The most common metric used in geochemistry for the oxidation state of organic molecules is the average oxidation state of carbon (ZC), which also goes by other names such as nominal oxidation state of carbon (NOSC) . This quantity measures the average degree of oxidation of carbon atoms in organic molecules. For a protein for which the primary sequence has the chemical formula CcHhNnOoSs, the value of ZC can be calculated from the following : 1ZC=-h+3n+2o+2sc.

The derivation of Eq. () is based on the relative electronegativities of the elements, expressed as oxidation numbers e.g.,. When bonded to carbon, H is assigned an oxidation number of +1, and N, O, and S have oxidation numbers of -3, -2, and -2. Equation () gives the remaining charge that must be present on each C atom, on average, to satisfy overall neutrality. Because of the relatively simple structures of amino acids and the primary structure of proteins, in which N, O, and S are bonded to only H and C, it is possible to calculate the average oxidation state of carbon using Eq. (). However, this equation is not necessarily valid for other classes of organic molecules or some types of post-translational modifications of proteins, including the formation of disulfide bonds. An important relation inherent in Eq. () is the redox neutrality of hydration and dehydration reactions; any pair of hypothetical (or real) proteins whose formulas differ only by some amount of H2O have equal carbon oxidation states.

A major premise of this study is that oxidation state and hydration state are two primary variables in geobiochemical systems. Accordingly, when choosing the basis species that can be combined to make the proteins, O2 and H2O are the only fixed requirements. This leaves three basis species that when combined with each other and with O2 and H2O must be able to give any possible formula written as CcHhNnOoSs. We reiterate that this analysis refers to the chemical formulas of polypeptide sequences, that is, the primary structure of proteins, not post-translational modifications or H2O molecules in the hydration shell of folded proteins.

Equation () is derived from electronegativity relations and therefore allows for the calculation of the carbon oxidation state from a given chemical formula, independent of any chemical reactions. In contrast, there is no way to count the number of H2O molecules in a chemical formula; H2O appears only in chemical reactions. But it is important to note that any particular reaction that involves only H2O is redox neutral. Conversely, the coefficient of O2 in redox reactions is closely related to the number of electrons transferred. Let us consider the 20 protein-forming amino acids as a baseline for compositional analysis; the numbers of H2O and O2 in the formation reactions of the amino acids from a particular set of basis species are denoted by nH2O and nO2. The choice of basis species in our study is guided by the dual objectives that (1) nH2O of amino acids should have very little correlation with ZC and (2) nO2 of amino acids should be strongly correlated with ZC. It should be emphasized that these are not criteria for “correctness”, since basis species, like thermodynamic components, only have to be the minimum number needed to represent the chemical composition of all the species that can be formed from them . Instead, basis species selected using these conditions yield a convenient mathematical projection of elemental composition; that is, nearly horizontal or vertical trends on nH2O–ZC scatterplots for proteins specifically reflect changes in oxidation state or hydration state, respectively.

An additional consideration is that a biologically meaningful set of basis species is likely to comprise metabolites that have high network connectivity, that is, are involved in reactions with many other metabolites. Reactions involving glutamine and glutamic acid (or its ionized form glutamate) are major steps of nitrogen metabolism , and these amino acids have been characterized as “nodal point” metabolites . Either methionine or cysteine would provide the sulfur required for the system, but cysteine is relevant as a constituent of the glutathione molecule, which has important roles in cellular redox chemistry . These considerations support the proposal of the amino acids glutamine, glutamic acid, and cysteine (collectively abbreviated QEC) together with O2 and H2O as a biologically relevant set of basis species for describing the chemical compositions of proteins . These three amino acids are among the top eight amino acids ranked by number of reactions in a metabolic model for E. coli (E: 52, S: 25, D: 23, Q: 18, A: 15, G: 15, M: 15, C: 13).

Here we compute the stoichiometric hydration state by analyzing the compositions of the 20 proteinogenic amino acids in detail. We start with a “default” set of basis species chosen for their common occurrence in overall catabolic reactions : CO2, NH3, H2S, H2O, and O2. Using these basis species (designated CHNOS), the theoretical formation reaction of alanine (C3H7NO2) is R13CO2+2H2O+NH3→C3H7NO2+3O2 and the oxygen and water content of the amino acid (i.e., nO2=-3 and nH2O=2) are the opposite of the coefficients on O2 and H2O in the reaction. Analogous reactions for the other amino acids were used to make Fig. a–b. Using glutamine (C5H10N2O3), glutamic acid (C5H9NO4), cysteine (C3H7NO2S), H2O, and O2 (the QEC basis species), the theoretical formation reaction of alanine is R20.4C5H10N2O3+0.2C5H9NO4+0.6H2O→C3H7NO2+0.3O2 showing that the oxygen and water content are nO2=-0.3 and nH2O=0.6. Calculations for all the amino acids using the QEC basis were used to make Fig. c–d.

As measured by R2 in linear regressions, the CHNOS basis yields a strong negative correlation between ZC and nH2O for the amino acids (Fig. a) but a relatively weak correlation between ZC and nO2 (Fig. b). The QEC basis provides a stronger association between ZC and nO2 and reduces the correlation between ZC and nH2O (Fig. c–d). However, there is still a small negative correlation for amino acids (Fig. c). A plot with the R2 values for all possible combinations of H2O, O2, and three amino acids indicates that QEC has relatively low R2 of nH2O–ZC and high R2 of nO2–ZC (Fig. e). Therefore, it is a suitable candidate to meet the objectives described above. Although another combination of amino acids – methionine, tryptophan, and tyrosine (MWY) – has even lower R2 for the nH2O–ZC fit (Fig. e), tryptophan and tyrosine are not highly connected metabolites and therefore are less preferable as basis species.

By strengthening the association between ZC and nO2, which represent alternative metrics for oxidation state, and by reducing the correlation between ZC and nH2O, the QEC basis species provides a more convenient projection of elemental composition than a default choice of inorganic species, such as CO2, NH3, H2S, H2O, and O2, which commonly appear in overall catabolic reactions . The selection of basis species is an evolving method, and further analysis with other metabolites may lead to a more convenient set of basis species to project the elemental composition of proteins into chemical variables.

For a given protein, the stoichiometric hydration state was calculated from 2nH2O=∑ninH2O,i-1+1∑ni, where ni is the frequency of the ith amino acid (i=1 to 20) in the protein and nH2O,i is the stoichiometric hydration state of that amino acid (Table ). The “-1” in the numerator accounts for the loss of H2O in the polymerization of amino acids, and the “+1” after the summation accounts for the N-terminal H and C-terminal OH of the polypeptide.

Unlike nH2O, ZC for proteins must be weighted by the number of carbon atoms in each amino acid, i.e., 3ZC=∑ninC,iZC,i∑ninC,i, where nC,i and ZC,i are the number of carbon atoms and carbon oxidation state of the ith amino acid (see Table 1). For example, ZC of the dipeptide Ala-Gly can be calculated as (3×0+2×1)/(3+2), where 3 and 2 are the numbers of carbon atoms and 0 and 1 are the ZC of Ala and Gly, respectively. The result, 0.4, can be checked by applying Eq. () to the chemical formula of alanylglycine (C5H10N2O3). The methods for calculating nH2O and ZC from elemental composition and amino acid composition are shown schematically in Fig. .

In a separate study, used carbon oxidation state as a metric for comparing proteomes of organisms containing the nitrogenase gene (nif). The evolution of these organisms is associated with rising atmospheric oxygen through geological history. In order to approximately replicate their results, amino acid compositions of all proteins for each bacterial, archaeal, and viral taxon in the NCBI Reference Sequence (RefSeq) database were compiled from RefSeq release 201 (July 2020). Scripts to do this and the resulting data file of amino acid compositions of 42 787 taxa are available in the JMDplots R package (see “Code and data availability” section). Names of organisms containing different nitrogenase (nif) homologs were extracted from Supplement Table S1A of . These names were matched to the closest organism name in RefSeq. Duplicated species (represented by different strains) were removed, as were matching organisms with fewer than 1000 RefSeq protein sequences. As a result, the numbers of organisms included in the present calculations (Nif-A: 155, Nif-B: 68, Nif-C: 14, Nif-D: 7) are less than those identified in . Note that values of ZC calculated here (Fig. a) are lower than those shown in Fig. 5 of . This difference is associated with the weighting by carbon number (described above), which was not performed by .

The grand average of hydropathicity (GRAVY) was calculated using published hydropathy values for amino acids . The isoelectric point (pI) was calculated using published pKa values for terminal groups and side chains ; however, the calculation does not implement position-specific adjustments . The pKa values used for calculating pI and transfer free energies used in the derivation of the GRAVY scale correspond to 25 ∘C and 1 bar, and no attempt was made here to account for the temperature effects on these properties. The charge for each ionizable group was precalculated from pH 0 to 14 at intervals of 0.01, and the isoelectric point was computed as the pH where the sum of charges of all groups in the protein is closest to zero. These calculations were implemented as new functions in the canprot R package (see “Code and data availability” section). Comparisons for selected proteins (UniProt IDs: LYSC_CHICK, RNAS1_BOVIN, AMYA_PYRFU) show that the calculated values of GRAVY and pI are equal to those obtained with the ProtParam tool .

Protein sequences were predicted from metagenomic reads using a previously described workflow . Briefly, reads were trimmed, filtered, and dereplicated using scripts adapted from the MG-RAST pipeline . For metatranscriptomic datasets, ribosomal RNA sequences were removed using SortMeRNA . Protein-coding sequences were identified using FragGeneScan , and the amino acid sequences of the predicted proteins were used in further calculations. For large datasets, only a portion of the available reads were processed (at least 500 000 reads; see Supplement Tables S1 and S2). This reduces the computational requirements without noticeably affecting the calculated average compositions .

Means and standard deviations of ZC, nH2O, GRAVY, and pI were calculated for 100 random subsamples of protein sequences from each metagenomic or metatranscriptomic dataset. The number of sequences included in each subsample was chosen to give a total length closest to 50 000 amino acids on average. The subsample density (or number of sequences included in each sample) depends on the average length of the metagenomic or metatranscriptomic sequences and is listed in Tables S1 and S2. This number ranges from 251 for the dataset with the highest mean protein fragment length (199.1; metagenome of hot-spring source of Bison Pool) to 1696 for the dataset with the lowest mean protein fragment length (29.5; metatranscriptome of site GS684 in the Baltic Sea).

To search for the hypothesized dehydration signal in metagenomic data, we began with redox gradients as a negative control. Submarine hydrothermal vents are zones of complex interactions between reduced endmember fluids and relatively oxidized seawater . Terrestrial hydrothermal systems, such as the hot springs in Yellowstone National Park, USA, provide a source of reduced fluids that are oxidized by degassing and mixing with air and surface groundwater as well as biological activity including sulfide oxidation . Redox gradients can also develop over smaller length scales. The surface of the Guerrero Negro microbial mat (Baja California Sur, Mexico) is exposed to ca. 1 m deep hypersaline, oxygenated water (approximately 200 µM O2), but in the mat, oxygen rises during the daytime and is depleted within a few millimeters, giving way to anoxic and then sulfidic conditions .

Using metagenomic data for these redox gradients , showed that the carbon oxidation states of DNA, messenger RNA, and proteins increase down the outflow channel of Bison Pool and between fluids from diffuse hydrothermal vents and relatively oxidizing seawater. Moreover, intact polar lipids extracted from the microbial communities of Bison Pool and other alkaline hot springs also exhibit downstream increases in carbon oxidation state , revealing that parallel compositional trends characterize many major types of biomacromolecules in these hot springs. The ZC of proteins increases more subtly toward the surface in the upper few millimeters of the Guerrero Negro microbial mat; it also increases at greater depths, perhaps due to heterotrophic degradation and/or horizontal gene transfer . Furthermore, an evolutionary trajectory associated with the occurrence of different homologs of nitrogenase (nif) in anaerobic and aerobic organisms is characterized by increasing ZC of the proteomes of these organisms .

The trends of carbon oxidation state described above are visible in the scatter plot in Fig. a, with an added dimension: stoichiometric hydration state. The guidelines in this plot are parallel to the nH2O–ZC trend for amino acids (Fig. c); their slope represents the background correlation between nH2O and ZC that is associated with the choice of basis species. Sample data for Bison Pool and the submarine vents are distributed parallel to these guidelines. Therefore, the decrease of nH2O along these redox gradients can be attributed to the background correlation in the stoichiometric analysis, and the differences between samples within each dataset are specifically associated with changes in carbon oxidation state and not stoichiometric hydration state. This is an expected outcome, as the redox gradients considered here do not have large changes in salinity. In particular, concentrations of Cl-, a conservative ion, increase by less than 10 % (6.1 to 6.6 mM) in the outflow of Bison Pool due to evaporation . The diffuse vents considered here have concentrations of Cl- between 515 and 624 mM, not greatly different from bottom seawater at 545 mM (Dataset S1 of ).

As a well known example of a regional salinity gradient, the Baltic Sea exhibits a freshwater to marine transition over 1800 km, but dissolved oxygen at the surface is at or near saturation with air , so this transect does not represent a redox gradient. For protein sequences derived from metagenomes in the 0.1–0.8 µm size fraction, there are large changes in stoichiometric hydration state along the Baltic Sea transect but relatively small differences in the carbon oxidation state (Fig. b). This pattern holds for samples from both the surface and chlorophyll a maximum (9–30 m deep; Fig. c).

The stoichiometric hydration state of proteins can be influenced by factors other than just salinity. Previous authors have observed large differences in microbial community composition between free-living and particle-associated fractions, which may be due in part to anoxic conditions arising from limited diffusion in particles . As described below, we found a trend of relatively low nH2O in particles compared to free-living fractions in both the Baltic Sea and Amazon River. This effect is probably associated with phylogenetic differences among the size fractions, but reduced accessibility to bulk water may be a contributing factor. Further support for the possible influence of physical accessibility is the reduced nH2O in the interior compared to upper layers of the Guerrero Negro microbial mat.

For the Baltic Sea metagenomes and metatranscriptomes, the 0.1–0.8 and 0.8–3.0 µm size fractions of particles that do not pass through the filter, which are used for subsequent DNA extraction and sequencing, represent free-living bacteria, while the 3.0–200 µm fraction contains particle-associated bacteria with average larger genome sizes and greater inferred metabolic and regulatory capacity . Figure a–c shows that proteins inferred from metagenomes for larger particles have lower nH2O than those for the smallest size fraction. The Guerrero Negro microbial mat offers another opportunity to compare exposed and interior environments. Unlike ZC, which reaches a minimum a few millimeters into the mat, nH2O decreases throughout the mat, but the changes are most pronounced in the upper few millimeters (Fig. a).

One hypothesis that could explain these findings is that the interiors of particles and the mat are sequestered to some extent from the surrounding aqueous environment. If limited accessibility to the aqueous phase were manifested as lower water activity, perhaps due to surface effects associated with geological nanomaterials and/or higher concentrations of solutes, it would provide a thermodynamic drive that favors lower nH2O of proteins. However, it should be noted that particles are also suitable habitats for multicellular and eukaryotic populations . Therefore, the trends in stoichiometric hydration state may require an explanation in terms of both physical and phylogenetic differences, which should be explored in future studies.

An important evolutionary transition is the emergence of heterotrophic metabolism, which is a later innovation than autotrophic core metabolism . It is notable that the deeper layers of the Guerrero Negro mat show greater evidence for heterotrophic metabolism ; likewise, heterotrophs in the “photosynthetic fringe” in Bison Pool may outcompete the autotrophs that dominate at higher and lower temperatures . These putative heterotroph-rich zones show locally lower values of nH2O (Fig. a). If decreasing stoichiometric hydration state is a common theme across some evolutionary transitions, then the relatively high nH2O in the proteomes of organisms carrying the ancestral nitrogenase Nif-D (Fig. a) is not unexpected. A better understanding of these trends would require more extensive phylogenetically resolved comparisons of the compositional differences as well as quantitative analyses of water fluxes in different metabolic pathways.

The Amazon River and ocean plume provide another example of a freshwater to marine transition, with salinities that range from below the scale of practical salinity units (PSU) in the river to 23–36 PSU in the plume . We used published metagenomic and metatranscriptomic data for filtered samples classified as free-living (0.2 to 2.0 µm) and particle-associated samples (2.0 to 156 µm) . River samples form a tight cluster on a plot of stoichiometric hydration state against carbon oxidation state of proteins, and the plume samples are scattered over lower ZC and low values of nH2O, particularly for the particle-associated fraction (Fig. a). For metatranscriptomes, there is a noticeable decrease of nH2O from the river to the ocean plume but little difference in carbon oxidation state (Fig. b), and the particle-associated samples again exhibit a generally lower nH2O than the free-living samples. Together with the lower nH2O for proteins inferred from metagenomes and metatranscriptomes in the larger size fractions from Baltic Sea samples, this could reflect a lower availability of H2O to organisms living near the particle surface due to physical separation from the bulk aqueous phase and associated diffusion limitation or lower water activity .

We also considered data used in a previous comparative study and data for hypersaline environments including evaporation ponds (salterns) and lakes in desert areas. characterized microbial communities using metagenomic data for various freshwater samples (lakes in the USA and Sweden) and marine locations. For hypersaline settings, we used metagenomic data from the Santa Pola salterns in Spain , natural soda lakes of the Kulunda Steppe in Serbia , and South Bay salterns in California, USA . The compositional analysis reveals a relatively low nH2O of proteins inferred from the marine metagenomes compared to freshwater samples in the Eiler et al. (2014) dataset (Fig. c). Surprisingly, hypersaline metagenomes have ranges of nH2O of proteins that are similar to marine environments but considerably higher ZC (Fig. c). To interpret these results, we considered other factors that are known to influence the amino acid compositions of proteins in halophiles.

“Salt-in” halophilic organisms have proteins with relatively low isoelectric point that remain soluble at high salt concentrations . It should be noted that proteins with a lower pI also tend to have relatively high ZC due to higher abundances of aspartic acid and glutamic acid, which are relatively oxidized (see ; ; and Fig. ). Consequently, the lower pI characteristic of salt-in organisms is also associated with an increase of carbon oxidation state. Because of the large pI differences (Fig. f), the increase of ZC in hypersaline environments can not be interpreted as an indicator of an environmental redox gradient. Some halophilic organisms are also known to have proteins that are less hydrophobic, with lower values of GRAVY . Because hydrophobic amino acids have relatively low values of ZC , a negative correlation between GRAVY and ZC is also expected.

Consistent with these well known features of halophilic adaptation, marine metagenomes exhibit lower hydrophobicity than most of the freshwater samples, and hypersaline metagenomes are shifted to both lower GRAVY and pI (Fig. f). However, there are irregular trends in the Amazon River data. Compared to the river, the proteins in plume metagenomes exhibit lower GRAVY and either higher or lower pI (Fig. d). Similarly, other authors have reported that although lower pI is a signature of many hypersaline environments, it does not clearly distinguish marine from lower-salinity environments . In contrast, the plume metatranscriptomes do show decreased pI but no major difference in GRAVY compared to river samples (Fig. e).

There is not enough space here to comprehensively examine all the available metagenomic data for environmental salinity gradients. However, we have identified one dataset that gives a contradictory result and therefore offers more perspective on the compositional relationships of proteins coded by metagenomes in salinity gradients. This dataset was generated in a time series study of microbial and viral community dynamics in a freshwater aquaculture facility (“tilapia channel” and “prebead bond”) and low-, medium-, and high-salinity salterns in southern California . Here, we have used only the reported microbial sequences (not the viral dataset) and considered all time points together. Contrary to our starting hypothesis, the stoichiometric hydration state of proteins is lowest in the freshwater samples, which is the reverse of the trend from the Baltic Sea (Fig. a–b). A side-by-side comparison of the Baltic Sea and the datasets by Rodriguez-Brito et al. (2010) shows large changes of GRAVY in the former but pI in the latter (Fig. c–d), which is another indication that these variables are responsive only in certain ranges of salinity.

This counterexample demonstrates that the sign of differences of nH2O is not predictable in all environments; however, the large negative offset in the freshwater samples may be a signal of some other influence, perhaps related to the human control of these ponds, which are used as fish nurseries. Specifically, the microbial communities in the aquaculture ponds may not be responding as they would in a typical natural system that is less nutrient rich. As noted above for putative heterotroph-rich zones in other systems, the lower stoichiometric hydration state could be associated with the enrichment of heterotrophic taxa, in this case due to the addition of organic compounds to the aquaculture ponds.

Considering all the datasets shown in Figs.  and , there appears to be no globally consistent metric for environmental salinity gradients that can be derived from amino acid composition. If we exclude the dataset, then nH2O exhibits a consistent decreasing trend in marine compared to freshwater samples. However, this trend does not continue into hypersaline environments.

While biomolecular data for environmental salinity gradients reflect both ecological and evolutionary differences, laboratory experiments provide information on the physiological effects of osmotic conditions on protein expression in particular organisms. It is also important to recognize that osmotic stress can be imposed by solutes other than NaCl; the effects of organic solutes differ in relation to their ability to permeate or depolarize cell membranes and to be sensed by cellular osmoregulatory systems . Because microbial acclimation to changes in osmotic conditions is a dynamic process, it is helpful to look at gene and protein expression data for a range of times and conditions that can be controlled in the lab.

We searched the literature to compile data for differential gene and protein expression in non-halophilic bacteria in NaCl or other osmotic stress conditions. As a general rule, we only included datasets with a minimum of 20 down-regulated and 20 up-regulated genes or proteins; however, smaller datasets were included if they are part of a study with larger datasets. This compilation consists of 49 transcriptomics and 30 proteomics datasets from 36 studies (note that different time points and treatments are considered separate datasets); descriptions and references for all datasets are given in Figures S1 and S2. In addition, four datasets for differential expression of proteins in halophilic archaea in hyperosmotic stress were located (see Fig. S3). This is a major update to an earlier compilation of data for hyperosmotic stress experiments , but we have limited the present compilation to data for bacteria or archaea; data for osmotic stress induced by NaCl or glucose in eukaryotic cells are considered in a separate paper .

We assembled the lists of up- and down-regulated proteins in each dataset or, for gene expression studies, the proteins corresponding to the up- and down-regulated genes and converted gene names or accession numbers to UniProt accessions using the UniProt mapping tool . The compiled data are available as CSV files in R packages (see the “Code and data availability” section). After removing genes or proteins with unavailable or duplicated UniProt IDs and those with ambiguous differences (appearing in both the down- and up-regulated groups), the amino acid compositions computed for protein sequences downloaded from UniProt were used for the compositional analysis of carbon oxidation state and stoichiometric hydration state. Median differences (i.e., ΔnH2O and ΔZC) were calculated as the median value for all up-regulated proteins minus the median value for all down-regulated proteins in each dataset.

Figure a shows results for time-course experiments for hyperosmotic stress. Note that all values are differences calculated relative to the same control (initial time point) in a given study. In transcriptomic experiments for a commensal species (Enterococcus faecalis), a soil bacterium (Methylocystis sp. strain SC2), and two pathogens (E. coli O157:H7 and Salmonella enterica serovar Typhimurium) , there is a marked progression toward lower ΔnH2O of the associated proteins with time. In a transcriptomic experiment for salt stress in Synechocystis sp. PCC 6803 , ΔnH2O is shifted negatively between 24 and 48 h but rises to a slightly positive value at 72 h. Proteomic data are available from two of these studies, indicating that the differentially expressed proteins in E. coli also show decreasing ΔnH2O with time, but in the proteomic experiment for Synechocystis sp. PCC 6803 , ΔnH2O changes sign from negative to positive between 24 and 48 h (Fig. a).

Perhaps the most striking result to emerge from this analysis is the strong dehydrating signal associated with osmotic stress imposed by organic solutes. We compared pairs of datasets from the same study for NaCl and another solute at concentrations that give similar total osmolalities. Transcriptomic data for sorbitol , sucrose , and glycerol compared to controls all show a lower ΔnH2O of the associated proteins than for NaCl compared to controls (Fig. b). Data from the study of are plotted at 1 and 6 h in the experiment, indicating a time-dependent decrease of ΔnH2O under both NaCl and glycerol treatment as well as more negative values for glycerol than NaCl. Experiments with different strains of E. coli show a slightly more positive value for sucrose than NaCl and a much larger positive difference for urea compared to NaCl . The available proteomic data also show lower nH2O for sucrose and glucose compared to NaCl (Fig. b). Note that the latter dataset is actually a comparison between growth on glucose and glucose with NaCl; growth on glucose alone produces a lower ΔnH2O of the differentially expressed proteins.

The marked decrease of ΔnH2O induced by solutes such as sorbitol, which does not permeate the plasma membrane, could result from a higher effective osmotic pressure compared to NaCl . Because it permeates cells, solutions of urea are not considered hypertonic , which may be one reason for the higher ΔnH2O for urea compared to NaCl. Sucrose, which permeates but unlike NaCl does not depolarize the plasma membrane , produces a slightly higher ΔnH2O than NaCl in one transcriptomics dataset for E. coli but has a more marked dehydrating effect in both transcriptomics and proteomics datasets for Caulobacter crescentus . The negative shift of ΔnH2O associated with most organic solutes compared to NaCl lends support to the notion that high organic loading could contribute to the relatively low nH2O of protein sequences from metagenomes of freshwater aquaculture systems (Fig. b).

Considering all transcriptomic datasets together (see Fig. S1 for references), the proteins coded by differentially expressed genes in non-halophilic bacteria under hyperosmotic stress do not show significant differences in ZC, nH2O, pI, or GRAVY (Fig. c–d). However, the average difference of nH2O would become more negative if the early time points in individual time-course experiments were excluded from the average (see Fig. a). Unlike the results for transcriptomes, the average value of GRAVY for all proteomics datasets (see Figs. S2 and S3 for references) increases significantly (Fig. f; p=0.011). The proteomic data also exhibit a small decrease of pI (p=0.083), which is expected for halophiles, but the increase of GRAVY – that is, higher hydrophobicity – is the opposite of the evolutionary trend for proteomes of halophilic organisms and the metagenomic comparisons described above. Overall, the proteomic experiments record a significant decrease of nH2O in hyperosmotic stress (Fig. e; p=0.016). We therefore conclude that nH2O is a metric with consistent behavior for field and laboratory datasets, since it records decreasing hydration state of proteins with increasing salinity in the Baltic Sea and Amazon River and plume and of differentially expressed proteins in microbial cells grown under hyperosmotic stress.