The ocean plays a central role in the fluxes and stability of Earth’s biogeochemistry (Dontsova et al., 2020; Field et al., 1998; Falkowski et al., 1998). Due to their abundance, diversity, and physiological versatility, microbes mediate the vast majority of organic matter transformations that underpin higher trophic levels (Brown et al., 2014; Mason et al., 2009). For example, marine microorganisms regulate a large fraction of the organic carbon pool (Ducklow and Doney, 2013), drive elemental cycling of nutrients like nitrogen (Zehr and Kudela, 2011), and participate in the ocean-atmosphere exchange of climatically important gasses (Vila-Costa et al., 2006). Starting in the 1980s, analysis of small-subunit ribosomal RNA genes began to reveal the identity of dominant clades of bacteria and archaea that were notable for their ubiquity and high abundance, and subsequent analyses highlighted their diverse physiological activities in the ocean (Giovannoni and Stingl, 2005). Phylogenetic studies showed that these clades are broadly distributed across the Tree of Life (ToL) and encompass a wide range of phylogenetic breadths (Giovannoni and Stingl, 2005). Cultivation-based studies and the large-scale generation of genomes from metagenomes have continued to make major progress in examining the genomic diversity and metabolism of these major marine clades, but we still lack a comprehensive understanding of the evolutionary events leading to their origin and diversification in the ocean.

Several independent studies have used molecular phylogenetic methods to date the diversification of some marine microbial lineages, such as the ammonia-oxidizing archaea of the order Nitrososphaerales (Marine Group I [MGI]) (Ren et al., 2019; Yang et al., 2021; Zhang et al., 2021), picocyanobacteria of the genera Synechococcus and Prochlorococcus (Sánchez-Baracaldo, 2015; Sánchez-Baracaldo et al., 2019; Zhang et al., 2021), and marine alphaproteobacterial groups that included the SAR11 and Roseobacter clades (Luo et al., 2013). Differences in the methodological frameworks used in these studies often hinder comparisons between lineages, however, and results for individual clades often conflict (Ren et al., 2019; Sánchez-Baracaldo, 2015; Yang et al., 2021; Zhang et al., 2021). Moreover, it has been difficult to directly compare bacterial and archaeal clades due to the vast evolutionary distances between these domains. For these reasons, it has remained challenging to evaluate the ages of different marine lineages and develop a comprehensive understanding of the timing of microbial diversification events in the ocean and their relationship with major geological events throughout Earth’s history.

To clarify the timing at which major lineages of bacteria and archaea diversified into the ocean, we developed an approach that leverages a multi-domain phylogenetic tree that allows for simultaneous dating of all major marine lineages. This method allows us to directly compare the ages of divergent lineages across the ToL and subsequently reconstruct a timeline in which these groups evolved into the ocean. Moreover, we can also map the acquisition of different protein families onto this phylogeny and thereby infer the genes that were gained by these marine lineages at the time of their emergence. Altogether, our study provides a comprehensive framework that sheds light on watershed events in the history of life on Earth that have given rise to contemporary biodiversity and biogeochemical dynamics in the ocean.

To begin analyzing the diversification of marine lineages of bacteria and archaea, we constructed a multi-domain phylogenetic tree that allowed us to directly compare the origin of 13 planktonic marine bacterial and archaeal clades that are notable for their abundance and major roles in marine biogeochemical cycles (Figure 1). We based tree reconstruction on a benchmarked set of marker genes that we have previously shown to be congruent for inter-domain phylogenetic reconstruction (Martinez-Gutierrez and Aylward, 2021; details in ‘Materials and methods,’ Supplementary file 2). Our phylogenetic framework included non-marine clades for phylogenetic context, and overall it recapitulates known relationships across the ToL, such as the clear demarcation of the Gracilicutes and Terrabacteria bacterial superphyla and the basal placement of the Thermatogales within Bacteria (Coleman et al., 2021; Martinez-Gutierrez and Aylward, 2021; Figure 1). To gain insight into the geological landscape in which these major marine clades first diversified, we performed a Bayesian relaxed molecular dating analysis on our benchmarked ToL using several calibrated nodes (Figure 1 and Table 1).

Figure 1

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Due to the limited representation of microorganisms in the fossil record and the difficulties to associate fossils to extant relatives, we employed geochemical evidence as temporal calibrations (Figure 1 and Table 1). Moreover, because of the uncertainty in the length of the branch linking bacteria and archaea, the crown node for each domain was calibrated independently. We used both the age of the presence of liquid water (as approximated through the dating of zircons; Valley et al., 2014) as well as the most ancient record of biogenic methane (broadly used as evidence of life on Earth; Ueno et al., 2006) as maximum and minimum prior ages for bacteria and archaea (4400 and 3460 My, respectively, Figure 1 and Table 1). For internal calibration, we used the recent identification of non-oxygenic Cyanobacteria to constrain the diversification node of oxygenic Cyanobacteria with a minimum age of 2320 My, the estimated age for the Great Oxidation Event (GOE) (Bekker et al., 2004; Holland, 2006; Holland, 2002). Similarly, we calibrated the crown node of aerobic ammonia-oxidizing archaea, aerobic Ca. Marinimicrobia, and the nitrite-oxidizing bacteria with a maximum age of 2320 My (GOE estimated age) due to their strict aerobic metabolism. Despite geological evidence pointing to the presence of oxygen before the GOE, our Bayesian estimates indicate an overall consistency of the priors used (Figure 2—figure supplement 2), and we recovered the ancient origin of major bacterial and archaeal supergroups, such as Asgardarchaeota, Euryarchaeota, Firmicutes, Actinobacteria, and Aquificota (Figure 2). Moreover, the date we found for oxygenic Cyanobacteria (2611 My, 95% CI 2589–2632; Figure 2) is in agreement with their diversification happening before the GOE (Ward et al., 2016). Please see Table 1 for a detailed explanation of all calibration dates used, together with our rationale for including each one.

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Our Bayesian estimates suggest that the lineages that emerged earliest are SAR202, aerobic Ca. Marinimicrobia, SAR324, and the Marine Group II of the phylum Euryarchaeota (MGII). The most ancient clade was the SAR202 (2479 My, 95% CI 2465–2492 My), whose diversification took place near before the GOE (Figure 2). Given the broadly distributed aerobic capabilities of SAR202, the diversification of this clade before the GOE suggests that SAR202 emerged during an oxygen oasis proposed to have existed in pre-GOE Earth (Anbar et al., 2007; Ossa Ossa et al., 2019; Reinhard and Planavsky, 2022). The ancient pre-GOE origin of SAR202 is consistent with a recent study that proposed that this clade played a role in the shift of the redox state of the atmosphere during the GOE. SAR202 is able to partially metabolize organic matter through a flavin-dependent Baeyer–Villiger monooxygenase, thereby enhancing the burial of organic matter and contributing to the net accumulation of oxygen in the atmosphere (Landry et al., 2017; Shang et al., 2022). After the GOE, we detected the diversification of aerobic Ca. Marinimicrobia (2196 My, 95% CI 2173–2219 My), the SAR324 clade (1686 My, 95% CI 1658–1715 My), and the MGII clade (1184 My, 95% CI 1166–1202 My) (Figure 2). Although these ancient clades may have first diversified under the oxic conditions derived from the GOE, it has been suggested that the initial oxygenation of Earth was followed by a relatively rapid drop in ocean and atmosphere oxygen levels (Alcott et al., 2019; Hodgskiss et al., 2019; Reinhard and Planavsky, 2022). It is therefore likely that these clades diversified in the microaerophilic and variable oxygen conditions that prevailed during this period (Bekker et al., 2004; Holland, 2006; Holland, 2002). Indeed, the oxygen landscape in which these marine clades first diversified is consistent with their current physiology. For example, these groups are capable of using oxygen and other compounds as terminal electron acceptors (e.g., nitrate and sulfate), and several representatives are prevalent in modern marine oxygen minimum (OMZs) (Pajares et al., 2020; Sheik et al., 2014; Thrash et al., 2017; Ulloa et al., 2012). The facultative aerobic or microaerophilic metabolism in these clades is potentially a vestige of the low oxygen environment of most of the Proterozoic Eon, and in this way OMZs can be considered to be modern-day refugia of these ancient ocean conditions. Of the clades that diversified as part of this early period, MGII and SAR324 show the youngest colonization dates, but we suspect that this may be due to the notably long branches that lead to the crown nodes of these lineages. These long branches are likely caused by the absence of basal-branching members of these clades – either due to extinction events or under-sampling of rare lineages in the available genome collection – that would have increased the age of these lineages if present in the tree.

According to our analysis, the next clades to diversify in the ocean are SAR116 (959 My, 95% CI 945–973 My), SAR11 (725 My, 95% CI 715–734 My), SAR86 (503 My, 95% CI 497–509 My), SAR92 (430 My, 95% CI 423–437 My), and Roseobacter (332 My, 95% CI 323–340 My) (Figure 2). The relatively late appearance of these heterotrophic lineages that are abundant in the open ocean today was potentially due to the low productivity and oxygen concentrations in both shallow and deep waters that prevailed in the Mid-Proterozoic (1800–800 My), a period previously described as the ‘boring billion’ (Anbar and Knoll, 2002; Crockford et al., 2018; Hodgskiss et al., 2019; Planavsky et al., 2014; Tang et al., 2016). The diversification of these clades may be indirectly associated with the Snowball event registered before the Neoproterozoic Oxidation Event (NOE, 800–540 My) (Anbar and Knoll, 2002; Hoffman et al., 1998; Shields-Zhou and Och, 2011), which was followed by an increased the availability of oxygen and inorganic nutrients in the ocean (Anbar and Knoll, 2002; Butterfield, 2001; Porter, 2004; Shields-Zhou and Och, 2011; Vidal and Moczydłowska-Vidal, 1997), and is also coincident with the widespread diversification of large eukaryotic algae during the Neoproterozoic (Anbar and Knoll, 2002; Butterfield, 2001; Porter, 2004; Shields-Zhou and Och, 2011; Vidal and Moczydłowska-Vidal, 1997). It is therefore plausible that an increase in nutrients as well as the broad diversification of eukaryotic plankton enhanced the mobility of organic and inorganic nutrients beyond the coastal areas, and increased the burial of organic matter that consequently led to the rise in atmospheric oxygen concentrations (Knoll et al., 2006; Shields-Zhou and Och, 2011). The scenario in which heterotrophic marine clades diversified in part as a consequence of the new niches built by marine eukaryotes has been previously proposed to have driven the diversification of the Roseobacter clade (Luo et al., 2013; Luo and Moran, 2014). The diversification timing of Roseobacter and other heterotrophic clades supports this phenomenon and suggests that the interaction with marine eukaryotes may have broadly influenced the diversification of prevalent lineages in the modern ocean. Similar to what we observed in MGII and SAR324, the Roseobacter clade shows a long branch leading to the crown node (Figure 1), suggesting that the diversification of this clade may have occurred earlier.

We also report the diversification of the chemolithoautotrophic archaeal lineage MGI into the ocean after the NOE (678 My, 95% CI 668–688 My) (Figure 2), which is comparable with the age reported by another independent study (Yang et al., 2021). This is consistent with an increase in the oxygen concentrations of the ocean during this period (Reinhard and Planavsky, 2022), a necessary requisite for ammonia oxidation. Moreover, the widespread sulfidic conditions that likely prevailed during the Mid-Proterozoic ocean may have limited the availability of redox-sensitive metals such as copper, which is necessary for ammonia monooxygenases (Anbar and Knoll, 2002; Hatzenpichler, 2012). It is therefore plausible that a low concentration of oxygen and limited inorganic nutrient availability before the NOE were limiting factors that delayed the colonization of AOA into the ocean.

The most recent lineages to emerge include the genera Synechococcus (243 My, 95% CI 238–247 My), Prochlorococcus (230 My, 95% CI 225–234 My), and the diazotroph Crocosphaera (228 My, 95% CI 218–237 My). Our results agree with an independent study that points to a relatively late emergence of the marine picocyanobacterial clades Prochlorococcus and Synechococcus (Sánchez-Baracaldo, 2015). Picocyanobacterial clades and Crocosphaera are critical components of phytoplanktonic communities in the modern open ocean due to their large contribution to carbon and nitrogen fixation, respectively (Flombaum et al., 2013; Montoya et al., 2004; Scanlan et al., 2009). For example, the nitrogen fixation activities of Crocosphaera watsonii in the open ocean today support the demands of nitrogen-starved microbial food webs found in oligotrophic waters (Hewson et al., 2009). The relatively late diversification of these lineages suggests that the oligotrophic open ocean is a relatively modern ecosystem. Moreover, the oligotrophic ocean today is characterized by the rapid turnover of nutrients that depends on the efficient mobilization of essential elements through the ocean (Karl, 2002). Due to its distance from terrestrial nutrient inputs, productivity in the open ocean is therefore dependent on local nitrogen fixation, which was likely enhanced after the widespread oxygenation of the ocean that made molybdenum widely available due to its high solubility in oxic seawater (Canfield et al., 2007; Scott et al., 2008; Wei et al., 2021). Such widespread oxygenation was registered 430–390 My in an event referred to here as the Paleozoic Oxidation Event (POE; Berner and Raiswell, 1983; Lenton et al., 2016; Sperling et al., 2015; Tostevin and Mills, 2020; Figure 2). The increase of oxygen to present-day levels in the atmosphere and the ocean was potentially the result of an increment of the burial of organic carbon in sedimentary rocks due to the diversification of the earliest land plants (Lenton et al., 2016; Planavsky et al., 2021; Reinhard and Planavsky, 2022). The POE has also been associated with increased phosphorus weathering rates (Bergman, 2004; Lenton et al., 2016), global impacts on the global element cycles (Dahl and Arens, 2020), and an increase in the overall productivity of the ocean (Planavsky et al., 2021). The late diversification of oligotrophic-specialized clades after the POE therefore suggests that the establishment of the oligotrophic open ocean as we know it today would only have been plausible once modern oxygen concentrations and biogeochemical dynamics were reached (Karl, 2002; Reinhard and Planavsky, 2022).

To shed further light on the drivers that allowed their colonization into the ocean, we investigated whether the diversification of marine microbial clades was linked to the acquisition of novel metabolic capabilities. We broadly classified the different clades as early-branching clades (EBC), late-branching clades (LBC), and late-branching phototrophs (LBP) based on the general timing of their diversification (Figure 2). To identify the enrichment of gene functions at the crown node of each marine clade (Figure 1), we performed a stochastic mapping analysis on each of the 112,248 protein families identified in our genome dataset (Supplementary file 1). We compared our results with a null hypothesis distribution in which a constant rate model was implemented unconditionally of observed data (see ‘Materials and methods’). Statistical comparisons of the stochastic and the null distribution show that each diversification phase was associated with the enrichment of specific functional categories that were consistent with the geochemical context of their diversification (Figures 3 and 4). For example, EBC gained a disproportionate number of genes involved in DNA repair, recombination, and glutathione metabolism, consistent with the hypothesis that the GOE led to a rise in reactive oxygen species that cause DNA damage (Zaikowski et al., 2010; Khademian and Imlay, 2021; Masip et al., 2006). Moreover, the EBC were enriched in proteins involved in ancient aerobic pathways, such as oxidative phosphorylation and the TCA cycle (Figure 3), as well as genes implicated in the degradation of fatty acids under aerobic conditions, such as the enzyme alkane 1-monooxygenase in MGII (Supplementary file 4). We also detected genes for the adaptation to marine environments, including genes for the anabolism of taurine (e.g., cysteine dioxygenase in MGII, Supplementary file 4), an osmoprotectant commonly found in marine bacteria (McParland et al., 2021). Our findings suggest that the diversification of EBC in the ocean was linked to the emergence of aerobic metabolism, the acquisition of metabolic capabilities to exploit the newly created niches that followed the increase of oxygen, and the expansion of genes involved in the tolerance to salinity and oxidative stress.

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Figure 4

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The emergence of LBC (Figures 3 and 4), whose diversification occurred around the time of the NOE and the initial diversification of eukaryotic algae (Parfrey et al., 2011), was characterized by the enrichment of substantially different gene repertories compared to EBC (Figure 3). For instance, the heterotrophic lineages Roseobacter, SAR116, and SAR92 show an enrichment of flagellar assembly and motility genes (Figure 3), including genes for flagellar biosynthesis, flagellin, and the flagellar basal-body assembly (Supplementary file 4). Motile marine heterotrophs like Roseobacter species have been associated with the marine phycosphere, a region surrounding individual phytoplankton cells releasing carbon-rich nutrients (Mühlenbruch et al., 2018; Seymour et al., 2017). Although the phycosphere can also be established between prokaryotic phytoplankton and heterotrophs (Croft et al., 2005; Seymour et al., 2017), given the late diversification of abundant marine prokaryotic phytoplankton (Figures 2 and 4), it is plausible that the emergence of these clades was closely related to the establishment of ecological proximity with large eukaryotic algae. The potential diversification of heterotrophic LBC due to their ecological interactions with eukaryotic algae is further supported by the enrichment of genes involved in vitamin B6 metabolism and folate biosynthesis, which are key nutrients involved in phytoplankton–bacteria associations (Seymour et al., 2017). LBC were also enriched in genes for the catabolism of taurine (e.g., taurine transport system permease in SAR11 and a taurine dioxygenase in SAR86 and SAR92), suggesting that LBC gained metabolic capabilities to utilize the taurine produced by other organisms as a substrate (Clifford et al., 2019), instead of producing it as an osmoprotectant. Furthermore, we identified the enrichment of genes involved in carotenoid biosynthesis, including spheroidene monooxygenase, carotenoid 1,2-hydratase, beta-carotene hydroxylase, and lycopene beta-cyclase (Supplementary file 4). The production of carotenoids is consistent with their use in proteorhodopsin, a light-driven proton pump that is a hallmark feature of most marine heterotrophic bacteria, in particular those that inhabit energy-depleted areas of the ocean today (de la Torre et al., 2003).

LBP that diversified around the time of the POE (Figure 2) showed a remarkable enrichment of transporters in Crocosphaera and Synechococcus (Figure 3). In particular, the diversification of Crocosphaera was characterized by the acquisition of transporters for inorganic nutrients like cobalt, nickel, iron, phosphonate, phosphate, ammonium, and magnesium, along with organic nutrients including amino acids and polysaccharides (Supplementary file 4). The acquisition of a wide diversity of transporters by the Crocosphaera is consistent with their boom-and-bust lifestyle seen in the oligotrophic open ocean today (Hewson et al., 2009; Wilson et al., 2017), which requires a rapid and efficient use of scarce nutrients. We also identified genes involved in osmotic pressure tolerance, for example, a Ca-activated chloride channel homolog, a magnesium exporter, and a fluoride exporter (Supplementary file 4). In contrast, our results show that Synechococcus only acquired transporters for inorganic nutrients (e.g., iron and sulfate, Supplementary file 4), whereas Prochlorococcus did not show the enrichment of transporters (Supplementary file 4). Similar to LBC, we identified the enrichment of taurine metabolism genes in Crocosphaera and Synechococcus, suggesting that its use as osmoprotectant and potential substrate is widespread among planktonic microorganisms (Clifford et al., 2019). Prochlorococcus exhibits enrichment in fewer categories than the rest of phototrophic clades diversifying during the same period, consistent with the streamlined nature of genomes from this lineage (Partensky and Garczarek, 2010). The genes acquired by Prochlorococcus are involved in photosynthesis, which supports previous findings that the diversification of this clade was accompanied by changes in the photosynthetic apparatus compared with Synechococcus, its sister group (Biller et al., 2015). Overall, the diversification of LBP was marked by the capacity to thrive in the oligotrophic ocean by exploiting organic and inorganic nutrients and by the modifying the photosynthetic apparatus as observed in Crocosphaera and Synechococcus, and Prochlorococcus, respectively.

The main code used in our study is deposited on GitHub: https://github.com/carolinaamg/enriched_OG (copy archived at Carolinaamg, 2023).

1. 1. Alcott LJ
   2. Mills BJW
   3. Poulton SW

   (2019) Stepwise Earth oxygenation is an inherent property of global biogeochemical cycling

   Science 366:1333–1337.

   https://doi.org/10.1126/science.aax6459

   • PubMed
   • Google Scholar
2. 1. Anbar AD
   2. Knoll AH

   (2002) Proterozoic ocean chemistry and evolution: a bioinorganic bridge?

   Science 297:1137–1142.

   https://doi.org/10.1126/science.1069651

   • PubMed
   • Google Scholar
3. 1. Anbar AD
   2. Duan Y
   3. Lyons TW
   4. Arnold GL
   5. Kendall B
   6. Creaser RA
   7. Kaufman AJ
   8. Gordon GW
   9. Scott C
   10. Garvin J
   11. Buick R

   (2007) A whiff of oxygen before the great oxidation event?

   Science 317:1903–1906.

   https://doi.org/10.1126/science.1140325

   • PubMed
   • Google Scholar
4. 1. Bekker A
   2. Holland HD
   3. Wang P-L
   4. Rumble D III
   5. Stein HJ
   6. Hannah JL
   7. Coetzee LL
   8. Beukes NJ

   (2004) Dating the rise of atmospheric oxygen

   Nature 427:117–120.

   https://doi.org/10.1038/nature02260

   • Google Scholar
5. 1. Bergman NM

   (2004) COPSE: A new model of biogeochemical cycling over Phanerozoic time

   American Journal of Science 304:397–437.

   https://doi.org/10.2475/ajs.304.5.397

   • Google Scholar
6. 1. Berner RA
   2. Raiswell R

   (1983) Burial of organic carbon and pyrite sulfur in sediments over phanerozoic time: a new theory

   Geochimica et Cosmochimica Acta 47:855–862.

   https://doi.org/10.1016/0016-7037(83)90151-5

   • Google Scholar
7. 1. Biller SJ
   2. Berube PM
   3. Lindell D
   4. Chisholm SW

   (2015) Prochlorococcus: the structure and function of collective diversity

   Nature Reviews. Microbiology 13:13–27.

   https://doi.org/10.1038/nrmicro3378

   • PubMed
   • Google Scholar
8. 1. Bollback JP

   (2006) SIMMAP: stochastic character mapping of discrete traits on phylogenies

   BMC Bioinformatics 7:88.

   https://doi.org/10.1186/1471-2105-7-88

   • PubMed
   • Google Scholar
9. 1. Brasier MD
   2. Lindsay JF

   (1998) A billion years of environmental stability and the emergence of eukaryotes: new data from northern Australia

   Geology 26:555–558.

   https://doi.org/10.1130/0091-7613(1998)026<0555:abyoes>2.3.co;2

   • PubMed
   • Google Scholar
10. 1. Brown MV
    2. Ostrowski M
    3. Grzymski JJ
    4. Lauro FM

    (2014) A trait based perspective on the biogeography of common and abundant marine bacterioplankton clades

    Marine Genomics 15:17–28.

    https://doi.org/10.1016/j.margen.2014.03.002

    • PubMed
    • Google Scholar
11. 1. Butterfield NJ

    (2000) Bangiomorpha pubescens n. gen., n. sp.: implications for the evolution of sex, multicellularity, and the Mesoproterozoic/Neoproterozoic radiation of eukaryotes

    Paleobiology 26:386–404.

    https://doi.org/10.1666/0094-8373(2000)026<0386:BPNGNS>2.0.CO;2

    • Google Scholar
12. 1. Butterfield NJ

    (2001) Paleobiology of the late Mesoproterozoic (ca. 1200 Ma) Hunting Formation, Somerset Island, arctic Canada

    Precambrian Research 111:235–256.

    https://doi.org/10.1016/S0301-9268(01)00162-0

    • Google Scholar
13. Book

    1. Butterfield NJ
    2. Knoll AH
    3. Swett K

    (2006) Paleobiology of the Neoproterozoic Svanbergfjellet Formation, Spitsbergen

    Wiley-Blackwell.

    https://doi.org/10.1111/j.1502-3931.1994.tb01558.x

    • Google Scholar
14. 1. Canfield DE
    2. Poulton SW
    3. Narbonne GM

    (2007) Late-Neoproterozoic deep-ocean oxygenation and the rise of animal life

    Science 315:92–95.

    https://doi.org/10.1126/science.1135013

    • PubMed
    • Google Scholar
15. 1. Capella-Gutiérrez S
    2. Silla-Martínez JM
    3. Gabaldón T

    (2009) trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses

    Bioinformatics 25:1972–1973.

    https://doi.org/10.1093/bioinformatics/btp348

    • PubMed
    • Google Scholar
16. Software

    1. Carolinaamg

    (2023) Enriched_Og, version swh:1:rev:2406728046a29cca0200e44bb64eaa59cbb322f5

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:ddc2721a147ab9f3a0f7b83ccdbd3e55eb106ae8;origin=https://github.com/carolinaamg/enriched_OG;visit=swh:1:snp:a74c413afaf3c454dba4c8f4f636462cd5503736;anchor=swh:1:rev:2406728046a29cca0200e44bb64eaa59cbb322f5
17. 1. Chaumeil PA
    2. Mussig AJ
    3. Hugenholtz P
    4. Parks DH

    (2019) GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database

    Bioinformatics 36:1925–1927.

    https://doi.org/10.1093/bioinformatics/btz848

    • PubMed
    • Google Scholar
18. 1. Clifford EL
    2. Varela MM
    3. De Corte D
    4. Bode A
    5. Ortiz V
    6. Herndl GJ
    7. Sintes E

    (2019) Taurine Is a Major Carbon and Energy Source for Marine Prokaryotes in the North Atlantic Ocean off the Iberian Peninsula

    Microbial Ecology 78:299–312.

    https://doi.org/10.1007/s00248-019-01320-y

    • PubMed
    • Google Scholar
19. 1. Coleman GA
    2. Davín AA
    3. Mahendrarajah TA
    4. Szánthó LL
    5. Spang A
    6. Hugenholtz P
    7. Szöllősi GJ
    8. Williams TA

    (2021) A rooted phylogeny resolves early bacterial evolution

    Science 372:eabe0511.

    https://doi.org/10.1126/science.abe0511

    • PubMed
    • Google Scholar
20. 1. Crockford PW
    2. Hayles JA
    3. Bao H
    4. Planavsky NJ
    5. Bekker A
    6. Fralick PW
    7. Halverson GP
    8. Bui TH
    9. Peng Y
    10. Wing BA

    (2018) Triple oxygen isotope evidence for limited mid-Proterozoic primary productivity

    Nature 559:613–616.

    https://doi.org/10.1038/s41586-018-0349-y

    • PubMed
    • Google Scholar
21. 1. Croft MT
    2. Lawrence AD
    3. Raux-Deery E
    4. Warren MJ
    5. Smith AG

    (2005) Algae acquire vitamin B12 through a symbiotic relationship with bacteria

    Nature 438:90–93.

    https://doi.org/10.1038/nature04056

    • PubMed
    • Google Scholar
22. 1. Dahl TW
    2. Arens SKM

    (2020) The impacts of land plant evolution on Earth’s climate and oxygenation state – An interdisciplinary review

    Chemical Geology 547:119665.

    https://doi.org/10.1016/j.chemgeo.2020.119665

    • Google Scholar
23. 1. de la Torre JR
    2. Christianson LM
    3. Béjà O
    4. Suzuki MT
    5. Karl DM
    6. Heidelberg J
    7. DeLong EF

    (2003) Proteorhodopsin genes are distributed among divergent marine bacterial taxa

    PNAS 100:12830–12835.

    https://doi.org/10.1073/pnas.2133554100

    • PubMed
    • Google Scholar
24. Book

    1. Dontsova K
    2. Balogh‐Brunstad Z
    3. Le Roux G

    (2020) Biogeochemical Cycles: Ecological Drivers and Environmental Impact

    John Wiley & Sons.

    https://doi.org/10.1002/9781119413332

    • Google Scholar
25. 1. Drummond AJ
    2. Ho SYW
    3. Phillips MJ
    4. Rambaut A

    (2006) Relaxed phylogenetics and dating with confidence

    PLOS Biology 4:e88.

    https://doi.org/10.1371/journal.pbio.0040088

    • PubMed
    • Google Scholar
26. 1. Ducklow HW
    2. Doney SC

    (2013) What is the metabolic state of the oligotrophic ocean? A debate

    Annual Review of Marine Science 5:525–533.

    https://doi.org/10.1146/annurev-marine-121211-172331

    • PubMed
    • Google Scholar
27. 1. Eddy SR
    2. Pearson WR

    (2011) Accelerated Profile HMM Searches

    PLOS Computational Biology 7:e1002195.

    https://doi.org/10.1371/journal.pcbi.1002195

    • PubMed
    • Google Scholar
28. 1. Falkowski PG
    2. Barber RT
    3. Smetacek VV

    (1998) Biogeochemical controls and feedbacks on ocean primary production

    Science 281:200–207.

    https://doi.org/10.1126/science.281.5374.200

    • PubMed
    • Google Scholar
29. 1. Field CB
    2. Behrenfeld MJ
    3. Randerson JT
    4. Falkowski P

    (1998) Primary production of the biosphere: integrating terrestrial and oceanic components

    Science 281:237–240.

    https://doi.org/10.1126/science.281.5374.237

    • PubMed
    • Google Scholar
30. 1. Flombaum P
    2. Gallegos JL
    3. Gordillo RA
    4. Rincón J
    5. Zabala LL
    6. Jiao N
    7. Karl DM
    8. Li WKW
    9. Lomas MW
    10. Veneziano D
    11. Vera CS
    12. Vrugt JA
    13. Martiny AC

    (2013) Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus

    PNAS 110:9824–9829.

    https://doi.org/10.1073/pnas.1307701110

    • PubMed
    • Google Scholar
31. 1. Giovannoni SJ
    2. Stingl U

    (2005) Molecular diversity and ecology of microbial plankton

    Nature 437:343–348.

    https://doi.org/10.1038/nature04158

    • PubMed
    • Google Scholar
32. 1. Hatzenpichler R

    (2012) Diversity, physiology, and niche differentiation of ammonia-oxidizing archaea

    Applied and Environmental Microbiology 78:7501–7510.

    https://doi.org/10.1128/AEM.01960-12

    • PubMed
    • Google Scholar
33. 1. Hewson I
    2. Poretsky RS
    3. Beinart RA
    4. White AE
    5. Shi T
    6. Bench SR
    7. Moisander PH
    8. Paerl RW
    9. Tripp HJ
    10. Montoya JP
    11. Moran MA
    12. Zehr JP

    (2009) In situ transcriptomic analysis of the globally important keystone N2-fixing taxon Crocosphaera watsonii

    The ISME Journal 3:618–631.

    https://doi.org/10.1038/ismej.2009.8

    • PubMed
    • Google Scholar
34. 1. Hodgskiss MSW
    2. Crockford PW
    3. Peng Y
    4. Wing BA
    5. Horner TJ

    (2019) A productivity collapse to end Earth’s Great Oxidation

    PNAS 116:17207–17212.

    https://doi.org/10.1073/pnas.1900325116

    • PubMed
    • Google Scholar
35. 1. Hoffman PF
    2. Kaufman AJ
    3. Halverson GP
    4. Schrag DP

    (1998) A neoproterozoic snowball earth

    Science 281:1342–1346.

    https://doi.org/10.1126/science.281.5381.1342

    • PubMed
    • Google Scholar
36. 1. Holland HD

    (2002) Volcanic gases, black smokers, and the great oxidation event

    Geochimica et Cosmochimica Acta 66:3811–3826.

    https://doi.org/10.1016/S0016-7037(02)00950-X

    • Google Scholar
37. 1. Holland HD

    (2006) The oxygenation of the atmosphere and oceans

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 361:903–915.

    https://doi.org/10.1098/rstb.2006.1838

    • PubMed
    • Google Scholar
38. 1. Kalyaanamoorthy S
    2. Minh BQ
    3. Wong TKF
    4. von Haeseler A
    5. Jermiin LS

    (2017) ModelFinder: fast model selection for accurate phylogenetic estimates

    Nature Methods 14:587–589.

    https://doi.org/10.1038/nmeth.4285

    • PubMed
    • Google Scholar
39. 1. Kanehisa M
    2. Goto S

    (2000) KEGG: kyoto encyclopedia of genes and genomes

    Nucleic Acids Research 28:27–30.

    https://doi.org/10.1093/nar/28.1.27

    • PubMed
    • Google Scholar
40. 1. Kanehisa M

    (2019) Toward understanding the origin and evolution of cellular organisms

    Protein Science 28:1947–1951.

    https://doi.org/10.1002/pro.3715

    • PubMed
    • Google Scholar
41. 1. Kanehisa M
    2. Furumichi M
    3. Sato Y
    4. Ishiguro-Watanabe M
    5. Tanabe M

    (2021) KEGG: integrating viruses and cellular organisms

    Nucleic Acids Research 49:D545–D551.

    https://doi.org/10.1093/nar/gkaa970

    • PubMed
    • Google Scholar
42. 1. Karl DM

    (2002) Nutrient dynamics in the deep blue sea

    Trends in Microbiology 10:410–418.

    https://doi.org/10.1016/s0966-842x(02)02430-7

    • PubMed
    • Google Scholar
43. 1. Khademian M
    2. Imlay JA

    (2021) How microbes evolved to tolerate oxygen

    Trends in Microbiology 29:428–440.

    https://doi.org/10.1016/j.tim.2020.10.001

    • PubMed
    • Google Scholar
44. 1. Knoll AH
    2. Javaux EJ
    3. Hewitt D
    4. Cohen P

    (2006) Eukaryotic organisms in Proterozoic oceans

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 361:1023–1038.

    https://doi.org/10.1098/rstb.2006.1843

    • PubMed
    • Google Scholar
45. 1. Landry Z
    2. Swan BK
    3. Herndl GJ
    4. Stepanauskas R
    5. Giovannoni SJ

    (2017) SAR202 genomes from the dark ocean predict pathways for the oxidation of recalcitrant dissolved organic matter

    mBio 8:e00413-17.

    https://doi.org/10.1128/mBio.00413-17

    • PubMed
    • Google Scholar
46. 1. Lartillot N
    2. Lepage T
    3. Blanquart S

    (2009) PhyloBayes 3: a Bayesian software package for phylogenetic reconstruction and molecular dating

    Bioinformatics 25:2286–2288.

    https://doi.org/10.1093/bioinformatics/btp368

    • PubMed
    • Google Scholar
47. 1. Lechner M
    2. Findeiss S
    3. Steiner L
    4. Marz M
    5. Stadler PF
    6. Prohaska SJ

    (2011) Proteinortho: detection of (co-)orthologs in large-scale analysis

    BMC Bioinformatics 12:124.

    https://doi.org/10.1186/1471-2105-12-124

    • PubMed
    • Google Scholar
48. 1. Lenton TM
    2. Dahl TW
    3. Daines SJ
    4. Mills BJW
    5. Ozaki K
    6. Saltzman MR
    7. Porada P

    (2016) Earliest land plants created modern levels of atmospheric oxygen

    PNAS 113:9704–9709.

    https://doi.org/10.1073/pnas.1604787113

    • PubMed
    • Google Scholar
49. 1. Lepage T
    2. Bryant D
    3. Philippe H
    4. Lartillot N

    (2007) A general comparison of relaxed molecular clock models

    Molecular Biology and Evolution 24:2669–2680.

    https://doi.org/10.1093/molbev/msm193

    • PubMed
    • Google Scholar
50. 1. Letunic I
    2. Bork P

    (2019) Interactive Tree Of Life (iTOL) v4: recent updates and new developments

    Nucleic Acids Research 47:W256–W259.

    https://doi.org/10.1093/nar/gkz239

    • PubMed
    • Google Scholar
51. 1. Luo H
    2. Csuros M
    3. Hughes AL
    4. Moran MA

    (2013) Evolution of divergent life history strategies in marine alphaproteobacteria

    mBio 4:e00373-13.

    https://doi.org/10.1128/mBio.00373-13

    • PubMed
    • Google Scholar
52. 1. Luo H
    2. Moran MA

    (2014) Evolutionary ecology of the marine Roseobacter clade

    Microbiology and Molecular Biology Reviews 78:573–587.

    https://doi.org/10.1128/MMBR.00020-14

    • PubMed
    • Google Scholar
53. 1. Martinez-Gutierrez CA
    2. Aylward FO

    (2021) Phylogenetic signal, congruence, and uncertainty across bacteria and archaea

    Molecular Biology and Evolution 38:5514–5527.

    https://doi.org/10.1093/molbev/msab254

    • PubMed
    • Google Scholar
54. 1. Masip L
    2. Veeravalli K
    3. Georgiou G

    (2006) The many faces of glutathione in bacteria

    Antioxidants & Redox Signaling 8:753–762.

    https://doi.org/10.1089/ars.2006.8.753

    • PubMed
    • Google Scholar
55. 1. Mason OU
    2. Di Meo-Savoie CA
    3. Van Nostrand JD
    4. Zhou J
    5. Fisk MR
    6. Giovannoni SJ

    (2009) Prokaryotic diversity, distribution, and insights into their role in biogeochemical cycling in marine basalts

    The ISME Journal 3:231–242.

    https://doi.org/10.1038/ismej.2008.92

    • PubMed
    • Google Scholar
56. 1. McParland EL
    2. Alexander H
    3. Johnson WM

    (2021) The Osmolyte Ties That Bind: genomic insights into synthesis and breakdown of organic osmolytes in marine microbes

    Frontiers in Marine Science 8:689306.

    https://doi.org/10.3389/fmars.2021.689306

    • Google Scholar
57. 1. Minh BQ
    2. Nguyen MAT
    3. von Haeseler A

    (2013) Ultrafast approximation for phylogenetic bootstrap

    Molecular Biology and Evolution 30:1188–1195.

    https://doi.org/10.1093/molbev/mst024

    • PubMed
    • Google Scholar
58. 1. Montoya JP
    2. Holl CM
    3. Zehr JP
    4. Hansen A
    5. Villareal TA
    6. Capone DG

    (2004) High rates of N2 fixation by unicellular diazotrophs in the oligotrophic Pacific Ocean

    Nature 430:1027–1032.

    https://doi.org/10.1038/nature02824

    • PubMed
    • Google Scholar
59. 1. Mühlenbruch M
    2. Grossart HP
    3. Eigemann F
    4. Voss M

    (2018) Mini-review: Phytoplankton-derived polysaccharides in the marine environment and their interactions with heterotrophic bacteria

    Environmental Microbiology 20:2671–2685.

    https://doi.org/10.1111/1462-2920.14302

    • PubMed
    • Google Scholar
60. 1. Nguyen L-T
    2. Schmidt HA
    3. von Haeseler A
    4. Minh BQ

    (2015) IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies

    Molecular Biology and Evolution 32:268–274.

    https://doi.org/10.1093/molbev/msu300

    • PubMed
    • Google Scholar
61. 1. Nordberg H
    2. Cantor M
    3. Dusheyko S
    4. Hua S
    5. Poliakov A
    6. Shabalov I
    7. Smirnova T
    8. Grigoriev IV
    9. Dubchak I

    (2014) The genome portal of the department of energy joint genome institute: 2014 updates

    Nucleic Acids Research 42:D26–D31.

    https://doi.org/10.1093/nar/gkt1069

    • PubMed
    • Google Scholar
62. 1. Och LM
    2. Shields-Zhou GA

    (2012) The Neoproterozoic oxygenation event: Environmental perturbations and biogeochemical cycling

    Earth-Science Reviews 110:26–57.

    https://doi.org/10.1016/j.earscirev.2011.09.004

    • Google Scholar
63. 1. Ossa Ossa F
    2. Hofmann A
    3. Spangenberg JE
    4. Poulton SW
    5. Stüeken EE
    6. Schoenberg R
    7. Eickmann B
    8. Wille M
    9. Butler M
    10. Bekker A

    (2019) Limited oxygen production in the Mesoarchean ocean

    PNAS 116:6647–6652.

    https://doi.org/10.1073/pnas.1818762116

    • PubMed
    • Google Scholar
64. 1. Pachiadaki MG
    2. Brown JM
    3. Brown J
    4. Bezuidt O
    5. Berube PM
    6. Biller SJ
    7. Poulton NJ
    8. Burkart MD
    9. La Clair JJ
    10. Chisholm SW
    11. Stepanauskas R

    (2019) Charting the complexity of the marine microbiome through single-cell genomics

    Cell 179:1623–1635.

    https://doi.org/10.1016/j.cell.2019.11.017

    • PubMed
    • Google Scholar
65. 1. Pajares S
    2. Varona-Cordero F
    3. Hernández-Becerril DU

    (2020) Spatial distribution patterns of bacterioplankton in the oxygen minimum zone of the tropical mexican pacific

    Microbial Ecology 80:519–536.

    https://doi.org/10.1007/s00248-020-01508-7

    • PubMed
    • Google Scholar
66. 1. Parfrey LW
    2. Lahr DJG
    3. Knoll AH
    4. Katz LA

    (2011) Estimating the timing of early eukaryotic diversification with multigene molecular clocks

    PNAS 108:13624–13629.

    https://doi.org/10.1073/pnas.1110633108

    • PubMed
    • Google Scholar
67. 1. Partensky F
    2. Garczarek L

    (2010) Prochlorococcus: advantages and limits of minimalism

    Annual Review of Marine Science 2:305–331.

    https://doi.org/10.1146/annurev-marine-120308-081034

    • PubMed
    • Google Scholar
68. 1. Philippe H
    2. Brinkmann H
    3. Lavrov DV
    4. Littlewood DTJ
    5. Manuel M
    6. Wörheide G
    7. Baurain D

    (2011) Resolving difficult phylogenetic questions: why more sequences are not enough

    PLOS Biology 9:e1000602.

    https://doi.org/10.1371/journal.pbio.1000602

    • PubMed
    • Google Scholar
69. 1. Planavsky NJ
    2. Reinhard CT
    3. Wang X
    4. Thomson D
    5. McGoldrick P
    6. Rainbird RH
    7. Johnson T
    8. Fischer WW
    9. Lyons TW

    (2014) Earth history. Low mid-Proterozoic atmospheric oxygen levels and the delayed rise of animals

    Science 346:635–638.

    https://doi.org/10.1126/science.1258410

    • PubMed
    • Google Scholar
70. 1. Planavsky NJ
    2. Crowe SA
    3. Fakhraee M
    4. Beaty B
    5. Reinhard CT
    6. Mills BJW
    7. Holstege C
    8. Konhauser KO

    (2021) Evolution of the structure and impact of Earth’s biosphere

    Nature Reviews Earth & Environment 2:123–139.

    https://doi.org/10.1038/s43017-020-00116-w

    • Google Scholar
71. 1. Porter SM

    (2004) The fossil record of early eukaryotic diversification

    The Paleontological Society Papers 10:35–50.

    https://doi.org/10.1017/S1089332600002321

    • Google Scholar
72. 1. Reinhard CT
    2. Planavsky NJ

    (2022) The history of ocean oxygenation

    Annual Review of Marine Science 14:331–353.

    https://doi.org/10.1146/annurev-marine-031721-104005

    • PubMed
    • Google Scholar
73. 1. Ren M
    2. Feng X
    3. Huang Y
    4. Wang H
    5. Hu Z
    6. Clingenpeel S
    7. Swan BK
    8. Fonseca MM
    9. Posada D
    10. Stepanauskas R
    11. Hollibaugh JT
    12. Foster PG
    13. Woyke T
    14. Luo H

    (2019) Phylogenomics suggests oxygen availability as a driving force in Thaumarchaeota evolution

    The ISME Journal 13:2150–2161.

    https://doi.org/10.1038/s41396-019-0418-8

    • PubMed
    • Google Scholar
74. 1. Revell LJ

    (2012) phytools: an R package for phylogenetic comparative biology (and other things)

    Methods in Ecology and Evolution 3:217–223.

    https://doi.org/10.1111/j.2041-210X.2011.00169.x

    • Google Scholar
75. 1. Salichos L
    2. Stamatakis A
    3. Rokas A

    (2014) Novel information theory-based measures for quantifying incongruence among phylogenetic trees

    Molecular Biology and Evolution 31:1261–1271.

    https://doi.org/10.1093/molbev/msu061

    • PubMed
    • Google Scholar
76. 1. Sánchez-Baracaldo P

    (2015) Origin of marine planktonic cyanobacteria

    Scientific Reports 5:17418.

    https://doi.org/10.1038/srep17418

    • PubMed
    • Google Scholar
77. 1. Sánchez-Baracaldo P
    2. Bianchini G
    3. Di Cesare A
    4. Callieri C
    5. Chrismas NAM

    (2019) Insights into the evolution of picocyanobacteria and phycoerythrin genes (mpeBA and cpeBA)

    Frontiers in Microbiology 10:00045.

    https://doi.org/10.3389/fmicb.2019.00045

    • Google Scholar
78. 1. Scanlan DJ
    2. Ostrowski M
    3. Mazard S
    4. Dufresne A
    5. Garczarek L
    6. Hess WR
    7. Post AF
    8. Hagemann M
    9. Paulsen I
    10. Partensky F

    (2009) Ecological genomics of marine picocyanobacteria

    Microbiology and Molecular Biology Reviews 73:249–299.

    https://doi.org/10.1128/MMBR.00035-08

    • PubMed
    • Google Scholar
79. 1. Scott C
    2. Lyons TW
    3. Bekker A
    4. Shen Y
    5. Poulton SW
    6. Chu X
    7. Anbar AD

    (2008) Tracing the stepwise oxygenation of the Proterozoic ocean

    Nature 452:456–459.

    https://doi.org/10.1038/nature06811

    • PubMed
    • Google Scholar
80. 1. Seymour JR
    2. Amin SA
    3. Raina JB
    4. Stocker R

    (2017) Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships

    Nature Microbiology 2:17065.

    https://doi.org/10.1038/nmicrobiol.2017.65

    • PubMed
    • Google Scholar
81. 1. Shang H
    2. Rothman DH
    3. Fournier GP

    (2022) Oxidative metabolisms catalyzed Earth’s oxygenation

    Nature Communications 13:1328.

    https://doi.org/10.1038/s41467-022-28996-0

    • PubMed
    • Google Scholar
82. 1. Sheik CS
    2. Jain S
    3. Dick GJ

    (2014) Metabolic flexibility of enigmatic SAR324 revealed through metagenomics and metatranscriptomics

    Environmental Microbiology 16:304–317.

    https://doi.org/10.1111/1462-2920.12165

    • PubMed
    • Google Scholar
83. 1. Shields-Zhou G
    2. Och L

    (2011) The case for a Neoproterozoic Oxygenation Event: Geochemical evidence and biological consequences

    GSA Today 21:4–11.

    https://doi.org/10.1130/GSATG102A.1

    • Google Scholar
84. 1. Sievers F
    2. Higgins DG

    (2018) Clustal Omega for making accurate alignments of many protein sequences

    Protein Science 27:135–145.

    https://doi.org/10.1002/pro.3290

    • PubMed
    • Google Scholar
85. 1. Smith SA
    2. O’Meara BC

    (2012) treePL: divergence time estimation using penalized likelihood for large phylogenies

    Bioinformatics 28:2689–2690.

    https://doi.org/10.1093/bioinformatics/bts492

    • PubMed
    • Google Scholar
86. 1. Sperling EA
    2. Wolock CJ
    3. Morgan AS
    4. Gill BC
    5. Kunzmann M
    6. Halverson GP
    7. Macdonald FA
    8. Knoll AH
    9. Johnston DT

    (2015) Statistical analysis of iron geochemical data suggests limited late Proterozoic oxygenation

    Nature 523:451–454.

    https://doi.org/10.1038/nature14589

    • PubMed
    • Google Scholar
87. 1. Stamatakis A

    (2014) RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

    Bioinformatics 30:1312–1313.

    https://doi.org/10.1093/bioinformatics/btu033

    • PubMed
    • Google Scholar
88. 1. Tang D
    2. Shi X
    3. Wang X
    4. Jiang G

    (2016) Extremely low oxygen concentration in mid-Proterozoic shallow seawaters

    Precambrian Research 276:145–157.

    https://doi.org/10.1016/j.precamres.2016.02.005

    • Google Scholar
89. 1. Thorne JL
    2. Kishino H
    3. Painter IS

    (1998) Estimating the rate of evolution of the rate of molecular evolution

    Molecular Biology and Evolution 15:1647–1657.

    https://doi.org/10.1093/oxfordjournals.molbev.a025892

    • PubMed
    • Google Scholar
90. 1. Thrash JC
    2. Seitz KW
    3. Baker BJ
    4. Temperton B
    5. Gillies LE
    6. Rabalais NN
    7. Henrissat B
    8. Mason OU

    (2017) Metabolic roles of uncultivated bacterioplankton lineages in the Northern Gulf of Mexico “Dead Zone.”

    mBio 8:e01017-17.

    https://doi.org/10.1128/mBio.01017-17

    • PubMed
    • Google Scholar
91. 1. Tostevin R
    2. Mills BJW

    (2020) Reconciling proxy records and models of Earth’s oxygenation during the Neoproterozoic and Palaeozoic

    Interface Focus 10:20190137.

    https://doi.org/10.1098/rsfs.2019.0137

    • PubMed
    • Google Scholar
92. 1. Ueno Y
    2. Yamada K
    3. Yoshida N
    4. Maruyama S
    5. Isozaki Y

    (2006) Evidence from fluid inclusions for microbial methanogenesis in the early Archaean era

    Nature 440:516–519.

    https://doi.org/10.1038/nature04584

    • PubMed
    • Google Scholar
93. 1. Ulloa O
    2. Canfield DE
    3. DeLong EF
    4. Letelier RM
    5. Stewart FJ

    (2012) Microbial oceanography of anoxic oxygen minimum zones

    PNAS 109:15996–16003.

    https://doi.org/10.1073/pnas.1205009109

    • PubMed
    • Google Scholar
94. 1. Valley JW
    2. Cavosie AJ
    3. Ushikubo T
    4. Reinhard DA
    5. Lawrence DF
    6. Larson DJ
    7. Clifton PH
    8. Kelly TF
    9. Wilde SA
    10. Moser DE
    11. Spicuzza MJ

    (2014) Hadean age for a post-magma-ocean zircon confirmed by atom-probe tomography

    Nature Geoscience 7:219–223.

    https://doi.org/10.1038/ngeo2075

    • Google Scholar
95. 1. Vidal G
    2. Moczydłowska-Vidal M

    (1997) Biodiversity, speciation, and extinction trends of Proterozoic and Cambrian phytoplankton

    Paleobiology 23:230–246.

    https://doi.org/10.1017/S0094837300016808

    • Google Scholar
96. 1. Vila-Costa M
    2. Simó R
    3. Harada H
    4. Gasol JM
    5. Slezak D
    6. Kiene RP

    (2006) Dimethylsulfoniopropionate uptake by marine phytoplankton

    Science 314:652–654.

    https://doi.org/10.1126/science.1131043

    • PubMed
    • Google Scholar
97. 1. Walter MR
    2. Buick R
    3. Dunlop JSR

    (1980) Stromatolites 3,400–3,500 Myr old from the North Pole area, Western Australia

    Nature 284:443–445.

    https://doi.org/10.1038/284443a0

    • Google Scholar
98. 1. Ward LM
    2. Kirschvink JL
    3. Fischer WW

    (2016) Timescales of Oxygenation Following the Evolution of Oxygenic Photosynthesis

    Origins of Life and Evolution of the Biosphere 46:51–65.

    https://doi.org/10.1007/s11084-015-9460-3

    • PubMed
    • Google Scholar
99. 1. Wei GY
    2. Planavsky NJ
    3. He T
    4. Zhang F
    5. Stockey RG
    6. Cole DB
    7. Lin YB
    8. Ling HF

    (2021) Global marine redox evolution from the late Neoproterozoic to the early Paleozoic constrained by the integration of Mo and U isotope records

    Earth-Science Reviews 214:103506.

    https://doi.org/10.1016/j.earscirev.2021.103506

    • Google Scholar
100. 1. Wilson ST
     2. Aylward FO
     3. Ribalet F
     4. Barone B
     5. Casey JR
     6. Connell PE
     7. Eppley JM
     8. Ferrón S
     9. Fitzsimmons JN
     10. Hayes CT
     11. Romano AE
     12. Turk-Kubo KA
     13. Vislova A
     14. Armbrust EV
     15. Caron DA
     16. Church MJ
     17. Zehr JP
     18. Karl DM
     19. DeLong EF

     (2017) Coordinated regulation of growth, activity and transcription in natural populations of the unicellular nitrogen-fixing cyanobacterium Crocosphaera

     Nature Microbiology 2:17118.

     https://doi.org/10.1038/nmicrobiol.2017.118

     • PubMed
     • Google Scholar
101. 1. Yang Y
     2. Zhang C
     3. Lenton TM
     4. Yan X
     5. Zhu M
     6. Zhou M
     7. Tao J
     8. Phelps TJ
     9. Cao Z

     (2021) The Evolution Pathway of Ammonia-Oxidizing Archaea Shaped by Major Geological Events

     Molecular Biology and Evolution 38:3637–3648.

     https://doi.org/10.1093/molbev/msab129

     • PubMed
     • Google Scholar
102. Book

     1. Zaikowski L
     2. Friedrich JM
     3. Seidel SR

     (2010) Chemical evolution II: from the origins of life to modern society

     In: Burrows CJ, editors. Surviving an Oxygen Atmosphere: DNA Damage and Repair. Washington DC: ACS Symposium Series. pp. 147–156.

     https://doi.org/10.1021/bk-2009-1025

     • Google Scholar
103. 1. Zehr JP
     2. Kudela RM

     (2011) Nitrogen cycle of the open ocean: from genes to ecosystems

     Annual Review of Marine Science 3:197–225.

     https://doi.org/10.1146/annurev-marine-120709-142819

     • PubMed
     • Google Scholar
104. 1. Zhang H
     2. Sun Y
     3. Zeng Q
     4. Crowe SA
     5. Luo H

     (2021) Snowball Earth, population bottleneck and evolution

     Proceedings. Biological Sciences 288:20211956.

     https://doi.org/10.1098/rspb.2021.1956

     • PubMed
     • Google Scholar

Author details

1. Carolina A Martinez-Gutierrez

   Department of Biological Sciences, Virginia Tech, Blacksburg, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   cmartinez@vt.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8365-6050

2. Josef C Uyeda

   Department of Biological Sciences, Virginia Tech, Blacksburg, United States

   Contribution

   Conceptualization, Resources, Formal analysis, Investigation, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

3. Frank O Aylward

   1. Department of Biological Sciences, Virginia Tech, Blacksburg, United States
   2. Center for Emerging, Zoonotic, and Arthropod-borne Pathogens, Virginia Tech, Blacksburg, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   faylward@vt.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1279-4050

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