benefits might explain why attached communities contain a significantly larger proportion of the aquifer microbiome, outnumbering planktonic cells by at least three orders of magnitude, independent of lithology (Lehman et al., 2001 ; Griebler et al., 2002 ; Magnabosco et al., 2018 ; Sharma et al., 2024). Given this dominance, the attached communities likely are crucial for providing groundwater ecosystem functions like contaminant degradation and nutrient recycling (Griebler & Avramov, 2015 ; Flynn et al., 2013 ; Griebler & Lueders, 2009 ). To gain insights into the processes driving the attached microbial community in a groundwater ecosystem, we focused on the carbonate aquifers within the Hainich Critical Zone Exploratory (CZE) in Thuringia, Germany (Kuesel et al., 2016). Previous bioreactor experiments with groundwater and rock material demonstrated rapid colonization of rock surfaces (Sharma et al., 2024). However, only a small subset of groundwater microbes attached in this artificial bioreactor setting, raising the question of whether such differentiation between attached and planktonic communities also occurs in situ , and if so, whether it translates into distinct functional traits with consequences for subsurface ecosystem processes. Using metagenomics, we provide an in-depth characterization of the taxonomy and functional potential of attached microbes in comparison to their planktonic counterparts. We hypothesize that attached and planktonic communities harbor distinct taxonomic groups and functional traits that reflect their contrasting lifestyles. To test this, we sampled microbial communities using passive samplers filled with crushed limestone rock, deployed in oxic and anoxic groundwater wells for up to one year. The resulting metagenome-assembled genomes (MAGs) were compared with a comprehensive dataset of previously recovered planktonic MAGs (Chaudhari et al., 2021 ; Overholt et al., 2022). By resolving the functional and ecological differentiation between attached and planktonic communities, our study addresses a major gap in subsurface microbial ecology and advances our understanding of how microbial lifestyles shape biogeochemical processes in carbonate aquifers. 2. Materials and Methods Passive sampler setup and DNA extraction Crushed rock materials with 2 to 4 mm diameter were prepared from limestone, specifically calcium mudstones of the Trochitenkalk formation, sourced from an outcrop located in the low-mountain ridge of the monitoring well transect within the Hainich Critical Zone Exploratory (CZE) in northwestern Thuringia, Germany. The wells access aquifer layers with distinct redox states: anoxic conditions in the upper Meissner Formation and oxic conditions (5.1 mg/L O₂) in the lower Trochitenkalk Formation. These differences reflect variations in surface connectivity, permeability, and flow dynamics of the sloping strata (Kohlhepp et al., 2017; Lehmann and Totsche, 2020). TOC levels remain below 2 mg/L throughout (Lehmann and Totsche, 2020). Four bags each of crushed rock material, enclosed in passive sampler casings (Hermann et al., 2017), were deployed in wells H41 (51.1150842 N, 10.4479713 E) and H52 (51.1193392 N, 10.4691776 E) in January 2022 for up to one year. Sampling occurred after 6, 18, 30, 42 and 54 weeks of exposure, with subsequent immediate freezing in dry ice and storage at -80°C. DNA was extracted using a phenol/chloroform-based protocol as previously described (Sharma et al., 2024). In brief, 10 g of passive sampler material was treated with 2.6 mL 2× SET buffer, 30 µL PMSF, and 350 µL 10% SDS, incubated at 55°C for 2 h with shaking, then centrifuged (4000 ×g, 10 min). The supernatant was extracted with phenol:chloroform:isoamyl alcohol (25:24:1), followed by chloroform:isoamyl alcohol (24:1) extraction. DNA was precipitated overnight with glycogen and ethanol, pelleted (4000 ×g, 30 min), washed with 80% ethanol, air-dried, and resuspended in 50 µL TE buffer. Microbial community profiling To select samples for metagenomic sequencing, first an amplicon-sequencing-based screening targeting bacterial 16S rRNA genes was conducted. Amplicon libraries were prepared using a two-step barcoding strategy as described by Krüger et al. ( 2021 ). The first PCR employed primer pair S-D-Bact-0341-b-S-17/S-D-Bact-0785-a-A-21, which amplify the V3–V4 region of the 16S rRNA gene (Herlemann et al., 2011 ; Klindworth et al., 2013 ), each modified with Illumina adaptor overhangs to enable subsequent indexing. Reactions (15 µL) were run in triplicate using 1–5 ng of template DNA, 0.4 µM of each primer, 1 µg/µL BSA, and 2× HotStartTaq Master Mix (Qiagen, Germany). Thermal cycling included initial denaturation at 95°C for 15 min, followed by 25–30 cycles of 94°C for 45 s, 55°C for 45 s, 72°C for 45 s, and a final extension at 72°C for 10 min. Amplicons were barcoded in a second PCR as previously described (Krüger et al., 2021 ), using 1 µL of first-round product, 0.5 µM of each barcoding primer, and 2× Ruby Taq Master Mix (Jena Bioscience, Germany). This step involved 6 cycles of 95°C for 45 s, 55°C for 45 s, and 72°C for 45 s. PCR products were checked for size and integrity via agarose gel electrophoresis. Sequencing was performed on an Illumina MiSeq platform (Illumina, Eindhoven, The Netherlands) with v3 chemistry in paired end mode (2 × 300 bp). Sequence reads were combined with previously published amplicon datasets from the planktonic groundwater community of the Hainich CZE (0.2 µm filter fraction), covering a time series from January 2022 to January 2023 (Wang et al., 2025). Data analysis was performed with the DADA2 (Divisive Amplicon Denoising Algorithm 2) pipeline (v1.22.0) using default settings following the core DADA2 algorithm using R software (v4.2.1) (Callahan et al., 2016 , R Core Team, 2021). The Vegan software package (v2.6-2) was used to analyze bacterial community patterns (Oksanen et al., 2007 ). Permutational multivariate analysis of variance (PERMANOVA), implemented via the adonis function (Li et al., 2022 ), was used to assess differences in community composition between attached and planktonic communities. The ggplot2 software package (v3.3.6) was used for graphical representation of the data (Wickham, 2011 ). Metagenomic sequencing Based on results from amplicon sequencing, DNA from passive sampler material at 6, 18, and 30 weeks from both wells (10 samples) were selected for metagenomic sequencing. Additionally, to improve the recovery of microbial genomes, metagenomic sequencing was performed on samples from a bioreactor experiment described in Sharma et al., 2024 (10 samples, incubation times 4 to 44 days). The quality of extracted DNA was determined on a 4200 TapeStation System (Agilent, Santa Clara, CA, USA) using the Genomic DNA ScreenTape (Agilent). Paired-end libraries were generated with an NEBNext Ultra II FS DNA preparation kit (New England Biolabs, Ipswich, MA, USA), and sequenced (2×150 bp) on an Illumina NovaSeq 6000 system (Illumina, San Diego, CA, USA) in Fritz Lipmann Institute, Jena, Germany. Yields of metagenomic sequencing per sample ranged from 20.5 to 35.4 Gbp (mean = 25.7 Gbp) (Supplementary Table S1 ). Metagenomic assembly, binning and refinement for attached communities The reads obtained from metagenomic sequencing were subjected to quality control using BBDuk (v39.01) (Dehasque et al., 2022 ) with the following parameters: statscolumns = 5, ktrim = r, qtrim = rl, trimq = 20, minlen = 50, k = 23, mink = 11, and hdist = 1. The quality-controlled reads of each individual sample were assembled using metaSPAdes (v3.15.2) (Nurk et al., 2017 ) with kmer sizes 21, 33 and 55. Scaffolds larger than 1 kb were used for downstream analyses. Genome binning was carried out using five binning algorithms: Abawaca (v.1.0.7) (Brown et al., 2015 ), metabat2 (v2.12.1) (Kang et al., 2015 ), CONCOCT (v.1.0.0) (Alneberg et al., 2013 ), BinSanity (v.0.2.7) (Graham et al., 2017 ), and maxbin2 (v.2.2.6) (Wu et al., 2016 ) with default parameters. Both the 40 and 107 marker gene sets were utilized in MaxBin2. BinSanity and Abawaca were used to generate bins using contigs of 3000 bp and above. For Abawaca binning, tetranucleotide frequencies were calculated from contigs with a minimum size of 5,000 bp and 10,000 bp. The generated bins were subsequently refined using metaWRAP (v1.3.2) (Uritskiy et al., 2018 ), with ≥ 50% completeness and ≤ 10% contamination, representing described thresholds for medium quality bins (Bowers et al., 2017 ). Bins were de-replicated using dRep (v3.4.0) (Olm et al., 2017 ) at 99% average nucleotide identity (ANI) at secondary clustering to remove strain-level redundancy across samples, resulting in 631 representative metagenome-assembled-genomes (MAGs) of the rock-attached communities. From these MAGs, we excluded 26 MAGs that were exclusively present in bioreactor samples to avoid the introduction of strains from the laboratory setting, leading to a final set of 605 MAGs from the attached community. Selection of planktonic groundwater MAGs for comparative analysis To compare the obtained MAGs from the rock-attached microbiome to planktonic MAGs from the same ecosystem, a dataset of previously recovered Hainich groundwater MAGs based on metagenomic sequencing of 12 samples from January 2019, including wells H14, H32, H41, H43, H51 and H52, was employed (Chaudhari et al., 2021 ; Overholt et al., 2022). From this dataset, a total of 891 MAGs, dereplicated with the same ≥ 50% completeness and ≤ 10% contamination threshold as the attached MAGs, were selected, as well as quality-filtered metagenomic reads from six samples of the 0.2 µm filter fraction of the groundwater, which contained the majority of the groundwater microbiome. These planktonic MAGs were analyzed in parallel to the attached MAGs as described in the following sections. Assessment of MAG quality metrics and genome statistics The quality (completeness and contamination/redundancy) of the 605 attached MAGs and the 891 planktonic MAGs was calculated based on domain-level bacterial/archaeal specific single copy marker gene sets using the checkM workflow (v.1.2.2) (Parks et al., 2015 ), with parameters --min-covered-fraction 0 and --methods mean. For MAGs affiliated with the Candidate Phyla Radiation (CPR), a CPR-specific single copy marker gene set was used. Corrected genome sizes of the MAGs were calculated by dividing by the number of marker gene sets present and multiplying with the expected number of marker gene sets based on the checkM analysis. The Wilcoxon signed-rank test was used to determine differences between sizes of attached and planktonic MAGs on phylum level, as for lower taxonomic levels, the number of MAGs would have been too low for meaningful comparisons. Determination of MAG taxonomy, abundance and functional potential The MAGs were taxonomically classified using GTDB-Tk (v2.3.0; Chaumeil et al., 2022 ) with the Genome Taxonomy Database (release 214) as reference. Furthermore, Kaiju (v.1.9.0) (Menzel et al., 2016 ) with the database nr_euk was used for taxonomic classification of metagenomic datasets on read level. To estimate the relative abundances of each MAG in the sampled communities, first genome coverages were calculated based on quality-filtered metagenomic data with coverM (v.0.6.1) (Aroney et al., 2025 ) in genome mode (parameters: --coupled --min-covered-fraction 0 --methods mean). Relative abundances were then calculated by dividing each MAG’s coverage by the total coverage of all genomes in the respective dataset (attached or planktonic), as previously described (Woodcroft et al., 2018). In addition, SingleM (v0.13.2) (Woodcroft et al., 2024 ) was used to determine abundances of taxa not covered on MAG level directly from metagenomic quality-filtered reads. SingleM identifies and classifies microbial taxa by detecting reads of single-copy marker genes. Functional annotation focusing on biofilm-related functions, biogeochemical cycling, and nutrient transporters, using pre-defined functional categories and individual genes derived from KEGG orthology, was done using DRAM (v.1.4.6) (Shaffer et al., 2020 ) and Metabolic-G (v.4.0) (Zhou et al., 2022 ) with default settings. For flagella, three key genes responsible for flagellar motor switch protein (FlgG, FliM and FliN/Y) were considered. For functions linked to iron oxidation and reduction, gene clusters defined by the FeGenie database (v1.2) (Garber et al., 2020 ) were used in conjunction with Metabolic-G. A function/pathway was considered present if more than 50% of the genes associated with that particular function were identified. For CO 2 fixation pathways, in addition to this cutoff, the presence or absence of key enzymes in the pathway, e.g., ribulose bisphosphate carboxylase/oxygenase (RuBisCO) for Calvin cycle, carbon monoxide dehydrogenase (CODH) for Wood Ljungdahl pathway, ATP citrate lyase for reverse tricarboxylic acid cycle, was also required. Determining active MAGs based on replication indices To identify actively replicating MAGs, first quality-filtered reads were mapped to MAGs using bowtie2 (v2.4.4) (Langmead et al., 2012). Mapping files were sorted and indexed via samtools (v1.13) (Danecek et al. 2021 ). Subsequently, indices of replication were determined based on the sequencing coverage trend resulting from bi-directional genome replication from a single origin of replication using iRep (v1.10) (Brown et al., 2016 ). Indices were only determined for MAGs with > 75% completeness and < 2% contamination, as previously suggested (Brown et al., 2016 ). A MAG was considered to be active when at least in one metagenomic sample of the respective dataset, R 2 values calculated between the coverage trend and the linear regression were ≥ 0.9, and reads were covering ≥ 98% of the respective MAG with at least 5x coverage (Brown et al., 2016 ). To identify functions enriched in active MAGs, ordinary least squares linear models fitted with the lm() function in R (v4.2.1) (R Core Team, 2021) were used to assess the correlation between functional categories and MAG activity. Functional categories with positive correlations and p-values below 0.05 were considered as significant predictors of microbial activity. Comparison of shared taxa in attached and planktonic communities MAGs that were present in both attached and planktonic communities were selected to investigate the adaptation mechanisms that facilitate their survival across lifestyles. For this, first microbial taxa present in both attached and planktonic communities were counted, revealing a total of 27 genera shared between 100 attached and 134 planktonic MAGs. Of these shared taxa, high-quality MAGs (≥ 50% completeness and ≤ 10% contamination) were selected to calculate pairwise average nucleotide identities using fastANI (v1.33) (Jain et al., 2018 ). Matches between planktonic and attached MAGs with > 90% ANI, a threshold chosen to enable genus-level or low-divergence comparisons (Konstantinidis & Tiedje, 2005) were selected for comparative analysis of their functional potential and genome sizes. A fold change in genome size was calculated between matched pairs of attached and planktonic MAGs. A Wilcoxon signed-rank test was then applied to evaluate whether these fold changes across all pairs were significantly different from zero. 3. Results Attachment is the primary determinant of groundwater microbial community composition When comparing microbial communities from groundwater and passive samplers of the same aquifers based on 16S rRNA gene amplicon data, the attachment preference (attached vs. planktonic) explained the largest proportion of variance (11%) in community composition (R 2 = 0.11, F value = 5.22) (Fig. 1 ). The presence of oxygen explained 8% of variance (R² = 0.08, F value = 3.99) and the time point of sampling explained 5% of variance (R 2 = 0.05, F value = 2.30). These patterns were also evident in the 16S rRNA gene taxonomic profiles of the communities (Supplementary Figure S1 ). Hence, even with the strong differences in hydrochemical conditions across the Hainich CZE, covering oxic as well as anoxic groundwater, attachment preference is the strongest driver for community composition. This justified a comparison of MAG datasets acquired at different sampling times (2019 for planktonic communities, 2022 for attached communities) to elucidate the differences between these communities. Attached and planktonic communities show drastic taxonomic differences Distinct community composition was also observed in metagenomic datasets of attached versus planktonic samples of the Hainich CZE groundwater. In the attached communities, Proteobacteria contributed 358 of the total of 605 MAGs (Fig. 2 A). In terms of relative abundance based on metagenomic coverage, these accounted for 60.3 ± 8.1% (mean ± st. dev.) of the attached community (Fig. 2 B). In contrast, Proteobacteria represented only 9.62 ± 5.64% relative abundance in the planktonic community, with 52 MAGs. Instead, 465 of the 891 MAGs in the planktonic community were affiliated with Cand. Patescibacteria, with 39.1 ± 11.8% relative abundance. In the attached community, Cand. Patescibacteria accounted for only 10.0 ± 4.6% relative abundance with 30 MAGs. The planktonic community also featured 102 archaeal MAGs, and 5.5% of the sequencing reads were classified as archaeal (Supplementary Figure S2 ). From the attached community, only 2 archaeal MAGs were recovered, with 0.48% of reads being archaeal. The dominant Proteobacteria in the attached communities featured highest abundances of genera like Rhodoferax (8.6 ± 6.7%), Aquabacterium (6.4 ± 2.1%), Hydrogenophaga (5.2 ± 3.6%), and Undibacterium (4.6 ± 4.0%). In planktonic communities, only two proteobacterial genera occurred at noteworthy proportions: Phenylobacterium (2.7 ± 5.4%, 17 MAGs) and Nitrosomonas (0.9 ± 1.4%). As the most abundant group in the planktonic community, Cand. Patescibacteria were dominated by Cand. Paceibacteria (19.5 ± 9.3%, 295 MAGs), ABY1 (4.5 ± 2.2%, 65 MAGs), and Cand. Microgenomatia (3.0 ± 1.1%, 61 MAGs) (Fig. 2 C). In attached communities, four classes were present, Cand. Saccharimonadia (4.2 ± 3.5%, 3 MAGs), JAEDAM01 (3.1 ± 3.5%, 6 MAGs), Cand. Paceibacteria (2.4 ± 1.4%, 17 MAGs), and ABY1 (0.2 ± 0.3%, 4 MAGs). In particular, in planktonic communities no MAG of class JAEDAM01 was observed, and this group had less than 0.01% read coverage, making it specific to the attached communities. Attached microbes feature larger genomes and widespread biofilm-related functions We found an attached lifestyle to be reflected by larger genomes. Significantly higher sizes of attached compared to planktonic MAGs were observed for the phyla Nitrospirota (p-value = 1.0 x 10 − 4 ), Proteobacteria (p-value = 2.1 x 10 − 15 ), and Bacteroidota (p-value = 1.6 x 10 − 2 ) (Supplementary Figure S3). The mean genome size of Proteobacteria in the attached fraction was 4.48 Mb, compared to 1.80 Mb in the planktonic fraction, corresponding to a 2.49-fold difference. Bacteroidota genomes were 1.87-fold larger (3.89 Mb vs. 2.08 Mb), and Nitrospirota showed a 1.15-fold difference (3.31 Mb vs. 2.88 Mb). This higher genome size also coincided with an increase in the organisms’ functional potential, in particular considering biofilm-related functions (Fig. 3 ). Attachment functions were encoded in up to 80% of attached MAGs: Pili were mostly present in Gammaproteobacteria, while Nitrospirota featured type 1 and type 3 secretion systems and Alphaproteobacteria showed a greater variety of these attachment genes. Only 8.2% of planktonic MAGs featured attachment genes, primarily for type 3 secretion systems. For formation of the biofilm matrix, various pathways were present in 98.7% of the attached MAGs: The Raetz pathway was primarily found in Gammaproteobacteria, while Nitrospirota featured Vibrio-type polysaccharide (VPS) biosynthesis. In planktonic communities, only 25.4% of the MAGs, with representatives of Nitrospirota, Proteobacteria and Planctomycetota, featured such biofilm formation genes. Among Cand. Patescibacteria, only class JAEDAM01 featured biofilm related functions, including pili, quorum sensing, VPS, and the Raetz pathway. Furthermore, diverse genes related to degradation of biofilms were present in nearly 65% of attached MAGs (Fig. 2 ). Some of these genes, primarily for glucosidases, were also present in 32% of planktonic MAGs, representing the only biofilm-related function prevalent in the planktonic microbes. Distinct CO 2 fixation pathways were prevalent in attached vs. planktonic communities Strikingly, we found a substantially higher fraction of putative chemolithoautotrophs in the attached than in the planktonic community. A MAG was considered to be putatively autotrophic if a CO 2 fixation pathway was more than 50% complete and the respective key enzyme was present. These criteria were fulfilled by 15.7% of attached MAGs but only 6.6% of planktonic MAGs. Putative autotrophs accounted for a significantly higher relative abundance of 20.7 ± 0.93% (mean ± st.dev) of the attached community compared to 12.7 ± 0.55% in the planktonic community (Mann–Whitney U test, p = 0.0448) (Fig. 4 A). The distribution of pathways was distinctly different: The Calvin cycle was by far the most prevalent CO 2 fixation pathway in the attached community, with 18.4 ± 1.02% of relative abundance. It was primarily found in the abundant proteobacterial genera like Rhodoferax , Aquabacterium and Undibacterium . In contrast, only 2.4 ± 0.12% of the planktonic community featured this pathway (Fig. 4 A). Instead, the Wood-Ljungdahl pathway, with 4.9 ± 0.62%, and the Arnon-Buchanan cycle, with 4.7 ± 0.91%, had the highest relative abundances in the planktonic community. These pathways primarily occurred in MAGs affiliated with Nitrospirota and Omnitrophota. Higher abundance of genes associated with other biogeochemical cycles in attached communities The metabolic potential for reduction and oxidation of inorganic compounds (Fe, S, N), essential for microbial energy acquisition in groundwater, was generally more widespread in attached than planktonic MAGs (Fig. 4 B). The cyc1 gene for iron oxidation was found in 42% of attached MAGs (mainly Gammaproteobacteria), compared to only 6% in planktonic MAGs. Iron reduction genes ( mtrBC , dmkAB , and cytochromes DFE_0448–0451 and DFE_0461–0465) were more widespread, found in 91% of attached MAG and 61% of planktonic MAGs. The sox cluster for sulfur oxidation was likewise present in 38% attached MAGs mainly Gammaproteobacteria and Myxococcota, but only 5% of planktonic MAGs including Nitrospirota and Chloroflexota. The sat - apr - dsr system, linked to both dissimilatory sulfate reduction and sulfur oxidation, was found in comparable percentages of MAGs, in 10.5% of attached and 8.6% of planktonic MAGs. For nitrogen metabolism, primarily reductive pathways were found, mostly in Proteobacteria and Nitrospirota of both communities. Genes specific for denitrification ( napAB , nirK / nirS , norBC , nosZ ) were found in up to 39% of attached MAGs and 11% of planktonic MAGs. The nirBD or nrfAH genes specific for dissimilatory nitrate reduction were present in 11.4% of attached MAGs and 2.6% of planktonic MAGs. In contrast, nitrification-specific genes ( amoCAB, hao ) were found in similarly low numbers in both attached (5.6%) and planktonic MAGs (5.1%). The most abundant attached genera, like Aquabacterium , Rhodoferax and Undibacterium , possessed genes for both iron and/or sulfur oxidation as well as denitrification. While a comparable number of attached and planktonic autotrophs harbored genes for nitrite oxidation (47% and 42%, respectively) and ammonia (14% and 12%) oxidation, a significantly higher proportion of attached autotrophs possessed genes for sulfur oxidation (73% vs. 28%) and iron oxidation (64% vs. 10%) than planktonic autotrophs. This provided evidence that sulfur and iron oxidation played a more important role in fueling autotrophy in the attached community. Nutrient transporters were more abundant in attached microbial community Transporters for uptake of inorganic electron donors and acceptors, were likewise more widespread in the attached community: Iron transporters were present in 90% of attached but only 24% of planktonic MAGs (Fig. 4 C). Transporters for sulfate and reduced sulfur compounds were present in 55% of attached but only 11% of planktonic MAGs. The distribution of transporters for nitrogen compounds was less skewed, with 27% attached and 13% of planktonic MAGs encoding them. Likewise, transporters responsible for the uptake of simple and complex sugars, such as arabinosaccharide, glucose, and mannose, were present in both attached (17%) and planktonic (10%) MAGs. Active replication in a higher portion of planktonic vs. attached MAGs To identify actively growing taxa and determine which metabolic functions correlate with activity, we calculated indices of replication (iRep) as previously described (Brown et al. 2016 ). Overall, the active attached community consists of fewer but more abundant MAGs compared to the planktonic community and possessed a broad functional repertoire to thrive in biofilms. A higher proportion of planktonic MAGs (42%, 373 MAGs) compared to attached MAGs (25%, 151 MAGs) featured iRep values indicating growth. In the attached community, the most abundant MAGs were active (Fig. 5 A). In comparison, in the planktonic community, a large proportion of the active MAGs were of low abundance. As a result, in terms of relative abundance a higher proportion of the attached community (57%) compared to the planktonic community (38%) was active. The active attached taxa included the proteobacterial key players such as Aquabacterium , Rhodoferax and Undibacterium , as well as the most abundant Actinobacteria, Myxococcota, Nitrospirota and Verrucomicrobiota MAGs. Contrastingly, in the planktonic community, the diverse Cand. Patescibacteria made up 61% of the active MAGs, while in the attached community, only Cand. Patescibacteria MAGs of group JAEDAM01 were active. More than 50% of the active attached MAGs exhibited functions related to biofilm formation and degradation, iron cycling, and denitrification (Fig. 4 B). Attached MAGs with genes for the Raetz pathway for EPS biosynthesis, the CBB cycle, denitrification, sulfate reduction, and F-type ATPases were active significantly more often than MAGs without these functions (Fig. 5 B). In contrast, biofilm-related functions were present in less than 15% of active planktonic MAGs (Fig. 5 C). Only genes for iron and nitrate reduction, glucosidases, and F-type ATPases were more common, and MAGs with genes for EPS degradation, dinitrogen fixation, and iron reduction were significantly more likely to be active. Additionally, in both communities the WL pathway and rTCA cycle were positively correlated with activity, indicating that both attached and planktonic MAGs with the capability to fix CO 2 were active significantly more often. Overlapping taxa exhibited functional differences To determine whether taxa present in both attached and planktonic communities represent microbes transitioning through the planktonic state to colonize new surfaces, we compared the genomic functions of these overlapping taxa. We found that only 27 genera (~ 7% of the total genera) were shared. These taxa were mostly affiliated with Proteobacteria, Nitrospirota, and Bacteroidota. Of these, only seven pairs of MAGs featured average nucleotide identity (ANI) values above 90%, indicating close relatedness. All attached MAGs exhibited a broad range of functions for flagellar biosynthesis and chemotaxis, as well as biofilm formation and quorum sensing (Fig. 6 ). In contrast, these functions were mostly absent in planktonic MAGs. Genes for biofilm degradation, sugar uptake and other transport systems were likewise more abundant in the attached compared to the planktonic MAGs. Functions for oxidation and reduction of iron, nitrogen and sulfur compounds showed strong differences in their distribution in the closely related attached and planktonic MAGs. For example, the planktonic Bacteroidota MAG contained nitrification genes that were missing from its attached counterpart, and the attached Nitrospirota 9FT-COMBO.42.15 contained nitrogen cycling genes absent in its planktonic relative. Attached MAGs had significantly larger genome sizes (1.40 ± 0.31 times, mean ± st. dev.) than planktonic MAGs (p-value = 0.01073, Wilcoxon signed-rank test) despite comparable completeness above 90% (Supplementary Table S7). Thus, although being closely related, the overlapping taxa showed lifestyle-specific differences. 4. Discussion Understanding the ecological principles driving the life of groundwater microbial communities is crucial for unraveling biogeochemical processes in the subsurface. Our results show that microbial lifestyle, i.e., the preference for attached or planktonic growth, is the strongest determinant of community structure in carbonate rock aquifers. Even severe differences in the hydrochemical conditions, such as redox gradients from oxic to anoxic settings, have less strong effects on the microbial communities. These findings are surprising in light of prior work emphasizing hydrochemistry and redox gradients as the primary drivers of groundwater community structure (Griebler & Lueders, 2009 ; Flynn et al., 2013 ), and suggest that a paradigm shift towards a focus on microbial lifestyles (attached vs. planktonic) is necessary for a better understanding of groundwater microbial ecology. Many groundwater studies have interpreted microbial community patterns through the lens of the seed bank hypothesis, which posits that the planktonic community acts as a reservoir of dormant or low-abundance taxa capable of colonizing surfaces when conditions permit. (Yamamoto et al., 2019, Lennon & Jones, 2011). This concept is supported in porous, unconsolidated aquifers, where dynamic exchange between water and sediment allows frequent microbial dispersal (Atencio et al., 2025 ; Coyte et al., 2016; Lennon & Jones, 2011). However, our genome-resolved analysis of a consolidated carbonate aquifer reveals a fundamentally different picture. Attached and planktonic communities were not only taxonomically distinct, but functionally segregated, with minimal overlap at the MAG level. Even these closely related taxa (with > 90% ANI) exhibited pronounced differences in genome size and metabolic potential depending on lifestyle, including biofilm formation, redox metabolism, and environmental sensing. These findings suggest that in fractured rock aquifers, biofilms are not seeded from the planktonic microbiome, but rather form functionally distinct and relatively isolated communities due to selective and stable ecological filtering. Our results thus challenge the broad applicability of the seed bank model in groundwater microbiology, and call for revised conceptual frameworks that recognize the limited connectivity and strong functional divergence between lifestyles in consolidated aquifers. The functional adaptations for biofilm formation observed in the attached community are widespread in the bacterial domain. Traits including adhesion functions and EPS production are distributed throughout taxa and environments (Besemer et al., 2012 ; Battin et al., 2016 ; Flemming and Wuertz, 2019 ; Gopalakrishnappa et al., 2022 ; Lennert et al., 2024 ). This prevalence aligns with the general tendency of microorganisms to colonize surfaces and live in biofilms, from ecosystems in the natural environment to host- and disease-associated communities (Niederdorfer et al., 2017 ; Flemming and Wurtz; Hall-Stoodley et al., 2004). The presence of biofilm-associated genes, together with larger genome sizes and metabolic versatility, positions attached microbes as functionally rich and ecologically stable anchors of the subsurface ecosystem. These biofilms are not passive but can influence mineral weathering and nutrient fluxes, reflecting the capacity of these communities to interact with and modify their local geochemical environment, acting as specialized ecosystem engineers (Nuppunen-Puputti et al., 2022; Mullin et al., 2020; Flynn et al., 2013 ). Biofilm formation by the attached organisms seems mainly driven by chemolithoautotrophic growth, based on CO 2 fixation via the CBB cycle and oxidation of sulfur and iron compounds (Fig. 7 ). Reduced sulfur and iron are available to attached microbes from minerals on the rock surfaces (Dong et al., 2022 ; Jones & Bennett, 2017 ), and their release might be promoted by microbially mediated dissolution processes (Jones & Santini, 2023 ; Bice et al., 2025). Attached key species like Rhodoferax and Undibacterium are known for chemolithoautotrophic growth on these electron donors (Kato and Ohkuma, 2021 ; Gülay et al., 2018 ). The sticky EPS matrix of the biofilms allows cells to remain in close proximity, promoting efficient nutrient exchange and uptake while reducing loss through groundwater flow (Stewart and Franklin, 2008 , Flemming and Wingender, 2010 ), thus offering various advantages in the oligotrophic conditions of the aquifer. The high abundance of genes for biofilm degradation furthermore implies that for subsequent heterotrophic colonizers, EPS can act as a source of organic carbon. Such genes for degradative enzymes were significant predictors of activity in planktonic MAGs as well, indicating that scavenging of dissolved biofilm material might play an important role also for the planktonic community (Fig. 7 ). The autotrophically-driven nature of the aquifer biofilms is likely linked to the oligotrophic conditions present in the groundwater, and is in contrast to observations made in environments with a higher availability of organic carbon: In marine ecosystems, organic particles are first colonized by heterotrophic degraders, and autotrophs might join at a later stage (Datta et al., 2016 ). Similarly, in stream sediments, organic matter recycling and respiration often precedes primary production, but the combined activity of autotrophs and heterotrophs can make the system self-sufficient in terms of carbon, if energy is present (Battin et al., 2016 ; Weaver & Jones, 2022 ). As such, autotrophy-driven biofilms might also play a different role in global carbon cycles. The high abundance of 20% attached autotrophs in the groundwater suggests that these biofilms form a sink for carbon in the subsurface. Efforts to assess subsurface CO 2 fixation so far primarily focused on planktonic communities (Overholt et al., 2022; Hutchins et al., 2016 ; Hubalek et al., 2016; Ben Maamar et al., 2015 ), leaving a gap in the global balances. As current methods for assessment of CO 2 fixation rates are reliant on the extraction of sufficient biomass, they might not be easily adaptable to attached communities. However, given the observed twofold higher relative abundance of autotrophs in the attached compared to the planktonic fraction, as well as the often drastically higher number of attached cells (Magnabosco et al., 2018 ; Sharma et al., 2024), the carbon sequestration by groundwater ecosystems might be substantially higher than previously assumed. Beyond chemolithoautotrophy, the attached community also exhibited broader general capacities for redox transformations of sulfur, iron, and nitrogen. Genes related to dissimilatory sulfite and nitrate reduction, iron reduction, and ammonia oxidation were consistently more abundant in attached MAGs, indicating a capacity for utilizing mineral-derived electron donors and acceptors (Casar et al., 2021; Flynn et al., 2013 ; Melton et al., 2014). These capabilities suggest that attached microbes not only serve as primary producers but also contribute actively to long-term nutrient turnover and geochemical transformations at the rock-water interface. In contrast, in both the total and active planktonic community, the functions investigated were rare and scattered, being present in only 6.8% of MAGs on average. Reduced nitrogen compounds seemed to play the primary role for sustaining the lower fraction of planktonic autotrophs (Fig. 7 ). Originating from surface inputs and biomass recycling (Gesink et al., 2022; Herrmann et al., 2015 ), reduced nitrogen might be more available in the groundwater than rock-derived compounds. Overall, the scattered functions in the planktonic community imply that the central functions sustaining it are carried out by only a small fraction of the organisms present, or that it might rely on the activity of the attached community. When comparing activity and abundance, we found distinct patterns in the two communities. In the attached community, most of the abundant MAGs showed active replication and less abundant MAGs were less likely to do so. In contrast, in the planktonic community, active and inactive MAGs showed a more random distribution, with some phyla explicitly showing activity in less abundant MAGs. These patterns indicate a higher dynamic of activity/inactivity in planktonic communities, supporting the argument that attachment supports more stable conditions for microbial growth (Park et al., 2025 ; Patel et al., 2024 ; Wu et al., 2017). Key active organisms in the attached community, like Rhodoferax , Aquabacterium , Hydrogenophaga , and Undibacterium , have previously been reported as abundant not only in rock-attached (Sharma et al., 2024; Lazar et al., 2019 ), but also in endolithic communities in these carbonate aquifers (Wegner et al., 2023). They might hence be key contributors to the stability and resilience of the attached community over time, contrasting the more dynamic conditions in the planktonic communities. A striking feature was also the activity of MAGs from the Cand. Patescibacterial group JAEDAM01. This group appeared exclusively in the attached community, where it was the only group of Cand. Patescibacteria that showed active replication. JAEDAM01 represents a sister lineage of Cand. Gracilibacteria and are among the few Cand. Patescibacteria obtained in stable co-cultures with their hosts, showing a specialized, predatory lifestyle (Moreira et al., 2021 ; Yakimov et al., 2022). Their exclusive presence in the attached community indicate that they benefit from the proximity to other cells. In our experiments, we ensured a de novo colonization of the exposed rock material, excluding pre-existing biofilms. The attachment of compositionally and functionally distinct organisms from the co-occurring planktonic community hence raises a key ecological question: Where do these colonizing microbes originate, if they are not abundant in the groundwater? One possibility is that they derive from the rare biosphere, existing at low abundance in the planktonic phase and below detection thresholds (Yamamoto et al., 2019). Alternatively, microbes may disperse through episodic detachment from other rock surfaces within the aquifer (Atencio et al., 2025 ), representing a low-frequency surface-to-surface exchange rather than continuous seeding from the water phase. It is also conceivable that colonizers arise from unsampled microhabitats such as rock pores or interfacial mineral zones that are not captured by standard planktonic sampling methods (Casar et al., 2021; Mullin et al., 2020). Our findings suggest colonization by long-resident, ecologically specialized taxa that persist on mineral surfaces throughout the aquifer. These microbes appear to exhibit low dispersal potential, rarely entering the planktonic phase, and are adapted for surface-associated life, as evidenced by their enriched functional repertoire (e.g., biofilm formation, chemolithoautotrophy), larger genome sizes, and active replication. Together, these patterns point to a structured, lithic microbiome that operates largely independent of the free-living groundwater community, challenging assumptions of high connectivity and functional redundancy between microbial lifestyles in consolidated aquifers. This observed lifestyle-driven divergence may carry important evolutionary implications. In spatially structured environments such as consolidated aquifers, physical separation between microenvironments (rock pores, mineral surfaces, the water phase) can act as a barrier to gene flow, facilitating ecological and evolutionary differentiation (Whitaker et al., 2003). Our genome-resolved comparisons of closely related MAGs revealed substantial differences in genome size and metabolic functions, suggesting that consolidated aquifers host parallel microbial lineages undergoing independent evolutionary trajectories, rather driven by ecological selection than by dispersal. These patterns align with broader evidence that microbial populations in heterogeneous systems evolve along parallel, niche-specific trajectories shaped more by selection than dispersal (Martiny et al., 2015). The effect of attached vs. planktonic lifestyle as key determining factor for microbial communities, however, varies greatly across environments. A comparable dissimilarity to our results has been observed in low-porosity granite aquifers, where Proteobacteria dominate attached communities while Cand. Patescibacteria and Desulfobacterota were more abundant in planktonic fractions (Dopson et al., 2023 ). Similar low overlaps of around 10% of ASVs were also reported from marine sediments and from a recent study of Lake Erie (Robinson et al., 2025 ; Dang & Lovell, 2016 ). However, a study from lake Baikal found analogous taxa in biofilms and plankton (Parfenova, Gladkikh & Belykh 2013 ), and a study on several Swedish streams found three most common genera to account for 33–41% in biofilms and 13–21% of suspended communities (Besemer 2012). Also in the subsurface, in highly porous aquifers like sandstone and gravel, attached and planktonic communities tend to be similar: In sandstone aquifers, Proteobacteria and Bacteroidota were identified as the two most abundant groups, constituting up to 70% of both the attached and planktonic communities (Rizoulis et al., 2013 ). Similarly, Proteobacteria, Geothrix , Burkholderiales, and Desulfuromonadaceae were reported in both communities in sand-gravel aquifers (Flynn et al., 2013 ). Differences in porosity hence may contribute to microbial differentiation in groundwater: In high porosity aquifers, higher permeability and fluid flow lead to a more uniform nutrient distribution, preventing the establishment of a free-living community distinct from their attached counterparts. Hydrodynamic effects were previously suggested to also drive interactions between attached and planktonic communities in groundwater and stream ecosystems (Atencio et al., 2025 ; Pan et al., 2021; Smith et al. 2018). A recent study using in situ bioreactors in groundwater, conversely, found higher differences between biofilms and planktonic communities in shallow alluvial aquifers with low porosity, but more similarity in anoxic bedrock groundwater (Park et al., 2025 ), attributing this distinction to be related to redox conditions. It can therefore be assumed that more complex interactions between lithology, porosity and hydrodynamics, as well as redox conditions, drive the differentiation of attached and planktonic communities. In summary, our findings call for a reevaluation of the seed bank model across aquifer types. While it may explain microbial dispersal in porous, hydrologically connected sediments (Lennon & Jones, 2011; Nelson et al., 2021), it does not capture the dynamics of consolidated aquifers, where dispersal is limited and biofilm-based communities are stable and functionally distinct (Casar et al., 2021; Flynn et al., 2013 ). Given the distinction between attached and planktonic communities, a targeted evaluation of functions in attached aquifer microbiomes is crucial to assess their role in carbon sequestration and biogeochemical cycling on a global level. Understanding such rock-hosted systems will also require different conceptual frameworks that account for lithological structure, hydrodynamics, and physical separation of microenvironments (Griebler & Lueders, 2009 ; Martiny et al., 2006), as well as novel methodological approaches to resolve the mechanisms of microbial activity, adaptation and persistence in spatially structured subsurface environments. Declarations Availability of data and materials The raw metagenomic sequencing reads for the attached communities is available at NCBI under BioProject accession PRJNA1280030. All MAGs from the attached communities are available from Open Science Framework (OSF) repository: https://osf.io/ekadx/?view_only=b2317cc646de4ba2972319b5b9fec864. Previously published data of planktonic samples is available at NCBI via accession PRJEB36505. Acknowledgements We are grateful to Robert Lehmann, Falko Gutmann and Heiko Minkmar for assistance with field work, and sampling of groundwater. We also thank Stefan Riedel for the preparative work for the MiSeq amplicon sequencing of the 16S rRNA gene. Additionally, we thank Muriel Ritsch, Ivonne Görlich and Marco Groth from the Core Facility DNA sequencing of the Leibniz Institute on Aging - Fritz Lipmann Institute in Jena for their help with Illumina sequencing. Funding This work was supported financially by the Deutsche Forschungsgemeinschaft via the Collaborative Research Centre AquaDiva (CRC 1076 AquaDiva - Project-ID 218627073) of the Friedrich Schiller University Jena. Martin Taubert gratefully acknowledges funding by the DFG under Germany’s Excellence Strategy, EXC 2051–Project-ID 390713860. Climate chambers to conduct experiments under controlled temperature conditions and the infrastructure for Illumina MiSeq sequencing were financially supported by the Thüringer Ministerium für Wirtschaft, Wissenschaft und Digitale Gesellschaft (TMWWDG, project B 715-09075 and project 2016 FGI 0024 “BIODIV”). Author information Authors and Affiliations Aquatic Geomicrobiology, Institute of Biodiversity, Ecology and Evolution, Friedrich Schiller University Jena, Dornburger Strasse 159, 07743, Jena, Germany Alisha Sharma, Kirsten Küsel, Carl-Eric Wegner, Olga Maria Pérez Carrascal & Martin Taubert Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Grüne Aue, 07745, Jena, Germany Kirsten Küsel, Olga Maria Pérez Carrascal & Martin Taubert German Center for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstrasse 4, 04103, Leipzig, Germany Kirsten Küsel BIOMICS-Group, Heinrich Heine University, Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany Carl-Eric Wegner Contributions MT, KK and AS designed and conceptualized the study. AS carried out the lab work followed by bioinformatics analysis and data interpretation with the help of MT, CEW and OMPC. AS and MT wrote the manuscript. All authors discussed the results and implications and commented on the manuscript at all stages. Corresponding author Correspondence to Martin Taubert ( [email protected] ) Ethics declaration Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Additional information Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References Alneberg, J, et al. (2013). CONCOCT: Clustering contigs on coverage and composition. arXiv preprint arXiv:1312.4038. Aroney, ST, Newell, RJ, Nissen, JN, Camargo, A.P., Tyson, G.W. and Woodcroft, B.J., 2025. CoverM: Read alignment statistics for metagenomics. arXiv preprint arXiv:2501.11217. Atencio, B., Malavin, S., Rubin-Blum, M., Ram, R., Adar, E., & Ronen, Z. (2025). 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Wiley interdisciplinary reviews: computational statistics , 3 (2), 180–185. Wolters, N., & Schwartz, W. (1956). Investigations into the occurrence and behavior of microorganisms in pure groundwater. Woodcroft, B. J., Aroney, S. T., Zhao, R., Cunningham, M., Mitchell, J. A., Blackall, L., & Tyson, G. W. (2024). SingleM and Sandpiper: Robust microbial taxonomic profiles from metagenomic data. Biorxiv , 2024-01. Woodcroft, B. J., Singleton, C. M., Boyd, J. A., Evans, P. N., Emerson, J. B., Zayed,A. A., … Tyson, G. W. (2018). Genome-centric view of carbon processing in thawing permafrost. Nature, 560(7716), 49–54. Wu, X., Pedersen, K., Edlund, J., Eriksson, L., Åström, M., Andersson, A. F., … Dopson,M. (2017). Potential for hydrogen-oxidizing chemolithoautotrophic and diazotrophic populations to initiate biofilm formation in oligotrophic, deep terrestrial subsurface waters. Microbiome, 5, 1–13. Wu, Y. W., Simmons, B. A., & Singer, S. W. (2016). MaxBin 2.0: An automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics, 32(4), 605–607. Yakimov, M. M., Merkel, A. Y., Gaisin, V. A., Pilhofer, M., Messina, E., Hallsworth,J. E., … Gorlenko, V. M. (2022). Cultivation of a vampire:‘Candidatus Absconditicoccus praedator’. Environmental microbiology, 24(1), 30–49. Zhou, Z., Tran, P. Q., Breister, A. M., et al. (2022). METABOLIC: High-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks. Microbiome, 10, 33. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures.docx Supplementary information Additional file 1: Supplementary_Figures.docx, containing supplementary figures 1 - 3 SupplementaryTables.xlsx Additional file 2: Supplementary_Tables.xlsx, containing supplementary tables S1 - S7 Cite Share Download PDF Status: Published Journal Publication published 31 Jan, 2026 Read the published version in Microbiome → Version 1 posted Editorial decision: Revision requested 15 Sep, 2025 Reviews received at journal 14 Sep, 2025 Reviews received at journal 13 Sep, 2025 Reviews received at journal 29 Aug, 2025 Reviewers agreed at journal 15 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviewers invited by journal 13 Aug, 2025 Editor assigned by journal 05 Aug, 2025 Submission checks completed at journal 15 Jul, 2025 First submitted to journal 15 Jul, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7131340","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485811189,"identity":"41fbe9c4-18a7-444d-ab52-cefd41fa26ee","order_by":0,"name":"Alisha Sharma","email":"","orcid":"","institution":"Friedrich Schiller University Jena","correspondingAuthor":false,"prefix":"","firstName":"Alisha","middleName":"","lastName":"Sharma","suffix":""},{"id":485811190,"identity":"0459d81b-5167-41f6-9f0c-d3c591548d0e","order_by":1,"name":"Kirsten Küsel","email":"","orcid":"","institution":"Friedrich Schiller University Jena","correspondingAuthor":false,"prefix":"","firstName":"Kirsten","middleName":"","lastName":"Küsel","suffix":""},{"id":485811191,"identity":"5e09eb17-afd7-48a6-9428-03e1c6f19918","order_by":2,"name":"Carl-Eric Wegner","email":"","orcid":"","institution":"Friedrich Schiller University Jena","correspondingAuthor":false,"prefix":"","firstName":"Carl-Eric","middleName":"","lastName":"Wegner","suffix":""},{"id":485811192,"identity":"5382d35c-3881-4003-a63f-41932ab9f094","order_by":3,"name":"Olga Maria Pérez-Carrascal","email":"","orcid":"","institution":"Friedrich Schiller University Jena","correspondingAuthor":false,"prefix":"","firstName":"Olga","middleName":"Maria","lastName":"Pérez-Carrascal","suffix":""},{"id":485811193,"identity":"5d8d15db-91f6-43a0-aef0-045318a52e18","order_by":4,"name":"Martin Taubert","email":"data:image/png;base64,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","orcid":"","institution":"Friedrich Schiller University Jena","correspondingAuthor":true,"prefix":"","firstName":"Martin","middleName":"","lastName":"Taubert","suffix":""}],"badges":[],"createdAt":"2025-07-15 13:53:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7131340/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7131340/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40168-025-02325-1","type":"published","date":"2026-01-31T15:58:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86876607,"identity":"cb3a53a3-1b4b-4adf-8b4d-e607fea33f8f","added_by":"auto","created_at":"2025-07-16 15:31:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":424242,"visible":true,"origin":"","legend":"\u003cp\u003eDistinction of microbial community composition of attached and planktonic aquifer samples. (A) Principal Coordinate Analysis (PCoA) depicts groundwater (blue) and passive sampler material (orange) communities from groundwater wells under oxic (light shades) and anoxic (darker shades) conditions. Data is derived from 16S rRNA gene amplicon sequencing. (B) Passive sampler and crushed rock material (2-4 mm in diameter) used to study attached communities.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7131340/v1/a11fbdeecbf783cd06509c0c.png"},{"id":86876615,"identity":"c2d392a1-f184-4177-b622-a9e3943af748","added_by":"auto","created_at":"2025-07-16 15:31:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":510277,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial community composition of attached and planktonic communities based on metagenomic data. (A) Distribution of metagenome-assembled genomes (MAGs) across major phyla in attached (orange) and planktonic (blue) metagenomes. Mean relative abundance of (B) overall phyla and (C) CPR classes in the attached and planktonic communities based on metagenomic coverage of MAGs. For details see Supplementary Table S2 \u0026amp; S3.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7131340/v1/b2e8c2b1cfef8ad42079b69b.png"},{"id":86876305,"identity":"95eb96b4-fca5-42fc-a3e5-8ed2f018bf63","added_by":"auto","created_at":"2025-07-16 15:23:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":687069,"visible":true,"origin":"","legend":"\u003cp\u003eBiofilm-related functions in attached and planktonic MAGs. Comparative analysis of genetic potential for motility, attachment, quorum sensing, biofilm formation, and biofilm degradation enzymes between attached (orange, 605 MAGs) and planktonic (blue, 891 MAGs) microbial communities. Functions were considered present if at least 50% of the key genes were identified. For details, see Supplementary Table S4 \u0026amp; S5.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7131340/v1/246e743a7964a33c48204cc0.png"},{"id":86876320,"identity":"69299a97-a538-43db-bfb3-abe24875fbe0","added_by":"auto","created_at":"2025-07-16 15:23:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1496873,"visible":true,"origin":"","legend":"\u003cp\u003eFunctions associated with chemolithoautotrophic growth in attached and planktonic MAGs. (A) Relative abundance of putative autotrophs with different CO\u003csub\u003e2\u003c/sub\u003e fixation pathways in attached and planktonic communities, based on metagenomic coverage. MAGs were included when pathways were at least 50 % complete and the following key enzymes were present: RuBisCO for Calvin cycle, CODH for Wood Ljungdahl pathway, ATP citrate lyase for rTCA cycle, Pyruvate synthase and PEP carboxylase for DC-HB cycle. The most abundant taxa for each pathway for attached and planktonic communities are indicated. (B) Presence of CO\u003csub\u003e2\u003c/sub\u003e fixation pathways and genes associated with biogeochemical cycling (nitrogen, sulfur, and iron), based on DRAM and Metabolic-G analysis, across the attached (orange) and planktonic (blue) MAGs. Functions were considered present if at least 50 % of the required genes were found. For CO\u003csub\u003e2\u003c/sub\u003e fixation pathways, shades indicate completeness. (C) Percentage of MAGs from attached versus planktonic communities featuring transport functions for inorganic electron donors and acceptors as well as organics and other compounds. For details, see Supplementary table S4 \u0026amp; S5.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7131340/v1/e098c02a65f1eafd26821ab8.png"},{"id":86876608,"identity":"845e75a0-ad28-468b-844f-74e9863994d5","added_by":"auto","created_at":"2025-07-16 15:31:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":996007,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic and functional overview of actively replicating attached and planktonic MAGs based on iRep analysis. (A) Taxonomic comparison of active MAGs. Each square represents one MAG, with its size corresponding to its mean relative abundance based on metagenomic coverage. Colors indicate taxonomy on phylum level, and darker shades indicate actively replicating MAGs. Lower panels show a functional overview of active MAGs from attached (B) and planktonic (C) communities. Orange and blue circles show the percentage of MAGs associated with each functional category. Asterisks indicate functions that are significantly enriched in the respective community (linear model, p-value ≤ 0.05). For details, see Supplementary Table S6.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7131340/v1/675d9a34e8591c686f53d212.png"},{"id":86876609,"identity":"03b3dd2f-dfdf-4ce2-95d2-3577c6703ee2","added_by":"auto","created_at":"2025-07-16 15:31:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":562590,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional comparison of closely related MAGs. The heatmap displays functional contrasts between closely related attached (orange) and planktonic (blue) bacterial taxa, represented by high-quality metagenome-assembled genomes (MAGs) with ANI values \u0026gt; 90%. The columns correspond to the MAGs of respective taxa, while each row indicates the presence or absence of specific functions, with categories derived from DRAM and Metabolic-G analyses. For details, see Supplementary Table S4 \u0026amp; S5.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7131340/v1/2dfe28531b2abce15863a7d6.png"},{"id":86876303,"identity":"b84c832d-4503-4bfd-a9ac-673765f1bb9a","added_by":"auto","created_at":"2025-07-16 15:23:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":199262,"visible":true,"origin":"","legend":"\u003cp\u003eAttached versus planktonic microbial lifestyles in carbonate aquifers. In attached communities, microbes drive CO\u003csub\u003e2\u003c/sub\u003e fixation and EPS formation, harnessing energy from rock-derived minerals. Planktonic microbes rely on groundwater-dissolved nutrients as electron donors for CO\u003csub\u003e2\u003c/sub\u003e fixation, and heterotrophs might scavenge nutrients from EPS.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7131340/v1/552ae5ceb683a79482da7134.png"},{"id":101690457,"identity":"e6fcb450-6f06-41be-81f0-af4480978fa5","added_by":"auto","created_at":"2026-02-02 16:03:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5607447,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7131340/v1/4bb587d5-b2a4-432c-83ee-ea102ad9729c.pdf"},{"id":86876301,"identity":"041adfa2-b0f4-4bda-b7fb-040764c4b1bf","added_by":"auto","created_at":"2025-07-16 15:23:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":393714,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional file 1: Supplementary_Figures.docx, containing supplementary figures 1 - 3\u003c/p\u003e","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7131340/v1/ffa276eaa3823bf4fd223754.docx"},{"id":86876317,"identity":"b5e996e6-9d14-4bf0-96a6-dd51efccced2","added_by":"auto","created_at":"2025-07-16 15:23:47","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":706171,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Supplementary_Tables.xlsx, containing supplementary tables S1 - S7\u003c/p\u003e","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7131340/v1/6a26823648c3a4cd3f44800e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Two worlds beneath: Distinct microbial strategies of the rock-attached and planktonic subsurface biosphere","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEarth\u0026rsquo;s ecosystems host diverse microbial communities that exist in either planktonic or attached lifestyles (Wolters \u0026amp; Schwartz, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e1956\u003c/span\u003e; Griebler et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Flemming \u0026amp; Wuertz, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Planktonic microbes are free-floating in the water phase, while attached microbes adhere to solid surfaces like rocks, mineral grains or organic particles (Dang et al., 2016; Battin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Esterhues et al., 2014; Karwautz, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Research often focuses on planktonic communities due to ease of access and handling. However, biofilms actually represent the major lifestyle of prokaryotes on Earth (Flemming and Wuertz, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite this universality, the mechanisms behind biofilm formation can vary depending on the environmental conditions present. Different energy sources like organic matter, light, or inorganic compounds can drive the costly biofilm formation process (Dang et al., 2016; Battin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Flemming \u0026amp; Wingender \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Especially under the oligotrophic conditions in pristine groundwater, the mechanisms of biofilm formation and the functional traits of the attached community are not well understood.\u003c/p\u003e\u003cp\u003eMicrobial attachment to the aquifer rock surfaces may offer several benefits. It provides access to rock-derived minerals, which can serve as electron donors for energy generation (Karwautz, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This process is not only sustaining microbial growth but also drives essential biogeochemical processes like rock dissolution and mineral precipitation (Griebler \u0026amp; Lueders, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Flynn et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, biofilms formed on the rock surfaces can provide protection from environmental stressors and predators (Flemming \u0026amp; Wingender, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Stewart \u0026amp; Franklin, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Following an initial attachment and coordinated by intercellular communication via quorum sensing, microbes produce extracellular polymeric substances (EPS) that form the biofilm matrix. After establishment of the biofilm, dispersal can occur to colonize new surfaces, perpetuating the biofilm cycle (Kapellos et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Flemming and Wuertz, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the nutrient-limited groundwater, EPS furthermore represents a source of organic carbon for growth of heterotrophic microbes (Flemming \u0026amp; Wuertz, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Griebler et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), providing them an advantage compared to a planktonic lifestyle. These benefits might explain why attached communities contain a significantly larger proportion of the aquifer microbiome, outnumbering planktonic cells by at least three orders of magnitude, independent of lithology (Lehman et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Griebler et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Magnabosco et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sharma et al., 2024). Given this dominance, the attached communities likely are crucial for providing groundwater ecosystem functions like contaminant degradation and nutrient recycling (Griebler \u0026amp; Avramov, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Flynn et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Griebler \u0026amp; Lueders, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo gain insights into the processes driving the attached microbial community in a groundwater ecosystem, we focused on the carbonate aquifers within the Hainich Critical Zone Exploratory (CZE) in Thuringia, Germany (Kuesel et al., 2016). Previous bioreactor experiments with groundwater and rock material demonstrated rapid colonization of rock surfaces (Sharma et al., 2024). However, only a small subset of groundwater microbes attached in this artificial bioreactor setting, raising the question of whether such differentiation between attached and planktonic communities also occurs \u003cem\u003ein situ\u003c/em\u003e, and if so, whether it translates into distinct functional traits with consequences for subsurface ecosystem processes.\u003c/p\u003e\u003cp\u003eUsing metagenomics, we provide an in-depth characterization of the taxonomy and functional potential of attached microbes in comparison to their planktonic counterparts. We hypothesize that attached and planktonic communities harbor distinct taxonomic groups and functional traits that reflect their contrasting lifestyles. To test this, we sampled microbial communities using passive samplers filled with crushed limestone rock, deployed in oxic and anoxic groundwater wells for up to one year. The resulting metagenome-assembled genomes (MAGs) were compared with a comprehensive dataset of previously recovered planktonic MAGs (Chaudhari et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Overholt et al., 2022). By resolving the functional and ecological differentiation between attached and planktonic communities, our study addresses a major gap in subsurface microbial ecology and advances our understanding of how microbial lifestyles shape biogeochemical processes in carbonate aquifers.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003ePassive sampler setup and DNA extraction\u003c/p\u003e\u003cp\u003eCrushed rock materials with 2 to 4 mm diameter were prepared from limestone, specifically calcium mudstones of the Trochitenkalk formation, sourced from an outcrop located in the low-mountain ridge of the monitoring well transect within the Hainich Critical Zone Exploratory (CZE) in northwestern Thuringia, Germany. The wells access aquifer layers with distinct redox states: anoxic conditions in the upper Meissner Formation and oxic conditions (5.1 mg/L O₂) in the lower Trochitenkalk Formation. These differences reflect variations in surface connectivity, permeability, and flow dynamics of the sloping strata (Kohlhepp et al., 2017; Lehmann and Totsche, 2020). TOC levels remain below 2 mg/L throughout (Lehmann and Totsche, 2020). Four bags each of crushed rock material, enclosed in passive sampler casings (Hermann et al., 2017), were deployed in wells H41 (51.1150842 N, 10.4479713 E) and H52 (51.1193392 N, 10.4691776 E) in January 2022 for up to one year. Sampling occurred after 6, 18, 30, 42 and 54 weeks of exposure, with subsequent immediate freezing in dry ice and storage at -80\u0026deg;C. DNA was extracted using a phenol/chloroform-based protocol as previously described (Sharma et al., 2024). In brief, 10 g of passive sampler material was treated with 2.6 mL 2\u0026times; SET buffer, 30 \u0026micro;L PMSF, and 350 \u0026micro;L 10% SDS, incubated at 55\u0026deg;C for 2 h with shaking, then centrifuged (4000 \u0026times;g, 10 min). The supernatant was extracted with phenol:chloroform:isoamyl alcohol (25:24:1), followed by chloroform:isoamyl alcohol (24:1) extraction. DNA was precipitated overnight with glycogen and ethanol, pelleted (4000 \u0026times;g, 30 min), washed with 80% ethanol, air-dried, and resuspended in 50 \u0026micro;L TE buffer.\u003c/p\u003e\u003cp\u003eMicrobial community profiling\u003c/p\u003e\u003cp\u003eTo select samples for metagenomic sequencing, first an amplicon-sequencing-based screening targeting bacterial 16S rRNA genes was conducted. Amplicon libraries were prepared using a two-step barcoding strategy as described by Kr\u0026uuml;ger et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The first PCR employed primer pair S-D-Bact-0341-b-S-17/S-D-Bact-0785-a-A-21, which amplify the V3\u0026ndash;V4 region of the 16S rRNA gene (Herlemann et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Klindworth et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), each modified with Illumina adaptor overhangs to enable subsequent indexing. Reactions (15 \u0026micro;L) were run in triplicate using 1\u0026ndash;5 ng of template DNA, 0.4 \u0026micro;M of each primer, 1 \u0026micro;g/\u0026micro;L BSA, and 2\u0026times; HotStartTaq Master Mix (Qiagen, Germany). Thermal cycling included initial denaturation at 95\u0026deg;C for 15 min, followed by 25\u0026ndash;30 cycles of 94\u0026deg;C for 45 s, 55\u0026deg;C for 45 s, 72\u0026deg;C for 45 s, and a final extension at 72\u0026deg;C for 10 min. Amplicons were barcoded in a second PCR as previously described (Kr\u0026uuml;ger et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), using 1 \u0026micro;L of first-round product, 0.5 \u0026micro;M of each barcoding primer, and 2\u0026times; Ruby Taq Master Mix (Jena Bioscience, Germany). This step involved 6 cycles of 95\u0026deg;C for 45 s, 55\u0026deg;C for 45 s, and 72\u0026deg;C for 45 s. PCR products were checked for size and integrity via agarose gel electrophoresis. Sequencing was performed on an Illumina MiSeq platform (Illumina, Eindhoven, The Netherlands) with v3 chemistry in paired end mode (2 \u0026times; 300 bp). Sequence reads were combined with previously published amplicon datasets from the planktonic groundwater community of the Hainich CZE (0.2 \u0026micro;m filter fraction), covering a time series from January 2022 to January 2023 (Wang et al., 2025). Data analysis was performed with the DADA2 (Divisive Amplicon Denoising Algorithm 2) pipeline (v1.22.0) using default settings following the core DADA2 algorithm using R software (v4.2.1) (Callahan et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, R Core Team, 2021). The Vegan software package (v2.6-2) was used to analyze bacterial community patterns (Oksanen et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Permutational multivariate analysis of variance (PERMANOVA), implemented via the adonis function (Li et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), was used to assess differences in community composition between attached and planktonic communities. The ggplot2 software package (v3.3.6) was used for graphical representation of the data (Wickham, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMetagenomic sequencing\u003c/p\u003e\u003cp\u003eBased on results from amplicon sequencing, DNA from passive sampler material at 6, 18, and 30 weeks from both wells (10 samples) were selected for metagenomic sequencing. Additionally, to improve the recovery of microbial genomes, metagenomic sequencing was performed on samples from a bioreactor experiment described in Sharma et al., 2024 (10 samples, incubation times 4 to 44 days). The quality of extracted DNA was determined on a 4200 TapeStation System (Agilent, Santa Clara, CA, USA) using the Genomic DNA ScreenTape (Agilent). Paired-end libraries were generated with an NEBNext Ultra II FS DNA preparation kit (New England Biolabs, Ipswich, MA, USA), and sequenced (2\u0026times;150 bp) on an Illumina NovaSeq 6000 system (Illumina, San Diego, CA, USA) in Fritz Lipmann Institute, Jena, Germany. Yields of metagenomic sequencing per sample ranged from 20.5 to 35.4 Gbp (mean\u0026thinsp;=\u0026thinsp;25.7 Gbp) (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMetagenomic assembly, binning and refinement for attached communities\u003c/p\u003e\u003cp\u003eThe reads obtained from metagenomic sequencing were subjected to quality control using BBDuk (v39.01) (Dehasque et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with the following parameters: statscolumns\u0026thinsp;=\u0026thinsp;5, ktrim\u0026thinsp;=\u0026thinsp;r, qtrim\u0026thinsp;=\u0026thinsp;rl, trimq\u0026thinsp;=\u0026thinsp;20, minlen\u0026thinsp;=\u0026thinsp;50, k\u0026thinsp;=\u0026thinsp;23, mink\u0026thinsp;=\u0026thinsp;11, and hdist\u0026thinsp;=\u0026thinsp;1. The quality-controlled reads of each individual sample were assembled using metaSPAdes (v3.15.2) (Nurk et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) with kmer sizes 21, 33 and 55. Scaffolds larger than 1 kb were used for downstream analyses. Genome binning was carried out using five binning algorithms: Abawaca (v.1.0.7) (Brown et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), metabat2 (v2.12.1) (Kang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), CONCOCT (v.1.0.0) (Alneberg et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), BinSanity (v.0.2.7) (Graham et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and maxbin2 (v.2.2.6) (Wu et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) with default parameters. Both the 40 and 107 marker gene sets were utilized in MaxBin2. BinSanity and Abawaca were used to generate bins using contigs of 3000 bp and above. For Abawaca binning, tetranucleotide frequencies were calculated from contigs with a minimum size of 5,000 bp and 10,000 bp. The generated bins were subsequently refined using metaWRAP (v1.3.2) (Uritskiy et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), with \u0026ge;\u0026thinsp;50% completeness and \u0026le;\u0026thinsp;10% contamination, representing described thresholds for medium quality bins (Bowers et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Bins were de-replicated using dRep (v3.4.0) (Olm et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) at 99% average nucleotide identity (ANI) at secondary clustering to remove strain-level redundancy across samples, resulting in 631 representative metagenome-assembled-genomes (MAGs) of the rock-attached communities. From these MAGs, we excluded 26 MAGs that were exclusively present in bioreactor samples to avoid the introduction of strains from the laboratory setting, leading to a final set of 605 MAGs from the attached community.\u003c/p\u003e\u003cp\u003eSelection of planktonic groundwater MAGs for comparative analysis\u003c/p\u003e\u003cp\u003eTo compare the obtained MAGs from the rock-attached microbiome to planktonic MAGs from the same ecosystem, a dataset of previously recovered Hainich groundwater MAGs based on metagenomic sequencing of 12 samples from January 2019, including wells H14, H32, H41, H43, H51 and H52, was employed (Chaudhari et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Overholt et al., 2022). From this dataset, a total of 891 MAGs, dereplicated with the same\u0026thinsp;\u0026ge;\u0026thinsp;50% completeness and \u0026le;\u0026thinsp;10% contamination threshold as the attached MAGs, were selected, as well as quality-filtered metagenomic reads from six samples of the 0.2 \u0026micro;m filter fraction of the groundwater, which contained the majority of the groundwater microbiome. These planktonic MAGs were analyzed in parallel to the attached MAGs as described in the following sections.\u003c/p\u003e\u003cp\u003eAssessment of MAG quality metrics and genome statistics\u003c/p\u003e\u003cp\u003eThe quality (completeness and contamination/redundancy) of the 605 attached MAGs and the 891 planktonic MAGs was calculated based on domain-level bacterial/archaeal specific single copy marker gene sets using the checkM workflow (v.1.2.2) (Parks et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), with parameters --min-covered-fraction 0 and --methods mean. For MAGs affiliated with the Candidate Phyla Radiation (CPR), a CPR-specific single copy marker gene set was used. Corrected genome sizes of the MAGs were calculated by dividing by the number of marker gene sets present and multiplying with the expected number of marker gene sets based on the checkM analysis. The Wilcoxon signed-rank test was used to determine differences between sizes of attached and planktonic MAGs on phylum level, as for lower taxonomic levels, the number of MAGs would have been too low for meaningful comparisons.\u003c/p\u003e\u003cp\u003eDetermination of MAG taxonomy, abundance and functional potential\u003c/p\u003e\u003cp\u003eThe MAGs were taxonomically classified using GTDB-Tk (v2.3.0; Chaumeil et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with the Genome Taxonomy Database (release 214) as reference. Furthermore, Kaiju (v.1.9.0) (Menzel et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) with the database nr_euk was used for taxonomic classification of metagenomic datasets on read level. To estimate the relative abundances of each MAG in the sampled communities, first genome coverages were calculated based on quality-filtered metagenomic data with coverM (v.0.6.1) (Aroney et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) in genome mode (parameters: --coupled --min-covered-fraction 0 --methods mean). Relative abundances were then calculated by dividing each MAG\u0026rsquo;s coverage by the total coverage of all genomes in the respective dataset (attached or planktonic), as previously described (Woodcroft et al., 2018). In addition, SingleM (v0.13.2) (Woodcroft et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) was used to determine abundances of taxa not covered on MAG level directly from metagenomic quality-filtered reads. SingleM identifies and classifies microbial taxa by detecting reads of single-copy marker genes.\u003c/p\u003e\u003cp\u003eFunctional annotation focusing on biofilm-related functions, biogeochemical cycling, and nutrient transporters, using pre-defined functional categories and individual genes derived from KEGG orthology, was done using DRAM (v.1.4.6) (Shaffer et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Metabolic-G (v.4.0) (Zhou et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with default settings. For flagella, three key genes responsible for flagellar motor switch protein (FlgG, FliM and FliN/Y) were considered. For functions linked to iron oxidation and reduction, gene clusters defined by the FeGenie database (v1.2) (Garber et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) were used in conjunction with Metabolic-G. A function/pathway was considered present if more than 50% of the genes associated with that particular function were identified. For CO\u003csub\u003e2\u003c/sub\u003e fixation pathways, in addition to this cutoff, the presence or absence of key enzymes in the pathway, e.g., ribulose bisphosphate carboxylase/oxygenase (RuBisCO) for Calvin cycle, carbon monoxide dehydrogenase (CODH) for Wood Ljungdahl pathway, ATP citrate lyase for reverse tricarboxylic acid cycle, was also required.\u003c/p\u003e\u003cp\u003eDetermining active MAGs based on replication indices\u003c/p\u003e\u003cp\u003eTo identify actively replicating MAGs, first quality-filtered reads were mapped to MAGs using bowtie2 (v2.4.4) (Langmead et al., 2012). Mapping files were sorted and indexed via samtools (v1.13) (Danecek et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Subsequently, indices of replication were determined based on the sequencing coverage trend resulting from bi-directional genome replication from a single origin of replication using iRep (v1.10) (Brown et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Indices were only determined for MAGs with \u0026gt;\u0026thinsp;75% completeness and \u0026lt;\u0026thinsp;2% contamination, as previously suggested (Brown et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A MAG was considered to be active when at least in one metagenomic sample of the respective dataset, R\u003csup\u003e2\u003c/sup\u003e values calculated between the coverage trend and the linear regression were \u0026ge;\u0026thinsp;0.9, and reads were covering\u0026thinsp;\u0026ge;\u0026thinsp;98% of the respective MAG with at least 5x coverage (Brown et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To identify functions enriched in active MAGs, ordinary least squares linear models fitted with the lm() function in R (v4.2.1) (R Core Team, 2021) were used to assess the correlation between functional categories and MAG activity. Functional categories with positive correlations and p-values below 0.05 were considered as significant predictors of microbial activity.\u003c/p\u003e\u003cp\u003eComparison of shared taxa in attached and planktonic communities\u003c/p\u003e\u003cp\u003eMAGs that were present in both attached and planktonic communities were selected to investigate the adaptation mechanisms that facilitate their survival across lifestyles. For this, first microbial taxa present in both attached and planktonic communities were counted, revealing a total of 27 genera shared between 100 attached and 134 planktonic MAGs. Of these shared taxa, high-quality MAGs (\u0026ge;\u0026thinsp;50% completeness and \u0026le;\u0026thinsp;10% contamination) were selected to calculate pairwise average nucleotide identities using fastANI (v1.33) (Jain et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Matches between planktonic and attached MAGs with \u0026gt;\u0026thinsp;90% ANI, a threshold chosen to enable genus-level or low-divergence comparisons (Konstantinidis \u0026amp; Tiedje, 2005) were selected for comparative analysis of their functional potential and genome sizes. A fold change in genome size was calculated between matched pairs of attached and planktonic MAGs. A Wilcoxon signed-rank test was then applied to evaluate whether these fold changes across all pairs were significantly different from zero.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eAttachment is the primary determinant of groundwater microbial community composition\u003c/p\u003e\u003cp\u003eWhen comparing microbial communities from groundwater and passive samplers of the same aquifers based on 16S rRNA gene amplicon data, the attachment preference (attached vs. planktonic) explained the largest proportion of variance (11%) in community composition (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.11, F value\u0026thinsp;=\u0026thinsp;5.22) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The presence of oxygen explained 8% of variance (R\u0026sup2; = 0.08, F value\u0026thinsp;=\u0026thinsp;3.99) and the time point of sampling explained 5% of variance (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.05, F value\u0026thinsp;=\u0026thinsp;2.30). These patterns were also evident in the 16S rRNA gene taxonomic profiles of the communities (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Hence, even with the strong differences in hydrochemical conditions across the Hainich CZE, covering oxic as well as anoxic groundwater, attachment preference is the strongest driver for community composition. This justified a comparison of MAG datasets acquired at different sampling times (2019 for planktonic communities, 2022 for attached communities) to elucidate the differences between these communities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAttached and planktonic communities show drastic taxonomic differences\u003c/p\u003e\u003cp\u003eDistinct community composition was also observed in metagenomic datasets of attached versus planktonic samples of the Hainich CZE groundwater. In the attached communities, Proteobacteria contributed 358 of the total of 605 MAGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In terms of relative abundance based on metagenomic coverage, these accounted for 60.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1% (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;st. dev.) of the attached community (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In contrast, Proteobacteria represented only 9.62\u0026thinsp;\u0026plusmn;\u0026thinsp;5.64% relative abundance in the planktonic community, with 52 MAGs. Instead, 465 of the 891 MAGs in the planktonic community were affiliated with \u003cem\u003eCand.\u003c/em\u003e Patescibacteria, with 39.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8% relative abundance. In the attached community, \u003cem\u003eCand.\u003c/em\u003e Patescibacteria accounted for only 10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6% relative abundance with 30 MAGs. The planktonic community also featured 102 archaeal MAGs, and 5.5% of the sequencing reads were classified as archaeal (Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). From the attached community, only 2 archaeal MAGs were recovered, with 0.48% of reads being archaeal.\u003c/p\u003e\u003cp\u003eThe dominant Proteobacteria in the attached communities featured highest abundances of genera like \u003cem\u003eRhodoferax\u003c/em\u003e (8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7%), \u003cem\u003eAquabacterium\u003c/em\u003e (6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1%), \u003cem\u003eHydrogenophaga\u003c/em\u003e (5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6%), and \u003cem\u003eUndibacterium\u003c/em\u003e (4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0%). In planktonic communities, only two proteobacterial genera occurred at noteworthy proportions: \u003cem\u003ePhenylobacterium\u003c/em\u003e (2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4%, 17 MAGs) and \u003cem\u003eNitrosomonas\u003c/em\u003e (0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4%). As the most abundant group in the planktonic community, \u003cem\u003eCand.\u003c/em\u003e Patescibacteria were dominated by \u003cem\u003eCand.\u003c/em\u003e Paceibacteria (19.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3%, 295 MAGs), ABY1 (4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2%, 65 MAGs), and \u003cem\u003eCand.\u003c/em\u003e Microgenomatia (3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1%, 61 MAGs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In attached communities, four classes were present, \u003cem\u003eCand.\u003c/em\u003e Saccharimonadia (4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5%, 3 MAGs), JAEDAM01 (3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5%, 6 MAGs), \u003cem\u003eCand.\u003c/em\u003e Paceibacteria (2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4%, 17 MAGs), and ABY1 (0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3%, 4 MAGs). In particular, in planktonic communities no MAG of class JAEDAM01 was observed, and this group had less than 0.01% read coverage, making it specific to the attached communities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAttached microbes feature larger genomes and widespread biofilm-related functions\u003c/p\u003e\u003cp\u003eWe found an attached lifestyle to be reflected by larger genomes. Significantly higher sizes of attached compared to planktonic MAGs were observed for the phyla Nitrospirota (p-value\u0026thinsp;=\u0026thinsp;1.0 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), Proteobacteria (p-value\u0026thinsp;=\u0026thinsp;2.1 x 10\u003csup\u003e\u0026minus;\u0026thinsp;15\u003c/sup\u003e), and Bacteroidota (p-value\u0026thinsp;=\u0026thinsp;1.6 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) (Supplementary Figure S3). The mean genome size of Proteobacteria in the attached fraction was 4.48 Mb, compared to 1.80 Mb in the planktonic fraction, corresponding to a 2.49-fold difference. Bacteroidota genomes were 1.87-fold larger (3.89 Mb vs. 2.08 Mb), and Nitrospirota showed a 1.15-fold difference (3.31 Mb vs. 2.88 Mb). This higher genome size also coincided with an increase in the organisms\u0026rsquo; functional potential, in particular considering biofilm-related functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Attachment functions were encoded in up to 80% of attached MAGs: Pili were mostly present in Gammaproteobacteria, while Nitrospirota featured type 1 and type 3 secretion systems and Alphaproteobacteria showed a greater variety of these attachment genes. Only 8.2% of planktonic MAGs featured attachment genes, primarily for type 3 secretion systems. For formation of the biofilm matrix, various pathways were present in 98.7% of the attached MAGs: The Raetz pathway was primarily found in Gammaproteobacteria, while Nitrospirota featured Vibrio-type polysaccharide (VPS) biosynthesis. In planktonic communities, only 25.4% of the MAGs, with representatives of Nitrospirota, Proteobacteria and Planctomycetota, featured such biofilm formation genes. Among \u003cem\u003eCand.\u003c/em\u003e Patescibacteria, only class JAEDAM01 featured biofilm related functions, including pili, quorum sensing, VPS, and the Raetz pathway. Furthermore, diverse genes related to degradation of biofilms were present in nearly 65% of attached MAGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Some of these genes, primarily for glucosidases, were also present in 32% of planktonic MAGs, representing the only biofilm-related function prevalent in the planktonic microbes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDistinct CO\u003csub\u003e2\u003c/sub\u003e fixation pathways were prevalent in attached vs. planktonic communities\u003c/p\u003e\u003cp\u003eStrikingly, we found a substantially higher fraction of putative chemolithoautotrophs in the attached than in the planktonic community. A MAG was considered to be putatively autotrophic if a CO\u003csub\u003e2\u003c/sub\u003e fixation pathway was more than 50% complete and the respective key enzyme was present. These criteria were fulfilled by 15.7% of attached MAGs but only 6.6% of planktonic MAGs. Putative autotrophs accounted for a significantly higher relative abundance of 20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93% (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;st.dev) of the attached community compared to 12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55% in the planktonic community (Mann\u0026ndash;Whitney U test, p\u0026thinsp;=\u0026thinsp;0.0448) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The distribution of pathways was distinctly different: The Calvin cycle was by far the most prevalent CO\u003csub\u003e2\u003c/sub\u003e fixation pathway in the attached community, with 18.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02% of relative abundance. It was primarily found in the abundant proteobacterial genera like \u003cem\u003eRhodoferax\u003c/em\u003e, \u003cem\u003eAquabacterium\u003c/em\u003e and \u003cem\u003eUndibacterium\u003c/em\u003e. In contrast, only 2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12% of the planktonic community featured this pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Instead, the Wood-Ljungdahl pathway, with 4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62%, and the Arnon-Buchanan cycle, with 4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91%, had the highest relative abundances in the planktonic community. These pathways primarily occurred in MAGs affiliated with Nitrospirota and Omnitrophota.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHigher abundance of genes associated with other biogeochemical cycles in attached communities\u003c/p\u003e\u003cp\u003eThe metabolic potential for reduction and oxidation of inorganic compounds (Fe, S, N), essential for microbial energy acquisition in groundwater, was generally more widespread in attached than planktonic MAGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The \u003cem\u003ecyc1\u003c/em\u003e gene for iron oxidation was found in 42% of attached MAGs (mainly Gammaproteobacteria), compared to only 6% in planktonic MAGs. Iron reduction genes (\u003cem\u003emtrBC\u003c/em\u003e, \u003cem\u003edmkAB\u003c/em\u003e, and cytochromes DFE_0448\u0026ndash;0451 and DFE_0461\u0026ndash;0465) were more widespread, found in 91% of attached MAG and 61% of planktonic MAGs. The \u003cem\u003esox\u003c/em\u003e cluster for sulfur oxidation was likewise present in 38% attached MAGs mainly Gammaproteobacteria and Myxococcota, but only 5% of planktonic MAGs including Nitrospirota and Chloroflexota. The \u003cem\u003esat\u003c/em\u003e-\u003cem\u003eapr\u003c/em\u003e-\u003cem\u003edsr\u003c/em\u003e system, linked to both dissimilatory sulfate reduction and sulfur oxidation, was found in comparable percentages of MAGs, in 10.5% of attached and 8.6% of planktonic MAGs.\u003c/p\u003e\u003cp\u003eFor nitrogen metabolism, primarily reductive pathways were found, mostly in Proteobacteria and Nitrospirota of both communities. Genes specific for denitrification (\u003cem\u003enapAB\u003c/em\u003e, \u003cem\u003enirK\u003c/em\u003e/\u003cem\u003enirS\u003c/em\u003e, \u003cem\u003enorBC\u003c/em\u003e, \u003cem\u003enosZ\u003c/em\u003e) were found in up to 39% of attached MAGs and 11% of planktonic MAGs. The \u003cem\u003enirBD\u003c/em\u003e or \u003cem\u003enrfAH\u003c/em\u003e genes specific for dissimilatory nitrate reduction were present in 11.4% of attached MAGs and 2.6% of planktonic MAGs. In contrast, nitrification-specific genes (\u003cem\u003eamoCAB, hao\u003c/em\u003e) were found in similarly low numbers in both attached (5.6%) and planktonic MAGs (5.1%).\u003c/p\u003e\u003cp\u003eThe most abundant attached genera, like \u003cem\u003eAquabacterium\u003c/em\u003e, \u003cem\u003eRhodoferax\u003c/em\u003e and \u003cem\u003eUndibacterium\u003c/em\u003e, possessed genes for both iron and/or sulfur oxidation as well as denitrification. While a comparable number of attached and planktonic autotrophs harbored genes for nitrite oxidation (47% and 42%, respectively) and ammonia (14% and 12%) oxidation, a significantly higher proportion of attached autotrophs possessed genes for sulfur oxidation (73% vs. 28%) and iron oxidation (64% vs. 10%) than planktonic autotrophs. This provided evidence that sulfur and iron oxidation played a more important role in fueling autotrophy in the attached community.\u003c/p\u003e\u003cp\u003eNutrient transporters were more abundant in attached microbial community\u003c/p\u003e\u003cp\u003eTransporters for uptake of inorganic electron donors and acceptors, were likewise more widespread in the attached community: Iron transporters were present in 90% of attached but only 24% of planktonic MAGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Transporters for sulfate and reduced sulfur compounds were present in 55% of attached but only 11% of planktonic MAGs. The distribution of transporters for nitrogen compounds was less skewed, with 27% attached and 13% of planktonic MAGs encoding them. Likewise, transporters responsible for the uptake of simple and complex sugars, such as arabinosaccharide, glucose, and mannose, were present in both attached (17%) and planktonic (10%) MAGs.\u003c/p\u003e\u003cp\u003eActive replication in a higher portion of planktonic vs. attached MAGs\u003c/p\u003e\u003cp\u003eTo identify actively growing taxa and determine which metabolic functions correlate with activity, we calculated indices of replication (iRep) as previously described (Brown et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Overall, the active attached community consists of fewer but more abundant MAGs compared to the planktonic community and possessed a broad functional repertoire to thrive in biofilms. A higher proportion of planktonic MAGs (42%, 373 MAGs) compared to attached MAGs (25%, 151 MAGs) featured iRep values indicating growth. In the attached community, the most abundant MAGs were active (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In comparison, in the planktonic community, a large proportion of the active MAGs were of low abundance. As a result, in terms of relative abundance a higher proportion of the attached community (57%) compared to the planktonic community (38%) was active. The active attached taxa included the proteobacterial key players such as \u003cem\u003eAquabacterium\u003c/em\u003e, \u003cem\u003eRhodoferax\u003c/em\u003e and \u003cem\u003eUndibacterium\u003c/em\u003e, as well as the most abundant Actinobacteria, Myxococcota, Nitrospirota and Verrucomicrobiota MAGs. Contrastingly, in the planktonic community, the diverse \u003cem\u003eCand.\u003c/em\u003e Patescibacteria made up 61% of the active MAGs, while in the attached community, only \u003cem\u003eCand.\u003c/em\u003e Patescibacteria MAGs of group JAEDAM01 were active.\u003c/p\u003e\u003cp\u003eMore than 50% of the active attached MAGs exhibited functions related to biofilm formation and degradation, iron cycling, and denitrification (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Attached MAGs with genes for the Raetz pathway for EPS biosynthesis, the CBB cycle, denitrification, sulfate reduction, and F-type ATPases were active significantly more often than MAGs without these functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). In contrast, biofilm-related functions were present in less than 15% of active planktonic MAGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Only genes for iron and nitrate reduction, glucosidases, and F-type ATPases were more common, and MAGs with genes for EPS degradation, dinitrogen fixation, and iron reduction were significantly more likely to be active. Additionally, in both communities the WL pathway and rTCA cycle were positively correlated with activity, indicating that both attached and planktonic MAGs with the capability to fix CO\u003csub\u003e2\u003c/sub\u003e were active significantly more often.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOverlapping taxa exhibited functional differences\u003c/p\u003e\u003cp\u003eTo determine whether taxa present in both attached and planktonic communities represent microbes transitioning through the planktonic state to colonize new surfaces, we compared the genomic functions of these overlapping taxa. We found that only 27 genera (~\u0026thinsp;7% of the total genera) were shared. These taxa were mostly affiliated with Proteobacteria, Nitrospirota, and Bacteroidota. Of these, only seven pairs of MAGs featured average nucleotide identity (ANI) values above 90%, indicating close relatedness. All attached MAGs exhibited a broad range of functions for flagellar biosynthesis and chemotaxis, as well as biofilm formation and quorum sensing (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In contrast, these functions were mostly absent in planktonic MAGs. Genes for biofilm degradation, sugar uptake and other transport systems were likewise more abundant in the attached compared to the planktonic MAGs.\u003c/p\u003e\u003cp\u003eFunctions for oxidation and reduction of iron, nitrogen and sulfur compounds showed strong differences in their distribution in the closely related attached and planktonic MAGs. For example, the planktonic Bacteroidota MAG contained nitrification genes that were missing from its attached counterpart, and the attached Nitrospirota 9FT-COMBO.42.15 contained nitrogen cycling genes absent in its planktonic relative. Attached MAGs had significantly larger genome sizes (1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31 times, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;st. dev.) than planktonic MAGs (p-value\u0026thinsp;=\u0026thinsp;0.01073, Wilcoxon signed-rank test) despite comparable completeness above 90% (Supplementary Table S7). Thus, although being closely related, the overlapping taxa showed lifestyle-specific differences.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eUnderstanding the ecological principles driving the life of groundwater microbial communities is crucial for unraveling biogeochemical processes in the subsurface. Our results show that microbial lifestyle, i.e., the preference for attached or planktonic growth, is the strongest determinant of community structure in carbonate rock aquifers. Even severe differences in the hydrochemical conditions, such as redox gradients from oxic to anoxic settings, have less strong effects on the microbial communities. These findings are surprising in light of prior work emphasizing hydrochemistry and redox gradients as the primary drivers of groundwater community structure (Griebler \u0026amp; Lueders, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Flynn et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and suggest that a paradigm shift towards a focus on microbial lifestyles (attached vs. planktonic) is necessary for a better understanding of groundwater microbial ecology.\u003c/p\u003e\u003cp\u003eMany groundwater studies have interpreted microbial community patterns through the lens of the seed bank hypothesis, which posits that the planktonic community acts as a reservoir of dormant or low-abundance taxa capable of colonizing surfaces when conditions permit. (Yamamoto et al., 2019, Lennon \u0026amp; Jones, 2011). This concept is supported in porous, unconsolidated aquifers, where dynamic exchange between water and sediment allows frequent microbial dispersal (Atencio et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Coyte et al., 2016; Lennon \u0026amp; Jones, 2011). However, our genome-resolved analysis of a consolidated carbonate aquifer reveals a fundamentally different picture. Attached and planktonic communities were not only taxonomically distinct, but functionally segregated, with minimal overlap at the MAG level. Even these closely related taxa (with \u0026gt;\u0026thinsp;90% ANI) exhibited pronounced differences in genome size and metabolic potential depending on lifestyle, including biofilm formation, redox metabolism, and environmental sensing. These findings suggest that in fractured rock aquifers, biofilms are not seeded from the planktonic microbiome, but rather form functionally distinct and relatively isolated communities due to selective and stable ecological filtering. Our results thus challenge the broad applicability of the seed bank model in groundwater microbiology, and call for revised conceptual frameworks that recognize the limited connectivity and strong functional divergence between lifestyles in consolidated aquifers.\u003c/p\u003e\u003cp\u003eThe functional adaptations for biofilm formation observed in the attached community are widespread in the bacterial domain. Traits including adhesion functions and EPS production are distributed throughout taxa and environments (Besemer et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Battin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Flemming and Wuertz, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gopalakrishnappa et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lennert et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This prevalence aligns with the general tendency of microorganisms to colonize surfaces and live in biofilms, from ecosystems in the natural environment to host- and disease-associated communities (Niederdorfer et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Flemming and Wurtz; Hall-Stoodley et al., 2004). The presence of biofilm-associated genes, together with larger genome sizes and metabolic versatility, positions attached microbes as functionally rich and ecologically stable anchors of the subsurface ecosystem. These biofilms are not passive but can influence mineral weathering and nutrient fluxes, reflecting the capacity of these communities to interact with and modify their local geochemical environment, acting as specialized ecosystem engineers (Nuppunen-Puputti et al., 2022; Mullin et al., 2020; Flynn et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBiofilm formation by the attached organisms seems mainly driven by chemolithoautotrophic growth, based on CO\u003csub\u003e2\u003c/sub\u003e fixation via the CBB cycle and oxidation of sulfur and iron compounds (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Reduced sulfur and iron are available to attached microbes from minerals on the rock surfaces (Dong et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jones \u0026amp; Bennett, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and their release might be promoted by microbially mediated dissolution processes (Jones \u0026amp; Santini, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bice et al., 2025). Attached key species like \u003cem\u003eRhodoferax\u003c/em\u003e and \u003cem\u003eUndibacterium\u003c/em\u003e are known for chemolithoautotrophic growth on these electron donors (Kato and Ohkuma, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; G\u0026uuml;lay et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The sticky EPS matrix of the biofilms allows cells to remain in close proximity, promoting efficient nutrient exchange and uptake while reducing loss through groundwater flow (Stewart and Franklin, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Flemming and Wingender, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), thus offering various advantages in the oligotrophic conditions of the aquifer. The high abundance of genes for biofilm degradation furthermore implies that for subsequent heterotrophic colonizers, EPS can act as a source of organic carbon. Such genes for degradative enzymes were significant predictors of activity in planktonic MAGs as well, indicating that scavenging of dissolved biofilm material might play an important role also for the planktonic community (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe autotrophically-driven nature of the aquifer biofilms is likely linked to the oligotrophic conditions present in the groundwater, and is in contrast to observations made in environments with a higher availability of organic carbon: In marine ecosystems, organic particles are first colonized by heterotrophic degraders, and autotrophs might join at a later stage (Datta et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, in stream sediments, organic matter recycling and respiration often precedes primary production, but the combined activity of autotrophs and heterotrophs can make the system self-sufficient in terms of carbon, if energy is present (Battin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Weaver \u0026amp; Jones, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As such, autotrophy-driven biofilms might also play a different role in global carbon cycles. The high abundance of 20% attached autotrophs in the groundwater suggests that these biofilms form a sink for carbon in the subsurface. Efforts to assess subsurface CO\u003csub\u003e2\u003c/sub\u003e fixation so far primarily focused on planktonic communities (Overholt et al., 2022; Hutchins et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hubalek et al., 2016; Ben Maamar et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), leaving a gap in the global balances. As current methods for assessment of CO\u003csub\u003e2\u003c/sub\u003e fixation rates are reliant on the extraction of sufficient biomass, they might not be easily adaptable to attached communities. However, given the observed twofold higher relative abundance of autotrophs in the attached compared to the planktonic fraction, as well as the often drastically higher number of attached cells (Magnabosco et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sharma et al., 2024), the carbon sequestration by groundwater ecosystems might be substantially higher than previously assumed.\u003c/p\u003e\u003cp\u003eBeyond chemolithoautotrophy, the attached community also exhibited broader general capacities for redox transformations of sulfur, iron, and nitrogen. Genes related to dissimilatory sulfite and nitrate reduction, iron reduction, and ammonia oxidation were consistently more abundant in attached MAGs, indicating a capacity for utilizing mineral-derived electron donors and acceptors (Casar et al., 2021; Flynn et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Melton et al., 2014). These capabilities suggest that attached microbes not only serve as primary producers but also contribute actively to long-term nutrient turnover and geochemical transformations at the rock-water interface. In contrast, in both the total and active planktonic community, the functions investigated were rare and scattered, being present in only 6.8% of MAGs on average. Reduced nitrogen compounds seemed to play the primary role for sustaining the lower fraction of planktonic autotrophs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Originating from surface inputs and biomass recycling (Gesink et al., 2022; Herrmann et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), reduced nitrogen might be more available in the groundwater than rock-derived compounds. Overall, the scattered functions in the planktonic community imply that the central functions sustaining it are carried out by only a small fraction of the organisms present, or that it might rely on the activity of the attached community.\u003c/p\u003e\u003cp\u003eWhen comparing activity and abundance, we found distinct patterns in the two communities. In the attached community, most of the abundant MAGs showed active replication and less abundant MAGs were less likely to do so. In contrast, in the planktonic community, active and inactive MAGs showed a more random distribution, with some phyla explicitly showing activity in less abundant MAGs. These patterns indicate a higher dynamic of activity/inactivity in planktonic communities, supporting the argument that attachment supports more stable conditions for microbial growth (Park et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Patel et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wu et al., 2017). Key active organisms in the attached community, like \u003cem\u003eRhodoferax\u003c/em\u003e, \u003cem\u003eAquabacterium\u003c/em\u003e, \u003cem\u003eHydrogenophaga\u003c/em\u003e, and \u003cem\u003eUndibacterium\u003c/em\u003e, have previously been reported as abundant not only in rock-attached (Sharma et al., 2024; Lazar et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), but also in endolithic communities in these carbonate aquifers (Wegner et al., 2023). They might hence be key contributors to the stability and resilience of the attached community over time, contrasting the more dynamic conditions in the planktonic communities.\u003c/p\u003e\u003cp\u003eA striking feature was also the activity of MAGs from the \u003cem\u003eCand.\u003c/em\u003e Patescibacterial group JAEDAM01. This group appeared exclusively in the attached community, where it was the only group of \u003cem\u003eCand.\u003c/em\u003e Patescibacteria that showed active replication. JAEDAM01 represents a sister lineage of \u003cem\u003eCand.\u003c/em\u003e Gracilibacteria and are among the few \u003cem\u003eCand.\u003c/em\u003e Patescibacteria obtained in stable co-cultures with their hosts, showing a specialized, predatory lifestyle (Moreira et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yakimov et al., 2022). Their exclusive presence in the attached community indicate that they benefit from the proximity to other cells.\u003c/p\u003e\u003cp\u003eIn our experiments, we ensured a \u003cem\u003ede novo\u003c/em\u003e colonization of the exposed rock material, excluding pre-existing biofilms. The attachment of compositionally and functionally distinct organisms from the co-occurring planktonic community hence raises a key ecological question: Where do these colonizing microbes originate, if they are not abundant in the groundwater? One possibility is that they derive from the rare biosphere, existing at low abundance in the planktonic phase and below detection thresholds (Yamamoto et al., 2019). Alternatively, microbes may disperse through episodic detachment from other rock surfaces within the aquifer (Atencio et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), representing a low-frequency surface-to-surface exchange rather than continuous seeding from the water phase. It is also conceivable that colonizers arise from unsampled microhabitats such as rock pores or interfacial mineral zones that are not captured by standard planktonic sampling methods (Casar et al., 2021; Mullin et al., 2020). Our findings suggest colonization by long-resident, ecologically specialized taxa that persist on mineral surfaces throughout the aquifer. These microbes appear to exhibit low dispersal potential, rarely entering the planktonic phase, and are adapted for surface-associated life, as evidenced by their enriched functional repertoire (e.g., biofilm formation, chemolithoautotrophy), larger genome sizes, and active replication. Together, these patterns point to a structured, lithic microbiome that operates largely independent of the free-living groundwater community, challenging assumptions of high connectivity and functional redundancy between microbial lifestyles in consolidated aquifers.\u003c/p\u003e\u003cp\u003eThis observed lifestyle-driven divergence may carry important evolutionary implications. In spatially structured environments such as consolidated aquifers, physical separation between microenvironments (rock pores, mineral surfaces, the water phase) can act as a barrier to gene flow, facilitating ecological and evolutionary differentiation (Whitaker et al., 2003). Our genome-resolved comparisons of closely related MAGs revealed substantial differences in genome size and metabolic functions, suggesting that consolidated aquifers host parallel microbial lineages undergoing independent evolutionary trajectories, rather driven by ecological selection than by dispersal. These patterns align with broader evidence that microbial populations in heterogeneous systems evolve along parallel, niche-specific trajectories shaped more by selection than dispersal (Martiny et al., 2015).\u003c/p\u003e\u003cp\u003eThe effect of attached vs. planktonic lifestyle as key determining factor for microbial communities, however, varies greatly across environments. A comparable dissimilarity to our results has been observed in low-porosity granite aquifers, where Proteobacteria dominate attached communities while \u003cem\u003eCand.\u003c/em\u003e Patescibacteria and Desulfobacterota were more abundant in planktonic fractions (Dopson et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similar low overlaps of around 10% of ASVs were also reported from marine sediments and from a recent study of Lake Erie (Robinson et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Dang \u0026amp; Lovell, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, a study from lake Baikal found analogous taxa in biofilms and plankton (Parfenova, Gladkikh \u0026amp; Belykh \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and a study on several Swedish streams found three most common genera to account for 33\u0026ndash;41% in biofilms and 13\u0026ndash;21% of suspended communities (Besemer 2012). Also in the subsurface, in highly porous aquifers like sandstone and gravel, attached and planktonic communities tend to be similar: In sandstone aquifers, Proteobacteria and Bacteroidota were identified as the two most abundant groups, constituting up to 70% of both the attached and planktonic communities (Rizoulis et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Similarly, Proteobacteria, \u003cem\u003eGeothrix\u003c/em\u003e, Burkholderiales, and Desulfuromonadaceae were reported in both communities in sand-gravel aquifers (Flynn et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDifferences in porosity hence may contribute to microbial differentiation in groundwater: In high porosity aquifers, higher permeability and fluid flow lead to a more uniform nutrient distribution, preventing the establishment of a free-living community distinct from their attached counterparts. Hydrodynamic effects were previously suggested to also drive interactions between attached and planktonic communities in groundwater and stream ecosystems (Atencio et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Pan et al., 2021; Smith et al. 2018). A recent study using \u003cem\u003ein situ\u003c/em\u003e bioreactors in groundwater, conversely, found higher differences between biofilms and planktonic communities in shallow alluvial aquifers with low porosity, but more similarity in anoxic bedrock groundwater (Park et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), attributing this distinction to be related to redox conditions. It can therefore be assumed that more complex interactions between lithology, porosity and hydrodynamics, as well as redox conditions, drive the differentiation of attached and planktonic communities.\u003c/p\u003e\u003cp\u003eIn summary, our findings call for a reevaluation of the seed bank model across aquifer types. While it may explain microbial dispersal in porous, hydrologically connected sediments (Lennon \u0026amp; Jones, 2011; Nelson et al., 2021), it does not capture the dynamics of consolidated aquifers, where dispersal is limited and biofilm-based communities are stable and functionally distinct (Casar et al., 2021; Flynn et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Given the distinction between attached and planktonic communities, a targeted evaluation of functions in attached aquifer microbiomes is crucial to assess their role in carbon sequestration and biogeochemical cycling on a global level. Understanding such rock-hosted systems will also require different conceptual frameworks that account for lithological structure, hydrodynamics, and physical separation of microenvironments (Griebler \u0026amp; Lueders, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Martiny et al., 2006), as well as novel methodological approaches to resolve the mechanisms of microbial activity, adaptation and persistence in spatially structured subsurface environments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe raw metagenomic sequencing reads for the attached communities is available at NCBI under BioProject accession PRJNA1280030. All MAGs from the attached communities are available from Open Science Framework (OSF) repository: https://osf.io/ekadx/?view_only=b2317cc646de4ba2972319b5b9fec864. Previously published data of planktonic samples is available at NCBI via accession PRJEB36505.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe are grateful to Robert Lehmann, Falko Gutmann and Heiko Minkmar for assistance with field work, and sampling of groundwater. We also thank Stefan Riedel for the preparative work for the MiSeq amplicon sequencing of the 16S rRNA gene. Additionally, we thank Muriel Ritsch, Ivonne G\u0026ouml;rlich and Marco Groth from the Core Facility DNA sequencing of the Leibniz Institute on Aging - Fritz Lipmann Institute in Jena for their help with Illumina sequencing.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported financially by the Deutsche Forschungsgemeinschaft via the Collaborative Research Centre AquaDiva (CRC 1076 AquaDiva - Project-ID 218627073) of the Friedrich Schiller University Jena. Martin Taubert gratefully acknowledges funding by the DFG under Germany\u0026rsquo;s Excellence Strategy, EXC 2051\u0026ndash;Project-ID 390713860. Climate chambers to conduct experiments under controlled temperature conditions and the infrastructure for Illumina MiSeq sequencing were financially supported by the Th\u0026uuml;ringer Ministerium f\u0026uuml;r Wirtschaft, Wissenschaft und Digitale Gesellschaft (TMWWDG, project B 715-09075 and project 2016 FGI 0024 \u0026ldquo;BIODIV\u0026rdquo;).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthor information\u003c/h2\u003e\n\u003ch3\u003eAuthors and Affiliations\u003c/h3\u003e\n\u003cp\u003eAquatic Geomicrobiology, Institute of Biodiversity, Ecology and Evolution, Friedrich Schiller University Jena, Dornburger Strasse 159, 07743, Jena, Germany\u003c/p\u003e\n\u003cp\u003eAlisha Sharma,\u003csup\u003e\u0026nbsp;\u003c/sup\u003eKirsten K\u0026uuml;sel, Carl-Eric Wegner, Olga Maria P\u0026eacute;rez Carrascal\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u0026amp; Martin Taubert\u003c/p\u003e\n\u003cp\u003eCluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena,\u0026nbsp;Gr\u0026uuml;ne Aue, 07745, Jena, Germany\u003c/p\u003e\n\u003cp\u003eKirsten K\u0026uuml;sel, Olga Maria P\u0026eacute;rez Carrascal\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u0026amp; Martin Taubert\u003c/p\u003e\n\u003cp\u003eGerman Center for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstrasse 4, 04103, Leipzig, Germany\u003c/p\u003e\n\u003cp\u003eKirsten K\u0026uuml;sel\u003c/p\u003e\n\u003cp\u003eBIOMICS-Group, Heinrich Heine University, D\u0026uuml;sseldorf, Universit\u0026auml;tsstra\u0026szlig;e 1, 40225 D\u0026uuml;sseldorf, Germany\u003c/p\u003e\n\u003cp\u003eCarl-Eric Wegner\u003c/p\u003e\n\u003ch3\u003eContributions\u003c/h3\u003e\n\u003cp\u003eMT, KK and AS designed and conceptualized the study. AS carried out the lab work followed by bioinformatics analysis and data interpretation with the help of MT, CEW and OMPC. AS and MT wrote the manuscript. All authors discussed the results and implications and commented on the manuscript at all stages.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eCorresponding author\u003c/h3\u003e\n\u003cp\u003eCorrespondence to Martin Taubert ([email protected])\u003c/p\u003e\n\u003ch2\u003eEthics declaration\u0026nbsp;\u003c/h2\u003e\n\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eAdditional information\u003c/h2\u003e\n\u003cp\u003ePublisher\u0026rsquo;s Note\u003c/p\u003e\n\u003cp\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlneberg, J, et al. 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Microbiome, 10, 33.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Biofilms, Chemolithoautotrophy, Carbonate rock, Groundwater, Metagenomics","lastPublishedDoi":"10.21203/rs.3.rs-7131340/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7131340/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMicroorganisms in groundwater ecosystems exist either as planktonic cells or as attached communities on aquifer rock surfaces. Attached cells outnumber planktonic ones by at least three orders of magnitude, suggesting a critical role in aquifer ecosystem function. However, particularly in consolidated carbonate aquifers, where research has predominantly focused on planktonic microbes, the metabolic potential and ecological roles of attached communities remain poorly understood.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eTo investigate the differences between attached and planktonic communities, we sampled the attached microbiome from passive samplers filled with crushed carbonate rock exposed to oxic and anoxic groundwater in the Hainich Critical Zone Exploratory and compared it to a previously published, extensive dataset of planktonic communities. Microbial lifestyle (attached vs. planktonic) emerged as the strongest determinant of community composition, explaining more variance than redox conditions. Metagenomic analysis revealed a striking taxonomic and functional segregation: the 605 metagenome-assembled genomes (MAGs) from attached communities were dominated by Proteobacteria (358 MAGs) and were enriched in genes for biofilm formation, chemolithoautotrophy, and redox cycling (e.g., iron and sulfur metabolism). In contrast, the 891 MAGs from planktonic communities were dominated by \u003cem\u003eCand.\u003c/em\u003e Patescibacteria (464 MAGs) and Nitrospirota (60 MAGs) and showed lower functional versatility. Only 7% of genera were shared, and even closely related MAGs (\u0026gt;\u0026thinsp;90% ANI) differed in genome size and metabolic traits, demonstrating lifestyle-specific functional adaptation. Analysis of active replication indicated that the active fraction of the attached community was primarily shaped by the most abundant MAGs. Planktonic communities featured more active MAGs, but overall with lower abundances.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe high abundance, metabolic specialization, and carbon fixation potential of attached microbes suggest that they are key drivers of subsurface biogeochemical processes. Carbonate aquifers may act as much larger inorganic carbon sinks than previously estimated based on CO\u003csub\u003e2\u003c/sub\u003e fixation rates of the planktonic communities alone. Our findings underscore the need to incorporate attached microbial communities into models of subsurface ecosystem function.\u003c/p\u003e","manuscriptTitle":"Two worlds beneath: Distinct microbial strategies of the rock-attached and planktonic subsurface biosphere","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-16 15:23:39","doi":"10.21203/rs.3.rs-7131340/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-15T14:19:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-15T02:49:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-13T17:41:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-29T10:07:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74689982861142728041865433760295121865","date":"2025-08-15T15:35:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4124809289452770683238016892954491940","date":"2025-08-14T05:54:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143004599879983304716450056997670702812","date":"2025-08-13T09:20:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-13T06:19:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-05T22:12:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-16T02:37:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbiome","date":"2025-07-15T13:44:05+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":"c996883c-aed5-4dde-91cf-1d2128cfeacf","owner":[],"postedDate":"July 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:00:50+00:00","versionOfRecord":{"articleIdentity":"rs-7131340","link":"https://doi.org/10.1186/s40168-025-02325-1","journal":{"identity":"microbiome","isVorOnly":false,"title":"Microbiome"},"publishedOn":"2026-01-31 15:58:29","publishedOnDateReadable":"January 31st, 2026"},"versionCreatedAt":"2025-07-16 15:23:39","video":"","vorDoi":"10.1186/s40168-025-02325-1","vorDoiUrl":"https://doi.org/10.1186/s40168-025-02325-1","workflowStages":[]},"version":"v1","identity":"rs-7131340","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7131340","identity":"rs-7131340","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}