Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Single cell activity reveals direct electron transfer in methanotrophic consortia

Abstract

Multicellular assemblages of microorganisms are ubiquitous in nature, and the proximity afforded by aggregation is thought to permit intercellular metabolic coupling that can accommodate otherwise unfavourable reactions. Consortia of methane-oxidizing archaea and sulphate-reducing bacteria are a well-known environmental example of microbial co-aggregation; however, the coupling mechanisms between these paired organisms is not well understood, despite the attention given them because of the global significance of anaerobic methane oxidation. Here we examined the influence of interspecies spatial positioning as it relates to biosynthetic activity within structurally diverse uncultured methane-oxidizing consortia by measuring stable isotope incorporation for individual archaeal and bacterial cells to constrain their potential metabolic interactions. In contrast to conventional models of syntrophy based on the passage of molecular intermediates, cellular activities were found to be independent of both species intermixing and distance between syntrophic partners within consortia. A generalized model of electric conductivity between co-associated archaea and bacteria best fit the empirical data. Combined with the detection of large multi-haem cytochromes in the genomes of methanotrophic archaea and the demonstration of redox-dependent staining of the matrix between cells in consortia, these results provide evidence for syntrophic coupling through direct electron transfer.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Examples of AOM consortia identified by FISH and paired anabolic activity measurement via nanoSIMS.
Figure 2: Activity relationships between archaea and bacteria in AOM consortia.
Figure 3: Multi-haem cytochrome genes, genomes and TEM visualization of haem group reactivity in representative ANME–SRB consortia.
Figure 4: A proposal for the energy metabolism of ANME-2a.

Accession codes

Primary accessions

GenBank/EMBL/DDBJ

Data deposits

Sequence for the ANME-2b multi-haem cytochrome protein was deposited in GenBank under the accession number KR811028.

References

  1. 1

    Tolker-Nielsen, T. & Molin, S. Spatial organization of microbial biofilm communities. Microb. Ecol. 40, 75–84 (2000)

    CAS  PubMed  Google Scholar 

  2. 2

    Rickard, A. H., Gilbert, P., High, N. J., Kolenbrander, P. E. & Handley, P. S. Bacterial coaggregation: an integral process in the development of multi-species biofilms. Trends Microbiol. 11, 94–100 (2003)

    CAS  Article  Google Scholar 

  3. 3

    Battin, T. J. et al. Microbial landscapes: new paths to biofilm research. Nature Rev. Microbiol. 5, 76–81 (2007)

    CAS  Article  Google Scholar 

  4. 4

    Kim, H. J., Boedicker, J. Q., Choi, J. W. & Ismagilov, R. F. Defined spatial structure stabilizes a synthetic multispecies bacterial community. Proc. Natl Acad. Sci. USA 105, 18188–18193 (2008)

    ADS  CAS  Article  Google Scholar 

  5. 5

    Wintermute, E. H. & Silver, P. A. Dynamics in the mixed microbial concourse. Genes Dev. 24, 2603–2614 (2010)

    CAS  Article  Google Scholar 

  6. 6

    Wessel, A. K., Hmelo, L., Parsek, M. R. & Whiteley, M. Going local: technologies for exploring bacterial microenvironments. Nature Rev. Microbiol. 11, 337–348 (2013)

    CAS  Article  Google Scholar 

  7. 7

    Momeni, B., Brileya, K. A., Fields, M. W. & Shou, W. Strong inter-population cooperation leads to partner intermixing in microbial communities. eLife 2, e00230 (2013)

    Article  Google Scholar 

  8. 8

    Kempes, C. P., Okegbe, C., Mears-Clarke, Z., Follows, M. J. & Dietrich, L. E. P. Morphological optimization for access to dual oxidants in biofilms. Proc. Natl Acad. Sci. USA 111, 208–213 (2014)

    ADS  CAS  Article  Google Scholar 

  9. 9

    Nielsen, A. T., Tolker-Nielsen, T., Barken, K. B. & Molin, S. Role of commensal relationships on the spatial structure of a surface-attached microbial consortium. Environ. Microbiol. 2, 59–68 (2000)

    CAS  Article  Google Scholar 

  10. 10

    Boetius, A. et al. A marine microbial consortium apparently mediating anaerobic oxidation of methane. Nature 407, 623–626 (2000)

    ADS  CAS  Article  Google Scholar 

  11. 11

    Orphan, V. J., House, C. H., Hinrichs, K. U., McKeegan, K. D. & DeLong, E. F. Methane-consuming archaea revealed by directly coupled isotopic and phylogenetic analysis. Science 293, 484–487 (2001)

    CAS  Article  Google Scholar 

  12. 12

    Orcutt, B. & Meile, C. Constraints on mechanisms and rates of anaerobic oxidation of methane by microbial consortia: process-based modeling of ANME-2 archaea and sulfate reducing bacteria interactions. Biogeosciences 5, 1587–1599 (2008)

    ADS  CAS  Article  Google Scholar 

  13. 13

    Alperin, M. J. & Hoehler, T. M. Anaerobic methane oxidation by archaea/sulfate-reducing bacteria aggregates: 1. thermodynamic and physical constraints. Am. J. Sci. 309, 869–957 (2009)

    ADS  CAS  Article  Google Scholar 

  14. 14

    Orphan, V. J., House, C. H., Hinrichs, K.-U., McKeegan, K. D. & DeLong, E. F. Multiple archaeal groups mediate methane oxidation in anoxic cold seep sediments. Proc. Natl Acad. Sci. USA 99, 7663–7668 (2002)

    ADS  CAS  Article  Google Scholar 

  15. 15

    Knittel, K., Lösekann, T., Boetius, A., Kort, R. & Amann, R. Diversity and distribution of methanotrophic archaea at cold seeps. Appl. Environ. Microbiol. 71, 467–479 (2005)

    CAS  Article  Google Scholar 

  16. 16

    Orphan, V. J., Turk, K. A., Green, A. M. & House, C. H. Patterns of 15N assimilation and growth of methanotrophic ANME-2 archaea and sulfate-reducing bacteria within structured syntrophic consortia revealed by FISH-SIMS. Environ. Microbiol. 11, 1777–1791 (2009)

    CAS  Article  Google Scholar 

  17. 17

    Nauhaus, K., Boetius, A., Krüger, M. & Widdel, F. In vitro demonstration of anaerobic oxidation of methane coupled to sulphate reduction in sediment from a marine gas hydrate area. Environ. Microbiol. 4, 296–305 (2002)

    CAS  Article  Google Scholar 

  18. 18

    Thauer, R. K. Anaerobic oxidation of methane with sulfate: on the reversibility of the reactions that are catalyzed by enzymes also involved in methanogenesis from CO2 . Curr. Opin. Microbiol. 14, 292–299 (2011)

    CAS  Article  Google Scholar 

  19. 19

    Moran, J. J. et al. Methyl sulfides as intermediates in the anaerobic oxidation of methane. Environ. Microbiol. 10, 162–173 (2008)

    CAS  PubMed  Google Scholar 

  20. 20

    Milucka, J. et al. Zero-valent sulphur is a key intermediate in marine methane oxidation. Nature 491, 541–546 (2012)

    ADS  CAS  Article  Google Scholar 

  21. 21

    Dolfing, J. The energetic consequences of hydrogen gradients in methanogenic ecosystems. FEMS Microbiol. Ecol. 101, 183–187 (1992)

    CAS  Article  Google Scholar 

  22. 22

    Schink, P. B. & Stams, A. J. M. in The Prokaryotes (eds Rosenberg, E., DeLong, E. F., Lory, S., Stackebrandt, E. & Thompson, F. ) 471–493 (Springer, 2013)

    Book  Google Scholar 

  23. 23

    Hoehler, T. M., Alperin, M. J., Albert, D. B. & Martens, C. S. Field and laboratory studies of methane oxidation in an anoxic marine sediment — evidence for a methanogen-sulfate reducer consortium. Glob. Biogeochem. Cycles 8, 451–463 (1994)

    ADS  CAS  Article  Google Scholar 

  24. 24

    Krüger, M., Wolters, H., Gehre, M., Joye, S. B. & Richnow, H.-H. Tracing the slow growth of anaerobic methane-oxidizing communities by (15)N-labelling techniques. FEMS Microbiol. Ecol. 63, 401–411 (2008)

    Article  Google Scholar 

  25. 25

    Schreiber, L., Holler, T., Knittel, K., Meyerdierks, A. & Amann, R. Identification of the dominant sulfate-reducing bacterial partner of anaerobic methanotrophs of the ANME-2 clade. Environ. Microbiol. 12, 2327–2340 (2010)

    CAS  PubMed  Google Scholar 

  26. 26

    Lovley, D. R. Electromicrobiology. Annu. Rev. Microbiol. 66, 391–409 (2012)

    CAS  Article  Google Scholar 

  27. 27

    Michelusi, N., Pirbadian, S., El-Naggar, M. Y. & Mitra, U. A stochastic model for electron transfer in bacterial cables. IEEE J. Sel. Areas Comm. 32, 2402–2416 (2014)

    Article  Google Scholar 

  28. 28

    Meysman, F. J. R., Risgaard-Petersen, N., Malkin, S. Y. & Nielsen, L. P. The geochemical fingerprint of microbial long-distance electron transport in the seafloor. Geochim. Cosmochim. Acta 152, 122–142 (2015)

    ADS  CAS  Article  Google Scholar 

  29. 29

    Meyerdierks, A. et al. Metagenome and mRNA expression analyses of anaerobic methanotrophic archaea of the ANME-1 group. Environ. Microbiol. 12, 422–439 (2010)

    CAS  Article  Google Scholar 

  30. 30

    Wang, F.-P. et al. Methanotrophic archaea possessing diverging methane-oxidizing and electron-transporting pathways. ISME J. 8, 1069–1078 (2014)

    CAS  Article  Google Scholar 

  31. 31

    Haroon, M. F. et al. Anaerobic oxidation of methane coupled to nitrate reduction in a novel archaeal lineage. Nature 500, 567–570 (2013)

    ADS  CAS  Article  Google Scholar 

  32. 32

    Strycharz-Glaven, S. M., Snider, R. M., Guiseppi-Elie, A. & Tender, L. M. On the electrical conductivity of microbial nanowires and biofilms. Energy Environ. Sci. 4, 4366–4379 (2011)

    CAS  Article  Google Scholar 

  33. 33

    Richardson, D. J. et al. The ‘porin-cytochrome’ model for microbe-to-mineral electron transfer. Mol. Microbiol. 85, 201–212 (2012)

    CAS  Article  Google Scholar 

  34. 34

    Okamoto, A., Hashimoto, K. & Nakamura, R. Long-range electron conduction of Shewanella biofilms mediated by outer membrane C-type cytochromes. Bioelectrochemistry 85, 61–65 (2012)

    CAS  Article  Google Scholar 

  35. 35

    Graham, R. C. & Karnovsky, M. J. The early stages of absorption of injected horseradish peroxidase in the proximal tubules of mouse kidney: ultrastructural cytochemistry by a new technique. J. Histochem. Cytochem. 14, 291–302 (1966)

    CAS  Article  Google Scholar 

  36. 36

    Litwin, J. A. Transition metal-catalysed oxidation of 3,3′-diaminobenzidine [DAB] in a model system. Acta Histochem. 71, 111–117 (1982)

    CAS  Article  Google Scholar 

  37. 37

    Welte, C. & Deppenmeier, U. Bioenergetics and anaerobic respiratory chains of aceticlastic methanogens. Biochim. Biophys. Acta 1837, 1130–1147 (2014)

    CAS  Article  Google Scholar 

  38. 38

    Summers, Z. M. et al. Direct exchange of electrons within aggregates of an evolved syntrophic coculture of anaerobic bacteria. Science 330, 1413–1415 (2010)

    ADS  CAS  Article  Google Scholar 

  39. 39

    Rotaru, A.-E. et al. Direct interspecies electron transfer between Geobacter metallireducens and Methanosarcina barkeri. Appl. Environ. Microbiol. 80, 4599–4605 (2014)

    Article  Google Scholar 

  40. 40

    Beal, E. J., House, C. H. & Orphan, V. J. Manganese- and iron-dependent marine methane oxidation. Science 325, 184–187 (2009)

    ADS  CAS  Article  Google Scholar 

  41. 41

    Kletzin, A. et al. Cytochromes c in Archaea: distribution, maturation, cell architecture and the special case of Ignicoccus hospitalis. Front. Microbiol. 6, 439 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful for the use of the facilities of the Beckman Resource Center for Transmission Electron Microscopy at Caltech (BRCem) and advice provided by A. McDowall, our collaborators T. Deerinck and M. Ellisman from the National Center for Microscopy and Imaging Research (NCMIR), C. Miele (UGA) and M. El-Naggar at USC. Metagenomic binning of ANME-2b was conducted by C. Skennerton and M. Haroon in collaboration with G. Tyson and M. Imelfort (University of Queensland). This work was supported by the US Department of Energy, Office of Science, Office of Biological Environmental Research under award numbers (DE-SC0004949 and DE-SC0010574) and a grant from the Gordon and Betty Moore foundation Marine Microbiology Initiative (grant number 3780). V.J.O. is supported by a DOE-BER early career grant (DE-SC0003940). S.E.M. acknowledges support from an Agouron Geobiology Option post-doctoral fellowship in the Division of Geological and Planetary Sciences at Caltech and C.P.K. was supported by the NASA Astrobiology Institute (award number NNA13AA92A). This is NAI-Life Underground Publication 049.

Author information

Affiliations

Authors

Contributions

V.J.O., S.M. and G.L.C. devised the study, S.M. and G.L.C. conducted the experiments and analyses and C.P.K. conducted the diffusion and electrical conductivity modelling, and all authors contributed to data interpretation and writing of the manuscript.

Corresponding author

Correspondence to Victoria J. Orphan.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Image processing workflow for single cell correlation between FISH and nanoSIMS data sets.

Representative example of data processing for an AOM consortium. a, Fiducial markers added to the FISH image. Marker points are shown in yellow, bacterial cells in red, archaeal cells in green. b, Corresponding fiducial markers identified on the nanoSIMS image. c, Overlay of the warped FISH image onto the nanoSIMS image, the transform function was defined by the points shown in a and b. d, Overlay of the original FISH image (yellow) and the warped FISH image (blue) highlighting a slight offset which becomes significant at single-cell resolutions. e, Centroids of the hand-drawn ROIs displayed on the nanoSIMS image, bacteria in red, archaea in green. f, Inverse transform applied to the ROIs drawn on the nanoSIMS image, bringing the centroid coordinates into ‘FISH space’ where we have more accurate measurement of distances between points.

Extended Data Figure 2 Spatial and geometric relationships for modelled aggregate geometries (well mixed to segregated) as a function of relative diffusivity (the ratio of growth rates to growth yields and diffusivity; see Supplementary Information) within the intermediate exchange model.

Slow diffusion is on the left (equivalent to roughly half the relative diffusivity of hydrogen compared to measured growth rates in our system) and fast on the right (equivalent to 103 times faster relative diffusivity than hydrogen compared with measured growth rates; see Supplementary Information). a, Total aggregate activity normalized to the group maximum as a function of the J spatial metric showing a strong dependency on geometry favouring well mixed (low J value) geometries under slow relative diffusion (left) and almost no relationship with J in fast-diffusion models (right). The average activity, normalized across all of the regimes rather than within a single regime, also changes dramatically from 0.002 to 0.99 as the relative diffusivity is increased. b, Total normalized archaeal population activity plotted against the total bacterial population activity within the same modelled aggregate. The total number of in silico consortia for rows a and b is 23. c, The normalized (z-score) activity for archaea (red) and bacteria (green) plotted against the distance to the nearest three partners. d, The z-score activity for archaea (green) and bacteria (red) plotted against the distance to environment-aggregate interface (that is, aggregate surface). In plots c and d the r-squared values for each correlation are given at the top of each plot in colours that correspond to the two cell types. The number of modelled in silico bacterial and archaeal cells from c and d plotted in the columns from left to right are: 1,138 bacterial and 1,162 archaeal cells; 1,163 bacterial and 1,137 archaeal cells; and 1,153 bacterial and 1,147 archaeal cells. As diffusion is increased in these models from left to right, the organisms within consortia become less dependent on each other and instead become less syntrophically coupled, relying on environmental exchange. This leads to the highest average activity rates per consortia (compare the top panel a to b).

Extended Data Figure 3 Summary of aggregate characteristics.

a, Histograms displaying the distribution of cell counts per aggregate for AS and AD consortia, blue and green respectively. b, Histograms displaying the average activity values for the AS and AD consortia, where anabolic activity is measured as fractional abundance of 15N per cell. c, Histograms of the number of AS and AD consortia associated with different levels of spatial mixing between syntrophic partners represented by the spatial mixing metric ‘J’ (see Supplementary Discussion for details on this metric). d, One-to-one relation between bacterial and archaeal cell counts in the AS and AD consortia analysed in this study. For all panels, the data set consists of 41 AS and 21 AD consortia. The number of cells in each aggregate can be found in the Source Data.

Source data

Extended Data Figure 4 Illustration of the value of single-cell resolution activity analysis.

a, Box plots showing the full range of archaeal and bacterial single-cell activities determined by 15NH4+ assimilation. The difference between the archaeal and bacterial mean activities across all aggregates (n = 62) is not significant (two sample t-test, P > 0.05). b, With our ability to quantify the activity for individual phylogenetically identified cells in AOM consortia, the average activity of the bacterial and archaeal populations within each consortium was revealed. Assessed at the level of paired populations, a significant difference in activity between the population of archaea and Deltaproteobacteria within aggregates is evident (n = 62, paired-sample t-test, P < 0.001). c, d, Adding phylogenetic resolution to this analysis by sub-grouping consortia based on their different deltaproteobacterial partners (AD and AS) reveals the difference between bacteria and archaea is only significant in the AS consortia (n = 41, paired-sample t-test, P < 0.001), while this population level offset in activity was not statistically supported within the AD group (n = 21, paired-sample t-test, P > 0.05), illustrating differential patterns in activity related to species membership. All axes represent 15N fractional abundance. The 8 consortia images shown in panels bd represent a subset of the total 62 consortia included in the analysis, with each image coloured by either archaeal 15N enrichment on the left (green) or bacterial 15N enrichment on the right (pink). The degree of brightness for each cell in the image reflects increasing levels of relative cellular 15N enrichment and the average population value for 15N fractional abundance is provided on the central axis.

Source data

Extended Data Figure 5 Evaluation of metrics for partner mixing.

The degree of partner intermixing within an aggregate was calculated using two metrics (see Supplementary Information for detailed description of metrics). For the modified join metric (J), 1 represents random mixing, while for Moran’s I, 0 represents random mixing. For both metrics increasing positive values represent increasing partner segregation and increasing negative values represent increasing ordered mixing (like a checkerboard). a, Examples of mock aggregates which were used to verify the behaviour of the two metrics. b, c, The determined values for either J or Moran’s I are represented by the large coloured data points for each of the 41 AS aggregates or 21 AD aggregates analysed in this study, respectively. The black data points represent the values for J or Moran’s I that were calculated in 300 permutation tests where the x and y coordinates of the archaea and bacteria cells were randomly reassigned. When the observed mixing was more segregated than 95% of the random permutation tests, the data points were coloured green and considered significant. Similarly, when the observed mixing was found to be more orderly mixed than 95% of the permutation tests the data points were coloured purple. When the observed mixing was found to be less extreme in either direction than 95% of the random test aggregates the data points were coloured red. The two metrics, while different in their formulation, gave very similar results. It is noteworthy that only a single aggregate contained cells that were more mixed than random. As expected, the permutation tests hover around the random mixing values for each metric, 1 and 0 for J and I, respectively.

Extended Data Figure 6 Insensitivity of cell activities to distance from nearest syntrophic partner for AD consortia.

Plots displaying all ROIs analysed of a given type for consortia composed of ANME-2b or ANME-2c and Deltaproteobacteria. Normalized activity (Z-scores) were calculated within each aggregate to allow for comparisons between consortia with large differences in average cellular activity. a, Normalized activities of archaea (n = 765 cells) within AD consortia as a function of distance to nearest syntrophic partner. b, Normalized activities of bacteria (n = 658 cells) within AD consortia as a function of distance to nearest syntrophic partner. From this analysis, it appears that distance to nearest syntrophic partner does not account for a significant amount of the variation in cellular activity within a consortium. The R2 values for linear regressions on the plotted data are shown in each panel. Dashed lines illustrate the 95% confidence intervals in slopes and intercepts of the linear regressions.

Source data

Extended Data Figure 7 Schematic of network analysis for microbial consortia.

a, FISH image of a representative ANME (green) and SRB (pink) consortium. b, Highlighted regions of interest false coloured by phylogenetic affiliation. c, Spheres of influence network of the consortia showing connectivity between cells. d, Identification of cells that share a border with a syntrophic partner (archaea adjacent to bacteria).

Extended Data Figure 8 Insensitivity of cell activities to distance from surface.

Plots displaying all ROIs analysed for a given population. Normalized activities (Z-scores) were taken within each consortium to allow for comparisons between aggregates with large differences in average cellular activity. a, Normalized activities of archaea within AS aggregates (n = 1,967 cells) as a function of distance to aggregate surface (that is, the external environment). b, Normalized activities of bacteria (n = 2,063 cells) within AS aggregates as a function of distance to aggregate surface. c, Normalized activities of archaea (n = 765 cells) within AD aggregates as a function of distance to aggregate surface. d, Normalized activities of bacteria (n = 658 cells) within AD aggregates as a function of distance to aggregate surface. From this analysis, the distance to the surface of the aggregate does not appear to explain a significant amount of the variation in cellular activity within each consortium. The R2 values for linear regressions on the plotted data are shown in each panel. Dashed lines illustrate the 95% confidence intervals in slopes and intercepts of the linear regressions.

Source data

Extended Data Figure 9 Spatial and geometric relationships for all modelled aggregate geometries as a function of relative conductivity within the direct electron transfer model.

a, Total aggregate activity normalized to the group maximum as a function of the J spatial metric, from well-mixed (low J) to segregated (high J) aggregate geometries (23 in silico aggregates in total). These plots illustrate how the total activity of all of aggregate geometries changes with the relative conductivity, with less dependency on geometry observed at the fastest conductance rates. Compare to Extended Data Fig. 2: in the case of electron exchange presented here, the least mixed aggregates (high J) have the highest activity. This is because our conductive treatment of the aggregate relies on the global electric potential of each consortia, which is the strongest when the cells are spatially organized. b, Normalized archaeal activity plotted against the normalized bacterial activity within the same modelled aggregate. c, The normalized (z-score) activity for archaea (green) and bacteria (red) plotted against the distance to the nearest three partners. d, The z-score activity for archaea (green) and bacteria (red) plotted against the distance to environment-aggregate interface (aggregate surface). In plots c and d the r-squared values for each correlation are given at the top of each plot in colours that correspond to the two cell types. The number of modelled in silico bacterial and archaeal cells from c and d plotted in the columns from left to right are: 1,138 bacterial and 1,162 archaeal cells; 1,161 bacterial cells and 1,139 archaeal cells; and 1,134 bacterial and 1,166 archaeal cells.

Supplementary information

Supplementary Information

This file contains Supplementary Text and Data, Supplementary Tables 1-4 and Supplementary References. (PDF 823 kb)

PowerPoint slides

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

McGlynn, S., Chadwick, G., Kempes, C. et al. Single cell activity reveals direct electron transfer in methanotrophic consortia. Nature 526, 531–535 (2015). https://doi.org/10.1038/nature15512

Download citation

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing