Abstract
Our view of genome size in Archaea and Bacteria has remained skewed as the data has been dominated by genomes of microorganisms that have been cultivated under laboratory settings. However, the continuous effort to catalog Earth’s microbiomes, specifically propelled by recent extensive work on uncultivated microorganisms, provides an opportunity to revise our perspective on genome size distribution. We present a meta-analysis that includes 26,101 representative genomes from 3 published genomic databases; metagenomic assembled genomes (MAGs) from GEMs and stratfreshDB, and isolates from GTDB. Aquatic and host-associated microbial genomes present on average the smallest estimated genome sizes (3.1 and 3.0 Mbp, respectively). These are followed by terrestrial microbial genomes (average 3.7 Mbp), and genomes from isolated microorganisms (average 4.3 Mbp). On the one hand, aquatic and host-associated ecosystems present smaller genomes sizes in genera of phyla with genome sizes above 3 Mbp. On the other hand, estimated genome size in phyla with genomes under 3 Mbp showed no difference between ecosystems. Moreover, we observed that when using 95% average nucleotide identity (ANI) as an estimator for genetic units, only 3% of MAGs cluster together with genomes from isolated microorganisms. Although there are potential methodological limitations when assembling and binning MAGs, we found that in genome clusters containing both environmental MAGs and isolate genomes, MAGs were estimated only an average 3.7% smaller than isolate genomes. Even when assembly and binning methods introduce biases, estimated genome size of MAGs and isolates are very similar. Finally, to better understand the ecological drivers of genome size, we discuss on the known and the overlooked factors that influence genome size in different ecosystems, phylogenetic groups, and trophic strategies.
Introduction
As microbiologists, how do we define what is a small or a big genome? Perhaps, researchers working on model organisms such as Escherichia coli with a genome size of ∼5 Mbp () would define “big” or “small” differently to researchers working on soil-dwelling bacteria with a genome size of 16 Mbp (). On the lower genome size scale, whereas genome sizes of bacterial endosymbionts of insects may have genomes merely larger than 100 kbp (), the abundant Prochlorococcus range between 1.6 and 1.9 Mbp for high-light and low-light ecotypes (). In summary, it is known that genome sizes of Archaea and Bacteria range between 100 kbp and 16 Mbp, but the genome size distribution in nature is still undefined. Therefore, the aim of this review is to provide an overview of the distribution of genome sizes in different ecosystems.
We leveraged recently published databases of archaeal and bacterial metagenome assembled genomes (MAGs) (; ) together with isolate genomes to revisit and acquire an updated understanding of the estimated genome size distribution across different ecosystems. In this review, we also discuss the ecological drivers that potentially influence genome sizes. In summary, we found that 76.3% of representative archaeal and bacterial genomes recovered through genome-resolved metagenomics present estimated genome sizes below 4 Mbp. Furthermore, all MAGs from five archaeal phyla (Micrarcheota, Ianarchaeota, Undinarchaeota, Nanohaloarchaeota, and Hadarchaeota) and two bacterial phyla (Coprothermobacterota and Dictyoglomota) were recovered exclusively from aquatic ecosystems and have genome sizes below 2 Mbp (Figures 1A,B).
FIGURE 1
Approximation of Genetic Units Using 95% Average Nucleotide Identity and Its Caveats
Species are widely considered congruent genetic and ecological units for sexual eukaryotes (
We used the 95% ANI boundary in published datasets (
Extant Genome Size Distribution in the Environment
In this review, we have included ∼64,500 environmental MAGs available via two recently published datasets, stratfreshDB and GEMs. StratfreshDB offers ∼12,000 MAGs (>40% completeness) from 41 stratified lakes and ponds assembled with Megahit (v1.1.13) and binned with Metabat (v2.12.1) (
Furthermore, using completeness estimates from CheckM, we compared the estimated genome size distribution of all MAGs vs. genomes from isolates. The estimated genome size was calculated by dividing the MAG’s assembly size by CheckM completeness (ranging from 0 to 1). Representative genomes from isolates have an average genome size of 4.3 Mbp which is significantly larger than that of MAGs (t-test p < 0.0001), both when comparing Archaea and Bacteria combined and separately. To compare estimated genome sizes between MAGs, ecosystem type was used according to the GEMs database. Although the ecosystem classification presented here is coarse and might contain countless niches, it still allowed us to see trends for genome sizes. Estimated genome sizes of aquatic MAGs have an average of 3.1 Mbp, host-associated MAGs average 3.0 Mbp, and terrestrial MAGs average 3.7 Mbp (Figure 1A). For the 540 mOTUs that contained both environmental MAGs and isolate genomes (Figure 1C), we found that MAGs were estimated on average 3.7% smaller than isolate genomes (Supplementary Figure 1). In other words, even when assembly and binning methods introduce biases, estimated genome size of MAGs and isolates are very similar. Overall, this suggests that the bias in metagenome assembly and binning would not account for the genome size difference observed between all isolate representatives and ecosystem MAGs, neither for the differences among ecosystem MAGs.
A reason for the difference in genome size between isolates and genomes reconstructed from metagenomes might be related to the fact that traditional isolation techniques select for rare microorganisms (Shade et al., 2012) and do not capture the entire ecosystem’s diversity (Figure 1C). For example, it is known that classical cultivation techniques with rich media bias the cultivation toward copiotrophic and fast-growing microorganisms (Swan et al., 2013). Cultivation biases our view of nature because it selects against slow growing microorganisms (
Placing archaeal and bacterial genome sizes in phylogenetic trees using GTDB-tk (Figures 2A,B) shows that the distribution of representative genomes and their estimated sizes varies widely between different phyla and within phyla. MAGs assigned to eight phyla in the domain Archaea were reconstructed exclusively from aquatic ecosystems, whereas eight other archaea phyla were reconstructed from multiple ecosystems. There was no significant difference between the genome sizes of aquatic archaea phyla or those from non-specific ecosystem (Figure 2C). However, estimated genome sizes in bacterial phyla were significantly larger than those in archaeal phyla. Moreover, genera from phyla with genome sizes below 3 Mbp, such as Halobacteriota, Thermoproteota, and Patescibacteria, do not show genome size variation in different ecosystems (Figures 2D,E,I). Nevertheless, genera from these smaller genome sizes phyla are significantly smaller than genera with more genome size variation in any ecosystem category (Figures 2K–N). For phyla with genome sizes above 3 Mbp, the genome sizes in aquatic or host-associated genera are significantly smaller than those in terrestrial or non-specific ecosystems (Figures 2F–J). We observe that while the microorganisms’ ecosystem can certainly be linked to genome size, phyla where genome sizes are mostly below 3 Mbp show no variation in estimated genome size across ecosystems.
FIGURE 2

Phylogenetic trees of archaeal (A) and bacterial (B) representative genomes show variation in genome size between and within phyla. The trees were constructed using GTDB-tk (v 1.5.0) using de novo workflow using aligned concatenated set of 122 and 120 single copy marker proteins for Archaea and Bacteria, respectively (
Clustering microorganisms together by the three ecosystem categories is not optimal since each contains innumerable niches. In each niche, there will be different selective pressures on the genome size. An example is clearly shown in a study (
Impact of Ecosystem and Trophic Strategy on Genome Size
Terrestrial ecosystems harbor immense microbial diversity (
In host-associated microbiomes, genetic drift, deletion biases and low populations sizes drive the reduction of genomes (
Small genomes exhibit either strong dependency on other community members or have specific nutrient requirements. Two diverging views on genome reduction have emerged to explain mechanisms of gene loss. On the one hand, genetic drift is more pronounced in species that have a small effective population size, such as host-associated endosymbiotic microorganisms. These microorganisms might thrive because hosts provide energy or nutrients. On the other hand, streamlining is the process of gene loss through selection and it is mainly observed in free-living microorganisms with high effective population sizes (
In this review, the largest fraction of MAGs is recovered from aquatic environments. The two main sub-ecosystems in our survey are freshwater with MAGs estimated average genome size of 3.2 Mbp significantly different (p < 0.0001) from marine genome size distribution with average estimated genome size of 2.9 Mbp. When comparing freshwater and marine environments, the most obvious difference is salinity followed by nutrient concentration. Further exploring the impact of differing levels of salinity on genome size is an interesting research prospect. Additionally, we compared the union of representative freshwater MAGs from both databases (StratfreshDB and GEMs) (Supplementary Figure 3). The difference of mean estimated genome size between the representatives from freshwater GEMs and StratfreshDB is 0.52 Mbp. However, this is because each database captures genetic units that were not found in the other database.
In general, aquatic environments are vertically structured by gradients of light penetration, temperature, oxygen, and nutrient (Supplementary Table 1). Moreover, microorganisms might experience a microscale spatial and nutrient structure due to the presence of heterogeneous particles. These aquatic structures are drivers of the genetic repertoire of aquatic microorganisms. Metagenomic sequencing reported the increase of genome sizes for Archaea and Bacteria with increasing depths (
Diversity and quantity of nutrients might be two understudied factors that drive ecology and genome size evolution. A recent example shows that polysaccharide xylan triggers microcolonies, whereas monosaccharide xylose promotes solitary growth in Caulobacter (
Conclusion
This review offers a broad overview of genome size distribution across three different ecosystem categories, showing that MAGs recovered from aquatic and host-associated ecosystems present smaller estimated genome sizes than those recovered from terrestrial ecosystems. Moreover, genomes obtained from environmental samples present a smaller estimated genome size than obtained by cultivation approaches. We find that the distribution of genome sizes across the phylogenetic tree of Archaea and Bacteria can be linked to the ecosystem type from which the microorganisms’ genomes have been extracted (aquatic, host-associated or terrestrial). Finally, we review the ecological factors that may cause the varying sizes of genomes in different ecosystems. In comparison with the aquatic and host-associated ecosystems, terrestrial ecosystems might harbor microorganisms with bigger estimated genome sizes mainly due to higher fluctuations in this ecosystem. Host-associations might shape genomes sizes differentially based on the type of host and level of intimacy between the microorganisms and the host. Genomes in aquatic ecosystems might be shaped by vertical stratification of abiotic factors such as nutrient distribution, light penetration, and temperature. Moreover, different trophic strategies such as auxotrophies might be connected to smaller genome sizes. We expect that as the microbial ecology field keeps moving forward with sequencing, bioinformatics, chemical analysis, and novel cultivation techniques, we will get a deeper resolution on physicochemical, metabolic, spatial, and biological drivers of archaeal and bacterial genome sizes.
Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Statements
Author contributions
SG, AR-G, and JN conceptualized the literature and data review idea. JN, MB, and FS gathered the data. AR-G and JN performed data analysis. SG, AR-G, and MM drafted the first manuscript. All authors did literature searches, contributed to the writing, and editing of the manuscript.
Funding
This work was supported by SciLifeLab and Kungl. Vetenskapsakademiens stiftelser grant CR2019-0060. The computations and data handling were enabled by resources in the project SNIC 2021/6-99 and SNIC 2021/5-133 provided by the Swedish National Infrastructure for Computing (SNIC) at UPPMAX, partially funded by the Swedish Research Council through grant agreement no. 2018-05973. The work conducted by the U.S. Department of Energy Joint Genome Institute, an Office of Science User Facility, made use of the National Energy Research Scientific Computing Center and was supported under Contract No. DE-AC02-05CH11231.
Acknowledgments
We are grateful to John Paul Balmonte, Sergio Tusso, and Alexander Probst for helpful discussions. AR-G thanks Fede Berckx for technical advice.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2021.761869/full#supplementary-material
References
1
AbramK.UdaondoZ.BlekerC.WanchaiV.WassenaarT. M.RobesonM. S. II.et al (2021). Mash-based analyses of Escherichia coli genomes reveal 14 distinct phylogroups.Commun. Biol.4:117. 10.1038/s42003-020-01626-5
2
AylwardF. O.SantoroA. E. (2020). Heterotrophic Thaumarchaea with small genomes are widespread in the dark ocean.mSystems5e00415–e00420. 10.1128/mSystems.00415-20
3
BentkowskiP.Van OosterhoutC.MockT. (2015). A model of genome size evolution for prokaryotes in stable and fluctuating environments.Genome Biol. Evol.72344–2351. 10.1093/gbe/evv148
4
BergmanB.SandhG.LinS.LarssonJ.CarpenterE. J. (2013). Trichodesmium–a widespread marine cyanobacterium with unusual nitrogen fixation properties.FEMS Microbiol. Rev.37286–302. 10.1111/j.1574-6976.2012.00352.x
5
BerubeP. M.BillerS. J.HacklT.HogleS. L.SatinskyB. M.BeckerJ. W.et al (2018). Single cell genomes of Prochlorococcus. Synechococcus, and sympatric microbes from diverse marine environments.Sci. Data5:180154. 10.1038/sdata.2018.154
6
BoussauB.KarlbergE. O.FrankA. C.LegaultB. A.AnderssonS. G. (2004). Computational inference of scenarios for alpha-proteobacterial genome evolution.Proc. Natl. Acad. Sci. U.S.A.1019722–9727. 10.1073/pnas.0400975101
7
BrewerT. E.HandleyK. M.CariniP.GilbertJ. A.FiererN. (2016). Genome reduction in an abundant and ubiquitous soil bacterium ‘Candidatus Udaeobacter copiosus’.Nat. Microbiol.2:16198. 10.1038/nmicrobiol.2016.198
8
Brochier-ArmanetC.DeschampsP.Lopez-GarciaP.ZivanovicY.Rodriguez-ValeraF.MoreiraD. (2011). Complete-fosmid and fosmid-end sequences reveal frequent horizontal gene transfers in marine uncultured planktonic archaea.ISME J.51291–1302. 10.1038/ismej.2011.16
9
BuckM.GarciaS. L.FernandezL.MartinG.Martinez-RodriguezG. A.SaarenheimoJ.et al (2021a). Comprehensive dataset of shotgun metagenomes from oxygen stratified freshwater lakes and ponds.Sci. Data8:131. 10.1038/s41597-021-00910-1
10
BuckM.MehrshadM.BertilssonS. (2021b). mOTUpan: a robust Bayesian approach to leverage metagenome assembled genomes for core-genome estimation.bioRxiv [Preprint]. 10.1101/2021.06.25.449606
11
CariniP.SteindlerL.BeszteriS.GiovannoniS. J. (2013). Nutrient requirements for growth of the extreme oligotroph ‘Candidatus Pelagibacter ubique’ HTCC1062 on a defined medium.ISME J.7592–602. 10.1038/Ismej.2012.122
12
ChaumeilP. A.MussigA. J.HugenholtzP.ParksD. H. (2020). GTDB-Tk: a toolkit to classify genomes with the genome taxonomy database.Bioinformatics361925–1927. 10.1093/bioinformatics/btz848
13
ChenM. Y.TengW. K.ZhaoL.HuC. X.ZhouY. K.HanB. P.et al (2021). Comparative genomics reveals insights into cyanobacterial evolution and habitat adaptation.ISME J.15211–227. 10.1038/s41396-020-00775-z
14
Cobo-SimonM.TamamesJ. (2017). Relating genomic characteristics to environmental preferences and ubiquity in different microbial taxa.BMC Genomics18:499. 10.1186/s12864-017-3888-y
15
CollingroA.KostlbacherS.HornM. (2020). Chlamydiae in the Environment.Trends Microbiol.28877–888. 10.1016/j.tim.2020.05.020
16
CrossK. L.CampbellJ. H.BalachandranM.CampbellA. G.CooperS. J.GriffenA.et al (2019). Targeted isolation and cultivation of uncultivated bacteria by reverse genomics.Nat. Biotechnol.371314–1321. 10.1038/s41587-019-0260-6
17
DedyshS. N. (2011). Cultivating uncultured bacteria from northern wetlands: knowledge gained and remaining gaps.Front. Microbiol.2:184. 10.3389/fmicb.2011.00184
18
Delgado-BaquerizoM.OliverioA. M.BrewerT. E.Benavent-GonzalezA.EldridgeD. J.BardgettR. D.et al (2018). A global atlas of the dominant bacteria found in soil.Science359320–325. 10.1126/science.aap9516
19
DharamshiJ. E.TamaritD.EmeL.StairsC. W.MartijnJ.HomaF.et al (2020). Marine sediments illuminate chlamydiae diversity and evolution.Curr. Biol.301032–1048.e7. 10.1016/j.cub.2020.02.016
20
D’SouzaG. G.PovoloV. R.KeegstraJ. M.StockerR.AckermannM. (2021). Nutrient complexity triggers transitions between solitary and colonial growth in bacterial populations.ISME J.152614–2626. 10.1038/s41396-021-00953-7
21
ElserJ. J.BrackenM. E.ClelandE. E.GrunerD. S.HarpoleW. S.HillebrandH.et al (2007). Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial ecosystems.Ecol. Lett.101135–1142. 10.1111/j.1461-0248.2007.01113.x
22
ErenA. M.KieflE.ShaiberA.VeseliI.MillerS. E.SchechterM. S.et al (2021). Community-led, integrated, reproducible multi-omics with anvi’o.Nat. Microbiol.63–6. 10.1038/s41564-020-00834-3
23
Figueroa-GonzalezP. A.BornemannT. L. V.AdamP. S.PlewkaJ.ReveszF.von HagenC. A.et al (2020). Saccharibacteria as Organic carbon sinks in hydrocarbon-fueled communities.Front. Microbiol.11:587782. 10.3389/fmicb.2020.587782
24
FrancheC.LindströmK.ElmerichC. (2008). Nitrogen-fixing bacteria associated with leguminous and non-leguminous plants.Plant Soil32135–59. 10.1007/s11104-008-9833-8
25
GarciaR.GemperleinK.MullerR. (2014). Minicystis rosea gen. nov., sp. nov., a polyunsaturated fatty acid-rich and steroid-producing soil myxobacterium.Int. J. Syst. Evol. Microbiol.643733–3742. 10.1099/ijs.0.068270-0
26
GarciaS. L. (2016). Mixed cultures as model communities: hunting for ubiquitous microorganisms, their partners, and interactions.Aquat. Microb. Ecol.7779–85. 10.3354/ame01796
27
GarciaS. L.BuckM.McMahonK. D.GrossartH. P.EilerA.WarneckeF. (2015). Auxotrophy and intrapopulation complementary in the “interactome’ of a cultivated freshwater model community.Mol. Ecol.244449–4459. 10.1111/mec.13319
28
GarciaS. L.MehrshadM.BuckM.TsujiJ. M.NeufeldJ. D.McMahonK. D.et al (2021). Freshwater chlorobia exhibit metabolic specialization among cosmopolitan and endemic populations.mSystems6e01196–e01220. 10.1128/mSystems.01196-20
29
GarciaS. L.StevensS. L. R.CraryB.Martinez-GarciaM.StepanauskasR.WoykeT.et al (2018). Contrasting patterns of genome-level diversity across distinct co-occurring bacterial populations.ISME J.12742–755. 10.1038/s41396-017-0001-0
30
GiovannoniS. J.Cameron ThrashJ.TempertonB. (2014). Implications of streamlining theory for microbial ecology.ISME J.81553–1565. 10.1038/ismej.2014.60
31
GroteJ.ThrashJ. C.HuggettM. J.LandryZ. C.CariniP.GiovannoniS. J.et al (2012). Streamlining and core genome conservation among highly divergent members of the SAR11 clade.Mbio3e00252–e00312. 10.1128/mBio.00252-12
32
GrzymskiJ. J.DussaqA. M. (2012). The significance of nitrogen cost minimization in proteomes of marine microorganisms.ISME J.671–80. 10.1038/ismej.2011.72
33
GweonH. S.BaileyM. J.ReadD. S. (2017). Assessment of the bimodality in the distribution of bacterial genome sizes.ISME J.11821–824. 10.1038/ismej.2016.142
34
HawkesJ. A.PatriarcaC.SjöbergP. J. R.TranvikL. J.BergquistJ. (2018). Extreme isomeric complexity of dissolved organic matter found across aquatic environments.Limnol. Oceanogr. Lett.321–30. 10.1002/lol2.10064
35
HensonM. W.PitreD. M.WeckhorstJ. L.LanclosV. C.WebberA. T.ThrashJ. C.et al (2016). Artificial seawater media facilitate cultivating members of the microbial majority from the gulf of Mexico.mSphere1e28–e116. 10.1128/mSphere.00028-16
36
HoehlerT. M.JorgensenB. B. (2013). Microbial life under extreme energy limitation.Nat. Rev. Microbiol.1183–94. 10.1038/nrmicro2939
37
ImachiH.NobuM. K.NakaharaN.MoronoY.OgawaraM.TakakiY.et al (2020). Isolation of an archaeon at the prokaryote-eukaryote interface.Nature577519–525. 10.1038/s41586-019-1916-6
38
JainC.Rodriguez-RL. M.PhillippyA. M.KonstantinidisK. T.AluruS. (2018). High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries.Nat. Commun.9:5114. 10.1038/s41467-018-07641-9
39
KangI.KimS.IslamM. R.ChoJ.-C. (2017). The first complete genome sequences of the acI lineage, the most abundant freshwater Actinobacteria, obtained by whole-genome-amplification of dilution-to-extinction cultures.Sci. Rep.7:42252. 10.1038/sre42252
40
KonstantinidisK. T.TiedjeJ. M. (2004). Trends between gene content and genome size in prokaryotic species with larger genomes.Proc. Natl. Acad. Sci. U.S.A.1013160–3165. 10.1073/pnas.0308653100
41
KonstantinidisK. T.TiedjeJ. M. (2005). Genomic insights that advance the species definition for prokaryotes.Proc. Natl. Acad. Sci. U.S.A.1022567–2572. 10.1073/Pnas.0409727102
42
LevyA.Salas GonzalezI.MittelviefhausM.ClingenpeelS.Herrera ParedesS.MiaoJ.et al (2017). Genomic features of bacterial adaptation to plants.Nat. Genet.50138–150. 10.1038/s41588-017-0012-9
43
LewisW. H.TahonG.GeesinkP.SousaD. Z.EttemaT. J. G. (2020). Innovations to culturing the uncultured microbial majority.Nat. Rev. Microbiol.19225–240. 10.1038/s41579-020-00458-8
44
LiL.LiuZ.ZhouZ.ZhangM.MengD.LiuX.et al (2021). Comparative genomics provides insights into the genetic diversity and evolution of the DPANN superphylum.mSystems6:e00602211. 10.1128/mSystems.00602-21
45
LloydK. G.SteenA. D.LadauJ.YinJ. Q.CrosbyL. (2018). Phylogenetically novel uncultured microbial cells dominate earth microbiomes.mSystems3e55–e118. 10.1128/mSystems.00055-18
46
MalletJ. (2008). Hybridization, ecological races and the nature of species: empirical evidence for the ease of speciation.Philos. Trans. R. Soc. Lond. B. Biol. Sci.3632971–2986. 10.1098/rstb.2008.0081
47
McLeanJ. S.BorB.KernsK. A.LiuQ.ToT. T.SoldenL.et al (2020). Acquisition and adaptation of ultra-small parasitic reduced genome bacteria to Mammalian hosts.Cell Rep.32:107939. 10.1016/j.celrep.2020.107939
48
MendeD. R.BryantJ. A.AylwardF. O.EppleyJ. M.NielsenT.KarlD. M.et al (2017). Environmental drivers of a microbial genomic transition zone in the ocean’s interior.Nat. Microbiol.21367–1373. 10.1038/s41564-017-0008-3
49
MezitiA.RodriguezR. L.HattJ. K.Pena-GonzalezA.LevyK.KonstantinidisK. T. (2021). The reliability of metagenome-assembled genomes (mags) in representing natural populations: insights from comparing mags against isolate genomes derived from the same fecal sample.Appl. Environ. Microbiol.87e02593–e02620. 10.1128/AEM.02593-20
50
MondavR.BertilssonS.BuckM.LangenhederS.LindstromE. S.GarciaS. L. (2020). Streamlined and abundant bacterioplankton thrive in functional cohorts.mSystems5e00316–e00420. 10.1128/mSystems.00316-20
51
MoranN. A.BennettG. M. (2014). The tiniest tiny genomes.Annu. Rev. Microbiol.68195–215. 10.1146/annurev-micro-091213-112901
52
MorrisJ. J.LenskiR. E.ZinserE. R. (2012). The black queen hypothesis: evolution of dependencies through adaptive gene loss.Mbio3e00036–e00112. 10.1128/mBio.00036-12
53
NayfachS.PollardK. S. (2015). Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome.Genome Biol.16:51. 10.1186/s13059-015-0611-7
54
NayfachS.RouxS.SeshadriR.UdwaryD.VargheseN.SchulzF.et al (2020). A genomic catalog of Earth’s microbiomes.Nat. Biotechnol.39499–509. 10.1038/s41587-020-0718-6
55
NelsonW. C.TullyB. J.MobberleyJ. M. (2020). Biases in genome reconstruction from metagenomic data.PeerJ.8:e101191.
56
OlmM. R.Crits-ChristophA.DiamondS.LavyA.Matheus CarnevaliP. B.BanfieldJ. F. (2020). Consistent metagenome-derived metrics verify and delineate bacterial species boundaries.mSystems5e00731–e00819. 10.1128/mSystems.00731-19
57
OrtizM.LeungP. M.ShelleyG.JirapanjawatT.NauerP. A.Van GoethemM. W.et al (2021). Multiple energy sources and metabolic strategies sustain microbial diversity in Antarctic desert soils.Proc. Natl. Acad. Sci. U.S.A.118:e2025322118. 10.1073/pnas.2025322118
58
ParksD. H.ChuvochinaM.ChaumeilP. A.RinkeC.MussigA. J.HugenholtzP. (2020). A complete domain-to-species taxonomy for Bacteria and Archaea.Nat. Biotechnol.381079–1086. 10.1038/s41587-020-0501-8
59
ParksD. H.ImelfortM.SkennertonC. T.HugenholtzP.TysonG. W. (2015). CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes.Genome Res.251043–1055. 10.1101/gr.186072.114
60
PatriarcaC.Sedano-NúñezV. T.GarciaS. L.BergquistJ.BertilssonS.SjöbergP. J. R.et al (2020). Character and environmental lability of cyanobacteria-derived dissolved organic matter.Limnol. Oceanogr.66496–509. 10.1002/lno.11619
61
RaesJ.KorbelJ.LercherM.von MeringC.BorkP. (2007). Prediction of effective genome size in metagenomic samples.Genome Biol.8:R10. 10.1186/gb-2007-8-1-r10
62
RocapG.LarimerF. W.LamerdinJ.MalfattiS.ChainP.AhlgrenN. A.et al (2003). Genome divergence in two Prochlorococcus ecotypes reflects oceanic niche differentiation.Nature4241042–1047. 10.1038/nature01947
63
RodriguezR. L.CastroJ. C.KyrpidesN. C.ColeJ. R.TiedjeJ. M.KonstantinidisK. T. (2018). How much do rRNA gene surveys underestimate extant bacterial diversity?Appl. Environ. Microbiol.84e00014–e00118. 10.1128/AEM.00014-18
64
RodriguezR. L.JainC.ConradR. E.AluruS.KonstantinidisK. T. (2021). Reply to: “re-evaluating the evidence for a universal genetic boundary among microbial species”.Nat. Commun.12:4060. 10.1038/s41467-021-24129-1
65
ShadeA.HoganC. S.KlimowiczA. K.LinskeM.McManusP. S.HandelsmanJ. (2012). Culturing captures members of the soil rare biosphere.Environ. Microbiol.142247–2252. 10.1111/j.1462-2920.2012.02817.x
66
ShapiroB. J.PolzM. F. (2015). Microbial speciation.Cold Spring Harb. Perspect. Biol.7:a0181431.
67
SteeleJ. H.BrinkK. H.ScottB. E. (2019). Comparison of marine and terrestrial ecosystems: suggestions of an evolutionary perspective influenced by environmental variation.ICES J. Mar. Sci.7650–59. 10.1093/icesjms/fsy149
68
SwanB. K.TupperB.SczyrbaA.LauroF. M.Martinez-GarciaM.GonzalezJ. M.et al (2013). Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean.Proc. Natl. Acad. Sci. U.S.A.11011463–11468. 10.1073/pnas.1304246110
69
TamasI.KlassonL.CanbackB.NaslundA. K.ErikssonA. S.WernegreenJ. J.et al (2002). 50 million years of genomic stasis in endosymbiotic bacteria.Science2962376–2379.
70
TianR.NingD.HeZ.ZhangP.SpencerS. J.GaoS.et al (2020). Small and mighty: adaptation of superphylum Patescibacteria to groundwater environment drives their genome simplicity.Microbiome8:51. 10.1186/s40168-020-00825-w
71
ToftC.AnderssonS. G. (2010). Evolutionary microbial genomics: insights into bacterial host adaptation.Nat. Rev. Genet.11465–475. 10.1038/nrg2798
72
VargheseN. J.MukherjeeS.IvanovaN.KonstantinidisK. T.MavrommatisK.KyrpidesN. C.et al (2015). Microbial species delineation using whole genome sequences.Nucleic Acids Res.436761–6771. 10.1093/nar/gkv657
73
WienhausenG.Noriega-OrtegaB. E.NiggemannJ.DittmarT.SimonM. (2017). The Exometabolome of two model strains of the roseobacter group: a marketplace of microbial metabolites.Front. Microbiol.8:1985. 10.3389/fmicb.2017.01985
Summary
Keywords
microbial ecology, genome size, bacteria, archaea, genomics
Citation
Rodríguez-Gijón A, Nuy JK, Mehrshad M, Buck M, Schulz F, Woyke T and Garcia SL (2022) A Genomic Perspective Across Earth’s Microbiomes Reveals That Genome Size in Archaea and Bacteria Is Linked to Ecosystem Type and Trophic Strategy. Front. Microbiol. 12:761869. doi: 10.3389/fmicb.2021.761869
Received
20 August 2021
Accepted
15 December 2021
Published
05 January 2022
Volume
12 - 2021
Edited by
M. Pilar Francino, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO), Spain
Reviewed by
Jennifer F. Biddle, University of Delaware, United States; Georg H. Reischer, Vienna University of Technology, Austria
Updates

Check for updates
Copyright
© 2022 Rodríguez-Gijón, Nuy, Mehrshad, Buck, Schulz, Woyke and Garcia.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Sarahi L. Garcia, sarahi.garcia@su.se
†These authors share first authorship
This article was submitted to Evolutionary and Genomic Microbiology, a section of the journal Frontiers in Microbiology
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.