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. 2023 Jun 29;8(3):e0001423.
doi: 10.1128/msystems.00014-23. Epub 2023 Jun 8.

Community- and genome-based evidence for a shaping influence of redox potential on bacterial protein evolution

Jeffrey M Dick  1 , Delong Meng  2
Affiliations

Community- and genome-based evidence for a shaping influence of redox potential on bacterial protein evolution

Jeffrey M Dick et al. mSystems. .

Abstract

Despite deep interest in how environments shape microbial communities, whether redox conditions influence the sequence composition of genomes is not well known. We predicted that the carbon oxidation state (ZC) of protein sequences would be positively correlated with redox potential (Eh). To test this prediction, we used taxonomic classifications for 68 publicly available 16S rRNA gene sequence data sets to estimate the abundances of archaeal and bacterial genomes in river & seawater, lake & pond, geothermal, hyperalkaline, groundwater, sediment, and soil environments. Locally, ZC of community reference proteomes (i.e., all the protein sequences in each genome, weighted by taxonomic abundances but not by protein abundances) is positively correlated with Eh corrected to pH 7 (Eh7) for the majority of data sets for bacterial communities in each type of environment, and global-scale correlations are positive for bacterial communities in all environments. In contrast, archaeal communities show approximately equal frequencies of positive and negative correlations in individual data sets, and a positive pan-environmental correlation for archaea only emerges after limiting the analysis to samples with reported oxygen concentrations. These results provide empirical evidence that geochemistry modulates genome evolution and may have distinct effects on bacteria and archaea. IMPORTANCE The identification of environmental factors that influence the elemental composition of proteins has implications for understanding microbial evolution and biogeography. Millions of years of genome evolution may provide a route for protein sequences to attain incomplete equilibrium with their chemical environment. We developed new tests of this chemical adaptation hypothesis by analyzing trends of the carbon oxidation state of community reference proteomes for microbial communities in local- and global-scale redox gradients. The results provide evidence for widespread environmental shaping of the elemental composition of protein sequences at the community level and establish a rationale for using thermodynamic models as a window into geochemical effects on microbial community assembly and evolution.

Keywords: Eh–pH diagram; geochemistry; global analysis; oxidation state; protein evolution; redox potential; thermodynamics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig 1
Fig 1
Thermodynamic model for the relationship between carbon oxidation state of reference proteomes and redox potential. (a) Eh–pH diagram for reference proteomes of five selected methanogen species. All proteomes are unstable relative to the basis species used in the calculations (glutamine, glutamic acid, cysteine, H2O, H+, and e -); the relative stability fields, therefore, represent the proteome that is least unstable at particular values of Eh and pH. The gray area represents reducing conditions that are beyond the stability limit of water. (b) Carbon oxidation state of relatively stable reference proteomes as a function of Eh or Eh7 at three pH values. The upper plot shows Z C of the most stable reference proteome at three pH values, corresponding to the vertical dashed lines in (a). Note, for instance, that the reference proteome for Mvo, which is relatively stable at pH = 5, has Z C that is intermediate between those of Mma and Msm, which are relatively stable at pH = 9. The lower plot shows the same profiles after correction to Eh7 using equation 1. (c) A simple conceptual model for equilibrium and non-equilibrium effects on the relation between carbon oxidation state and Eh7. The first plot shows theoretical equilibrium values of Z C sampled at equal intervals of Eh7, taken from the profile for pH = 7 in (b). The second plot shows Z C for randomly selected archaeal and bacterial species from RefSeq, representing non-equilibrium biological variability; this particular choice of random species displays essentially zero correlation in the plot. The third plot shows a linear combination of the first and second plots. Abbreviations: Mli, Methanofollis liminatans; Mma, Methanococcus maripaludis; Msm, Methanobrevibacter smithii; Mva, Methanococcus vannielii; Mvo, Methanococcus voltae.
Fig 2
Fig 2
Z C of reference proteomes compared with oxygen tolerance and with metaproteomes. (a) Carbon oxidation state (Z C) of reference proteomes for strictly anaerobic and aerotolerant genera in the RefSeq database. The oxygen tolerance of prokaryotic genera was taken from Table S1 of reference (104). Of the genus names in that table, 64 were not matched to RefSeq and were omitted from the comparison. Student’s two-sided t-test was used to calculate P value. (b) Comparison of metaproteomes and community reference proteomes. Z C was calculated for all proteins identified in each metaproteome. Independently, Z C was also calculated for community reference proteomes derived from 16S rRNA sequences and RefSeq proteomes. The dashed line is the theoretical 1:1 line; the solid line shows linear regression of all data points with Pearson correlation coefficient (r) and slope (m) indicated in the legend.
Fig 3
Fig 3
Overview of methods and chemical depth profiles in different Winogradsky columns. (a) Schematic overview of data and methods used in this study. (b) Measurements of oxidation–reduction potential (denoted here as Eh; dashed line) in a Winogradsky column made with acidic sediment, taken from Fig. S5 of Diez-Ercilla et al. (17) with the depth scale adjusted so the sediment–water interface is at 0 cm. Values of Eh7 computed from Equation 1 are also shown (solid line). (c) Values of Z C for community reference proteomes computed in this study using 16S rRNA gene sequences reported by Rundell et al. (16) for Winogradsky columns composed of non-acidic sediment from ponds in Massachusetts, USA (BioProject PRJNA234104). The box-and-whisker plot represents data for samples collected from the same depth intervals in different columns. At each depth, N is the number of samples, and the center line, box width, whiskers, and points denote the median, interquartile range (IQR), the most extreme values within 1.5× IQR, and values outside this range. “Top” indicates samples collected by scraping the biofilm on the top surface of the sediment and “SWI” indicates samples of the sediment–water interface collected by drilling into the side of the column (16).
Fig 4
Fig 4
Sample locations and Eh–pH diagram. (a) Sample locations were obtained from NCBI BioSample metadata or primary publications. If the location of source material for laboratory or mesocosm studies was not specified, the institutional address was used. Small open circles indicate sampling transects for paddy soils in East Asia (76), hot springs in the Southern Tibetan Plateau (38), and water samples from the Three Gorges Reservoir (25); small filled triangles represent 20 worldwide ports (14). (b) Eh–pH diagram for all environment types. The outline is redrawn from Baas Becking et al. (90) and represents the range of natural environments described in that study. Note that this figure shows values of Eh, not Eh7 as in the following figures.
Fig 5
Fig 5
Associations between Eh7 and Z C at local scales. (a) Selected data sets for sediments and soil. Scatterplots between Eh7 and bacterial Z C are shown with linear regressions. Legends indicate the number of samples (N), Pearson correlation coefficient (r), and slope of the linear regression (m) ± the margin of error for the 95% confidence interval. (b) Slopes of linear regressions plotted against the decimal logarithm of numbers of samples in each data set for bacterial communities. The data sets with the largest effect sizes (i.e., those for which the absolute value of slope is > 0.1/V) are indicated by larger points. Of these nine data sets, eight have positive slopes; the only one with a negative slope is the data set for Daya Bay shown in (a). (c) Linear regressions for bacterial and archaeal communities in geothermal areas. Colors are used to represent sample characteristics (acidic water, circumneutral to alkaline water, and sediment). Numbers for data sets are (1) acidic and (2) circumneutral to alkaline New Zealand hot springs (15), (3) Eastern Tibetan Plateau (36), (4) Uzon Caldera (including nine samples for acidic water and sediment and one high-pH sample) (37), and (5) Southern Tibetan Plateau (38).
Fig 6
Fig 6
Global-scale associations between Eh7 and Z C. (a) Sample values and linear regressions for bacterial and archaeal communities in each environment type. The legend text is grayed out for environments represented by fewer than five data sets with archaeal sequences analyzed in this study. (b) Pan-environmental comparison. The legends indicate the number of data sets (top left), number of samples and slope of the linear regression ± the margin of error for the 95% confidence interval (top right), and Pearson correlation coefficient (bottom right).
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