The deep terrestrial biosphere encompasses the life below the photosynthesis-fueled surface that perseveres in typically nutrient and energy depleted anoxic groundwaters. The composition and cycling of this vast dissolved organic matter (DOM) reservoir relevant to the global carbon cycle remains to be deciphered. Here we show that recent Baltic Sea-influenced to ancient pre-Holocene saline Fennoscandian Shield deep bedrock fracture waters carried DOM with a strong terrigenous signature and varying contributions from abiotic and biotic processes. Removal of easily degraded carbon at the surface-to-groundwater transition and corresponding microbial community assembly processes likely resulted in the highly similar DOM signatures across the notably different water types that selected for a core microbiome. In combination with the aliphatic character, depleted δ13C signatures in DOM indicated recent microbial production in the oldest, saline groundwater. Our study revealed the persistence of terrestrially-sourced carbon in severely energy limited deep continental groundwaters supporting deep microbial life.
Groundwater is the earth’s largest active source of freshwater1 containing a vast store of poorly characterized dissolved organic matter (DOM)2, an actively cycled key component in the global carbon cycle. As the water moves into deeper layers, nutrients and available organic carbon are removed, severely limiting the energy supply to the deep biosphere life estimated to contain 2–6 × 1029 cells3 that is approximately one quarter of the global microbial biomass4. Key processes in deep subsurface carbon cycling depend on the recalcitrance of the organic matter to microbial utilization5, input of metabolic oxidants and reductants, and aquifer permeability6. While acting as a nutrient and energy source for heterotrophic life7, the composition and concentration of the DOM also impacts trace element and contaminant transport8,9, especially in anthropogenically impacted groundwater systems. Climate change and increasing urbanization exert pressures on the deep groundwater systems affecting DOM input, storage, and turnover2,10. Yet to date, few studies have addressed DOM composition and its associated bioavailability in deep terrestrial groundwaters.
Plant litter and soil comprise the main sources of DOM in shallow groundwaters11. Plant-derived molecular constituents and a high overlap with riverine fulvic acids, carrying a large number of preferentially biodegraded nitrogen- and sulfur-containing compounds, were attributed to groundwater DOM at depths <3 m12,13. Sulfur-containing organic matter can be a dominant component of rainwater14, but can also be released by microbes or abiotically produced in anoxic settings, enhancing the recalcitrance of the organic matter15,16,17. Permeating through soils and sediments, selective interactions of the organic matter occur with microbes, minerals, and water that integrated in the “regional chromatography” effect shape a homogenized DOM signature in the saturated zone18,19. Nevertheless, the source and history of groundwater DOM remain imprinted in its composition, with bulk organic carbon and isotopic compositions similar to surface soils indicating a surface origin and vertical transport into aquifer systems20 that varies seasonally21,22. The proximity and connectivity to organic-rich layers drive microbial abundance and activity as well as microbial community composition23,24 and DOM from chemolithoautotrophic communities in deep basaltic or granitic intrusions may be important as the long timescales increase the proportion of microbial-derived and transformed DOM over allochthonous, terrigenous material in the oligotrophic systems25,26,27.
The Äspö Hard Rock Laboratory (HRL) on the Swedish Baltic Sea coast facilitates access to deep continental groundwaters in the Fennoscandian Shield. Its 3.6 km long tunnel partially extends below the Baltic Sea at depths down to 460 meters below sea level (mbsl). The groundwater components consist of old saline water, brackish water from the overlying Baltic Sea and its predecessors such as the Littorina Sea, and fresh waters from temperate and cold climate (glacial meltwaters) of the Pleistocene28,29,30. The present groundwaters broadly fall into four main types according to water chemistry signatures: marine, meteoric, old saline, and glacial30. While the brackish Baltic Sea water is classified as a marine source, its DOM is of predominantly terrestrial origin, i.e., comprising about 75% in the Baltic Proper31. During the 30 years since the Äspö HRL tunnels were constructed, complex mixing has occurred where some fracture zones facilitate downward percolation of Baltic Sea water whereas other fractures promote upward flux of deep saline waters. The Fennoscandian Shield microbial community has a core microbiome between the different groundwater types often comprised of small cells with streamlined genome sizes that likely represent adaptations to an oligotrophic lifestyle32,33,34. While isotopically light carbon indicates slow, but persistent microbial transformation of surface-derived DOM35, the link between the DOM reservoir and the microbial community is largely unknown.
In this study, DOM concentration and molecular composition in conjunction with stable and radiogenic carbon and water isotopic investigations, water chemistry, and microbial community structure in Äspö HRL fracture waters (Fig. 1) of different depths, ages, and sources were analyzed to disentangle mechanisms behind organic matter cycling in the deep biosphere. We include a saline pre-Holocene groundwater, potentially representative of often overlooked natural saline groundwaters worldwide36. We hypothesized that terrestrial surface DOM fuels microbial life in the deep continental bedrock fractures. In addition, we suggest that abiotic transformations occurring on long timescales and microbial reworking of the aged DOM is likely in the low carbon and energy groundwaters with cycling of sulfur compounds playing an important role.
Type and origin of the groundwaters
Using chloride concentrations, δ18O values, and Cl/Mg plus Br/Cl ratios to distinguish water origin in the aquifer at Äspö HRL28,30 grouped the samples (Table 1 & Fig. 2) into: (i) ‘Baltic Sea’ water with typical salinities and δ18O values for these brackish waters30,37; (ii) ‘BrMarine1’ recent brackish-marine waters intruded into the boreholes; (iii) ‘BrMarine2’ Holocene marine waters older than present day Baltic Sea; (iv) ‘Transition’ waters that carry signatures from both BrMarine and old saline waters; and (v) brine-type old ‘Saline’ water of unknown origin with residence times in the bedrock of ≥100,000 years38 mixed to various degrees with old meteoric water of cold climate origin, likely glacial and pre-Holocene. The origin of the brine component is complex and may involve leaching of marine sediments (e.g., Palaeozoic evaporites) later influenced by water rock interaction through time38. The Baltic Sea samples clustered with BrMarine1 borehole SA1229A that accessed a large section with high connectivity to the Baltic Sea. All other BrMarine1 and BrMarine2 groundwaters grouped within the area of mixed water origin, with one borehole sample (KA2051A01_5, BrMarine1) moved toward meteoric characteristics. KA2865A01_1 and KA3385A_1 waters associated closest with KA3510A_2 classified as Transition-type waters of intermediate Cl/Mg ratios.
Dissolved organic matter in fracture waters
DOC concentrations decreased with increasing conductivity and were below 1.15 mg L−1 in all Saline waters while the Linnaeus Microbial Observatory (LMO) and near Äspö Island Baltic Sea values framed the BrMarine samples (Table 1). Dissolved organic nitrogen (DON) concentrations were higher in waters of lower conductivity. With the exception of borehole SA1229A_1 that had >5 mg L−1 total dissolved nitrogen (TDN) mostly as ammonium of an unknown source, dissolved C/N ratios were not significantly different between water types at an average of 19.4 ± 6.1.
Solid-phase carbon extraction efficiencies were 81 ± 4% and 66 ± 4% across all deep fracture waters and the two Baltic Sea samples, respectively (Table 1). The DOM composition comprised 12,728 monoisotopic molecular formulas (MFs) assignments between 35 samples (average 7961 ± 464; Table S1) in the 96–1000 Da mass range from FT-ICR-MS analysis. Of these, 3772 MFs were found in all samples and accounted for on average 47 ± 3% of the MFs by count and 85 ± 2% of the relative intensities per sample. 309 MFs (0.23 ± 0.13% of relative intensities) were unique to the Saline-type DOM that plotted at high H/C and low O/C ratios in van Krevelen space used to visualize tentative structural characteristics of MFs39, including few O-poor aromatics and mostly highly unsaturated O-poor or unsaturated MFs, some containing N (Supplementary Fig. S1). 110 MFs (0.21 ± 0.04% of relative intensities) were unique to the two Baltic Sea samples that plotted in the region of tannin-like and condensed aromatic MFs of terrigenous origin. The unique MFs of the BrMarine samples (n = 438; 0.26 ± 0.11% relative intensity) were widely spread across van Krevelen space besides the area of O-rich aromatics and highly unsaturated MFs.
A gradient from Baltic Sea, through Transition, to Saline groundwater origins was partially reflected by the distribution of DOM compositions along the first axis of NMDS ordination (Fig. 3). Hydrochemical composition based on eight main parameters (Na, K, Ca, Mg, δ18O, δ2H, SO42−, D, and Cl−) similar to those used previously for water type classification at Äspö HRL29 and DOM composition were significantly related (distance-based redundancy analysis, db-RDA), 9999 permutations, n = 15, F = 3.6, p < 0.01). Overall, hydrochemistry explained about 83% of DOM variability with some differences in classifications remaining. BrMarine1 samples KA2511A_5 and SA1229A_1 grouped closest with the Baltic Sea waters, while KA2051A01_5 associated with BrMarine2 and Transition boreholes KA3105A_3 and KA2865A01_1. The Transition DOM formed a tight group in the hydrochemical classification, but their mixed origin stood out in DOM molecular space. The Transition-type waters KA3510A_2 and KA3385A_1 grouped closest to Saline KA1755A_3 and HA2780A_1, respectively, while KA2865A01_1 more closely affiliated with BrMarine2 borehole KA3105A_3.
The Baltic and BrMarine1 waters showed similar H/CMF and P/CMF ratios (weighted means from MF assignments) that were low compared to older groundwater samples (Fig. 4). Although the BrMarine group DOM composition was not coherent, its average mass tended to be higher than that of Transition- and Saline-type DOM. The N/CMF ratio varied across all water types while the O/CMF ratio and S/CMF ratios were most different for Baltic Sea DOM. Sulfate and sulfide concentrations negatively related in BrMarine1, BrMarine2, and KA2865A01_1, denoting ongoing sulfate reduction in the most recent waters. The DOM S/CMF ratio strongly correlated with the relative contribution of AbioS40 peaks, indicative of abiotic sulfurization under anoxic conditions, of which 13 (out of 15) were detected in this dataset (R = 0.94, p < 0.001). The AbioS peaks were not present in Baltic Sea water and barely present in KA2051A01_5 (BrMarine1) and KA3105A_3 (BrMarine2). In addition, the S/CMF ratio was highly correlated to the number of S-containing MFs (R = 0.95, p < 0.001), implying the production of new S-containing MFs rather than addition to already existing ones. The presumed lability of the DOM based on the lability index MLBw was highest in the oldest, Saline- and some Transition-type waters (Fig. 5). The aromaticity, assessed via the modified aromaticity index AImod, covered a wide range between the Baltic Sea samples closest to the coast that decreased toward Transition and Saline DOM.
The radiocarbon age of the bulk inorganic and organic carbon fractions was assessed as percent modern carbon (pMC_DOC, pMC_DIC; Fig. 6) with pMC values around 100 corresponding to recent carbon and 60 corresponding to a mean residence time of around 4200 years. pMC_DOC ranged from 105.7 in borehole KA2051A01_5 (BrMarine2) to 58.2 in borehole KA1755A_3 (Saline), but most old Saline waters had a DOC radiocarbon age of around 69 pMC corresponding to ~3000 years. pMC_DIC also strongly decreased from Baltic Sea (105.6 near Äspö Island) and BrMarine groundwaters compared to values of 40–50 in most Saline waters, a Transition-type water (KA3510_2), and the BrMarine2 water from borehole KA2511A_5. pMC_DOC and pMC_DIC were significantly positively correlated (R = 0.66, p < 0.001) but the organic fraction showed a higher pMC value than the inorganic fraction, except for the Baltic Sea waters, that was mainly attributed to dissolution of calcite within the overburden or in the uppermost fractures. The relatively young age of the inorganic and organic carbon in the Saline-type fracture waters was surprising regarding their implicit origin. The saline component endmember (45,000 mg Cl L−1) has a long residence time manifested in Mg/Cl, Br/Cl ratios, He content, and δ18O/δH ratio38. However, the Cl− concentrations were below 15,000 mg L−1 with the dilute components being of glacial origin or possibly an older meteoric water. Introduction of modern water portions or contamination through e.g., biofilm formation in the sampling system is unlikely as sampling section and tubing were flushed with several volumes of fracture water before each sample was taken, similar carbon extraction efficiencies were found over all section and tubing lengths, and FT-ICR-MS spectra showed no obvious contaminants. The detected trends between water types thus likely hold true but nevertheless, these potential caveats need to be considered especially for the low-DOC Saline-type water.
δ13C-DIC values ranged from −0.7‰ in Baltic Sea water to −18.5‰ in Saline groundwaters while δ13C-DOC values ranged from −24.0 to −28.7, ‰ respectively (Fig. 6) except for one sample from borehole SA2600A_1 that showed very different values between batch 1 (δ13C-DOC = −28.6‰) and batch 2 (δ13C = −23.70‰). As all other samples were consistent between the years and the second sampling of SA2600A_1 was not otherwise conspicuous, this value was disregarded during further interpretations. δ13C values of organic and inorganic carbon were positively related (R = 0.70, p < 0.01) with Δ13CDIC−DOC values of −16.6 ± 4.1‰ for all samples and −14.1 ± 3.3‰ for old saline waters. The solid-phase extracted DOC stable carbon isotope signature (δ13C-SPE-DOC) did not mirror the full extent of δ13C-DOC and ranged from −26.1 to −28.4‰ (R = 0.42, p < 0.05). The agreement between δ13C-SPE-DOC and δ13C-DOC values was good, but deviation was highest for Baltic Sea water with the lowest extraction efficiencies (Supplementary Fig. S2). On DOM molecular level, the δ13C values were most strongly correlated to the MF fraction assigned to the aromatic, oxygen-rich molecular class (R = 0.70, p < 0.01) that comprised between 4 and 9% of the relative intensity.
Microbial communities and DOM
The microbial communities in Äspö HRL groundwaters were dominated by 16 S rRNA gene amplicon sequence variants (ASVs) that aligned within the Proteobacteria (28% mean relative abundance) including classes Gamma- and Alphaproteobacteria, Patescibacteria (24%), Campylobacterota (13%), and Desulfobacterota (8%) (Supplementary Fig. S3). Microbial communities partly clustered according to the water types BrMarine, Transition, and Saline although the BrMarine boreholes KA3105A_3 and KA2051A01_5 communities clustered with Saline samples (Supplementary Fig. S4). A db-RDA using only unique samples and the same eight water chemistry parameters as for DOM revealed that the water chemistry explained almost 92% of the variability (9999 permutations, F = 1.40, n = 10) of the microbial community composition. However, this relationship was not statistically significant, likely due to the low number of samples used in this study (p = 0.134, n = 10). Comparison of the DOM composition and the Äspö HRL groundwater microbial community compositions via Procrustes analysis resulted in an intermediate m2 of 0.65 (9999 permutations, p = 0.097) that indicated the datasets were not significantly related along main gradients of variability. Therefore, individual associations between microbial ASVs and DOM MFs were analyzed via network analysis based on proportionality. The MFs retained in the network were centered in van Krevelen space (Supplementary Fig. S5) but only comprised around 2% of the total relative sample intensities. Network MFs were overall of higher molecular weight and had higher heteroelements-to-carbon ratios than the original dataset (Table S2). In addition, the presumed lability of the MFs was higher as they contained less unsaturated hydrocarbon-type, more tannin-type, and less lignin-type MFs than the original dataset (Table S3). The composition of the ASVs found to be related to MFs also differed from the total community composition. DOM-related ASVs were dominated by Patescibacteria (12% mean relative abundance), Proteobacteria (9%), ‘unclassified’ taxon (5%), Desulfobacterota (4%), and Chloroflexota (3%). The resulting network consisted of 1617 nodes (1388 MFs and 229 ASVs), 2506 edges, 67 modules (Supplementary Data 1), and a network modularity of 0.89. Ten out of the 67 modules contained more than 50 nodes (3–23 ASV nodes and 54–259 MF nodes; Fig. 7, Tables S4, S5) that accounted for 63% of the whole network. The ten largest modules were comprised of MFs with distinct characteristics as reflected by varying H/CMF and O/CMF ratios ranging from 0.91 to 1.48 and 0.35 to 0.55 that formed clusters in specific areas of the van Krevelen diagram (Supplementary Fig. S5). Accordingly, the proportion of MFs assigned to specific compound classes differed between the modules (Table S4) with most MFs classified as lignin-like (ranging from 38 to 74% of the nodes per module).
On the basis of within- and between- module connectivity, 12 connectors, 45 module hubs, and 1560 peripheral nodes were identified in the whole network (Supplementary Fig. S6). While most connectors were MFs except for ASV165 (‘unclassified’ taxon), all but one module hubs were ASVs. All connector MFs contained the heteroelements N, S, or P (CHON, CHOS, CHOP, and CHONS) and were mostly lignin-like of varying lability and molecular weight. Most connectors were positively and negatively related to several microbial ASVs from different phyla such as Patescibacteria, Desulfobacterota, and Chloroflexota. Of the microbial module hubs, 16 (36%) were classified as Patescibacteria and seven (16%) as Desulfobacterota (Fig. 7).
Regional chromatography shapes the fracture water signatures
Selective interactions of organic substances, especially N-rich materials, with water, microbes, and mineral surfaces during groundwater transport was reported by Aiken41 and termed “regional chromatography”19. This concept has been applied to describe groundwater DOM compositions18,42 and explains the refractory signal (i.e., low MLBw, low C/NMF) and relatively low variability in the Äspö HRL fracture system where lignin-like MFs contributed >50% of the relative intensities in all water types. In addition, that the sixfold change of bulk DOC concentrations between Baltic Sea and the deepest groundwaters plus the increased DON in the Baltic Sea were not mirrored in the narrow variation in DOM molecular characteristics can partially be attributed to the analytical window inherent to the applied methods43. However, the higher deep groundwater solid-phase extraction efficiencies suggested they contained a lower proportion of poorly captured high molecular weight material, colloidal organic matter, or small monomeric compounds compared to the Baltic Sea water. Despite a considerable terrestrial fingerprint31, the Baltic Sea DOM carries freshly produced matter comprising e.g., proteins, amino acids, and carbohydrates44. These compounds are preferentially respired in the subsurface water column and may adsorb to sediment grains leading to the strongly terrigenous DOM signature entering the deep fractures. In line with the changes in DOM composition with depth and groundwater type from the Baltic Sea downwards, Äspö HRL groundwater microbial communities supported by the available DOM also considerably differ from the overlying Baltic Sea water, suggesting species sorting occurred prior to the infiltrated waters reaching the Äspö HRL boreholes45. Thus, the existence of a Fennoscandian Shield core microbial community adapted to the oligotrophic subsurface32 may be at least partially due to the low variability and recalcitrant nature of the DOM in the different Äspö HRL groundwaters.
Mixing dominates DOM signatures of waters from different origins
Groundwaters carrying >1 mg L−1 organic carbon are commonly located close to organic-rich sediments or receive organic-rich recharge46. The shallow BrMarine1 and BrMarine2 groundwaters with high surface connectivity exhibited large contributions of lignin- and tannin-like MFs of high aromaticity, similar to the high humic and fulvic acid concentrations from soil carbon of aromatic and (highly) unsaturated molecular classes detected in shallow soil-derived DOM12,20,21 and the Baltic Sea31. Toward the old Saline-type waters, DOC concentration, molecular mass and aromaticity, as well as O/CMF and P/CMF ratios consistently followed the dominant mixing process. The intrusion of terrigenous DOM into deep aquifers may confound previous interpretations of a largely chemolithoautotrophic origin of DOC in other systems26,47.
The Äspö HRL is built in Paleoproterozoic granitoids with fracture surfaces carrying calcite, chlorite, pyrite, and clay minerals48,49. Adsorption on surfaces or formation of insoluble complexes with hydrolyzing metals, especially Fe and Al, preferentially removes aromatic and lignin-like DOM moieties from solution50,51. The relatively high aromaticity of the BrMarine and partially the Transition-type DOM together with the high DOC concentrations indicated saturated surfaces and/or sufficient recharge29. Overall, that more than two thirds of the variability in DOM composition was explained by water chemistry parameters confirmed mixing of different source waters as the main driver of water compositions in the deep bedrock. The remaining variability points toward additional abiotic or biotic processes including sulfurization, sorption, condensation, or microbial uptake and reworking6 shaping DOM composition in Fennoscandian Shield groundwaters.
Evidence for abiotic and biotic sulfurization of DOM
The high sulfate concentration in Saline waters and two Transition-type boreholes originated from Baltic Sea input and gypsum dissolution and were consistent with previous investigations in a nearby fracture system52,53. Microbial community data indicate the presence (Supplementary Fig. S3) and activity of autotrophic and heterotrophic sulfate reducing bacteria in Äspö HRL groundwaters34,54 that is not reflected in the alkalinity due to subsequent CO2 uptake in reciprocal microbial partnerships with acetogens, methanogens and fermenters32,55,56. In sulfidic sediments, organic matter reacts with reduced sulfur species potentially from sulfate reduction forming organic sulfur compounds as recently shown for DOM experimentally and in anoxic porewaters15,16, increasing organic matter preservation at low temperatures17. At Äspö HRL, the DOM S/CMF ratio was elevated in all anoxic groundwaters (S/CMF = 0.007; Supplementary Table S1) in comparison to Baltic Sea water (S/CMF = 0.003). The S-content of the SPE-DOM (count of S-containing MFs or S/CMF ratio) correlated to the relative contribution of the AbioS index indicative of abiotic sulfurization40, while the double bond equivalent (DBE) of S-containing MFs of the groundwaters was on average one unit lower than that of CHO-only MFs. Together with a negative relationship of S-containing and CHO-only contributions to sample compositions, this could indicate the formation of new DOC compounds through bisulfide addition16. However, the deviation in DBE was even higher at around two DBE for the oxic Baltic Sea samples, where a different mechanism must take place.
Sulfurization, manifested in increased S/CMF ratios, of organic matter is thought to enhance preservation17. It may therefore seem counterintuitive that a higher S/CMF ratio characterized DOM in the old Saline-type DOM that overall also comprised DOM of higher lability as indicated by the MLBw. Notably, on average, MFs in the dataset carrying at least one S (N) atom contributed approximately 13% (17%) of the total sample intensities. Separating relative contributions included in the MLBw (H/CMF > 1.5), CHO MFs contributed 65 ± 5% followed by S- (29 ± 4%) and N-containing (5 ± 2%) MFs. S-containing MFs thus contributed disproportionally highly to the labile DOM pool, especially compared to the often bioavailable N-containing compounds. The abiotic sulfurization process is not selective in regard to saturation, aromaticity, degree of oxidation, or heteroelement content of the precursor compound, and bisulfide nucleophilic addition shifts the MF toward higher H/CMF ratios15,16. Furthermore, sulfur-containing metabolites produced though bacterial assimilatory S-reduction likely contributed to the DOS pool57. Hence, abiotic sulfurization of the subsurface DOM was an important process in the Äspö HRL fracture waters, but sulfur integration in DOM not only acted as a stabilizer but also increased the assumed lability of the DOM (% MLBw), an ambiguous role that needs further investigation in this system.
Microbial imprint in old saline fracture waters
Microbial remineralization, transformation, and production affected the deep groundwater DOM compositions in addition to the dominant mixing signature. The microbial imprint was not only reflected in the old Saline DOM fingerprints, but also in the carbon isotopic composition. δ13C-DIC carbon isotopic values of −0.7 to −18.5‰ decreased with conductivity and represented recharging groundwaters with BrMarine influence, terrigenous organic matter oxidation and dissolution of calcite from different generations with only minor contributions of extremely depleted DIC derived from anaerobic oxidation of thermogenic (δ13C −30 to −50‰) or biogenic methane (δ13C −60 to −90‰) present at low concentrations in the fracture waters38,53. δ13C-DOC correlated with δ13C-DIC, but isotopic compositions were in a narrow range from −23.7 to −28.7‰ that were close the values reported for boreal forest soil (−24 to −29‰58), the dominant river DOM source in the region. In addition, the Δ13C(DIC-DOC) values were mostly higher than the range of measured fractionation factors for the reverse tricarboxylic acid cycle (−2 to −12‰), emphasizing that CO2 fixation was not the main driver of DOC production as suggested for the hot Costa Rican backarc system47. Degradation by microbes typically lowers the δ13C of the remaining DOC pool59 and the most depleted δ13C-DOC value of −28.6‰ were found for the old saline DOM, indicating a stronger imprint in these waters to which a minor contribution of higher-plant derived material was previously attributed60. Usually, higher H/CMF ratios or lower aromaticity of DOM are associated with younger DOM that is potentially more accessible to microbial utilization61,62,63, opposite to what was observed at the Äspö HRL. Here, in conjunction with the low AImod, low molecular mass, and high H/CMF ratios, permanent reworking and release of an aged but apparently labile organic matter fraction through the resident microbes was likely in the Saline-type waters10. In addition, the MFs unique to the deep Saline DOM were enriched in low-O/CMF and high-H/CMF MFs (Supplementary Fig. S1). These contrasting trends with ageing were recently attributed to the different oxygen availability and photochemical processing in oxic aquatic versus anoxic, deep systems where photodegradable compounds and aerobically biolabile formulas can accumulate10. In agreement with this and the DOM signature of microbial reworking at Äspö HRL, ref. 26 describe a deep groundwater DOM comprised of aliphatics, unsaturated, and lignin-like compounds also containing low molecular weight organic acids and minor contribution aromatic compounds likely of microbial origin in a deep South African fracture zone. These MFs were presumably either released by the resident microbial community through metabolic activity or after senescence, or indicated that allochthonous DOM transported in the aquifer had undergone methylation processes under anoxic conditions64. Possible methylation was supported by the higher average molecular weight of the MFs unique to Saline-type DOM (475 ± 26 Da) compared to the average molecular weight of all samples (397 ± 5 Da). Methane in the deep crystalline bedrock can originate from abiotic (e.g., Fischer-Tropsch) or biotic processes via methanogenesis65, but methanotrophic contribution to the DOM pool was low at Äspö according to the stable isotopic composition of DOC.
With ongoing microbial degradation of the DOM, N- and P-containing molecules are often preferentially utilized by heterotrophic bacteria13,66. However, these studies deal with DOM originating at least partially from recent primary production, whereas the DOM examined in the anoxic Fennoscandian Shield fractures had a terrigenous signature and had cycled through the subsurface bedrock fractures for long timescales. The fundamentally different source DOM quality and environment can entail that the opposite trend was found for the P/CMF ratio with increasing age in the Äspö HRL fracture waters, and no consistent change in N/CMF ratio was observed. Although terrestrial organic matter is generally depleted in nitrogen compared to marine organics, both the DON and DOC concentrations were lower in Transition- and Saline-type waters compared to the more recent BrMarine with no significant differences in bulk C/N ratios. The nitrogen-containing DOM may still be rapidly cycled in the environment limited in soluble nitrogen and phosphorus67, keeping the concentrations measured at discrete time points low. The increase in P/CMF ratio was not easily explained and literature including DOP on MF level analysis is scarce. Phosphate was depleted in BrMarine2, Transition, and Saline waters, such that DOP comprises a potential P-source for microbial life rather than being released to the DOM pool. Yet, with the increase in P/CMF ratio from Baltic Sea to Saline waters, also the number of P-containing MFs increased from around 50 to 150, pointing toward new production and no P-limitation of the subsurface microbes.
The possible reasons why the microbial imprint was primarily observed in the deep Saline-type DOM were threefold: (i) the DOC concentration was the lowest such that minor removal or dilution of terrigenous organics resulted in a visible microbial imprint in the relative abundance spectra; (ii) microbial adaptation to the refractory C sources takes time and thus only occurs in the oldest waters; or (iii) more accessible energy sources were available in other fracture waters. Previous studies show that the deep biosphere microbial community is viable and active, rapidly recycling dead cells as substrate68. Reworking of DOM due to microbial reuse of necromass adds to the labile characteristics of Saline-type DOM. In addition, carbon dioxide and hydrogen geogas-driven microbial metabolism may leave little imprint in the DOM and the fast uptake of products of this metabolism by other microbes further diminishes the detectable imprint34. However, all DOM fingerprints included in this study shared more than 85% of the MFs relative intensity, so that the proportion of the DOM responsible for the labile signature in the deep Saline waters was small in comparison to the recalcitrant, terrigenous signature common to all groundwaters. This potentially explains the slow growth rates and “stop-and-start” cell replication processes that are suggested to be activated or deactivated as and when carbon and energy sources are intermittently available to the deep fracture water microbial community32.
Links between specific molecules and microbes
While both DOM and microbial community composition were to certain degrees linked to water type, overall compositions were not significantly related to each other. Nevertheless, network analysis revealed potential connections between DOM MFs and microbial ASVs in the form of correlations that require experimental evidence for confirmation. While microbial network ASVs accounted for around 37% of the total relative abundance, the network MFs only comprised around 2% of the total relative intensities. Considering that MFs in the network presumably represented compounds that were either produced or consumed by microorganisms, a low cumulative relative intensity of the respective MFs would be expected. In contrast, highly abundant MFs were more likely compounds of low bioavailability that accumulate in the groundwaters. This was mirrored in the higher lability (MLBw) of the network MFs and in line with previous studies reporting major proportions of refractory DOM in aquatic systems18. Most ASV nodes were affiliated with the phylum Patescibacteria that also constituted more than one-third of the module hubs that were highly connected and proposed to be keystone taxa69. These module hub Patescibacteria belonged to the classes Paceibacteria, ABY1, and Gracilibacteria, which were also present in a shallow groundwater system70 and are suggested to be prevalent in deep groundwaters by their ease of mobilization from soil coupled to their adaptation to low energy conditions71. Patescibacteria were not linked with specific DOM MFs, but with MFs of varying characteristics and all compound classes. In addition, they mostly co-occurred with either other Patescibacteria ASVs or ASVs affiliated with Chloroflexota, Dusulfobacterota, and ‘Unclassified’ phyla in the same module. This indicated coupling of the production and consumption of DOM compounds via a symbiotic host-associated lifestyle72. The coupled breakdown of complex DOM compounds was also supported by connector MFs, representing nodes that connect different modules (e.g., modules 3, 9, and 11) and therefore might reflect key intermediate compounds during microbial DOM degradation69. These connector MFs were positively and negatively linked to various microbial taxa, which might represent potential producers and consumers, respectively. Members of the phylum Desulfobacterota were also abundant in the network with module hub ASVs belonging to Desulfocapsa, Desulfatiglans, Desulfobacula, and unclassified genera of the order Syntrophales. Characterized members of the Desulfatiglans and Desulfobacula are heterotrophs and able to degrade aromatic compounds like phenol73, thus potentially being capable of degrading complex aromatic compounds like tannins and lignins in line with links between these two genera and mostly lignin-like MFs in the network (e.g., ASVs 218, 232, and 233; Fig. 7).
The small percentage of retained MFs suggested that although DOM was present, the large majority was resistant to microbial degradation. This supported the extremely long cell turnover times that have been calculated in the 100–1000 s of years for the deep marine biosphere74 and that the microbial community in terrestrial deep Saline waters in particular have been described as in “metabolic standby”56.
Organic carbon has a range of reactivities that are determined by the nature of the organic compounds, along with the biological, geochemical, and physical attributes of the environment75. The carbon advectively transported from Baltic Sea, meteoric, and soil sources into the deep continental bedrock fractures was mainly of terrigenous origin. The terrigenous DOM signature was retained and recalcitrant to degradation, potentially leading to the assembly of a core deep biosphere microbial community composition, supported by the low percentage of MFs associated to the community with Patescibacteria and Desulfobacterota as possible keystone taxa. Thus, deep Fennoscandian Shield microbes likely survive on the small pool of available molecules, such as the geogases carbon dioxide and methane plus the consumption of necromass as imprinted in the oldest, saline waters. This further supports the suggested “metabolic standby” of the communities, extremely long cell turnover rates, and “stop-and-start” replication processes. Future degradation experiments of the deep groundwater DOM under in situ conditions will aid to precisely disentangle mechanisms of subsurface DOM processing and its rates.
Sampling and water chemistry
In total, 13 packed-off borehole sections at depths ranging from 170 m to 507 mbsl were sampled for groundwater chemical components and isotopes (Fig. 176) at the Äspö HRL on Äspö Island in the Misterhult archipelago in Kalmar County, Sweden. Since the tunnels were constructed between 1990 and 1995, contamination from excavation has been minimized although the boreholes have been instrumented for varying times that has introduced materials into the deep groundwaters. Samples from boreholes SA1229A_1, KA3105A_3, SA1730A_1, KA3600F_2, and SA2600A_1 were taken in November 2018 (batch 1) followed by all other samples in May 2019 (batch 2; Tables 1 and S1). The boreholes were chosen to cover relatively recent brackish-marine to pre-Holocene, saline waters. In addition, two surface Baltic Sea locations were sampled: one coastal site from Borholmsfjärden in the vicinity of the Äspö HRL (surface water, N 57°25.296’, E 16°39.583’) and one offshore sample at the LMO located 11 km offshore Kårehamn, Öland (2 m water depth, N 56°55.854’, E 17°3.642’).
Hydrochemical variables were determined in the ISO 17025 rated Äspö HRL chemical laboratory as follows: pH was determined potentiometrically (±0.10 pH units), Cl− concentrations were determined by potentiometric titration (0.1 M AgNO3) with an analytical uncertainty of 6% (Swedish Standard 028136 edition 1), and SO42− concentrations were determined using ion chromatography (DIN EN ISO 10 304-1: 2009) with an analytical uncertainty of 12% (4.5–70 mg L−1) and 31% (0.5–4.5 mg L−1). Dissolved Fe2+ and total iron (Fetotal) concentrations were determined immediately after sampling using a spectrophotometer (UVPC2401; Shimadzu, Kyoto Japan) and a modified ferrozine method77 with an analytical uncertainly of ±0.005% (0.02–0.05 mg L−1), 8% (0.05–1 mg L−1), and 13% (1–3 mg L−1) depending on concentration range. Oxygen concentrations could not be reliably measured due to the setup installed to obtain water from the boreholes, but Fe2+ concentrations equal to the Fetotal indicated anoxic conditions in all samples except those from the Baltic Sea. HS− concentrations were determined with a spectrophotometer (as above) with an analytical uncertainty of 32% (Swedish Standard SIS 02 81 15). Concentrations of Ca, K, Mg, and Na cations were determined with inductively coupled plasma atomic emission spectroscopy at ALS Scandinavia AB in Luleå (Sweden) with an analytical uncertainty of 12%. Analysis of the water 18O/16O and 2H/1H ratio (per mil deviation from Standard Mean Ocean Water, SMOW) were determined by laser spectrometry (Los Gatos Research; Triple-Liquid Water Isotope Analyzer) with an analytical uncertainly of ±0.25 and ±1.5 unit, respectively. δ13C- and pMC-DIC and DOC were determined on whole water samples at the Ångström Laboratory in Uppsala (Sweden) using Accelerator Mass Spectrometry, with an analytical uncertainty of 0.3‰ PDB for δ13C and 0.2–0.5 pMC.
Dissolved organic matter
All glassware used for sampling was pre-combusted (4 h, 400 °C) or thoroughly soaked in ultrapure water at pH 2 and rinsed with sample before use. Samples for DOM quantification and molecular characterization were filtered (Durapore PVDF 0.22 µm) and acidified to pH 2 with HCl before shipping to the University of Oldenburg (Germany). DOC and TDN were quantified from duplicate subsamples using a Shimadzu TOC-L with an ASI-L autosampler with concentrations determined via an L-arginine standard curve. Then, ~1 L of each sample was extracted onto 1 g solid-phase extraction columns (Bond Elut PPL, Agilent) and eluted with 6 mL methanol (ULC grade). DON was calculated from TDN − (nitrate + nitrite + ammonium). Extract DOC concentrations were determined by drying an aliquot of the extract and re-dissolving it in ultrapure water at pH 2. The extracts were then stored in the dark at −20 °C until further analysis. Stable carbon isotope composition of solid-phase extracted DOM (δ13C-SPE-DOC) was analyzed to better characterize the analytical window of the SPE method. Aliquots containing ~10 µmol C were dried in doubled Sn caps (IVA, Germany) and analyzed using an Elemental Analyzer (Thermo Scientific Flash) coupled to a continuous‐flow isotope ratio mass spectrometer (Thermo Scientific Delta V) via an Advantage Conflo IV interface. Values were corrected with procedural blanks, using the dried extracts from ultrapure water processing in doubled Sn caps. Technical replicates deviated <0.3‰, accuracy was better than 0.2‰.
Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) analysis was performed on a 15 T Solarix (Bruker Daltonics, Billerica, MA, USA, equipped with a ParaCell). Samples were diluted to yield a concentration of ~5 ppm in ultrapure water and methanol 50:50 (vol/vol). This dilution was filtered through pre-cleaned 0.2 µm polycarbonate syringe filters before analysis performed in random order. Electrospray ionization in negative mode (Bruker Apollo II) was done at 200 °C and the capillary voltage was set to 4.5 kV. The sample was injected at a flow rate of 120 µL h−1, the accumulation time was set to 0.05 s, and 300 scans were co-added for each spectrum in a mass range of 92–2000 Da. Batch 1 samples were analyzed once while batch 2 samples were run once when replicate extractions existed and twice from the same extract if only one extraction existed. Each spectrum was internally calibrated with lists of known masses. Mass spectra were exported from the Bruker Data Analysis software at a signal-to-noise ratio of 0 and MFs were assigned using the ICBM Ocean tool78. All analyses (batch 1 and 2) were processed together. The method detection limit was set to 3. Junction of mass lists along m/z was performed via fast join at a tolerance of 0.5 ppm while standard smooth and additional isotope tolerance was 10‰. Singlet peaks occurring only once in the dataset were removed, then MF attribution was done with a tolerance of 0.5 ppm in the range m/z 0–1000 within the limits C1–100H1–100O0–70N0–4S0–2P0–1. For isotopic verification of MF attributions, up to one 13C, 15N, and 34S isotopologue was allowed. Additional tolerance using quantile-based isotope ratio tolerances was set to 1000‰ and isotope ratio mismatches for peaks below the method detection limit <5 were permitted. MF suggestions with >3 heteroelements (except N4) and four common contaminant MFs were removed (C16H32O2, C12H26O4S, C17H28O3S, C10H15NO2S; likely NBBS from tubing) were removed from all samples. Before statistical analysis, relative intensities were normalized to the sum of intensities per sample and the highest minimum between all samples was set as detection limit and all peaks with lower normalized relative abundance were set to 0 to improve comparability between spectra. Furthermore, MFs were only retained in the dataset if they occurred at least three times across all samples. Molecular indices indicating lability through relative contribution of MFs above the molecular lability boundary of H/CMF = 1.5, MLBw79, and aromaticity as modified aromaticity index, AImod80, were calculated. MFs were assigned to molecular categories within the boundaries described by ref. 81 as unsaturated oxygen-poor (0 < O/C ≤ 0.29, 1.6 ≤ H/C ≤ 2.5), unsaturated (0.29 < O/C ≤ 0.6, 1.5 ≤ H/C ≤ 2.5), amino sugar- and carbohydrate-like (0.6 ≤ O/C ≤ 1.2, 1.5 ≤ H/C ≤ 2.5), unsaturated hydrocarbon-like (0 < O/C ≤ 0.29, 1 ≤ H/C ≤ 1.6), condensed aromatics (0 ≤ O/C ≤ 0.4, 0 ≤ H/C ≤ 0.7), lignin-like (0.29 < O/C ≤ 0.65, 0.7 < H/C < 1.5), and tannin-like (0.65 < O/C ≤ 1.2, 0.5 ≤ H/C < 1.5). MFs that could not be assigned to a class and MFs assigned to multiple categories were designated as unclassified. Elemental ratios calculated from FT-ICR-MS formula assignments were weighted by peak intensities and denoted throughout the manuscript via subscript MF (e.g., S/CMF).
Microbial community composition
The Äspö HRL microbial community data used in this study was sampled in spring 2017 and published by ref. 33. While the groundwaters were sampled for microbial community analysis prior to the DOM sampling, studies at the Äspö HRL show that the water chemistry is relatively uniform and the microbial community’s rRNA and mRNA based composition and activities are stable over several years55. Microbial communities and the corresponding water chemistry data were sampled for boreholes SA1229A_1, KA2051A01_5, KA2511A_5, KA3105A_3, KA3600F_2, KA3385A_1, SA2600A_1, KA1755A_3, KA2862A_1, and SA1730A_1, whereas no data was available for the boreholes KA2865A01_1, KA3510A_2, and HA2780A_1. For a detailed description on sampling and sample processing refer to ref. 33. Briefly, planktonic microbial communities in Äspö HRL groundwaters were sampled in triplicates by connecting high-pressure filter holders (Millipore) to the borehole. Cells were captured on 0.1 µm pore size membrane filters and immediately flash frozen in liquid nitrogen. DNA from filters was extracted (MO BIO PowerWater DNA isolation kit) and the 16 S rRNA gene was amplified with the bacterial primers 341 F and 805R82. High-throughput sequencing was done on the Illumina MiSeq platform at the Science for Life Laboratory (Sweden) according to ref. 34. For this study, the sequencing data were reanalyzed using the DADA2 software package83, 1.14.1) in R (R Core Team; version 3.6.0). After the filter and trim step, remaining primer sequences were removed with Cutadapt (ref. 84, version 2.3). ASVs were inferred from denoised reads and after merging read pairs, ASVs were classified using the GTDB database (ref. 85; release 89). Details of the Äspö HRL DNA concentrations are provided in ref. 33. Details of the DADA2 reprocessed data are provided in Table S2. Rarefaction curves indicated a sufficient sequencing depth to capture the microbial diversity (Supplementary Fig. S7).
Three different datasets were used throughout the manuscript: (1) DOM composition was analyzed based on all available samples including technical and analytical replicates (n = 35). (2) Statistical analyses including DOM and water chemistry were based on a dataset containing mean spectra of DOM from the same borehole regardless of sampling date (n = 15). (3) For statistical analyses including DOM composition, hydrochemistry, and microbial community compositions, the number of samples was further reduced due to the availability of the sequencing data (n = 10). Relative intensities of spectra, relative abundances of microbial ASVs, and environmental parameters were averaged per borehole for the analyses including more than one of these datasets.
Bray Curtis dissimilarities were used to assess the overall dissimilarity of the DOM fingerprints and microbial community composition and major gradients of variability were visualized with non-metric multidimensional scaling (NMDS). The congruence of DOM molecular and microbial community composition was analyzed via Procrustes rotation. The impact of mixing on DOM and microbial community composition was assessed via distance-based redundancy analysis. These statistical analyses were performed using R (Version 4.0.3) and the vegan package86. Pairwise associations between microbial ASVs and DOM MFs were investigated via proportionality analysis to account for the compositional nature of both datasets87. For this, the overlapping datasets for DOM and microbial community composition were used (n = 10). MFs and ASVs were only considered for the analysis if they were present in at least five samples; low-abundant ASVs were removed (relative abundance < 0.1%); zeros were replaced by their respective minimum value/10 to account for the fact that the range of relative intensities and abundances covered several orders of magnitude; and each dataset was center log-ratio (clr) transformed separately. Proportionality measure ρ was then calculated for the combined ASV and MF data matrix (propr package). The selection of a cutoff for ρ was based on permutation of the false-discovery rate (FDR) for different ρ88. To control for a FDR < 5%, |ρ | ≥ 0.85 was chosen as cutoff for network construction. The resulting network retained a relationship between the relative abundances of 229 ASVs and relative intensities of 1388 MFs. Network visualization was done in Gephi (version 0.9.289). Network modularity, module separation and roles of nodes were determined via the interdomain ecological network analysis pipeline (IDENAP; http://mem.rcees.ac.cn:808190) with the fast greedy modularity optimization91. The potential ecological role of each node was assigned based on within-module connectivity (z) and among-module connectivity (P)92 and nodes were classified into four categories: peripheral nodes (z ≤ 2.5, P ≤ 0.62), connectors (z ≤ 2.5, P > 0.62), module hubs (z > 2.5, P ≥ 0.62), and network hubs (z > 2.5, P > 0.62)69.
Ferguson, G. et al. Crustal Groundwater Volumes Greater Than Previously Thought. Geophys. Res. Lett. 48, e2021GL093549 (2021).
McDonough, L. K. et al. Changes in global groundwater organic carbon driven by climate change and urbanization. Nat. Commun. 11, 1279 (2020).
Magnabosco, C. et al. The biomass and biodiversity of the continental subsurface. Nat. Geosci. 11, 707–717 (2018).
Flemming, H.-C. & Wuertz, S. Bacteria and archaea on Earth and their abundance in biofilms. Nat. Rev. Microbiol. 17, 247–260 (2019).
Hansell, D. A. Recalcitrant Dissolved Organic Carbon Fractions. Annu. Rev. Mar. Sci. 5, 421–445 (2013).
Lang, S. Q., Osburn, M. R. & Steen, A. D. In Deep Carbon: Past to Present (eds Beth N. Orcutt, Isabelle Daniel, & Rajdeep Dasgupta) 480–523 (Cambridge University Press, 2019).
Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol.-Prog. Ser. 10, 257–263 (1983).
Kerr, S. C., Shafer, M. M., Overdier, J. & Armstrong, D. E. Hydrologic and Biogeochemical Controls on Trace Element Export from Northern Wisconsin Wetlands. Biogeochemistry 89, 273–294 (2008).
Krop, H. B., van Noort, P. C. M. & Govers, H. A. J. In Reviews of Environmental Contamination and Toxicology: Continuation of Residue Reviews (ed. George W. Ware) 1–122 (Springer New York, 2001).
McDonough, L. et al. A new conceptual framework for the transformation of groundwater dissolved organic matter. Nat. Commun. 13, https://doi.org/10.1038/s41467-022-29711-9 (2022).
Kalbitz, K., Solinger, S., Park, J. H., Michalzik, B. & Matzner, E. Controls on the dynamics of dissolved organic matter in soils: a review. Soil Sci. 165 (2000). https://doi.org/10.1097/00010694-200004000-00001
Longnecker, K. & Kujawinski, E. B. Composition of dissolved organic matter in groundwater. Geochim. et. Cosmochim. Acta 75, 2752–2761 (2011).
Hach, P. F. et al. Rapid microbial diversification of dissolved organic matter in oceanic surface waters leads to carbon sequestration. Sci. Rep. 10, 13025–13025 (2020).
Altieri, K. E., Turpin, B. J. & Seitzinger, S. P. Oligomers, organosulfates, and nitrooxy organosulfates in rainwater identified by ultra-high resolution electrospray ionization FT-ICR mass spectrometry. Atmos. Chem. Phys. 9, 2533–2542 (2009).
Pohlabeln, A. M., Gomez-Saez, G. V., Noriega-Ortega, B. E. & Dittmar, T. Experimental Evidence for Abiotic Sulfurization of Marine Dissolved Organic Matter. Front. Marine Sci. 4, https://doi.org/10.3389/fmars.2017.00364 (2017).
Abdulla, H. A., Burdige, D. J. & Komada, T. Abiotic formation of dissolved organic sulfur in anoxic sediments of Santa Barbara Basin. Org. Geochem. 139, 103879 (2020).
Sinninghe Damste, J. S. & De Leeuw, J. W. Analysis, structure and geochemical significance of organically-bound sulphur in the geosphere: State of the art and future research. Org. Geochem. 16, 1077–1101 (1990).
Shen, Y., Chapelle, F. H., Strom, E. W. & Benner, R. Origins and bioavailability of dissolved organic matter in groundwater. Biogeochemistry 122, 61–78 (2015).
Hedges, J. I. et al. Origins and processing of organic matter in the Amazon River as indicated by carbohydrates and amino acids. Limnol. Oceanogr. 39, 743–761 (1994).
Murphy, E. M., Davis, S. N., Long, A., Donahue, D. & Jull, A. J. T. Characterization and isotopic composition of organic and inorganic carbon in the Milk River Aquifer. Water Resour. Res. 25, 1893–1905 (1989).
Routh, J., Grossman, E. L., Murphy, E. M. & Benner, R. Characterization and origin of dissolved organic carbon in Yegua ground water in Brazos County, Texas. Ground Water 39, 760–767 (2001).
Lapworth, D. J., Gooddy, D. C., Butcher, A. S. & Morris, B. L. Tracing groundwater flow and sources of organic carbon in sandstone aquifers using fluorescence properties of dissolved organic matter (DOM).Appl. Geochem. 23, 3384–3390 (2008).
Chapelle, F. H. & Lovley, D. R. Rates of microbial metabolism in deep coastal plain aquifers. Appl. Environ. Microbiol. 56, 1865–1874 (1990).
Casar, C. P., Kruger, B. R. & Osburn, M. R. Rock-Hosted Subsurface Biofilms: Mineral Selectivity Drives Hotspots for Intraterrestrial Life. Front. Microbiol. 12, 658988 (2021).
Magnabosco, C. et al. A metagenomic window into carbon metabolism at 3 km depth in Precambrian continental crust. ISME J. 10, 730–741 (2016).
Kieft, T. L. et al. Dissolved organic matter compositions in 0.6–3.4 km deep fracture waters, Kaapvaal Craton, South Africa. Org. Geochem. 118, 116–131 (2018).
Benk, S. A. et al. Fueling Diversity in the Subsurface: Composition and Age of Dissolved Organic Matter in the Critical Zone. Front. Earth Sci. 7, https://doi.org/10.3389/feart.2019.00296 (2019).
Laaksoharju, M., Tullborg, E.-L., Wikberg, P., Wallin, B. & Smellie, J. Hydrogeochemical conditions and evolution at the Äspö HRL, Sweden. Appl. Geochem. 14, 835–859 (1999).
Laaksoharju, M., Gascoyne, M. & Gurban, I. Understanding groundwater chemistry using mixing models. Appl. Geochem. 23, 1921–1940 (2008).
Mathurin, F., Astrom, M., Laaksoharju, M., Kalinowski, B. & Tullborg, E.-L. Effect of Tunnel Excavation on Source and Mixing of Groundwater in a Coastal Granitoidic Fracture Network. Environ. Sci. Technol. 46, https://doi.org/10.1021/es301722b (2012).
Seidel, M. et al. Composition and Transformation of Dissolved Organic Matter in the Baltic Sea. Front. Earth Sci. 5, 31 (2017).
Mehrshad, M. et al. Energy efficiency and biological interactions define the core microbiome of deep oligotrophic groundwater. Nat. Commun. 12, 4253 (2021).
Lopez-Fernandez, M., Åström, M., Bertilsson, S. & Dopson, M. Depth and Dissolved Organic Carbon Shape Microbial Communities in Surface Influenced but Not Ancient Saline Terrestrial Aquifers. Front. Microbiol. 9, https://doi.org/10.3389/fmicb.2018.02880 (2018).
Wu, X. et al. Microbial metagenomes from three aquifers in the Fennoscandian shield terrestrial deep biosphere reveal metabolic partitioning among populations. ISME J. 10, 1192–1203 (2016).
Banwart, S. et al. Organic carbon oxidation induced by large-scale shallow water intrusion into a vertical fracture zone at the Äspö Hard Rock Laboratory (Sweden). J. Contaminant Hydrol. 21, 115–125 (1996).
van Weert, F., van der Gun, J. & Reckman, J. Global Overview of Saline Groundwater Occurrence and Genesis (Report number: GP 2009-1). IGRAC - U. N. Int. Groundw. Resour. Assess. Cent., 1–32 (Utrecht, Netherlands, 2009).
Deutsch, B., Alling, V., Humborg, C., Korth, F. & Mörth, C. M. Tracing inputs of terrestrial high molecular weight dissolved organic matter within the Baltic Sea ecosystem. Biogeosciences 9, 4465–4475 (2012).
Laaksoharju, M. et al. Bedrock hydrogeochemistry Laxemar. Site descriptive modelling SDM-Site Laxemar. SKB Report R-08-93. (SKB, 2009).
Kim, S., Kramer, R. W. & Hatcher, P. G. Graphical method for analysis of ultrahigh-resolution broadband mass spectra of natural organic matter, the van Krevelen diagram. Anal. Chem. 75, 5336–5344 (2003).
Gomez-Saez, G. V. et al. Sulfurization of dissolved organic matter in the anoxic water column of the Black Sea. Sci. Adv. 7, eabf6199 (2021).
Aiken, G. R. Organic matter in groundwater. U.S. Geological Survey Open File Report 02-89, 20–23. U.S. Geological Survey Artificial RechargeWorkshop Proceedings, (eds George R. Aiken and Eve L. Kuniansky) April 2-4, (Sacramento, California, 2002).
Chapelle, F. H. The Bioavailability of Dissolved, Particulate, and Adsorbed Organic Carbon in Groundwater Systems. Ground Water 59, 226–235 (2021).
Raeke, J., Lechtenfeld, O. J., Wagner, M., Herzsprung, P. & Reemtsma, T. Selectivity of solid phase extraction of freshwater dissolved organic matter and its effect on ultrahigh resolution mass spectra. Environ. Sci.: Process. Impacts 18, 918–927 (2016).
Nausch, M. & Kerstan, E. The relationship between dissolved carbohydrates and carbohydrate-degrading enzymes in the salinity gradient of the Pomeranian Bight (Southern Baltic). Oceanologia 45, 16 (2003).
Westmeijer, G. et al. Connectivity of Fennoscandian Shield terrestrial deep biosphere microbiomes with surface communities. Commun. Biol. 5, 37 (2022).
Thurman, E. M. In Humic substances in soil, sediment, and water: Geochemistry, isolation, and characterization (eds G. R. Aiken, D.M. McKnight, R. L. Wershaw, & Peter M. McCarthy) Ch. 87–103, (Wiley, 1985).
Fullerton, K. M. et al. Effect of tectonic processes on biosphere–geosphere feedbacks across a convergent margin. Nat. Geosci. 14, 301–306 (2021).
Drake, H., Sandström, B. & Tullborg, E.-L. Mineralogy and geochemistry of rocks and fracture fillings from Forsmark and Oskarshamn: Compilation of data for SR-Can. Report No. R-06-109, (SKB (Swedish Nuclear Fuel and Waste Management Company, 2006).
Mathurin, F. A., Åström, M. E., Drake, H., Maskenskaya, O. M. & Kalinowski, B. E. REE and Y in groundwater in the upper 1.2km of Proterozoic granitoids (Eastern Sweden) – Assessing the role of composition and origin of groundwaters, geochemistry of fractures, and organic/inorganic aqueous complexation. Geochim. et. Cosmochim. Acta 144, 342–378 (2014).
Riedel, T., Biester, H. & Dittmar, T. Molecular fractionation of dissolved organic matter with metal salts. Environ. Sci. Technol. 46, 4419–4426 (2012).
Rebhun, M. & Lurie, M. Control of Organic Matter by Coagulation and Floc Separation. Water Sci. Technol. 27, 1–20 (1993).
Drake, H., Åström, M. E., Tullborg, E.-L., Whitehouse, M. & Fallick, A. E. Variability of sulphur isotope ratios in pyrite and dissolved sulphate in granitoid fractures down to 1km depth – Evidence for widespread activity of sulphur reducing bacteria. Geochim. et. Cosmochim. Acta 102, 143–161 (2013).
Gimeno, M. J., Auqué, L. F., Acero, P. & Gómez, J. B. Hydrogeochemical characterisation and modelling of groundwaters in a potential geological repository for spent nuclear fuel in crystalline rocks (Laxemar, Sweden). Appl. Geochem. 45, 50–71 (2014).
Bell, E. et al. Biogeochemical Cycling by a Low-Diversity Microbial Community in Deep Groundwater. Front. Microbiol. 9, https://doi.org/10.3389/fmicb.2018.02129 (2018).
Lopez-Fernandez, M., Broman, E., Simone, D., Bertilsson, S. & Dopson, M. Statistical Analysis of Community RNA Transcripts between Organic Carbon and Geogas-Fed Continental Deep Biosphere Groundwaters. mBio 10, https://doi.org/10.1128/mBio.01470-19 (2019).
Lopez-Fernandez, M. et al. Metatranscriptomes Reveal That All Three Domains of Life Are Active but Are Dominated by Bacteria in the Fennoscandian Crystalline Granitic Continental Deep Biosphere. mBio 9, https://doi.org/10.1128/mBio.01792-18 (2018).
Kang, P.-G., Mitchell, M. J., Mayer, B. & Campbell, J. L. Isotopic Evidence for Determining the Sources of Dissolved Organic Sulfur in a Forested Catchment. Environ. Sci. Technol. 48, 11259–11267 (2014).
Ladd, B. et al. Carbon isotopic signatures of soil organic matter correlate with leaf area index across woody biomes. J. Ecol. 102, 1606–1611 (2014).
Hullar, M., Fry, B., Peterson, B. J. & Wright, R. T. Microbial utilization of estuarine dissolved organic carbon: a stable isotope tracer approach tested by mass balance. Appl. Environ. Microbiol. 62, 2489–2493 (1996).
Vane, C. H. et al. Molecular Characterisation of Dissolved Organic Matter (DOM) in Groundwaters from the Äspö Underground Research Laboratory, Sweden: A Novel Finger Printing Tool for Palaeohydrological Assessment. MRS Proc. 1107, 557 (2011).
Medeiros, P. M. et al. Microbially-Mediated Transformations of Estuarine Dissolved Organic Matter. Front. Mar. Sci. 4, 69 (2017).
Shah Walter, S. R. et al. Microbial decomposition of marine dissolved organic matter in cool oceanic crust. Nat. Geosci. 11, 334–339 (2018).
Osterholz, H., Niggemann, J., Giebel, H. A., Simon, M. & Dittmar, T. Inefficient microbial production of refractory dissolved organic matter in the ocean. Nat. Commun. 6, 7422 (2015).
Schmidt, F. et al. Diagenetic Transformation of Dissolved Organic Nitrogen Compounds under Contrasting Sedimentary Redox Conditions in the Black Sea. Environ. Sci. Technol. 45, 5223–5229 (2011).
Kietäväinen, R. & Purkamo, L. The origin, source, and cycling of methane in deep crystalline rock biosphere. Front. Microbiol. 6, https://doi.org/10.3389/fmicb.2015.00725 (2015).
Vorobev, A. et al. Identifying labile DOM components in a coastal ocean through depleted bacterial transcripts and chemical signals. Environ. Microbiol. 20, 3012–3030 (2018).
Ghiorse, W. C. & Wilson, J. T. In Advances in Applied Microbiology Vol. 33 (ed. Allen I. Laskin) 107–172 (Academic Press, 1988).
Lopez-Fernandez, M. et al. Investigation of viable taxa in the deep terrestrial biosphere suggests high rates of nutrient recycling. FEMS Microbiol. Ecol. 94, https://doi.org/10.1093/femsec/fiy121 (2018).
Olesen, J. M., Bascompte, J., Dupont, Y. L. & Jordano, P. The modularity of pollination networks. Proc. Natl Acad. Sci. 104, 19891 (2007).
Chaudhari, N. M. et al. The economical lifestyle of CPR bacteria in groundwater allows little preference for environmental drivers. bioRxiv, 2021.2007.2028.454184, https://doi.org/10.1101/2021.07.28.454184 (2021).
Herrmann, M. et al. Predominance of Cand. Patescibacteria in Groundwater Is Caused by Their Preferential Mobilization From Soils and Flourishing Under Oligotrophic Conditions. Front. Microbiol. 10, https://doi.org/10.3389/fmicb.2019.01407 (2019).
Anantharaman, K. et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat. Commun. 7, 13219 (2016).
Suzuki, D., Li, Z., Cui, X., Zhang, C. & Katayama, A. Reclassification of Desulfobacterium anilini as Desulfatiglans anilini comb. nov. within Desulfatiglans gen. nov., and description of a 4-chlorophenol-degrading sulfate-reducing bacterium, Desulfatiglans parachlorophenolica sp. nov. Int. J. Syst. Evolut. Microbiol. 64, 3081–3086 (2014).
Lomstein, B. A., Langerhuus, A. T., D’Hondt, S., Jørgensen, B. B. & Spivack, A. J. Endospore abundance, microbial growth and necromass turnover in deep sub-seafloor sediment. Nature 484, 101–104 (2012).
LaRowe, D. E. et al. The fate of organic carbon in marine sediments - New insights from recent data and analysis. Earth-Sci. Rev. 204, 103146 (2020).
Nilsson, A.-C. et al. Methodology for Hydrogeochemical Sampling to Characterise Groundwaters in Crystalline Bedrock: Developments Made within the Swedish Radwaste Programme. Geofluids 2020, 8740492 (2020).
To, T. B., Nordstrom, D. K., Cunningham, K. M., Ball, J. W. & McCleskey, R. B. New Method for the Direct Determination of Dissolved Fe(III) Concentration in Acid Mine Waters. Environ. Sci. Technol. 33, 807–813 (1999).
Merder, J. et al. ICBM-OCEAN: Processing Ultrahigh-Resolution Mass Spectrometry Data of Complex Molecular Mixtures. Anal. Chem. 92, 6832–6838 (2020).
D’Andrilli, J., Cooper, W. T., Foreman, C. M. & Marshall, A. G. An ultrahigh-resolution mass spectrometry index to estimate natural organic matter lability. Rapid Commun. Mass Spectrom. 29, 2385–2401 (2015).
Koch, B. P. & Dittmar, T. From mass to structure: an aromaticity index for high-resolution mass data of natural organic matter. Rapid Commun. Mass Spectrom. 20, 926–932 (2006).
D’Andrilli, J., Junker, J. R., Smith, H. J., Scholl, E. A. & Foreman, C. M. DOM composition alters ecosystem function during microbial processing of isolated sources. Biogeochemistry 142, 281–298 (2019).
Herlemann, D. P. R. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal; Vol 17, No 1: Next Generation Sequencing Data AnalysisDO − https://doi.org/10.14806/ej.17.1.200 (2011).
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).
vegan: Community Ecology Package. R package version 2.5-6. https://CRAN.R-project.org/package=vegan (2019).
Quinn, T. P., Richardson, M. F., Lovell, D. & Crowley, T. M. propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis. Sci. Rep. 7, 16252 (2017).
Quinn, T. P. et al. A field guide for the compositional analysis of any-omics data. GigaScience 8, https://doi.org/10.1093/gigascience/giz107 (2019).
Bastian, M., Heymann, S. & Jacomy, M. Gephi: An Open Source Software for Exploring and Manipulating Networks. Proc. Int. AAAI Conf. Web Soc. Media 3, 361–362 (2009).
Feng, K., Zhang, Y., He, Z., Ning, D. & Deng, Y. Interdomain ecological networks between plants and microbes. Mol. Ecol. Resour. 19, 1565–1577 (2019).
Newman, M. E. J. Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133 (2004).
Guimerà, R. & Nunes Amaral, L. A. Functional cartography of complex metabolic networks. Nature 433, 895–900 (2005).
Osterholz, H. et al. Water chemistry of unfiltered groundwater samples from the Fennoscandian Shield deep terrestrial biosphere in 2018 and 2019. PANGAEA, https://doi.org/10.1594/PANGAEA.939654 (2021).
Alakangas, L. J., Mathurin, F. A. & Åström, M. E. Diverse fractionation patterns of Rare Earth Elements in deep fracture groundwater in the Baltic Shield – Progress from utilisation of Diffusive Gradients in Thin-films (DGT) at the Äspö Hard Rock Laboratory. Geochim. et. Cosmochim. Acta 269, 15–38 (2020).
The authors thank Jarone Pinhassi and the LMO sampling team, especially Benjamin Pontiller, for providing the LMO Baltic Sea water sample. The authors are grateful to Iris Liskow and Maren Voss (Leibniz Institute for Baltic Sea Research Warnemünde, Germany) for stable carbon isotope analyses of solid phase DOM extracts. High-throughput sequencing was carried out at the National Genomics Infrastructure hosted by the Science for Life Laboratory. Bioinformatics analyses were carried out utilizing the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) at Uppsala University (projects SNIC 2021/22-628 and SNIC 2021/6-256). The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at UPPMAX partially funded by the Swedish Research Council through grant agreement no. 2016-07213. M.D. wishes to thank the Swedish Research Council (contract 2018-04311) for support.
Open Access funding enabled and organized by Projekt DEAL.
This study was partially funded by The Swedish Nuclear Fuel and Waste Management Company (SKB) as part of the Swedish investigation of the safety of disposal concepts for long-term storage of nuclear waste that employs authors L.A. and B.K. The remaining authors declare no competing interests.
Peer review information
Nature Communications thanks Harshad Kulkarni and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Osterholz, H., Turner, S., Alakangas, L.J. et al. Terrigenous dissolved organic matter persists in the energy-limited deep groundwaters of the Fennoscandian Shield. Nat Commun 13, 4837 (2022). https://doi.org/10.1038/s41467-022-32457-z
This article is cited by
Communications Earth & Environment (2023)
Thiobacillus as a key player for biofilm formation in oligotrophic groundwaters of the Fennoscandian Shield
npj Biofilms and Microbiomes (2023)
Communications Earth & Environment (2023)