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Genome-centric view of carbon processing in thawing permafrost

Nature volume 560, pages 49–54 (2018)Cite this article

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Abstract

As global temperatures rise, large amounts of carbon sequestered in permafrost are becoming available for microbial degradation. Accurate prediction of carbon gas emissions from thawing permafrost is limited by our understanding of these microbial communities. Here we use metagenomic sequencing of 214 samples from a permafrost thaw gradient to recover 1,529 metagenome-assembled genomes, including many from phyla with poor genomic representation. These genomes reflect the diversity of this complex ecosystem, with genus-level representatives for more than sixty per cent of the community. Meta-omic analysis revealed key populations involved in the degradation of organic matter, including bacteria whose genomes encode a previously undescribed fungal pathway for xylose degradation. Microbial and geochemical data highlight lineages that correlate with the production of greenhouse gases and indicate novel syntrophic relationships. Our findings link changing biogeochemistry to specific microbial lineages involved in carbon processing, and provide key information for predicting the effects of climate change on permafrost systems.

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Fig. 1: Genome-resolved view of the microbial communities at Stordalen Mire.
Fig. 2: Carbon metabolism across the thaw gradient.
Fig. 3: Ca. ‘Acidiflorens’ geochemical correlations and metabolic reconstruction.

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Acknowledgements

This study was funded by the Genomic Science Program of the United States Department of Energy (DOE) Office of Biological and Environmental Research (BER), grants DE-SC0004632, DE-SC0010580 and DE-SC0016440. B.J.W. and P.N.E. are supported by Australian Research Council Discovery Early Career Research Awards #DE160100248 and #DE170100428, respectively. C.M.S. and J.A.B. are supported by Australian Government Research Training Program (RTP) Scholarships, and G.W.T. is supported by Australian Research Council Future Fellowship FT170100070. A portion of the research was performed using Environmental Molecular Sciences Laboratory (EMSL), a DOE Office of Science User Facility, and a portion was performed under the Facilities Integrating Collaborations for User Science (FICUS) initiative with resources at both the DOE Joint Genome Institute and EMSL. Both facilities are sponsored by the Office of BER and operated under contracts DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL). We thank the IsoGenie 1 and 2 Project Teams and the 2010–2012 field teams for sample collection, particularly T. Logan, as well as the Abisko Scientific Research Station for sampling infrastructure and support. We thank P. Hugenholtz, D. Parks, S. Robbins, B. Kemish, M. Chuvochina, S. Low and M. Butler for helpful discussion and infrastructure support.

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Nature thanks S. Allison, J. Jansson and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Author notes

  1. Joanne B. Emerson

    Present address: Department of Plant Pathology, University of California, Davis, CA, USA

  2. These authors contributed equally: Ben J. Woodcroft, Caitlin M. Singleton

Authors and Affiliations

  1. Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia

    Ben J. Woodcroft, Caitlin M. Singleton, Joel A. Boyd, Paul N. Evans, Robert D. Hoelzle, Timothy O. Lamberton & Gene W. Tyson

  2. Department of Microbiology, The Ohio State University, Columbus, OH, USA

    Joanne B. Emerson, Ahmed A. F. Zayed & Virginia I. Rich

  3. Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY, USA

    Carmody K. McCalley

  4. Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, USA

    Suzanne B. Hodgkins, Rachel M. Wilson & Jeffrey P. Chanton

  5. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

    Samuel O. Purvine & Carrie D. Nicora

  6. Earth Systems Research Center, Institute for the Study of Earth, Oceans and Space, University of New Hampshire, Durham, NH, USA

    Changsheng Li & Steve Frolking

  7. Department of Geological Sciences and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden

    Patrick M. Crill

  8. Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA

    Scott R. Saleska

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Contributions

C.L., S.F., J.P.C., P.M.C., S.R.S., V.I.R. and G.W.T. designed the overall study of microbial and biogeochemical dynamics of permafrost thaw and procured funding. V.I.R. coordinated sampling efforts, and S.B.H., J.P.C. and G.W.T. collected samples. B.J.W., C.M.S., J.A.B., V.I.R. and G.W.T. designed experiments around specific microbial hypotheses. J.B.E., A.A.F.Z., S.O.P., and C.D.N performed the protein extractions and analyses. C.K.M., S.B.H. and R.M.W. performed geochemical analyses. B.J.W., C.M.S., J.A.B., P.N.E, R.D.H. and T.O.L. carried out microbial experiments and integration of microbial and geochemical data. B.J.W., C.M.S. and G.W.T. wrote the manuscript with contributions from J.A.B., P.N.E. and R.D.H. All authors except C.L. edited, reviewed and approved the final manuscript.

Corresponding author

Correspondence to Gene W. Tyson.

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Extended data figures and tables

Extended Data Fig. 1 Phylogenetic distribution of MAGs recovered from Stordalen Mire.

a, Phylogenetic tree of 647 dereplicated MAGs. Numbers in parentheses show total MAGs recovered and phylogenetic gain of Stordalen MAGs compared to publicly available genomes for each phylum. Red text indicates previously poorly represented phyla. b, Acidobacteria subtree showing the Ca. ‘Acidiflorens’ lineages. c, Eremiobacteraeota subtree incorporating the CARN191 MAG. d, Dormibacteraeota subtree, showing Ca. ‘Changshengia’. e, Subtree of Ca. ‘Methanoflorentales’ MAGs, and closest neighbouring orders. In b–e, pie charts show phylogenetic gain, red lines indicate Stordalen MAGs, black lines indicate public genomes, blue triangles indicate clustered public genomes and red triangles indicate clustered Stordalen MAGs. Black dots indicate bootstrap values 70–100%.

Extended Data Fig. 2 Microbial community profile of the thaw gradient.

a, Relative abundance of each phylum estimated through the recovery of 16S rRNA gene reads, averaged within each thaw stage. The 15 phyla with the highest relative abundance across all samples are shown. b, Number of MAGs recovered from each of the phyla in a, showing that broadly, MAGs recovered are from lineages highest in abundance. c, Principal coordinates analysis of weighted UniFrac compositional differences between samples, based on average coverage of each recovered genome of reads mapped to the dereplicated genome set. Colours indicate thaw stage: brown = palsa (P), green = sphagnum/bog (S), blue = eriophorum/fen (E). Depth: S = surface, M = mid-depth, D = deep, X = extra-deep. Goodness of fit was 0.57 for PCoA 1 and 0.65 for PCoA 2. Sample numbers: n = 53, 65 and 70 biologically independent samples for palsa, bog and fen, respectively. d, Quantitative PCR analysis of samples taken in 2012. The number of cells per gram of soil is shown for three depths at the three thaw stages, after correcting for 16S rRNA gene copy number variation (see Methods). Fen samples contained significantly more cells per gram of soil than bog and palsa samples (average 2.6×, P = 7 × 10–8, n = 103, two-sided Mann–Whitney U-test). Sample numbers: n = 8, 9, and 8 for biologically independent samples palsa surface, mid and deep, respectively, n = 9, 8, 9 and 10, 7 and 9 for bog and fen, respectively. e, f, Relative abundances of phyla and classes within the Proteobacteria across the thaw gradient, respectively. The depth of each sample is indicated by the colour of the box (surface: red, mid-depth: green, deep: blue, extra-deep: purple). Each data point is the sum of relative abundances of all lineages assigned to the phylum in a sample after adding a 0.1% pseudocount to all phyla (so the y axis is not dominated by small values visually). Box plots are shown plotted on a log-scale y axis, with phyla and classes ordered by decreasing average relative abundance across all samples. Relative abundance was calculated based on the fraction of the community with recovered genomes (see Methods). Sample numbers: n = 53, 65 and 70 biologically independent samples for palsa, bog and fen, respectively.

Extended Data Fig. 3 Prevalence of individual MAGs across the thaw gradient.

a, Number of samples where each Stordalen MAG is present at >1% relative abundance among each stage of the thaw gradient. Vertical red lines indicate the number of samples sequenced in total from that environment. Only one MAG, ‘Deltaproteobacteria_fen_1087’, was found in a high abundance across fen sites, detected at >1% relative abundance in 96% of fen sites. b, The same information stratified by depth of the sample in the soil column. The specific MAGs prevalent are detailed in Extended Data Table 1, showing that a small number of populations were prevalent at a specific depth of a specific site. c, Stordalen genomes that changed significantly in abundance with depth. For each site, genomes that show the largest absolute difference in abundance between shallow and deep samples are shown. Genomes that are more abundant in shallow samples compared to deep are positive, and those more abundant in deep samples relative to shallow samples are negative. Only those lineages with a mean absolute difference of >1% and that are significantly different (P < 0.05, two-sided Mann–Whitney U-test) are shown. Sample numbers: n = 53, 65 and 70 biologically independent samples for palsa, bog and fen, respectively. Each bar indicates a 97% dereplicated MAG that changes in relative abundance between surface and deep samples and the colour of each indicates the phylum the genome belongs to. The fen is less stratified between the surface and deep, which is reflected in the fewer population abundances significantly changing in abundance between shallow and deep samples. Recovered congeneric genomes that showed significant but inverse differential abundance between surface and deep samples are shown in Supplementary Data 7. Genomes depicted in c in order are Acidobacteria_palsa_348 = 1, Acidobacteria_palsa_246 = 2, Actinobacteria_palsa_463 = 3, Actinobacteria_palsa_558 = 4, Acidobacteria_palsa_312 = 5, Alphaproteobacteria_palsa_929 = 6, Actinobacteria_palsa_504 = 7, Acidobacteria_palsa_125 = 8, WPS2_palsa_1515 = 9, Acidobacteria_palsa_289 = 10, WPS2_palsa_1516 = 11, Acidobacteria_palsa_310 = 12, Alphaproteobacteria_palsa_913 = 13, Actinobacteria_palsa_693 = 14, Actinobacteria_palsa_465 = 15, Actinobacteria_palsa_691 = 16, Alphaproteobacteria_palsa_895 = 17, Actinobacteria_palsa_505 = 18, Actinobacteria_bog_593 = 19, Actinobacteria_palsa_462 = 20, Acidobacteria_palsa_199 = 21, Acidobacteria_palsa_362 = 22, Acidobacteria_palsa_313 = 23, Gammaproteobacteria_palsa_1209 = 24, Acidobacteria_palsa_267 = 25, Planctomycetes_palsa_1347 = 26, Acidobacteria_palsa_143 = 27, Verrucomicrobia_palsa_1397 = 28, Actinobacteria_palsa_641 = 29, Actinobacteria_palsa_733 = 30, Acidobacteria_palsa_420 = 31, Actinobacteria_palsa_736 = 32, Verrucomicrobia_palsa_1413 = 33, Alphaproteobacteria_palsa_910 = 34, Acidobacteria_palsa_286 = 35, Acidobacteria_palsa_122 = 36, Acidobacteria_palsa_343 = 37, Deltaproteobacteria_palsa_1114 = 38, Gemmatimonadetes_palsa_1248 = 39, Acidobacteria_palsa_340 = 40, Acidobacteria_palsa_141 = 41, Alphaproteobacteria_palsa_922 = 42, WPS2_palsa_1496 = 43, Actinobacteria_bog_635 = 44, Actinobacteria_bog_766 = 45, Actinobacteria_bog_592 = 46, Gammaproteobacteria_bog_1200 = 47, Actinobacteria_bog_594 = 48, Acidobacteria_bog_329 = 49, Verrucomicrobia_bog_1475 = 50, Actinobacteria_bog_723 = 51, Acidobacteria_bog_233 = 52, Verrucomicrobia_bog_1402 = 53, WPS2_bog_1492 = 54, Alphaproteobacteria_bog_899 = 55, WPS2_bog_1527 = 56, Actinobacteria_bog_769 = 57, Acidobacteria_bog_377 = 58, Actinobacteria_bog_637 = 59, FCPU426_bog_1183 = 60, Alphaproteobacteria_bog_900 = 61, Acidobacteria_bog_234 = 62, WPS2_bog_1502 = 63, Verrucomicrobia_bog_1421 = 64, Gammaproteobacteria_bog_1206 = 65, Alphaproteobacteria_bog_908 = 66, Betaproteobacteria_bog_994 = 67, Acidobacteria_fen_416 = 68, Actinobacteria_fen_548 = 69, Acidobacteria_bog_445 = 70, Acidobacteria_bog_96 = 71, Acidobacteria_bog_202 = 72, Actinobacteria_fen_455 = 73, AD3_bog_854 = 74, Acidobacteria_bog_218 = 75, Actinobacteria_bog_806 = 76, Acidobacteria_bog_390 = 77, Actinobacteria_bog_524 = 78, Euryarchaeota_bog_81 = 79, Verrucomicrobia_bog_1459 = 80, AD3_bog_876 = 81, Actinobacteria_bog_808 = 82, Acidobacteria_bog_226 = 83, Actinobacteria_bog_576 = 84, Acidobacteria_bog_406 = 85, Acidobacteria_fen_408 = 86, Deltaproteobacteria_fen_1088 = 87, Nitrospirae_fen_1304 = 88, Bacteroidetes_fen_982 = 89, Bacteroidetes_fen_956 = 90, Acidobacteria_fen_335 = 91, Euryarchaeota_fen_63 = 92, Gammaproteobacteria_fen_1191 = 93, Deltaproteobacteria_fen_1087 = 94, Gammaproteobacteria_fen_1218 = 95, Gammaproteobacteria_fen_1219 = 96, Actinobacteria_fen_730 = 97, Deltaproteobacteria_fen_1138 = 98, Chloroflexi_fen_1050 = 99, Actinobacteria_fen_453 = 100, Acidobacteria_fen_408 = 101, Actinobacteria_fen_548 = 102, Chloroflexi_fen_1019 = 103, Acidobacteria_fen_414 = 104, Actinobacteria_fen_455 = 105.

Extended Data Fig. 4 Cellulase, xylanase and β-glucosidase gene expression across the thaw gradient.

a, b, Cellulose; c, d, xylanase; e, f, β-glucosidase. Samples analysed with metatranscriptomics are described by the date of sampling, core number and depth. a, c, e, Relative contribution of each phylum to the total TPM of the enzyme class observed in the metatranscriptomes. b, d, f, Total TPM of all expressed genes in the sample.

Extended Data Fig. 5 Monosaccharide degradation pathway prevalence at Stordalen Mire.

a, As in Fig. 2, 97% dereplicated MAGs are shown as circles (‘MAG abundance’), where the radius of the circle represents the average relative abundance of that genome in the palsa, bog or fen. b, As in Fig. 2, the total relative abundance of genomes encoding the pathway is shown among the entire community. Sample numbers: n = 53, 65 and 70 biologically independent samples for palsa, bog and fen, respectively.

Extended Data Fig. 6 Xylose degradation pathways at Stordalen Mire.

a, Diagram of xylose degradation pathways. b, Venn diagram showing how each xylose breakdown pathway is shared among the Stordalen Mire MAGs. Percentages represent the proportion compared to all Stordalen genomes encoding a xylose degradation pathway. In the metaproteomes, genomes Acidobacteria_bog_390, Actinobacteria_fen_455 and Actinobacteria_bog_808 expressed a protein specific to oxidoreductase pathways and a protein specific to the isomerase pathway. In the metatranscriptomes, Acidobacteria_palsa_248, Acidobacteria_bog_370, Acidobacteria_bog_390, Actinobacteria_fen_455, Actinobacteria_bog_586, Actinobacteria_bog_808 and Planctomycetes_fen_1346 expressed a protein specific to oxidoreductase pathways and a protein specific to the isomerase pathway. c–h, Gene expression of xylose degradation pathways. Average expression of genes in the canonical bacterial xylose isomerase (c, d), oxidoreductase (e, f) and xylanate dehydratase pathways (g, h) are depicted across the thaw gradient. Samples analysed with metatranscriptomics are described by the date of sampling, core number and depth. c, e, g, Relative contribution of each phylum to the total TPM of the enzyme class observed in the metatranscriptomes. d, f, h, Total TPM of all expressed genes in the sample.

Extended Data Fig. 7 Gene expression of fermentation pathways.

Samples analysed with metatranscriptomics are described by the date of sampling, core number and depth. a, c, e, g, Total TPM of each fermentation pathway in the metatranscriptomes. b, d, f, h, Relative contribution of each phylum to the total TPM of each pathway.

Extended Data Fig. 8 Correlation of microbial and geochemical data.

a, CO2 and CH4 concentrations in porewater derived from the bog and fen. The blue line shown is a line of best fit, forced through the origin. Dots indicate the samples, with colours indicating the sample depth. The concentrations are correlated, and the CH4 concentrations are much lower than the CO2 concentrations in both sites. Sample numbers: n = 51 (bog) and 61 (fen) biologically independent samples. b, Methanogenesis versus methanotrophy rates. Each point represents the average relative abundance of methanotrophs and methanogens across all samples in a single core, multiplied by the rate of methane generation or consumption inferred from previous culture-based measurements (2.345 and 20.1 fmol CH4 h–1 per cell of methanogenesis and methanotrophy, respectively, see Methods). The line represents the 1:1 ratio. Inferred fluxes were calculated using relative abundance of methanogenic or methanotrophic lineages so rates are only intended for comparison between the x and y axes, rather than as an absolute measure of CH4 flux. Methanotrophy appears to mitigate a significant proportion of the CH4 generated in the bog sites. c, Correlation of the relative abundance of Ca. ‘Methanoflorens stordalenmirensis’ with the isotopic fractionation of methane (αC) dissolved in paired porewater samples taken from the bog. Previously observed in 2011 using 16S rRNA gene amplicon sequencing33, the correlation is confirmed here using genome-centric metagenomic techniques on the 2011 samples, as well as in a new year of sampling in 2012. Sample numbers: n = 23 (2011) and 24 (2012) biologically independent samples. d, e, Expression of methanogenesis marker gene mcrA across the thaw gradient. Samples analysed with metatranscriptomics are described by the date of sampling, core number and depth. d, Relative contribution of each methanogenic order to the total TPM. e, Relative contribution of all mcrA genes in the metatranscriptome. Metaproteomes revealed the expression of 289 hydrogenotrophic McrA proteins across 13 samples, as well as 78 acetoclastic McrA proteins across eight samples (Supplementary Data 2). f, Linear regression analysis for predicting effective fractionation (αc) of CH4 from environmental variables and Ca. ‘Methanoflorens stordalenmirensis’ abundances in the bog. Ca. ‘Methanoflorens stordalenmirensis’ abundance exceeds bulk geochemical parameters in predicting the effective fractionation of CH4. Each line is the result of a linear regression of the specified measurement against the αc of CH4 in bog porewater samples taken in 2011 and 2012 (n = 47 biologically independent samples).

Extended Data Fig. 9 Candidate phylum Dormibacteraeota (AD3) genus Ca. ‘Changshengia’ at Stordalen Mire.

a, Total relative abundance of the genus Ca. ‘Changshengia’ correlated with the fraction of the concentration of C mineralized to CO2 versus CH4 in the bog porewater samples (R2 = 0.19, P = 0.001, n = 51 biologically independent samples). Each point represents an individual sample from 2012, with its colour representing the depth in the core from which the sample was taken. b, Metabolic reconstruction of genomes belonging to the candidate phylum AD3 genus Ca. ‘Changshengia’ correlating with the CH4:CO2 concentration ratio in porewater from 2012 bog samples. Genomes from four clades within the AD3 were assembled from across Stordalen Mire. Enzyme colour indicates the families that share that metabolic potential, as outlined in the legend on the left. Arrow colouring indicates whether expression was detected (red arrows) or not detected (black arrows) for genes encoding the enzyme in any of the 24 metatranscriptomes. Orange stars indicate detection of protein expression in any of the 22 metaproteomes from the Ca. ‘Changshengia’ and related genomes. All four lineages encode the potential to oxidize glycerol anaerobically through glycerol transporter (glpF), glycerol kinase (glpK) and a membrane-bound glycerol-3-phosphate dehydrogenase (glpABC), entering glycolysis via dihydroxyacetone phosphate processed to glyceraldehyde-3-phosphate by the triosephosphate isomerase (tpiA). Other glycerol derivatives such as glycerol-3-phosphate could be imported (glpT) by this and other family members, and dihydroxyacetone phosphate can also be processed using the PTS-dependent dihydroxyacetone kinase (dhaLMK) complex. Sinks for the electrons generated from the oxidation of glycerol also varied between the different lineages, with Ca. ‘Changshengia’ and clade 1 having a H+-translocating complex I NADH:oxidoreductase, while clade 1 also has a high affinity cytochrome oxidase complex IV, clade 2 genomes encode only a nitrate reductase (narGHI) and clade 4 genomes only a fumarate reductase (sdhABCD). These differences are likely to lead to the differentiation of the niches that each lineage occupies across different sites and depths of the mire. Lineages were considered positive for genes or complexes based on the presence of sequences with 80% homology in 50% of the genomes. c, Phylogenetic subtree showing the family groupings of AD3 for the metabolic analysis. Representative genomes from the 97% average nucleotide identity (ANI) dereplication are indicated in red. Bootstrap support is indicated at the nodes for values over 70% or 90% in grey and black, respectively. Blue clade indicates cluster of seven UBA and RefSeq genomes.

Extended Data Table 1 Genomes with high prevalence in specific sites and depths
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Extended Data Table 2 Overview of proteins detected using metaproteomics
Full size table

Supplementary information

Supplementary Information

This file contains the Supplementary Notes, Supplementary References and a guide to Supplementary Data files 10–14, which are available on figshare.

Reporting Summary

Supplementary Data 1

Details about each sample included in this study.

Supplementary Data 2

Proteins identified as expressed through metaproteomics, including the sample, the name of each protein, where the first 3 elements in snake case refer to the genome, and the final number refers to the protein ID within that genome, peptide and assigned annotation (KEGG orthology group or hydrolysis enzyme annotation).

Supplementary Data 3

Characteristics of recovered MAGs.

Supplementary Data 4

Abundances of representative lineages across the thaw gradient. Each column represents the relative abundance of a lineage in a particular sample. The column 'coverage' indicates the trimmed mean pileup (BamM 'tpmean') coverage of reads, 'relative_abundance' represents the fraction of the total community that lineage is estimated to represent, and 'relative_abundance_of_recovered' represents the fraction of the recovered part of the community that genome represents.

Supplementary Data 5

Pathways encoded by each MAG detected through automated methods.

Supplementary Data 6

Geochemical characteristics measured at each site.

Supplementary Data 7

Congeneric genomes which show significant differential abundance between surface and deep samples.

Supplementary Data 8

Copyrighter database of the GreenGenes 2013 taxonomy.

Supplementary Data 9

Custom KEGG module definitions used to define metabolic pathways assigned to each MAG.

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Woodcroft, B.J., Singleton, C.M., Boyd, J.A. et al. Genome-centric view of carbon processing in thawing permafrost. Nature 560, 49–54 (2018). https://doi.org/10.1038/s41586-018-0338-1

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