Ecology and biogeochemistry of the microbial underworld in two sister soda lakes

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Paquette, Srijak Bhatnagar, Agasteswar Vadlamani, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3861392/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Approximately 3.7 billion years ago, microbial life may have emerged in phosphate-rich salty ponds. Surprisingly, analogs of these environments are present in alkaline lake systems, recognized as highly productive biological ecosystems. Investigating the microbial ecology of two Canadian soda lake sediment systems characterized by naturally high phosphate levels. Using a comprehensive approach involving geochemistry, metagenomics, and amplicon sequencing, we discovered that groundwater infiltration into Lake Goodenough sediments supported stratified layers of microbial metabolisms fueled by decaying mats. Effective degradation of microbial mats resulted in unexpectedly low net productivity. Evaporation of water from Last Chance Lake and its sediments led to saturation of brines and a habitat dominated by inorganic precipitation reactions, with low productivity, low organic matter turnover and little biological uptake of phosphorus, leading to high phosphate concentrations. Our research highlights that modern analogs for origin-of-life conditions might be better represented by soda lakes with low phosphate concentrations. Highly alkaline brines were found to be dominated by potentially dormant spore-forming bacteria. These saturated brines also hosted potential symbioses between Halobacteria and Nanoarchaeaota , as well as Lokiarchaea and bacterial sulfate reducers. Metagenome-assembled genomes of Nanoarchaeaota lacked strategies for coping with salty brines and were minimal for Lokiarchaea . Thus, highly alkaline brine environments could be too extreme to support origin of life scenarios. These findings shed light on the complex interplay of microbial life in extreme environments and contribute to our understanding of early Earth environments. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Alkaline soda lakes are distributed worldwide and are typically highly productive ecosystems, with pH > 9, low calcium and magnesium concentrations, and a high abundance of sodium and carbonate species (HCO 3 − and CO 3 2− ) [ 1 – 4 ]. These lakes are located in arid and semi-arid environments and form in closed basins where evaporation exceeds inflow. At Northern latitudes, they undergo extreme desiccation in the summer months and complete freezing in winter. Thus, alongside a pH of ~ 10 and high salt concentrations, the microorganisms inhabiting these lakes must also survive a periodic dearth of water and adapt to extreme changes in temperature. From an origin of life context, the periodic cycling of precipitation and desiccation may be central to the formation of prebiotic molecules, by enabling RNA phosphorylation [ 5 ]. Alkaline soda lakes have been proposed as a promising setting for the origins of life due to the higher abundance of available phosphate in comparison to other aqueous environments on Earth [ 6 , 7 ]. The high pH of soda lakes ensures that calcium precipitates as calcium carbonate instead of calcium phosphate, resulting in total phosphorus concentrations ranging from 1–17 mM and up to 50 mM in concentrated brine pools resulting from lake desiccation in the autumn [ 6 – 13 ]. These lakes are not only interesting from a perspective of probiotic chemistry but also for astrobiologists investigating microbial survival strategies in high-salt, low-temperature environments as found in the icy moons of the outer solar system [ 14 ]. Here, we compare the sediment geochemistry and microbial ecology of two very different soda lakes on the Cariboo Plateau, central British Columbia, Canada, separated by a glacial moraine less than 50 m wide [ 7 , 8 , 15 , 16 ]. Both lakes generally freeze over in November and remain covered in ice for 4–6 months. One of these two lakes, Lake Goodenough, holds water year-round and features prolific microbial mats that bloom during the ice-free spring and summer. In Lake Goodenough, phosphate is largely locked biologically into the mats and underlying sediments leading to a wide range of dissolved phosphate concentrations in the water (0.3–1.4 mM). Neighboring Last Chance Lake evaporates nearly completely during summer, forming a series of polygonal pools with extensive salt crystallization. The lake is relatively barren, with no microbial mats. A recent study of Last Chance Lake, used isotopic analysis of N 2 fixation rates to conclude that rates of biological nitrogen fixation are suppressed by the high lake salinity, causing a lack of phosphate uptake, resulting in phosphate buildup in the lake [ 6 ]. In this lake, dissolved phosphate concentrations were comparatively high ranging from 4.4–38.0 mM, consistent with proposed origin-of-life scenarios [ 6 ]. Six sediment cores (three per lake) were sampled in high resolution and were investigated using a combination of 18S, and 16S rRNA gene amplicon sequencing, pore water, and sedimentary geochemistry. Geochronology was used to analyze the biogeochemical processes occurring in the sediments of Last Chance and Goodenough Lake. In addition, we provide a comprehensive analysis of the sediment microbial community of Last Chance Lake using shotgun metagenomics. We investigate the metabolic pathways and adaptations found in a type of ecosystem that, according to some, may have spawned life on Earth. 2. Results and discussion 2.1 Inorganic versus biological diagenesis Twenty 30–48 cm cores were recovered from Last Chance and Lake Goodenough (Fig. 1 ). Cores from Goodenough Lake were distinctly different from Last Chance Lake. Goodenough lake cores were darker, grayish blue in color, and had a pungent smell of sulfide. In comparison, cores from Last Chance were lighter, grayish brown to pale olive in color, and the smell of sulfide was not as evident (Fig. 1 B). X-ray fluorescence (XRF) showed that elemental sulfur content was higher in Last Chance, 104–600 counts/sec compared to 23–168 counts/sec in Goodenough (SI Fig. 5 C and SI Table 1&2). The upper 4–8 cm of the Goodenough cores consisted of a microbial mat (Fig. 1 B) that likely gets buried into the sediment, together with decaying mosquito, fly, and shrimp matter found in the top sediment layers. This was consistent with high organic matter content throughout the cores (Fig. 2 D, E, and F, SI Fig. 1 , and SI Table 1). Decay of organic matter might have driven sulfate reduction, explaining the observed sulfide smell and blue color of the cores. The Last Chance cores showed no visible microbial mat or other organic material (Fig. 1 B, Fig. 2 A, B, and C, SI Fig. 1 , and SI Table 2). Instead, Last Chance cores featured ubiquitously large (0.5-3 cm) white natron crystals, most abundant between − 20 cm and − 30 cm (SI Fig. 2 ). Natron crystals have been reported since 1924 [ 17 ], when very large masses of interlocking crystal structures were found throughout and below the pools [ 8 , 17 ]. The amount of natron crystal in Last Chance Lake was estimated at that time to be 70,000 tons. Precipitation of these crystals may have been driven by a gradual loss of water (Fig. 2 ) caused by seasonal evaporation of the lake above. In summary, whereas Goodenough sediments were defined by microbial turnover of organic matter culminating in sulfate reduction, diagenesis in Last Chance was dominated by inorganic processes leading to crystallization of natron and other minerals. To get an idea of the timescales these lake sediments were on, we performed age dating on a core of each lake. In the Goodenough core, the activity of 210 Pb was much higher than 226 Ra indicating atmospheric sourcing of 210 Pb (used for dating the core). 210 Pb activity (DPM/g dry weight) decreased with depth (SI Fig. 3 ). Using linear regression between 6–16 cm, the sediment accumulation rate in Goodenough Core 5 was calculated to be relatively low, 0.23 cm/yr (SI Fig. 3 ). In contrast, Last Chance 226 Ra was not significantly different from 210 Pb (SI Fig. 3 ), indicating a lack of detectable atmospherically sourced 210 Pb (making age dating impossible). The lack of 210 Pb might be explained by the seasonal evaporation of the overlying lake followed by the loss of deposited 210 Pb via wind erosion. Although Last Chance sediments could thus not be dated, it is likely that a lower influx of spring water and associated minerals, combined with lower organic matter production [ 18 ], leads to extremely slow sediment accumulation. We compared the geochemistry of the two lake sediments in Fig. 3 , including phosphate, the focus of origin-of-life scenarios. In Last Chance Lake, the sediment phosphate concentration (~ 4 mM) was in agreement with previous studies [ 6 ] and was comparable to the lake water (~ 4.1 mM) [ 16 ]. However, the low sediment water content makes these sediments otherwise quite inhospitable to life, as they harbored concentrated brines with pH, sodium, carbonate alkalinity, and nearly all other ions all much higher than neighboring Lake Goodenough (Fig. 3 , SI Fig. 4, and SI Tables 1 & 2). In Last Chance, most concentrations peaked at 20–30 cm, indicating potential (very slow) upward penetration of water into these sediments from deeper down (Fig. 3 , SI Fig. 4, and SI Tables 1 & 2). Sulfide was an exception as it displayed higher concentrations in Goodenough, 0.18–1.70 mM throughout, in comparison to Last Chance, 0.006–0.022 mM, with peaks near the sediment-water interface and at 20–26 cm depth. High sulfide concentrations have also been reported in overlying microbial mats in Goodenough Lake [ 15 ], which would additionally suggest that sulfide is probably not suppressing biological N 2 fixation, as previously suggested [ 6 ]. Last Chance Lake peaks in sulfide concentrations might be associated with hotspots in microbial sulfate reduction (Fig. 3 E & G, SI Fig. 5 B. SI Tables 1 & 2). It was also observed in the XRF data that iron abundance was 40x higher in the sediment of Last Chance Lake, and sulfur abundance was low in the sediment, suggesting that iron is precipitating mainly as iron oxide minerals instead of iron sulfide minerals, possibly resulting in the ocher colour observed in the Last Chance cores (Fig. 1 and SI Table 1&2). Summarizing these results, the phosphate concentration in present-day Last Chance Lake is high because the alkaline chemistry in carbonate-rich lakes prevents the binding of phosphate to calcium [ 7 ]. Further, the lake is otherwise a challenging environment for life, limiting biological uptake. The lake itself dries up seasonally, and water losses from underlying sediments fill the pore cavities with a concentrated brine. From a biological perspective, the extreme geochemistry of Last Chance Lake may thus not perfectly model conditions that gave rise to the origin of life. However, given the presence of potential hotspots of sulfate reduction, microbial communities may still inhabit these sediments. This is all the more interesting because the turnover of organic matter may be extremely slow, and slow growth makes coping with high salt and pH even more challenging for living cells. 2.2 Sediment microbial communities of Goodenough and Last Chance Lake Figure 4 : Microbial Sediment Community Analysis of Last Chance and Goodenough Lake. (A) Non-metric multidimensional scaling (NMDS) plots using Bray-Curtis dissimilarity to compare Goodenough and Last Chance Lake microbial sediment communities. (B) Bubble plots showing the total relative abundance of algae and cyanobacteria in the sediment of both lakes. (C) Bar graph showing the relative abundance of bacterial genera potentially involved in the sulfur cycle. (D) Major groups of microbes found in Last Chance sediments. A-C based on 16S rRNA gene amplicon sequencing; D based on small subunit rRNA retrieved from the metagenomic dataset of Last Chance core 1 with phyloFlash. Dethiobacteria and Desulfobulbia in C are part of Firmicutes and Desulfobacterota in D, respectively. Nonlinear multidimensional scaling of 16S rRNA amplicon data showed sediment communities in both lakes were completely different (Fig. 4A). Last Chance Lake also had fewer bacterial species-level operational taxonomic units (OTUs, clustered at 97% similarity) than Lake Goodenough, 1,300 and 2,400 OTUs, respectively (SI Fig. 6 ). This distinct difference in community richness was most likely due to the challenging living conditions of Last Chance. In Goodenough, a source spring close to Goodenough Core 1 appeared to influence community composition (Fig. 4A and SI Fig. 6 A), probably caused by the lower concentration of salts in the spring water compared to the rest of the lake. A stark difference between the two lakes was the drastic decrease in cyanobacterial abundance with depth in Lake Goodenough compared to Last Chance Lake, where cyanobacteria remained present throughout the core or even increased with depth (Fig. 4B). This indicated that the degradation of cyanobacteria was ineffective in Last Chance sediments, potentially because of “pickling” by its concentrated brines. The same trend was observed in the 18S rRNA amplicon sequencing data. Eukaryotic rRNA was not amplified below − 2 cm in Goodenough sediments, unlike Last Chance sediments where eukaryotic rRNA was amplified up to -30 cm (SI Fig. 7). At -30 cm, the main remaining eukaryotic species was Dunaliella , a unicellular alga commonly known as the primary producer in hypersaline environments [ 19 – 21 ]. In Goodenough sediments, the Eukaryotic community was dominated by macro-organisms affiliated with Nematoda (g. Monhysterida ), Arthropoda (g. Notostraca ), and Rotifera (c. Monogononta). All of these likely feeded off and contributed to the faster degradation of the mats and their cyanobacteria. The lack of microbial mats and slower degradation in Last Chance Lake directly affected the amount of organic matter available for the downstream biogeochemical cycles, such as the sulfur cycle. Gram-positive Dethiobacteria dominated all cores in both lake sediments, accounting for between ~ 10–78% of the bacterial 16S rRNA gene amplicon abundance (Fig. 4C and SI Fig. 6 A & B) and comprising 79 species-level OTUs. In Last Chance metagenomes, 21 Dethiobacteria genomes were assembled and binned (completeness > 81%, contamination < 6.6%, SI Table 3). Dethiobacteria diversity and abundance were much higher than previously found in Central Asian soda lakes [ 22 ]. Dethiobacteria are known as obligately anaerobic, sulfur disproportionating bacteria that use the Wood–Ljungdahl pathway to fix CO 2 . Both pure culture strains reported in the literature have been isolated from alkaline soda lakes [ 23 – 26 ]. Together, the 21 metagenome assembled genomes (MAGs) found here displayed considerable metabolic diversity, with with the majority associated with sulfate reduction and oxidation (adenosine-5′-phosphosulfate reductase; 62% of Dethiobacter ), sulfite reduction (anaerobic sulfite reductase; 62% of Dethiobacter ) and potentially, sulfur disproportionation (persulfide dioxygenase; 62% of Dethiobacter ) (SI Table 4; SI Fig. 10). All Dethiobacteria appeared capable of endospore formation, with MAGs containing up to 50 genes encoding all steps in endospore synthesis. Endospore formation is a physiological capability that provides a distinct ecological advantage to Dethiobacteria , allowing them to sporulate when environmental conditions become unfavorable. It is possible that when Dethiobacteria are not actually growing in Last Chance sediments they are capable of biding their time until conditions improve. Sulfate reducing bacteria (SRB) were identified in both lakes (Fig. 4C and SI Fig. 6 A & B). In Goodenough sediments they were present throughout the cores, at abundances between 3–15%. This could be explained by the high organic matter content of these sediments and was consistent with low sulfate and high sulfide concentrations found in these cores (Fig. 3 E & G, SI Fig. 5 B). In Last Chance sediments, they were much less abundant (generally 18%. This peak corresponded to an area of high sulfide concentrations and might be explained by the penetration of lower salinity groundwater from below, reducing brine concentrations and enabling these bacteria to feast on the preserved organic matter. SRB were mainly affiliated with Desulfobulbia , Desulfurivibrionia , and Desulfuromonadia . In metagenomes of Last Chance sediment, six MAGs (> 84% complete, < 4% contaminated) were affiliated with Desulfobacterota . Key genes for sulfate reduction were found for most SRB (SI Fig. 10 and SI Table 5). Archaeal communities were strikingly different between both lakes, with Last Chance dominated by Halobacteria and Nanoarchaeaota [ 27 ] and Goodenough Lake composed of a more diverse archaeal community (Fig. 5 A & B). In both lakes, cores taken from close proximity to the underground spring feeding the lakes (core 1) had a markedly different community composition in comparison to other cores (Fig. 5 A & B). Last Chance core 1 contained a higher percentage of Lokiarchaea (Fig. 5 A), and Goodenough core 1 had an increased abundance of Bathyarchaeia and Thermoplasmata (Fig. 5 B). A notable characteristic of all cores was the relationship between Nanoarchaeota and Halobacteria . When the abundance of the Nanoarchaeota was high, the abundance of Halobacteria was low, and vice versa (Fig. 5 A & B), as shown by co-occurrence analysis (SI Fig. 11). Co-occurrence analysis also showed a strong correlation between Lokiarchaeota and Desulfurivibrio (Figs. 4CD, 5B, SI Figs. 6 , 11). This was consistent with co-enrichment of Lokiarchaeota and SRB during lab cultivation [ 28 , 29 ]. They could have a syntrophic relationship with SRB feeding off hydrogen and formate produced by Lokiarchaeota [ 28 ]. Archaea synthesize domain specific isoprenoid dialkyl glycerol diether lipids. The C25 extended archaeol homologue, “extended archaeol” (EXT-AR), is specific to hypersaline/evaporitic environments and considered a taxonomic marker of Halobacteria [ 30 , 31 ], and more specifically the order Natrialbales [ 32 , 33 ]. Extended archaeol lipids are thought to create a “zip-link” membrane, effective against the high alkalinity and extreme osmotic stress [ 34 ] prevalent in soda lake environments. We detected EXT-AR in sediments of both lakes. Consistent with the high abundance of Natrialbales in Last Chance sediments, EXT-AR abundance was also highest there (SI Fig. 8). However, at some depths EXT-AR abundance was high with hardly any Natrialbales detected. This indicated that these lipids might not always be associated with living Natrialbales. Intriguingly, in those samples high abundances of Pacearchaeota (part of Nanoarchaeota , “DPANN”) were observed. Analysis of Pacearchaeota MAGs in this study, revealed they do not contain the genetic machinery capable of encoding lipid biosynthesis, a feature supporting the hypothesis that representatives from the DPANN superphylum lead a symbiotic or parasitic lifestyle [ 35 , 36 ] and could assimilate the lipids of their hosts into their cell membranes. A relatively complete MAG (completeness and contamination estimated at 98% and 3%) affiliated with GTDB genus “Tc-Br11_E2g” (SI Fig. 13) within Natrialbales encoded complete metabolic pathways for the citric acid cycle and glycolysis as well as three different transporters associated with osmoadaptation via the salt-in strategy (Fig. 6 and SI Table 6). This is typical for Halobacteria (Fig. 6 ). Nanoarchaeota MAGs “Bin133” and “Bin105” were affiliated with Pacearchaeota (SI Fig. 14) and appeared to be less complete, but CheckM is known to underestimate completeness of minimal genomes like this. These MAGs encoded genes for ribosomal proteins and aminoacyl-tRNA synthetases, typical archaeal genes for diphthamide synthesis and S-layer proteins and finally, a very minimal carbon metabolism. They contained virulence factors such as type II/ type IV protein secretion systems, toxin-antitoxin systems and genes similar to patatin-like phospholipases. These enzymes cleave fatty acids from membrane lipids and are potent virulence factors if injected into the cytoplasms of the host [ 37 ]. It remains unknown how these Nanoarchaeota cope with the high salt levels since no obvious osmoadaptation genes were present (Fig. 6 and SI Table 6). The closest relatives of these Nanoarchaeota are found in Lake Tanganyika, Tanzania (SI Fig. 14) [ 38 ]. Lake Tanganyika is a freshwater lake, so it almost appears these Archaea are somehow not affected by salinity at all, for example because they live inside the cytoplasm of a host [ 39 ]. Finally, Lokiarchaeota MAG “Bin83” was affiliated with GTDB family “SOKP0” (SI Fig. 12) and had an estimated completeness and contamination of 96% and 5% respectively. It encoded the tetrahydromethanopterin-dependent archaeal variant of the Wood-Ljundahl pathway (Fig. 6 and SI Table 6). This would enable the associated organism to perform acetogenesis from inorganic carbon sources [ 40 – 42 ] in anoxic Last Chance sediments. The encoded ectoine biosynthesis indicated it might use a salt-out strategy to cope with the high salinity. That strategy would cost more energy than the salt-in strategy used by Halobacteria. Considering the extensive cell-surface area observed for Lokiarchaeota, it is a mystery how these organisms could persist in these extremely saline sediments at a likely very low rate of ATP regeneration. First, our work shows that high phosphate concentrations and high rates of carbon fixation do not necessarily lead to high net productivity in alkaline soda lakes. Instead, sedimentation rates ranged from low (0.23 cm/yr) to undetectable, despite the presence of prolific microbial mats in Lake Goodenough. Second, because of a high phosphate solubility, alkaline soda lakes have been suggested to be modern analogues for origin-of-life or extraterrestrial life scenarios [ 7 ]. Among alkaline soda lakes Last Chance stands out with an exceptionally high phosphate concentration. However, we showed that the phosphate concentration in Last Chance Lake is high because the lake is barren, and its environmental conditions are extremely hostile to life. This is supported by the findings of Haas et al., [ 6 ] who conclude that the high salinity limits biological N 2 fixation, and associated microbiomes. Instead, neighboring Lake Goodenough, might be a better modern-day analogue for these scenarios. It also has high phosphate solubility, but it has a lower concentration, because phosphate is locked up in its biosphere. From that perspective, detection of phosphate in brines from Enceladus ejected into space [ 14 ] does not bode well for the chances of finding a biosphere underneath its ice cap. We also note that it is not certain if phosphate played a role in primordial metabolism [ 43 ]. During the Hadean time period, it is thought that extreme volcanic activity resulted in an abundance of phosphate rich rocks, which were more susceptible to weathering under the CO 2 rich atmosphere of the time. Due to physical weathering processes and biological activity over the last 4.7 billion years, the high abundance of Hadean phosphate has been encapsulated and removed from biological access for the current day processes. Finally, our study provides a first view on the exciting microbial ecology of inhospitable Last Chance brines. This ecology was characterized by two major Archaeal symbioses: Firstly, diverse Natrialbales that appeared to be extremely salt resistant faced off against diverse Pacearchaeota that had no detectable strategies to cope with salt. Secondly, bacterial sulfate reducers appeared to partner with Lokiarchaea , known for the extreme surface to volume ratio of their cells, which appears completely incompatible with survival in salty brines. Last but not least, the habitat was dominated by diverse Dethiobacteria which might persist there as dormant endospores. On early earth the geological and climatic conditions were volatile and extreme survival strategies such as sporulation might have been essential. Comparative genomics and phylogeny focusing on bacterial cell envelope architectures indicated the last bacterial common ancestor likely formed spores [ 44 ]. From that perspective, Last Chance Lake remains an interesting topic of current study. 3. Methods 3.1 Study Site: The Cariboo Plateau is a region in central British Columbia, Canada, located between the Coastal ranges and the Rocky Mountains, elevated between 1050 and 1250 m. The plateau lithology is basaltic lava flow uplifted during the Pliocene and overlain by glacial till deposited ~ 10 Ka, which comprises the uppermost 1–5 m of soil [ 45 ]. Complex, shallow groundwater flows in the region associated with the glacial debris have led to the development of over 1,000 shallow lakes, many of which are saline and alkaline due to a combination of carbonate-rich groundwater infiltration through marginal clastic sediments and semi-arid climatic conditions [ 8 ]. As a result, the Cariboo lakes vary substantially in appearance, chemical composition, and size; some lakes have completely dried out, some dry out ephemerally, and others contain a perennial water column [ 8 , 46 ]. 3.2 Sample Collection: Duplicate sediment cores were collected in April 2019 from Last Chance Lake (51.327950, -121.635105) and Goodenough Lake (51.330151, -121.643639). Sediment cores were taken from 5 locations within each lake (Fig. 1 ) using a 1.5-m single-drive Griffith corer from LacCore: National Lacustrine Core Facility (University of Minnesota). The sediment cores ranged in length from 25–50 cm, and deeper coring was prevented by underlying impenetrable rock or mineral formations. To reduce the mixing of water and upper sediment layers in the cores, Zorbitrol was used as a gelling agent to stabilize the sediment-water interface during transport, subsequently, cores were stored upright at -20°C. At each location, replicate cores were taken 20 cm apart. One was used for core description and another for biogeochemical analyses, microbial community analyses, and geochronology analyses. In total, 20 cores were collected from both lakes, and 6 cores (3 from each lake and from 3 different locations within each lake) were used for this study. 3.3 Sample Preparation: Cores were removed from the − 20°C freezer and defrosted at room temperature (22°C) for 2 hours. Cores were horizontally sliced into 2 cm disks using a Dremel Multi-Max MM50 oscillating saw at the lowest speed, a smaller blade was used to reduce blade contact with the sediment. The blade was sterilized with 70% ethanol before each core section was sampled. To avoid the potential risk of contamination from the core liner or during sectioning, sediment in contact with the core liner was removed and disposed of, and the inner core was transferred to a 50 mL tube, sealed, and stored at -20°C. Pore water and sediment were separated by centrifuging at 4500 rpm for 30 minutes (Allegra X-22R, Beckman Coulter, USA). The amount of porewater collected ranged from 1 to 10 mL and was stored in sterile 1.5 mL eppendorf tubes at -20°C. 3.4 Analysis of Sediment 3.4.1 CHN Analysis, Pb-210 dating and XRF: The wet weight of the sediment was determined by thawing a sample of the sediment core from each 2 cm section and weighing it using a weighing boat. The samples were then re-frozen overnight at -80°C. Frozen sediment was then transferred to a benchtop freeze dryer, lyophilized at -50°C, and at a pressure of 1 mPa (Labconco, Kansas City, MO). After 72 hours, the freeze-dried biomass was removed, and the carbon, nitrogen, and hydrogen content was determined using a CHN Elemental Analyzer (Perkin Elmer 2400 Series II CHNS/O, Massachusetts, USA) [ 47 ]. Samples of frozen sediment from Goodenough core − 5 (GL-5) and Last Chance core − 1 (LC-1) were analyzed for geochronological diagnostic isotopes at Flett Research Ltd., Winnipeg, Manitoba, Canada. To estimate the sediment accumulation rate, Pb-210 was determined indirectly by measuring the Po-210 using alpha spectrometry using a modified method of Eakins and Morrison, 1978 [ 48 ]. Radium (Ra-226) was determined using radon-222 emanation using the modified method of Mathieu et al., 1988 [ 49 ]. Cesium‐137 (Cs‐137) isotopes were measured by determining the gamma-ray emitting radionuclides in the sediment with gamma spectrometry and an HPGe detector. Cs-137 is used as an independent tracer to validate the Pb-210 dating. In theory, this core could be dated using the unsupported Pb-210 activities. However, the unsupported Pb-210 model assumes a constant input of Pb-210 directly onto the sediment, and due to the unknown influences of the overlying microbial mat, the requirement of the regression model assumption is not valid for this situation. As an alternative, the Pb-210 linear regression model was calibrated using the 1966 maximum Cs-137 inventory (Supplementary Figure S3 C), and core 5 was dated using two main assumptions: 1) the 1966 Cs-137 maximum inventory is recorded at the midpoint depth of section GL_S5_-3 (adjusted depth 10–12 cm), then the age at 11 cm depth should be 2019 − 1966 = 53 years; 2) and average sediment accumulation rate is 0.2299 cm/yr (by linear regression model) for the 6–16 cm core interval was used. Elemental sediment content was determined by X-Ray Fluorescence (XRF) analysis using an ITRAX XRF Corescanner (Large Lakes Observatory at the University of Minnesota Duluth). 3.4.2 LOI and Imaging of Cores: Replica cores from each sampling location were processed, described, and archived at the Continental Scientific Drilling Facility (CSD Facility), University of Minnesota, USA. Cores were split using sterilized medical cast saws, and a sterilized utility knife was used to cut the liner and caps, and the sediment was sliced using abrasion-resistant thread (Spectra fiber fishing line). Once the cores were sectioned, half of each core was archived at 4 ℃, and the other half was used for photographic imaging and Loss-on-Ignition (LOI) analysis. Before analyzing the working half of the core, the surface of each core and potential contamination were removed using a sterile microscope slide. Cores were then imaged with a Geoscan V (Geotek, United Kingdom) and scanned at 10 pixels/mm (~ 254 dpi). After imaging the cores, the sediment-water and organic content were determined by sampling every two centimeters and weighing the subsamples. Next, samples were heated at 100°C overnight to remove the water content, then heated at 550°C for 4 hours to combust the organic material, and finally heated at 1000°C for 2 hours to remove carbonates. After each heating step, the weight of the sample was recorded and used to calculate the dry density, water content, organic content, carbonate content, and non-carbonate content [ 50 ]. 3.5 Pore water: 3.5.1 pH and TA: The pH of the pore water was measured using a calibrated pH meter (Seven CompactTM S220, Mettler Toledo, USA), and pore water total alkalinity (TA) was determined using a G20 compact titrator (Mettler Toledo, USA). Briefly, 50 µL of pore water was added to 39.95 mL of water in a beaker and titrated with 0.2N H 2 SO 4 until the samples reached an end-point of pH = 4.3 [ 51 ]. 3.5.2 TC/TIC/TOC/TN Total carbon (TC), total organic carbon (TOC), and total nitrogen (TN) in the pore water were measured using a Shimadzu Total Organic Carbon TOC-V Analyzer equipped with an ASI-V autosampler (Shimadzu, Columbia, MD, USA). Samples were diluted 100x for total carbon and total nitrogen analysis and 150x for total organic carbon analysis. The TC, TOC, and TN content was estimated from a calibration curve (range: 0–1100 mg/L) using glucose as the standard for TC and TOC, while potassium nitrate was used as the standard for the TN calibration curve (range: 0 -100 mg/L). Total inorganic carbon (TIC) was calculated by subtracting TOC from TC. 3.5.3 Anions and Cations: Pore water concentrations of sodium (Na), calcium (Ca), magnesium (Mg), potassium (K), and iron (Fe) were measured using Inductively Coupled Plasma Mass Spectrometry (ICP-QQQ, Agilent Technologies, Tokyo, Japan) [ 47 ]. Briefly, pore water samples were diluted 400x with 5% nitric acid to reach a pH of ~ 3.0 and then filtered using 0.2 µm Acrodisc® PSF Syringe Filters (Pall Corporation, East Hills, NY). Samples were then analyzed using ICP-QQQ equipped with an SPS4 autosampler, which used 3 mL of sample for analysis. A multi-element standard was used to derive calibration curves for each element over a standard range of ppb - ppm. Calibration curves were then used to determine the concentration of each element in the sediment samples. Concentrations of sulfate, phosphate, nitrate, and chloride were measured using an ion chromatography system equipped with an IonPac AS18 anion-exchange column and a conductivity detector (DIONEX ICS-5000+, Thermo Fisher Scientific, USA)[ 47 , 51 ]. Pore water samples were diluted 100x using Mili-Q water and filtered using 0.2 µm mixed cellulose esters (MCE) membrane filters. Concentrations of sulfate, phosphate, nitrate, and chloride were determined from calibration curves that were generated using NaNO 3 (range: 0–100 mg/L), KH 2 PO 4 (range: 0–150 mg/L), MgSO 4 ·7H 2 O (range: 0–700 mg/L), and NaCl (range: 0–500 mg/L) as standards. A core from each lake (Goodenough Core 4 and Last Chance Core 5) was used to determine the concentration of sulfide in the porewater. Pore water sulfide was determined spectrophotometrically (UV-vis-spectrophotometer, Thermo Fisher Scientific) using methylene blue [ 52 ]. 3.6 Lipid extraction and analysis After sampling, lake sediments were frozen, freeze-dried, and stored at − 20°C until extraction. Solid samples were ground and homogenized before weighing (~ 3 g) into vials pre-cleaned with solvent. Samples were then ultrasonically extracted 3 × using methanol (MeOH), 3 × dichloromethane (DCM)/MeOH (1:1, v:v), and 3 × DCM or using 5 × 2:1 DCM: MeOH. Extracts were combined to produce a total lipid extract which was subsequently dried under a stream of N 2 . Extracts were reconstituted into 200 µL 9:1 MeOH:DCM before analysis. Samples were analyzed using the atmospheric pressure chemical ionization, 45-minute reverse phase method of Rattray & Smittenberg, 2020 [ 53 ] on a Shimadzu 8050 high-performance liquid chromatograph (HPLC) coupled to a Shimadzu triple quadrupole mass spectrometer (MS/MS). Isoprenoid glycerol dialkyl glycerol tetraethers (GDGTs) and branched GDGTs were determined using positive ion spectra created by selective ion monitoring over a range of m / z 741.2-1304.5. 3.7 DNA extraction, amplicon sequencing, and data analysis Nucleic acids were extracted from sediment samples following the protocol from a FastDNA Spin Kit for Soil (MP Biomedicals, USA) with the modification of heating at 55°C for 5 minutes before the final elution step. To generate 18S and 16S rRNA gene libraries, DNA samples were PCR-amplified in triplicates with a two-step method using the protocol from Sharp et al 2017 [ 54 ]. The following primer sets were used for the first step PCR: 1) A519 (5’-CAGCMGCCGCGGTAA-3’ and A915 (5’-GTGCTCCCCCGCCAATTCCT-3”) for identifying archaeal and bacterial species [ 55 – 57 ]; 2) 926wF (5′-AAACTYAAAKGAATTGRCGG3′) and 1392R (5′-ACGGGCGGTGTGTRC3′) to identify bacterial species [ 58 ]; and 3) TAReuk454FWD (5′-CCAGCASCYGCGGTAATTCC-3′) and TAReukREV3 (5′-ACTTTCGTTCTTGATYRA-3′) [ 59 ] for identifying eukaryotic species. Two-step PCR-amplified samples were sequenced on a MiSeq Personal Sequencer using the 2 × 300 bp MiSeq Reagent Kit v3 (Illumina). Sequencing data were analyzed using MetaAmp, an online amplicon data analysis pipeline [ 60 ]. Briefly, paired-end raw reads with greater than 100 base pairs (bp) overlap, and less than 8 mismatches were merged. Merged reads were checked for the presence of primers and mismatches in the primer sequences. Sequences containing forward and reverse primers and 1 or fewer mismatches were quality filtered. The remaining reads were trimmed to a fixed length of 350 bp. These sequences were checked for chimeras and clustered into operational taxonomic units (OTUs) with a similarity cutoff of 97%. OTUs were classified using the Silva database [ 61 ] (version 138.1). The microbial association network of the archaeal and bacterial community was determined using NetCoMi (v. 1.0.2) package in R [ 62 ]. To run the NetCoMi package first a phyloseq object had to be created using phyloseq package [ 63 ], which needed three components to make the phyloseq object: 1) OTU table; 2) taxonomy table; and 3) the metadata. Once the phyloseq object was made, it was filtered to remove the OTUs that appeared less than 50 times in 80% of the samples and aggregated to the genus level. To create the network, a Pearson correlation and unsigned transformation were used as the association measure. The following parameters were set for the network creation, which included a centred log-ratio transformation, zero handling in relation to the pseudo count, and a threshold of 0.4 for the sparsification method. Analysis of the network was completed using the cluster method called cluster fast greedy. After the analysis, the network was plotted using the parameters specified in Peschel et al., 2021 [ 62 ]. 3.8 Metagenome sequencing, assembly, and binning Extracted DNA (0.5-2 ng/uL) from Last Chance core #1 (Samples: LC1 -11 to -21) was sent to the Centre for Health Genomics and Informatics in the Cumming School of Medicine (University of Calgary) for the metagenomic library preparation and sequencing with a NextSeq 500 System (Illumina Inc., San Diego, CA, USA). Assembly and binning of the metagenomes followed the same procedure as Dong et al., (2020) [ 64 ] and Zorz et al., (2019) [ 16 ]. In brief, quality control of the raw reads was conducted using BBDuk from the BBTools package, which involved the removal of primers, adapters, and low-quality reads. Those reads that passed quality control were co-assembled using MEGAHIT and assembled separately for each sample with metaSPades (version 3.12.0.). Contigs shorter than 500 bp were removed, and the remaining assembled contigs were annotated using MetaErg version 2.3.x [ 65 ]. Mapping of the quality-controlled reads back to assemblies was completed with BBmap. Assembled contigs were then binned into metagenome-assembled-genomes (MAGs) using MetaBat. The individual samples and the co-assembly were binned separately. Completeness and contamination of each MAG were estimated with CheckM [ 22 ], and then the MAGs were classified using GTDB-Tk version 2.1.0 [ 66 ]. PhyloFlash (version 3.4.0.) [ 67 ] was used to summarize the taxonomic diversity of the MAGs. MAGs that were more than 80% complete and with less than 5% contamination were analyzed further (SI Table 3). 3.9 Data Visualization and Statistical Analysis The statistical analysis and graphical representation were carried out using GraphPad Prism 10.1.2 and R version 4.2.3 [ 68 ]. Utilizing the metaMDS function from the vegan package in R, nonmetric multidimensional scaling of Bray-Curtis dissimilarity was computed and then visualized with the ggplot2 package [ 69 , 70 ]. Other packages used for data visualization were tidyverse (v2.0.0), reshape2 (v1.4.4), and viridis (v0.6.3) [ 71 , 72 ]. 3.10 Phylogenetic analysis of the archaeal MAGs Conserved single-copy proteins were identified using hmmsearch with a set of hmm (Hidden Markov Model) profiles used by gtdbtk [ 73 , 74 ]. Next, sets of orthologs among the identified proteins were identified, as previously described [ 75 ]. Sets that lacked a representative in 40% of the species were discarded and 100–134 remaining sets of orthologous proteins (listed in Supplementary Table 6 of Parks et al., 2017 [ 74 ]) were aligned using clustalo [ 76 , 77 ]. Poorly aligned regions were eliminated with BMGE[ 78 ] and all individual gene alignments were concatenated, leading to three concatenated alignments with 50,120 − 67,742 positions, one for Asgard, one for Halo- and one for DPANN Archaea. From these, trees were created using fasttree [ 79 , 80 ]. Declarations Acknowledgments The authors are thankful for the assistance provided by Brian Ballie for running the samples on ICP-MS and the Total Inorganic Carbon-Total Organic Carbon analysis; Stephen Schroeder for helping collect the sediment cores from the soda lakes. This research was undertaken thanks in part to funding from the Natural Sciences and Engineering Research Council (NSERC), Canada First Research Excellence Fund (CFREF), Alberta Innovates, the Government of Alberta, and the University of Calgary. Author contributions AJP, HD, MS, and JER conceived and planned the experiments. AJP, AV, TG, BN, and JER contributed to sample preparation and carried out the experiments. AJP, SB, AV, TG, BN, VK, HD, MS, and JER contributed to the interpretation of the results. AJP and JER took the lead in writing the manuscript. All authors provided critical feedback and helped shape the research, analysis, and final version of the manuscript. Data Availability: Amplicon sequences can be found under the Bioproject PRJNA377096. The 16 S rRNA sequence Biosamples are: SAMN38476395-SAMN38476456, the 18 S rRNA sequence Biosamples are: SAMN3877441-SAMN3877459, and the 16 S rRNA sequences amplified using the A519 and A915 primer set are Biosamples: SAMN38477990-SAMN38478044. The metagenome raw reads and metagenome-assembled-genomes can also be found under the Bioproject PRJNA377096. Code availability The metagenomics pipeline can be located on GitHub page: https://github.com/xiaoli-dong/metagenomics_crash_course. The NetCoMi pipeline used to construct and analyze microbial networks can be found on GitHub page: https://github.com/stefpeschel/NetCoMi. References Boros E, Kolpakova M. A review of the defining chemical properties of soda lakes and pans: An assessment on a large geographic scale of Eurasian inland saline surface waters. PloS one. 2018;13(8):e0202205; doi: 10.1371/journal.pone.0202205 . Grant WD, Sorokin DY. Distribution and Diversity of Soda Lake Alkaliphiles. In: Horikoshi K, editor. 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Molecular biology and evolution. 2009;26(7):1641–50. Price MN, Dehal PS, Arkin AP. FastTree 2–approximately maximum-likelihood trees for large alignments. PloS one. 2010;5(3):e9490. Additional Declarations No competing interests reported. Supplementary Files SITable1.xlsx SITable2.xlsx SITable3.xlsx SITable4.xlsx SITable5.xlsx SITable6.xlsx SodalakemanuscriptSupplementaryInformationMicrobiomeJanuary13th2024.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3861392","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267094080,"identity":"89ba3c60-31f9-4c2d-973e-c9f037df7f01","order_by":0,"name":"Alexandre J. Paquette","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABQklEQVRIie3Sv0vDQBQH8BcCV4fXZn0h2PwLFwJiaWn+lUqgXRSdnIoUCtclqaui/hEuxTHloC6xc8eK0KmFTiUiVq+ZiqEFN8F8h3c/uA8P7g4gT56/mEJa9e0tPVLleDfRfxBCYI3N+CuCfC8xuvqI3p+q5wYUh8uFqF15hWBFSZvq13cxh6SdISSZ74Rxq3LbKfnmg2gS4svADEbk34xPuRaMsm0kOtOikJxHyC01IaSzgQWMfIiR68AywpbGcvipiBeh+1EUX4T2fGbBmnw7JesM4RK1t7QL4JHqEqkuyCxNUJ1viCYyxJHMdQ9Fi5MsNSv3Y98M4qZbCfvUcGJ2MQz7GVJ+7s7MhahyoxfKyfyybhR68nWSrGpeOdYfp8lq52Wrp0EA3FqfdOCAR3sApOe3iac+03S/yJMnT55/km/JNWGrT9F/IQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Calgary","correspondingAuthor":true,"prefix":"","firstName":"Alexandre","middleName":"J.","lastName":"Paquette","suffix":""},{"id":267094081,"identity":"38c7a835-6f49-4e46-a0d7-4ec1dc34aa16","order_by":1,"name":"Srijak Bhatnagar","email":"","orcid":"","institution":"Athabasca University","correspondingAuthor":false,"prefix":"","firstName":"Srijak","middleName":"","lastName":"Bhatnagar","suffix":""},{"id":267094082,"identity":"6939cfef-35c6-4073-a3df-5c3c5343e63c","order_by":2,"name":"Agasteswar Vadlamani","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Agasteswar","middleName":"","lastName":"Vadlamani","suffix":""},{"id":267094083,"identity":"892f594c-28dc-4fc3-8c9f-e0f5e8730737","order_by":3,"name":"Timber Gillis","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Timber","middleName":"","lastName":"Gillis","suffix":""},{"id":267094084,"identity":"480def71-4c14-49a0-acce-69662a3f9e56","order_by":4,"name":"Varada Khot","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Varada","middleName":"","lastName":"Khot","suffix":""},{"id":267094085,"identity":"909e8b29-06e6-4086-9b13-527bb45ee1c2","order_by":5,"name":"Breda Novotnik","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Breda","middleName":"","lastName":"Novotnik","suffix":""},{"id":267094086,"identity":"d6197a57-ac77-40d7-9691-b40f72508143","order_by":6,"name":"Hector De la Hoz Siegler","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Hector","middleName":"De la Hoz","lastName":"Siegler","suffix":""},{"id":267094087,"identity":"673cbf83-6fcc-4fbb-862a-213ba69d359a","order_by":7,"name":"Marc Strous","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Marc","middleName":"","lastName":"Strous","suffix":""},{"id":267094088,"identity":"653ef63a-f5a5-4da6-a4b5-85f45dfb79ed","order_by":8,"name":"Jayne E. Rattray","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Jayne","middleName":"E.","lastName":"Rattray","suffix":""}],"badges":[],"createdAt":"2024-01-13 21:59:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3861392/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3861392/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49706496,"identity":"89a423c9-4f63-445c-be7c-e53bb11ce095","added_by":"auto","created_at":"2024-01-16 18:27:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1878481,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation of sediment cores extracted from Goodenough and Last Chance Lake, British Columbia, Canada\u003c/strong\u003e. (A) ArcGIS image of Goodenough (GL) and Last Chance (LC) Lakes, showing the location of sediment cores extracted during April 2019 sampling. Approximate location of springs based on (Renaut and Long, 1989). (B) Core images were taken using a Geoscan V (Geotek, United Kingdom) and scanned at 10 pixels/mm (~254 dpi). The scale on the left side of each core is in cm.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3861392/v1/515294b6aeb834f3840268a8.png"},{"id":49706493,"identity":"dc9a76c8-ff5e-43e3-8bab-9e6b9a1ad390","added_by":"auto","created_at":"2024-01-16 18:27:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58835,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSediment physical properties as determined by Loss On Ignition (LOI).\u003c/strong\u003e(A, B, C) – three sediment cores from Goodenough (D, E, F) – three sediment cores from Last Chance.\u003c/p\u003e","description":"","filename":"Onlinefloatimage29.png","url":"https://assets-eu.researchsquare.com/files/rs-3861392/v1/e065aeb5a4354952600fb340.png"},{"id":49706497,"identity":"12b25250-03ac-48dd-884d-6e12027b7219","added_by":"auto","created_at":"2024-01-16 18:27:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125922,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePorewater geochemistry in Lake Goodenough and Last Chance Lake sediment cores.\u003c/strong\u003e Circles represent the porewater from Lake Goodenough, and squares represent the porewater from Last Chance Lake.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3861392/v1/34d5a5999c05a8bc639fea91.png"},{"id":49706500,"identity":"9c61d6c7-cbb6-431c-b864-3d236b3ea13b","added_by":"auto","created_at":"2024-01-16 18:27:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":223487,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial Sediment Community Analysis of Last Chance and Goodenough Lake.\u003c/strong\u003e(A) Non-metric multidimensional scaling (NMDS) plots using Bray-Curtis dissimilarity to compare Goodenough and Last Chance Lake microbial sediment communities. (B) Bubble plots showing the total relative abundance of algae and cyanobacteria in the sediment of both lakes. (C) Bar graph showing the relative abundance of bacterial genera potentially involved in the sulfur cycle. (D) Major groups of microbes found in Last Chance sediments. A-C based on 16S rRNA gene amplicon sequencing; D based on small subunit rRNA retrieved from the metagenomic dataset of Last Chance core 1 with phyloFlash. \u003cem\u003eDethiobacteria\u003c/em\u003eand \u003cem\u003eDesulfobulbia\u003c/em\u003e in C are part of \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eDesulfobacterota\u003c/em\u003ein D, respectively.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3861392/v1/6da39256912ad17a8abd0cb5.png"},{"id":49706830,"identity":"1bbb07b4-5c55-430a-a4b0-1d9fb9c1962d","added_by":"auto","created_at":"2024-01-16 18:35:27","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":867755,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative abundance of the archaeal populations in the sediment of Goodenough (A) and Last Chance Lake (B).\u003c/strong\u003e Data was obtained from 16S rRNA gene amplicon sequencing to identify bacteria and archaea at the phylum and class level. The number beside each classification represents the number of OTUs detected.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3861392/v1/d336aefa8c308c6bb0dfa7dd.jpeg"},{"id":49706501,"identity":"c0ab2ea8-c6c4-4b10-ad2d-7479cf64bbc7","added_by":"auto","created_at":"2024-01-16 18:27:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":125615,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolic potential of Last Chance Lake's archaeal community.\u003c/strong\u003e The heatmap shows the metabolic and osmoadaptation potential of the four highest quality archaeal MAGs representing each of the major Archaeal clades. The orange color intensity represents how complete the pathway was based on the number of genes detected. Green and blue mark the pathways involved in osmoregulation via the “salt out” and “salt in” strategy respectively.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3861392/v1/11af63a65ec23085a2e7273f.png"},{"id":51139516,"identity":"81b16fe2-403c-4519-a3de-40043f6c8c22","added_by":"auto","created_at":"2024-02-14 19:38:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3283833,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3861392/v1/f51d2907-1f9e-4b30-a26a-8c98927a0d1d.pdf"},{"id":49706829,"identity":"61581de9-2601-429a-9e2a-41ad63f25c7f","added_by":"auto","created_at":"2024-01-16 18:35:27","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":62349,"visible":true,"origin":"","legend":"","description":"","filename":"SITable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3861392/v1/00bac85b13f116562cb77400.xlsx"},{"id":49706499,"identity":"97e3877c-a699-4512-be57-ee1ccab89a5e","added_by":"auto","created_at":"2024-01-16 18:27:27","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":57854,"visible":true,"origin":"","legend":"","description":"","filename":"SITable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3861392/v1/8a88821a85f64d4ad9acf3ee.xlsx"},{"id":49706831,"identity":"20256adf-b361-4b2b-99f0-1a229737330c","added_by":"auto","created_at":"2024-01-16 18:35:27","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":20775,"visible":true,"origin":"","legend":"","description":"","filename":"SITable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3861392/v1/3deb593f9473e69705c9c45c.xlsx"},{"id":49706494,"identity":"6caf34d5-e535-4fdc-81ae-c0ec933c6826","added_by":"auto","created_at":"2024-01-16 18:27:27","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":68992,"visible":true,"origin":"","legend":"","description":"","filename":"SITable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3861392/v1/9bfed3b2b2fde514dcc70187.xlsx"},{"id":49706504,"identity":"9751fa99-010a-46a6-89db-2f34f2b1d6ad","added_by":"auto","created_at":"2024-01-16 18:27:28","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":49195,"visible":true,"origin":"","legend":"","description":"","filename":"SITable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3861392/v1/7e75fd8707e06dd3a56a2dad.xlsx"},{"id":49706498,"identity":"c1079e4e-6ccd-4417-bbd7-5ab65feb8400","added_by":"auto","created_at":"2024-01-16 18:27:27","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":31417,"visible":true,"origin":"","legend":"","description":"","filename":"SITable6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3861392/v1/af3230029db19f1f551e49c9.xlsx"},{"id":49706505,"identity":"5d0b63eb-78db-43fa-a3de-ca6c66b11657","added_by":"auto","created_at":"2024-01-16 18:27:28","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":2961044,"visible":true,"origin":"","legend":"","description":"","filename":"SodalakemanuscriptSupplementaryInformationMicrobiomeJanuary13th2024.docx","url":"https://assets-eu.researchsquare.com/files/rs-3861392/v1/7f9fb797359260e43262626f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ecology and biogeochemistry of the microbial underworld in two sister soda lakes","fulltext":[{"header":"1. Introduction","content":" \u003cp\u003eAlkaline soda lakes are distributed worldwide and are typically highly productive ecosystems, with pH\u0026thinsp;\u0026gt;\u0026thinsp;9, low calcium and magnesium concentrations, and a high abundance of sodium and carbonate species (HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and CO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e) [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These lakes are located in arid and semi-arid environments and form in closed basins where evaporation exceeds inflow. At Northern latitudes, they undergo extreme desiccation in the summer months and complete freezing in winter. Thus, alongside a pH of ~\u0026thinsp;10 and high salt concentrations, the microorganisms inhabiting these lakes must also survive a periodic dearth of water and adapt to extreme changes in temperature. From an origin of life context, the periodic cycling of precipitation and desiccation may be central to the formation of prebiotic molecules, by enabling RNA phosphorylation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlkaline soda lakes have been proposed as a promising setting for the origins of life due to the higher abundance of available phosphate in comparison to other aqueous environments on Earth [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The high pH of soda lakes ensures that calcium precipitates as calcium carbonate instead of calcium phosphate, resulting in total phosphorus concentrations ranging from 1\u0026ndash;17 mM and up to 50 mM in concentrated brine pools resulting from lake desiccation in the autumn [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These lakes are not only interesting from a perspective of probiotic chemistry but also for astrobiologists investigating microbial survival strategies in high-salt, low-temperature environments as found in the icy moons of the outer solar system [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHere, we compare the sediment geochemistry and microbial ecology of two very different soda lakes on the Cariboo Plateau, central British Columbia, Canada, separated by a glacial moraine less than 50 m wide [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Both lakes generally freeze over in November and remain covered in ice for 4\u0026ndash;6 months. One of these two lakes, Lake Goodenough, holds water year-round and features prolific microbial mats that bloom during the ice-free spring and summer. In Lake Goodenough, phosphate is largely locked biologically into the mats and underlying sediments leading to a wide range of dissolved phosphate concentrations in the water (0.3\u0026ndash;1.4 mM). Neighboring Last Chance Lake evaporates nearly completely during summer, forming a series of polygonal pools with extensive salt crystallization. The lake is relatively barren, with no microbial mats. A recent study of Last Chance Lake, used isotopic analysis of N\u003csub\u003e2\u003c/sub\u003e fixation rates to conclude that rates of biological nitrogen fixation are suppressed by the high lake salinity, causing a lack of phosphate uptake, resulting in phosphate buildup in the lake [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In this lake, dissolved phosphate concentrations were comparatively high ranging from 4.4\u0026ndash;38.0 mM, consistent with proposed origin-of-life scenarios [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSix sediment cores (three per lake) were sampled in high resolution and were investigated using a combination of 18S, and 16S rRNA gene amplicon sequencing, pore water, and sedimentary geochemistry. Geochronology was used to analyze the biogeochemical processes occurring in the sediments of Last Chance and Goodenough Lake. In addition, we provide a comprehensive analysis of the sediment microbial community of Last Chance Lake using shotgun metagenomics. We investigate the metabolic pathways and adaptations found in a type of ecosystem that, according to some, may have spawned life on Earth.\u003c/p\u003e"},{"header":"2. Results and discussion","content":"\u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Inorganic versus biological diagenesis\u003c/h2\u003e \u003cp\u003eTwenty 30\u0026ndash;48 cm cores were recovered from Last Chance and Lake Goodenough (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Cores from Goodenough Lake were distinctly different from Last Chance Lake. Goodenough lake cores were darker, grayish blue in color, and had a pungent smell of sulfide. In comparison, cores from Last Chance were lighter, grayish brown to pale olive in color, and the smell of sulfide was not as evident (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). X-ray fluorescence (XRF) showed that elemental sulfur content was higher in Last Chance, 104\u0026ndash;600 counts/sec compared to 23\u0026ndash;168 counts/sec in Goodenough (SI Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and SI Table\u0026nbsp;1\u0026amp;2).\u003c/p\u003e \u003cp\u003eThe upper 4\u0026ndash;8 cm of the Goodenough cores consisted of a microbial mat (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) that likely gets buried into the sediment, together with decaying mosquito, fly, and shrimp matter found in the top sediment layers. This was consistent with high organic matter content throughout the cores (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, E, and F, SI Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and SI Table\u0026nbsp;1). Decay of organic matter might have driven sulfate reduction, explaining the observed sulfide smell and blue color of the cores. The Last Chance cores showed no visible microbial mat or other organic material (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B, and C, SI Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and SI Table\u0026nbsp;2). Instead, Last Chance cores featured ubiquitously large (0.5-3 cm) white natron crystals, most abundant between \u0026minus;\u0026thinsp;20 cm and \u0026minus;\u0026thinsp;30 cm (SI Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Natron crystals have been reported since 1924 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], when very large masses of interlocking crystal structures were found throughout and below the pools [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The amount of natron crystal in Last Chance Lake was estimated at that time to be 70,000 tons. Precipitation of these crystals may have been driven by a gradual loss of water (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) caused by seasonal evaporation of the lake above. In summary, whereas Goodenough sediments were defined by microbial turnover of organic matter culminating in sulfate reduction, diagenesis in Last Chance was dominated by inorganic processes leading to crystallization of natron and other minerals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo get an idea of the timescales these lake sediments were on, we performed age dating on a core of each lake. In the Goodenough core, the activity of \u003csup\u003e210\u003c/sup\u003ePb was much higher than \u003csup\u003e226\u003c/sup\u003eRa indicating atmospheric sourcing of \u003csup\u003e210\u003c/sup\u003ePb (used for dating the core). \u003csup\u003e210\u003c/sup\u003ePb activity (DPM/g dry weight) decreased with depth (SI Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Using linear regression between 6\u0026ndash;16 cm, the sediment accumulation rate in Goodenough Core 5 was calculated to be relatively low, 0.23 cm/yr (SI Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, Last Chance \u003csup\u003e226\u003c/sup\u003eRa was not significantly different from \u003csup\u003e210\u003c/sup\u003ePb (SI Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), indicating a lack of detectable atmospherically sourced \u003csup\u003e210\u003c/sup\u003ePb (making age dating impossible). The lack of \u003csup\u003e210\u003c/sup\u003ePb might be explained by the seasonal evaporation of the overlying lake followed by the loss of deposited \u003csup\u003e210\u003c/sup\u003ePb via wind erosion. Although Last Chance sediments could thus not be dated, it is likely that a lower influx of spring water and associated minerals, combined with lower organic matter production [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], leads to extremely slow sediment accumulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe compared the geochemistry of the two lake sediments in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, including phosphate, the focus of origin-of-life scenarios. In Last Chance Lake, the sediment phosphate concentration (~\u0026thinsp;4 mM) was in agreement with previous studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and was comparable to the lake water (~\u0026thinsp;4.1 mM) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, the low sediment water content makes these sediments otherwise quite inhospitable to life, as they harbored concentrated brines with pH, sodium, carbonate alkalinity, and nearly all other ions all much higher than neighboring Lake Goodenough (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, SI Fig.\u0026nbsp;4, and SI Tables\u0026nbsp;1 \u0026amp; 2). In Last Chance, most concentrations peaked at 20\u0026ndash;30 cm, indicating potential (very slow) upward penetration of water into these sediments from deeper down (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, SI Fig.\u0026nbsp;4, and SI Tables\u0026nbsp;1 \u0026amp; 2). Sulfide was an exception as it displayed higher concentrations in Goodenough, 0.18\u0026ndash;1.70 mM throughout, in comparison to Last Chance, 0.006\u0026ndash;0.022 mM, with peaks near the sediment-water interface and at 20\u0026ndash;26 cm depth. High sulfide concentrations have also been reported in overlying microbial mats in Goodenough Lake [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which would additionally suggest that sulfide is probably not suppressing biological N\u003csub\u003e2\u003c/sub\u003e fixation, as previously suggested [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Last Chance Lake peaks in sulfide concentrations might be associated with hotspots in microbial sulfate reduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE \u0026amp; G, SI Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB. SI Tables\u0026nbsp;1 \u0026amp; 2). It was also observed in the XRF data that iron abundance was 40x higher in the sediment of Last Chance Lake, and sulfur abundance was low in the sediment, suggesting that iron is precipitating mainly as iron oxide minerals instead of iron sulfide minerals, possibly resulting in the ocher colour observed in the Last Chance cores (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and SI Table\u0026nbsp;1\u0026amp;2).\u003c/p\u003e \u003cp\u003eSummarizing these results, the phosphate concentration in present-day Last Chance Lake is high because the alkaline chemistry in carbonate-rich lakes prevents the binding of phosphate to calcium [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Further, the lake is otherwise a challenging environment for life, limiting biological uptake. The lake itself dries up seasonally, and water losses from underlying sediments fill the pore cavities with a concentrated brine. From a biological perspective, the extreme geochemistry of Last Chance Lake may thus not perfectly model conditions that gave rise to the origin of life. However, given the presence of potential hotspots of sulfate reduction, microbial communities may still inhabit these sediments. This is all the more interesting because the turnover of organic matter may be extremely slow, and slow growth makes coping with high salt and pH even more challenging for living cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sediment microbial communities of Goodenough and Last Chance Lake\u003c/b\u003e \u003c/h2\u003e \u003cp\u003e \u003cb\u003eFigure 4\u003c/b\u003e: \u003cb\u003eMicrobial Sediment Community Analysis of Last Chance and Goodenough Lake.\u003c/b\u003e (A) Non-metric multidimensional scaling (NMDS) plots using Bray-Curtis dissimilarity to compare Goodenough and Last Chance Lake microbial sediment communities. (B) Bubble plots showing the total relative abundance of algae and cyanobacteria in the sediment of both lakes. (C) Bar graph showing the relative abundance of bacterial genera potentially involved in the sulfur cycle. (D) Major groups of microbes found in Last Chance sediments. A-C based on 16S rRNA gene amplicon sequencing; D based on small subunit rRNA retrieved from the metagenomic dataset of Last Chance core 1 with phyloFlash. \u003cem\u003eDethiobacteria\u003c/em\u003e and \u003cem\u003eDesulfobulbia\u003c/em\u003e in C are part of \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eDesulfobacterota\u003c/em\u003e in D, respectively.\u003c/p\u003e \u003cp\u003eNonlinear multidimensional scaling of 16S rRNA amplicon data showed sediment communities in both lakes were completely different (Fig.\u0026nbsp;4A). Last Chance Lake also had fewer bacterial species-level operational taxonomic units (OTUs, clustered at 97% similarity) than Lake Goodenough, 1,300 and 2,400 OTUs, respectively (SI Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This distinct difference in community richness was most likely due to the challenging living conditions of Last Chance. In Goodenough, a source spring close to Goodenough Core 1 appeared to influence community composition (Fig.\u0026nbsp;4A and SI Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), probably caused by the lower concentration of salts in the spring water compared to the rest of the lake.\u003c/p\u003e \u003cp\u003eA stark difference between the two lakes was the drastic decrease in cyanobacterial abundance with depth in Lake Goodenough compared to Last Chance Lake, where cyanobacteria remained present throughout the core or even increased with depth (Fig.\u0026nbsp;4B). This indicated that the degradation of cyanobacteria was ineffective in Last Chance sediments, potentially because of \u0026ldquo;pickling\u0026rdquo; by its concentrated brines. The same trend was observed in the 18S rRNA amplicon sequencing data. Eukaryotic rRNA was not amplified below \u0026minus;\u0026thinsp;2 cm in Goodenough sediments, unlike Last Chance sediments where eukaryotic rRNA was amplified up to -30 cm (SI Fig.\u0026nbsp;7). At -30 cm, the main remaining eukaryotic species was \u003cem\u003eDunaliella\u003c/em\u003e, a unicellular alga commonly known as the primary producer in hypersaline environments [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In Goodenough sediments, the Eukaryotic community was dominated by macro-organisms affiliated with Nematoda (g. \u003cem\u003eMonhysterida\u003c/em\u003e), Arthropoda (g. \u003cem\u003eNotostraca\u003c/em\u003e), and Rotifera (c. Monogononta). All of these likely feeded off and contributed to the faster degradation of the mats and their cyanobacteria. The lack of microbial mats and slower degradation in Last Chance Lake directly affected the amount of organic matter available for the downstream biogeochemical cycles, such as the sulfur cycle.\u003c/p\u003e \u003cp\u003eGram-positive \u003cem\u003eDethiobacteria\u003c/em\u003e dominated all cores in both lake sediments, accounting for between ~\u0026thinsp;10\u0026ndash;78% of the bacterial 16S rRNA gene amplicon abundance (Fig.\u0026nbsp;4C and SI Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA \u0026amp; B) and comprising 79 species-level OTUs. In Last Chance metagenomes, 21 \u003cem\u003eDethiobacteria\u003c/em\u003e genomes were assembled and binned (completeness\u0026thinsp;\u0026gt;\u0026thinsp;81%, contamination\u0026thinsp;\u0026lt;\u0026thinsp;6.6%, SI Table\u0026nbsp;3). \u003cem\u003eDethiobacteria\u003c/em\u003e diversity and abundance were much higher than previously found in Central Asian soda lakes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. \u003cem\u003eDethiobacteria\u003c/em\u003e are known as obligately anaerobic, sulfur disproportionating bacteria that use the Wood\u0026ndash;Ljungdahl pathway to fix CO\u003csub\u003e2\u003c/sub\u003e. Both pure culture strains reported in the literature have been isolated from alkaline soda lakes [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Together, the 21 metagenome assembled genomes (MAGs) found here displayed considerable metabolic diversity, with with the majority associated with sulfate reduction and oxidation (adenosine-5\u0026prime;-phosphosulfate reductase; 62% of \u003cem\u003eDethiobacter\u003c/em\u003e), sulfite reduction (anaerobic sulfite reductase; 62% of \u003cem\u003eDethiobacter\u003c/em\u003e) and potentially, sulfur disproportionation (persulfide dioxygenase; 62% of \u003cem\u003eDethiobacter\u003c/em\u003e) (SI Table\u0026nbsp;4; SI Fig.\u0026nbsp;10). All \u003cem\u003eDethiobacteria\u003c/em\u003e appeared capable of endospore formation, with MAGs containing up to 50 genes encoding all steps in endospore synthesis. Endospore formation is a physiological capability that provides a distinct ecological advantage to \u003cem\u003eDethiobacteria\u003c/em\u003e, allowing them to sporulate when environmental conditions become unfavorable. It is possible that when \u003cem\u003eDethiobacteria\u003c/em\u003e are not actually growing in Last Chance sediments they are capable of biding their time until conditions improve.\u003c/p\u003e \u003cp\u003eSulfate reducing bacteria (SRB) were identified in both lakes (Fig.\u0026nbsp;4C and SI Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA \u0026amp; B). In Goodenough sediments they were present throughout the cores, at abundances between 3\u0026ndash;15%. This could be explained by the high organic matter content of these sediments and was consistent with low sulfate and high sulfide concentrations found in these cores (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE \u0026amp; G, SI Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). In Last Chance sediments, they were much less abundant (generally\u0026thinsp;\u0026lt;\u0026thinsp;4%), except below 22 cm in cores 1 and 4, where their abundance peaked at \u0026gt;\u0026thinsp;18%. This peak corresponded to an area of high sulfide concentrations and might be explained by the penetration of lower salinity groundwater from below, reducing brine concentrations and enabling these bacteria to feast on the preserved organic matter. SRB were mainly affiliated with \u003cem\u003eDesulfobulbia\u003c/em\u003e, \u003cem\u003eDesulfurivibrionia\u003c/em\u003e, and \u003cem\u003eDesulfuromonadia\u003c/em\u003e. In metagenomes of Last Chance sediment, six MAGs (\u0026gt;\u0026thinsp;84% complete, \u0026lt;\u0026thinsp;4% contaminated) were affiliated with \u003cem\u003eDesulfobacterota\u003c/em\u003e. Key genes for sulfate reduction were found for most SRB (SI Fig.\u0026nbsp;10 and SI Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eArchaeal communities were strikingly different between both lakes, with Last Chance dominated by \u003cem\u003eHalobacteria\u003c/em\u003e and \u003cem\u003eNanoarchaeaota\u003c/em\u003e [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and Goodenough Lake composed of a more diverse archaeal community (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA \u0026amp; B). In both lakes, cores taken from close proximity to the underground spring feeding the lakes (core 1) had a markedly different community composition in comparison to other cores (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA \u0026amp; B). Last Chance core 1 contained a higher percentage of \u003cem\u003eLokiarchaea\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), and Goodenough core 1 had an increased abundance of \u003cem\u003eBathyarchaeia\u003c/em\u003e and \u003cem\u003eThermoplasmata\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). A notable characteristic of all cores was the relationship between \u003cem\u003eNanoarchaeota\u003c/em\u003e and \u003cem\u003eHalobacteria\u003c/em\u003e. When the abundance of the \u003cem\u003eNanoarchaeota\u003c/em\u003e was high, the abundance of \u003cem\u003eHalobacteria\u003c/em\u003e was low, and \u003cem\u003evice versa\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA \u0026amp; B), as shown by co-occurrence analysis (SI Fig.\u0026nbsp;11).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCo-occurrence analysis also showed a strong correlation between \u003cem\u003eLokiarchaeota\u003c/em\u003e and \u003cem\u003eDesulfurivibrio\u003c/em\u003e (Figs.\u0026nbsp;4CD, 5B, SI Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, 11). This was consistent with co-enrichment of \u003cem\u003eLokiarchaeota\u003c/em\u003e and SRB during lab cultivation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. They could have a syntrophic relationship with SRB feeding off hydrogen and formate produced by Lokiarchaeota [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eArchaea synthesize domain specific isoprenoid dialkyl glycerol diether lipids. The C25 extended archaeol homologue, \u0026ldquo;extended archaeol\u0026rdquo; (EXT-AR), is specific to hypersaline/evaporitic environments and considered a taxonomic marker of \u003cem\u003eHalobacteria\u003c/em\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and more specifically the order \u003cem\u003eNatrialbales\u003c/em\u003e [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Extended archaeol lipids are thought to create a \u0026ldquo;zip-link\u0026rdquo; membrane, effective against the high alkalinity and extreme osmotic stress [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] prevalent in soda lake environments. We detected EXT-AR in sediments of both lakes. Consistent with the high abundance of \u003cem\u003eNatrialbales\u003c/em\u003e in Last Chance sediments, EXT-AR abundance was also highest there (SI Fig.\u0026nbsp;8). However, at some depths EXT-AR abundance was high with hardly any \u003cem\u003eNatrialbales detected.\u003c/em\u003e This indicated that these lipids might not always be associated with living \u003cem\u003eNatrialbales.\u003c/em\u003e Intriguingly, in those samples high abundances of \u003cem\u003ePacearchaeota\u003c/em\u003e (part of \u003cem\u003eNanoarchaeota\u003c/em\u003e, \u0026ldquo;DPANN\u0026rdquo;) were observed. Analysis of \u003cem\u003ePacearchaeota\u003c/em\u003e MAGs in this study, revealed they do not contain the genetic machinery capable of encoding lipid biosynthesis, a feature supporting the hypothesis that representatives from the DPANN superphylum lead a symbiotic or parasitic lifestyle [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and could assimilate the lipids of their hosts into their cell membranes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA relatively complete MAG (completeness and contamination estimated at 98% and 3%) affiliated with GTDB genus \u0026ldquo;Tc-Br11_E2g\u0026rdquo; (SI Fig.\u0026nbsp;13) within \u003cem\u003eNatrialbales\u003c/em\u003e encoded complete metabolic pathways for the citric acid cycle and glycolysis as well as three different transporters associated with osmoadaptation via the salt-in strategy (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e and SI Table\u0026nbsp;6). This is typical for \u003cem\u003eHalobacteria\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eNanoarchaeota\u003c/em\u003e MAGs \u0026ldquo;Bin133\u0026rdquo; and \u0026ldquo;Bin105\u0026rdquo; were affiliated with \u003cem\u003ePacearchaeota\u003c/em\u003e (SI Fig.\u0026nbsp;14) and appeared to be less complete, but CheckM is known to underestimate completeness of minimal genomes like this. These MAGs encoded genes for ribosomal proteins and aminoacyl-tRNA synthetases, typical archaeal genes for diphthamide synthesis and S-layer proteins and finally, a very minimal carbon metabolism. They contained virulence factors such as type II/ type IV protein secretion systems, toxin-antitoxin systems and genes similar to patatin-like phospholipases. These enzymes cleave fatty acids from membrane lipids and are potent virulence factors if injected into the cytoplasms of the host [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. It remains unknown how these \u003cem\u003eNanoarchaeota\u003c/em\u003e cope with the high salt levels since no obvious osmoadaptation genes were present (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e and SI Table\u0026nbsp;6). The closest relatives of these Nanoarchaeota are found in Lake Tanganyika, Tanzania (SI Fig.\u0026nbsp;14) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Lake Tanganyika is a freshwater lake, so it almost appears these Archaea are somehow not affected by salinity at all, for example because they live inside the cytoplasm of a host [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, Lokiarchaeota MAG \u0026ldquo;Bin83\u0026rdquo; was affiliated with GTDB family \u0026ldquo;SOKP0\u0026rdquo; (SI Fig.\u0026nbsp;12) and had an estimated completeness and contamination of 96% and 5% respectively. It encoded the tetrahydromethanopterin-dependent archaeal variant of the Wood-Ljundahl pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e and SI Table\u0026nbsp;6). This would enable the associated organism to perform acetogenesis from inorganic carbon sources [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] in anoxic Last Chance sediments. The encoded ectoine biosynthesis indicated it might use a salt-out strategy to cope with the high salinity. That strategy would cost more energy than the salt-in strategy used by Halobacteria. Considering the extensive cell-surface area observed for Lokiarchaeota, it is a mystery how these organisms could persist in these extremely saline sediments at a likely very low rate of ATP regeneration.\u003c/p\u003e \u003cp\u003eFirst, our work shows that high phosphate concentrations and high rates of carbon fixation do not necessarily lead to high \u003cem\u003enet\u003c/em\u003e productivity in alkaline soda lakes. Instead, sedimentation rates ranged from low (0.23 cm/yr) to undetectable, despite the presence of prolific microbial mats in Lake Goodenough.\u003c/p\u003e \u003cp\u003eSecond, because of a high phosphate solubility, alkaline soda lakes have been suggested to be modern analogues for origin-of-life or extraterrestrial life scenarios [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Among alkaline soda lakes Last Chance stands out with an exceptionally high phosphate concentration. However, we showed that the phosphate concentration in Last Chance Lake is high because the lake is barren, and its environmental conditions are extremely hostile to life. This is supported by the findings of Haas et al., [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] who conclude that the high salinity limits biological N\u003csub\u003e2\u003c/sub\u003e fixation, and associated microbiomes. Instead, neighboring Lake Goodenough, might be a better modern-day analogue for these scenarios. It also has high phosphate solubility, but it has a lower concentration, because phosphate is locked up in its biosphere. From that perspective, detection of phosphate in brines from Enceladus ejected into space [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] does not bode well for the chances of finding a biosphere underneath its ice cap. We also note that it is not certain if phosphate played a role in primordial metabolism [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. During the Hadean time period, it is thought that extreme volcanic activity resulted in an abundance of phosphate rich rocks, which were more susceptible to weathering under the CO\u003csub\u003e2\u003c/sub\u003e rich atmosphere of the time. Due to physical weathering processes and biological activity over the last 4.7\u0026nbsp;billion years, the high abundance of Hadean phosphate has been encapsulated and removed from biological access for the current day processes.\u003c/p\u003e \u003cp\u003eFinally, our study provides a first view on the exciting microbial ecology of inhospitable Last Chance brines. This ecology was characterized by two major Archaeal symbioses: Firstly, diverse \u003cem\u003eNatrialbales\u003c/em\u003e that appeared to be extremely salt resistant faced off against diverse \u003cem\u003ePacearchaeota\u003c/em\u003e that had no detectable strategies to cope with salt. Secondly, bacterial sulfate reducers appeared to partner with \u003cem\u003eLokiarchaea\u003c/em\u003e, known for the extreme surface to volume ratio of their cells, which appears completely incompatible with survival in salty brines. Last but not least, the habitat was dominated by diverse \u003cem\u003eDethiobacteria\u003c/em\u003e which might persist there as dormant endospores. On early earth the geological and climatic conditions were volatile and extreme survival strategies such as sporulation might have been essential. Comparative genomics and phylogeny focusing on bacterial cell envelope architectures indicated the last bacterial common ancestor likely formed spores [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. From that perspective, Last Chance Lake remains an interesting topic of current study.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study Site:\u003c/h2\u003e \u003cp\u003eThe Cariboo Plateau is a region in central British Columbia, Canada, located between the Coastal ranges and the Rocky Mountains, elevated between 1050 and 1250 m. The plateau lithology is basaltic lava flow uplifted during the Pliocene and overlain by glacial till deposited\u0026thinsp;~\u0026thinsp;10 Ka, which comprises the uppermost 1\u0026ndash;5 m of soil [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Complex, shallow groundwater flows in the region associated with the glacial debris have led to the development of over 1,000 shallow lakes, many of which are saline and alkaline due to a combination of carbonate-rich groundwater infiltration through marginal clastic sediments and semi-arid climatic conditions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. As a result, the Cariboo lakes vary substantially in appearance, chemical composition, and size; some lakes have completely dried out, some dry out ephemerally, and others contain a perennial water column [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Sample Collection:\u003c/h2\u003e \u003cp\u003eDuplicate sediment cores were collected in April 2019 from Last Chance Lake (51.327950, -121.635105) and Goodenough Lake (51.330151, -121.643639). Sediment cores were taken from 5 locations within each lake (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) using a 1.5-m single-drive Griffith corer from LacCore: National Lacustrine Core Facility (University of Minnesota). The sediment cores ranged in length from 25\u0026ndash;50 cm, and deeper coring was prevented by underlying impenetrable rock or mineral formations. To reduce the mixing of water and upper sediment layers in the cores, Zorbitrol was used as a gelling agent to stabilize the sediment-water interface during transport, subsequently, cores were stored upright at -20\u0026deg;C. At each location, replicate cores were taken 20 cm apart. One was used for core description and another for biogeochemical analyses, microbial community analyses, and geochronology analyses. In total, 20 cores were collected from both lakes, and 6 cores (3 from each lake and from 3 different locations within each lake) were used for this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sample Preparation:\u003c/h2\u003e \u003cp\u003eCores were removed from the \u0026minus;\u0026thinsp;20\u0026deg;C freezer and defrosted at room temperature (22\u0026deg;C) for 2 hours. Cores were horizontally sliced into 2 cm disks using a Dremel Multi-Max MM50 oscillating saw at the lowest speed, a smaller blade was used to reduce blade contact with the sediment. The blade was sterilized with 70% ethanol before each core section was sampled. To avoid the potential risk of contamination from the core liner or during sectioning, sediment in contact with the core liner was removed and disposed of, and the inner core was transferred to a 50 mL tube, sealed, and stored at -20\u0026deg;C. Pore water and sediment were separated by centrifuging at 4500 rpm for 30 minutes (Allegra X-22R, Beckman Coulter, USA). The amount of porewater collected ranged from 1 to 10 mL and was stored in sterile 1.5 mL eppendorf tubes at -20\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Analysis of Sediment\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 CHN Analysis, Pb-210 dating and XRF:\u003c/h2\u003e \u003cp\u003eThe wet weight of the sediment was determined by thawing a sample of the sediment core from each 2 cm section and weighing it using a weighing boat. The samples were then re-frozen overnight at -80\u0026deg;C. Frozen sediment was then transferred to a benchtop freeze dryer, lyophilized at -50\u0026deg;C, and at a pressure of 1 mPa (Labconco, Kansas City, MO). After 72 hours, the freeze-dried biomass was removed, and the carbon, nitrogen, and hydrogen content was determined using a CHN Elemental Analyzer (Perkin Elmer 2400 Series II CHNS/O, Massachusetts, USA) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSamples of frozen sediment from Goodenough core \u0026minus;\u0026thinsp;5 (GL-5) and Last Chance core \u0026minus;\u0026thinsp;1 (LC-1) were analyzed for geochronological diagnostic isotopes at Flett Research Ltd., Winnipeg, Manitoba, Canada. To estimate the sediment accumulation rate, Pb-210 was determined indirectly by measuring the Po-210 using alpha spectrometry using a modified method of Eakins and Morrison, 1978 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Radium (Ra-226) was determined using radon-222 emanation using the modified method of Mathieu et al., 1988 [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Cesium‐137 (Cs‐137) isotopes were measured by determining the gamma-ray emitting radionuclides in the sediment with gamma spectrometry and an HPGe detector. Cs-137 is used as an independent tracer to validate the Pb-210 dating. In theory, this core could be dated using the unsupported Pb-210 activities. However, the unsupported Pb-210 model assumes a constant input of Pb-210 directly onto the sediment, and due to the unknown influences of the overlying microbial mat, the requirement of the regression model assumption is not valid for this situation. As an alternative, the Pb-210 linear regression model was calibrated using the 1966 maximum Cs-137 inventory (Supplementary Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eC), and core 5 was dated using two main assumptions: 1) the 1966 Cs-137 maximum inventory is recorded at the midpoint depth of section GL_S5_-3 (adjusted depth 10\u0026ndash;12 cm), then the age at 11 cm depth should be 2019\u0026thinsp;\u0026minus;\u0026thinsp;1966\u0026thinsp;=\u0026thinsp;53 years; 2) and average sediment accumulation rate is 0.2299 cm/yr (by linear regression model) for the 6\u0026ndash;16 cm core interval was used. Elemental sediment content was determined by X-Ray Fluorescence (XRF) analysis using an ITRAX XRF Corescanner (Large Lakes Observatory at the University of Minnesota Duluth).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 LOI and Imaging of Cores:\u003c/h2\u003e \u003cp\u003eReplica cores from each sampling location were processed, described, and archived at the Continental Scientific Drilling Facility (CSD Facility), University of Minnesota, USA. Cores were split using sterilized medical cast saws, and a sterilized utility knife was used to cut the liner and caps, and the sediment was sliced using abrasion-resistant thread (Spectra fiber fishing line). Once the cores were sectioned, half of each core was archived at 4 ℃, and the other half was used for photographic imaging and Loss-on-Ignition (LOI) analysis. Before analyzing the working half of the core, the surface of each core and potential contamination were removed using a sterile microscope slide. Cores were then imaged with a Geoscan V (Geotek, United Kingdom) and scanned at 10 pixels/mm (~\u0026thinsp;254 dpi). After imaging the cores, the sediment-water and organic content were determined by sampling every two centimeters and weighing the subsamples. Next, samples were heated at 100\u0026deg;C overnight to remove the water content, then heated at 550\u0026deg;C for 4 hours to combust the organic material, and finally heated at 1000\u0026deg;C for 2 hours to remove carbonates. After each heating step, the weight of the sample was recorded and used to calculate the dry density, water content, organic content, carbonate content, and non-carbonate content [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Pore water:\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 pH and TA:\u003c/h2\u003e \u003cp\u003eThe pH of the pore water was measured using a calibrated pH meter (Seven CompactTM S220, Mettler Toledo, USA), and pore water total alkalinity (TA) was determined using a G20 compact titrator (Mettler Toledo, USA). Briefly, 50 \u0026micro;L of pore water was added to 39.95 mL of water in a beaker and titrated with 0.2N H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e until the samples reached an end-point of pH\u0026thinsp;=\u0026thinsp;4.3 [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 TC/TIC/TOC/TN\u003c/h2\u003e \u003cp\u003eTotal carbon (TC), total organic carbon (TOC), and total nitrogen (TN) in the pore water were measured using a Shimadzu Total Organic Carbon TOC-V Analyzer equipped with an ASI-V autosampler (Shimadzu, Columbia, MD, USA). Samples were diluted 100x for total carbon and total nitrogen analysis and 150x for total organic carbon analysis. The TC, TOC, and TN content was estimated from a calibration curve (range: 0\u0026ndash;1100 mg/L) using glucose as the standard for TC and TOC, while potassium nitrate was used as the standard for the TN calibration curve (range: 0 -100 mg/L). Total inorganic carbon (TIC) was calculated by subtracting TOC from TC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3 Anions and Cations:\u003c/h2\u003e \u003cp\u003ePore water concentrations of sodium (Na), calcium (Ca), magnesium (Mg), potassium (K), and iron (Fe) were measured using Inductively Coupled Plasma Mass Spectrometry (ICP-QQQ, Agilent Technologies, Tokyo, Japan) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Briefly, pore water samples were diluted 400x with 5% nitric acid to reach a pH of ~\u0026thinsp;3.0 and then filtered using 0.2 \u0026micro;m Acrodisc\u0026reg; PSF Syringe Filters (Pall Corporation, East Hills, NY). Samples were then analyzed using ICP-QQQ equipped with an SPS4 autosampler, which used 3 mL of sample for analysis. A multi-element standard was used to derive calibration curves for each element over a standard range of ppb - ppm. Calibration curves were then used to determine the concentration of each element in the sediment samples. Concentrations of sulfate, phosphate, nitrate, and chloride were measured using an ion chromatography system equipped with an IonPac AS18 anion-exchange column and a conductivity detector (DIONEX ICS-5000+, Thermo Fisher Scientific, USA)[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Pore water samples were diluted 100x using Mili-Q water and filtered using 0.2 \u0026micro;m mixed cellulose esters (MCE) membrane filters. Concentrations of sulfate, phosphate, nitrate, and chloride were determined from calibration curves that were generated using NaNO\u003csub\u003e3\u003c/sub\u003e (range: 0\u0026ndash;100 mg/L), KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e (range: 0\u0026ndash;150 mg/L), MgSO\u003csub\u003e4\u003c/sub\u003e\u0026middot;7H\u003csub\u003e2\u003c/sub\u003eO (range: 0\u0026ndash;700 mg/L), and NaCl (range: 0\u0026ndash;500 mg/L) as standards. A core from each lake (Goodenough Core 4 and Last Chance Core 5) was used to determine the concentration of sulfide in the porewater. Pore water sulfide was determined spectrophotometrically (UV-vis-spectrophotometer, Thermo Fisher Scientific) using methylene blue [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Lipid extraction and analysis\u003c/h2\u003e \u003cp\u003eAfter sampling, lake sediments were frozen, freeze-dried, and stored at \u0026minus;\u0026thinsp;20\u0026deg;C until extraction. Solid samples were ground and homogenized before weighing (~\u0026thinsp;3 g) into vials pre-cleaned with solvent. Samples were then ultrasonically extracted 3 \u0026times; using methanol (MeOH), 3 \u0026times; dichloromethane (DCM)/MeOH (1:1, v:v), and 3 \u0026times; DCM or using 5 \u0026times; 2:1 DCM: MeOH. Extracts were combined to produce a total lipid extract which was subsequently dried under a stream of N\u003csub\u003e2\u003c/sub\u003e. Extracts were reconstituted into 200 \u0026micro;L 9:1 MeOH:DCM before analysis. Samples were analyzed using the atmospheric pressure chemical ionization, 45-minute reverse phase method of Rattray \u0026amp; Smittenberg, 2020 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] on a Shimadzu 8050 high-performance liquid chromatograph (HPLC) coupled to a Shimadzu triple quadrupole mass spectrometer (MS/MS). Isoprenoid glycerol dialkyl glycerol tetraethers (GDGTs) and branched GDGTs were determined using positive ion spectra created by selective ion monitoring over a range of \u003cem\u003em\u003c/em\u003e/\u003cem\u003ez\u003c/em\u003e 741.2-1304.5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7 DNA extraction, amplicon sequencing, and data analysis\u003c/h2\u003e \u003cp\u003eNucleic acids were extracted from sediment samples following the protocol from a FastDNA Spin Kit for Soil (MP Biomedicals, USA) with the modification of heating at 55\u0026deg;C for 5 minutes before the final elution step. To generate 18S and 16S rRNA gene libraries, DNA samples were PCR-amplified in triplicates with a two-step method using the protocol from Sharp et al 2017 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The following primer sets were used for the first step PCR: 1) A519 (5\u0026rsquo;-CAGCMGCCGCGGTAA-3\u0026rsquo; and A915 (5\u0026rsquo;-GTGCTCCCCCGCCAATTCCT-3\u0026rdquo;) for identifying archaeal and bacterial species [\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]; 2) 926wF (5\u0026prime;-AAACTYAAAKGAATTGRCGG3\u0026prime;) and 1392R (5\u0026prime;-ACGGGCGGTGTGTRC3\u0026prime;) to identify bacterial species [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]; and 3) TAReuk454FWD (5\u0026prime;-CCAGCASCYGCGGTAATTCC-3\u0026prime;) and TAReukREV3 (5\u0026prime;-ACTTTCGTTCTTGATYRA-3\u0026prime;) [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] for identifying eukaryotic species. Two-step PCR-amplified samples were sequenced on a MiSeq Personal Sequencer using the 2 \u0026times; 300 bp MiSeq Reagent Kit v3 (Illumina).\u003c/p\u003e \u003cp\u003eSequencing data were analyzed using MetaAmp, an online amplicon data analysis pipeline [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Briefly, paired-end raw reads with greater than 100 base pairs (bp) overlap, and less than 8 mismatches were merged. Merged reads were checked for the presence of primers and mismatches in the primer sequences. Sequences containing forward and reverse primers and 1 or fewer mismatches were quality filtered. The remaining reads were trimmed to a fixed length of 350 bp. These sequences were checked for chimeras and clustered into operational taxonomic units (OTUs) with a similarity cutoff of 97%. OTUs were classified using the Silva database [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] (version 138.1). The microbial association network of the archaeal and bacterial community was determined using NetCoMi (v. 1.0.2) package in R [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. To run the NetCoMi package first a phyloseq object had to be created using phyloseq package [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], which needed three components to make the phyloseq object: 1) OTU table; 2) taxonomy table; and 3) the metadata. Once the phyloseq object was made, it was filtered to remove the OTUs that appeared less than 50 times in 80% of the samples and aggregated to the genus level. To create the network, a Pearson correlation and unsigned transformation were used as the association measure. The following parameters were set for the network creation, which included a centred log-ratio transformation, zero handling in relation to the pseudo count, and a threshold of 0.4 for the sparsification method. Analysis of the network was completed using the cluster method called cluster fast greedy. After the analysis, the network was plotted using the parameters specified in Peschel et al., 2021 [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Metagenome sequencing, assembly, and binning\u003c/h2\u003e \u003cp\u003eExtracted DNA (0.5-2 ng/uL) from Last Chance core #1 (Samples: LC1 -11 to -21) was sent to the Centre for Health Genomics and Informatics in the Cumming School of Medicine (University of Calgary) for the metagenomic library preparation and sequencing with a NextSeq 500 System (Illumina Inc., San Diego, CA, USA). Assembly and binning of the metagenomes followed the same procedure as Dong et al., (2020) [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] and Zorz et al., (2019) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In brief, quality control of the raw reads was conducted using BBDuk from the BBTools package, which involved the removal of primers, adapters, and low-quality reads. Those reads that passed quality control were co-assembled using MEGAHIT and assembled separately for each sample with metaSPades (version 3.12.0.). Contigs shorter than 500 bp were removed, and the remaining assembled contigs were annotated using MetaErg version 2.3.x [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Mapping of the quality-controlled reads back to assemblies was completed with BBmap. Assembled contigs were then binned into metagenome-assembled-genomes (MAGs) using MetaBat. The individual samples and the co-assembly were binned separately. Completeness and contamination of each MAG were estimated with CheckM [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and then the MAGs were classified using GTDB-Tk version 2.1.0 [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. PhyloFlash (version 3.4.0.) [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] was used to summarize the taxonomic diversity of the MAGs. MAGs that were more than 80% complete and with less than 5% contamination were analyzed further (SI Table\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Data Visualization and Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis and graphical representation were carried out using GraphPad Prism 10.1.2 and R version 4.2.3 [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Utilizing the metaMDS function from the vegan package in R, nonmetric multidimensional scaling of Bray-Curtis dissimilarity was computed and then visualized with the ggplot2 package [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Other packages used for data visualization were tidyverse (v2.0.0), reshape2 (v1.4.4), and viridis (v0.6.3) [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Phylogenetic analysis of the archaeal MAGs\u003c/h2\u003e \u003cp\u003eConserved single-copy proteins were identified using hmmsearch with a set of hmm (Hidden Markov Model) profiles used by gtdbtk [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Next, sets of orthologs among the identified proteins were identified, as previously described [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Sets that lacked a representative in 40% of the species were discarded and 100\u0026ndash;134 remaining sets of orthologous proteins (listed in Supplementary Table\u0026nbsp;6 of Parks et al., 2017 [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]) were aligned using clustalo [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Poorly aligned regions were eliminated with BMGE[\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e] and all individual gene alignments were concatenated, leading to three concatenated alignments with 50,120\u0026thinsp;\u0026minus;\u0026thinsp;67,742 positions, one for Asgard, one for Halo- and one for DPANN Archaea. From these, trees were created using fasttree [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch3\u003eAcknowledgments\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe authors are thankful for the assistance provided by Brian Ballie for running the samples on ICP-MS and the Total Inorganic Carbon-Total Organic Carbon analysis; Stephen Schroeder for helping collect the sediment cores from the soda lakes. This research was undertaken thanks in part to funding from the Natural Sciences and Engineering Research Council (NSERC), Canada First Research Excellence Fund (CFREF), Alberta Innovates, the Government of Alberta, and the University of Calgary.\u003c/p\u003e\n\u003ch3\u003eAuthor contributions\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAJP, HD, MS, and JER conceived and planned the experiments. AJP, AV, TG, BN, and JER contributed to sample preparation and carried out the experiments. AJP, SB, AV, TG, BN, VK, HD, MS, and JER contributed to the interpretation of the results. AJP and JER took the lead in writing the manuscript. All authors provided critical feedback and helped shape the research, analysis, and final version of the manuscript.\u003c/p\u003e\n\u003ch3\u003eData Availability:\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAmplicon sequences can be found under the Bioproject PRJNA377096. The 16\u0026thinsp;S rRNA sequence Biosamples are: SAMN38476395-SAMN38476456, the 18\u0026thinsp;S rRNA sequence Biosamples are: SAMN3877441-SAMN3877459, and the 16 S rRNA sequences amplified using the A519 and A915 primer set are Biosamples: SAMN38477990-SAMN38478044. The metagenome raw reads and metagenome-assembled-genomes can also be found under the Bioproject PRJNA377096.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eCode availability\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe metagenomics pipeline can be located on GitHub page: https://github.com/xiaoli-dong/metagenomics_crash_course. The NetCoMi pipeline used to construct and analyze microbial networks can be found on GitHub page: https://github.com/stefpeschel/NetCoMi.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBoros E, Kolpakova M. 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PloS one. 2010;5(3):e9490.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3861392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3861392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eApproximately 3.7\u0026nbsp;billion years ago, microbial life may have emerged in phosphate-rich salty ponds. Surprisingly, analogs of these environments are present in alkaline lake systems, recognized as highly productive biological ecosystems. Investigating the microbial ecology of two Canadian soda lake sediment systems characterized by naturally high phosphate levels. Using a comprehensive approach involving geochemistry, metagenomics, and amplicon sequencing, we discovered that groundwater infiltration into Lake Goodenough sediments supported stratified layers of microbial metabolisms fueled by decaying mats. Effective degradation of microbial mats resulted in unexpectedly low net productivity. Evaporation of water from Last Chance Lake and its sediments led to saturation of brines and a habitat dominated by inorganic precipitation reactions, with low productivity, low organic matter turnover and little biological uptake of phosphorus, leading to high phosphate concentrations. Our research highlights that modern analogs for origin-of-life conditions might be better represented by soda lakes with low phosphate concentrations. Highly alkaline brines were found to be dominated by potentially dormant spore-forming bacteria. These saturated brines also hosted potential symbioses between \u003cem\u003eHalobacteria\u003c/em\u003e and \u003cem\u003eNanoarchaeaota\u003c/em\u003e, as well as \u003cem\u003eLokiarchaea\u003c/em\u003e and bacterial sulfate reducers. Metagenome-assembled genomes of \u003cem\u003eNanoarchaeaota\u003c/em\u003e lacked strategies for coping with salty brines and were minimal for \u003cem\u003eLokiarchaea\u003c/em\u003e. Thus, highly alkaline brine environments could be too extreme to support origin of life scenarios. These findings shed light on the complex interplay of microbial life in extreme environments and contribute to our understanding of early Earth environments.\u003c/p\u003e","manuscriptTitle":"Ecology and biogeochemistry of the microbial underworld in two sister soda lakes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-16 18:27:22","doi":"10.21203/rs.3.rs-3861392/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ac9a9ef1-ec2a-45da-8f69-41d0bba808e8","owner":[],"postedDate":"January 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-02T12:49:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-16 18:27:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3861392","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3861392","identity":"rs-3861392","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}