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Distribution and diversity of rhodopsin-producing microbes in the Chesapeake Bay 1

Julia A. Marescaa,#

, Kelsey J. Millerb, Jessica L. Keffer

a,*, Chandran R. Sabanayagam

c, 2

Barbara J. Campbelld 3

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Running head: Microbial rhodopsins in the Chesapeake 5

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aDepartment of Civil and Environmental Engineering, University of Delaware, Newark, 7

Delaware USA 8

bDepartment of Biological Sciences, University of Delaware, Newark, Delaware USA 9

cDelaware Biotechnology Institute, University of Delaware, Newark, Delaware USA 10

dDepartment of Biological Sciences, Clemson University, Clemson, South Carolina USA 11

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#To whom correspondence should be addressed: [email protected] 13

*Present address: Delaware Biotechnology Institute, University of Delaware, Newark, 14

Delaware USA 15

AEM Accepted Manuscript Posted Online 27 April 2018Appl. Environ. Microbiol. doi:10.1128/AEM.00137-18Copyright © 2018 American Society for Microbiology. All Rights Reserved.

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Abstract (250) 16

Although sunlight is an abundant source of energy in surface environments, less than 17

0.5% of the available photons are captured by (bacterio)chlorophyll dependent 18

photosynthesis in plants and bacteria. Metagenomic data indicate that 30-60% of the 19

bacterial genomes in some environments encode rhodopsins, retinal-based photosystems 20

found in heterotrophs, suggesting that sunlight may provide energy for more life than 21

previously suspected. However, quantitative data on the number of cells that produce 22

rhodopsins in environmental systems is limited. Here, we use total internal reflection 23

fluorescence microscopy to show that the number of free-living microbes that produce 24

rhodopsins increases along the salinity gradient in the Chesapeake Bay. We correlate this 25

functional data with environmental data to show that rhodopsin abundance is positively 26

correlated with salinity and with indicators of active heterotrophy during the day. 27

Metagenomic and metatranscriptomic data suggest that the microbial rhodopsins in the 28

low-salinity samples are primarily found in Actinobacteria and Bacteroidetes, while those 29

in the high-salinity samples are associated with SAR-11 type Alphaproteobacteria. 30

31

Importance (150) 32

33

Microbial rhodopsins are common light-activated ion pumps in heterotrophs, and 34

previous work has proposed that heterotrophic microbes use them to conserve energy 35

when organic carbon is limiting. If this hypothesis is correct, rhodopsin-producing cells 36

should be most abundant where nutrients are most limited. Our results indicate that in the 37

Chesapeake Bay, rhodopsin gene abundance is correlated with salinity, and that 38


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functional rhodopsin production is correlated with nitrate, bacterial production, and 39

chlorophyll a. We propose that in this environment, where carbon and nitrogen are likely 40

not limiting, heterotrophs do not need to use rhodopsins to supplement ATP synthesis. 41

Rather, the light-generated proton motive force in nutrient-rich environments could be 42

used to power energy-dependent membrane-associated processes such as active transport 43

of organic carbon and cofactors, enabling these organisms to more efficiently utilize 44

exudates from primary producers. 45

46

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Introduction 48

Light is an abundant resource: 5.34 x 1034

photons reach the earth every second 49

(calculated from http://rredc.nrel.gov/solar/spectra/am1.5/). Organisms in all domains of 50

life use light for photosynthesis, vision, or as an environmental cue (1, 2). Photosystems 51

capable of converting photons to stored chemical energy provide photoheterotrophic 52

microorganisms with a way to supplement their energy metabolism, taking advantage of 53

the abundance of light in an often nutrient-limited world (3–7). Photoheterotrophs using 54

photosystems with bacteriochlorophyll a as the primary light-capturing pigment typically 55

comprise up to 10% of the microbial community in aquatic and marine environments (7–56

12). In contrast, the much simpler rhodopsin-type light-harvesting systems are found in 57

30-60% of the microbial genomes in surface environments (13–17), even though 58

theoretical calculations suggest that they may return significantly less energy to the cell 59

than the bacteriochlorophyll a-utilizing photosystems (18). 60

61

Microbial rhodopsins consist of one polypeptide and a single organic cofactor, retinal, 62

whose biosynthesis is encoded by a total of 7 genes, and most are light-activated proton 63

pumps. Because they are so simple, it is often suggested that they provide a metabolically 64

inexpensive mechanism for supplementing the proton motive force when organic carbon 65

is limiting (3, 19). If microbial rhodopsins are critical to the starvation response, they 66

should only be expressed when cells are carbon- or energy-limited. Upregulation of 67

proton-pumping rhodopsin expression in carbon- or energy-limited conditions has been 68

observed in some bacterial isolates (3–6, 20–23). However, other work has identified 69

bacterial species with rhodopsins that pump ions other than protons (24, 25) and proton-70


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pumping rhodopsins that energize active transport rather than ATP synthesis (26), as well 71

as species whose rhodopsin expression is associated with anaplerotic CO2 assimilation in 72

the light (27–29). Rhodopsin expression may also be correlated with salinity or osmotic 73

stress (30, 31). In sum, the existing data clearly suggests that rhodopsins may play many 74

physiological roles and may be important in a variety of environmental conditions. 75

Despite their array of apparent physiological roles, the distribution of functional 76

rhodopsins in relation to environmental parameters other than nutrient availability has 77

been under-explored. 78

79

To begin to identify the environmental conditions under which rhodopsins are most 80

active and thus presumably most important, a method for quantification of cells 81

containing active rhodopsins in natural environments is necessary. The low fluorescence 82

yield of rhodopsins has hampered direct detection and counting methods. Previous 83

estimates of abundance relied on metagenomic sequence data, amplicon sequencing, 84

qPCR , or cultivation, which are imperfect indicators of functional rhodopsin abundance 85

(32). 86

87

Total internal reflection fluorescence (TIRF) microscopy can be used to identify and 88

quantify cells with active rhodopsins (33). Here, we combine TIRF microscopy, a 89

quantitative method that we use to measure abundance of rhodopsin-producing cells, with 90

qualitative analyses (qPCR, and metatranscriptomics) that identify the types of 91

rhodopsins present, to assess the kinds and abundances of rhodopsins in bacterioplankton 92

in the Chesapeake Bay. The Chesapeake Bay is a mesotrophic system with a salinity 93


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gradient along its length (34). Because prior research indicates that rhodopsin 94

transcription in natural environments is upregulated in the light (35–38) and that 95

rhodopsins are more abundant in marine environments than in fresh water (13–16), we 96

predicted that functional rhodopsin abundance would be greater during the day than at 97

night and would increase as light intensity increased, and that rhodopsin gene abundance 98

would increase with salinity. Here, we used qPCR along the length of the Bay and 99

metagenomic data from 3 sites to confirm the hypothesis that rhodopsin genes and their 100

transcription increase in abundance as salinity increases along the length of the estuary. 101

We further demonstrate that functional rhodopsins are more abundant during the day than 102

at night, and that their abundance pattern is similar to the patterns of chlorophyll a (Chl 103

a) abundance and bacterial production. 104

105

Results 106

Environmental parameters 107

Samples were collected in April 2015 from the R/V Sharpe along a transect from 108

the headwaters of the Chesapeake Bay, near the Susquehanna River, to the mouth of the 109

Chesapeake Bay (Fig. 1). One additional sample was also collected off the coast of 110

Assateague Island (Fig. 1, site 36). This transect followed a gradient of increasing salinity, 111

from nearly fresh (0.07 parts per thousand salinity) to marine (35 ppt salinity; Fig. 2A). 112

At each site, samples were collected and analyzed for nitrate, ammonium, phosphate, and 113

silicate (Fig. 2 and Table S1), as well as total cell counts (by enumeration of DAPI-114

stained cells), bacterial production (by quantification of 3H-leucine incorporation), and 115

chlorophyll a (Chl a; Fig. 2). Nitrate and silicate concentrations decreased along the 116


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length of the Bay as salinity increased, as did bacterial production (Fig. 2). Phosphate 117

was below the detection limit in nearly all samples, suggesting that this was the limiting 118

nutrient at the time of collection. 119

The R functions rcorr and corrplot (39, 40) were used to identify and plot 120

correlations between abiotic environmental parameters (salinity, nitrate, ammonium, 121

silicate, and light intensity) and between abiotic and biological parameters (cell counts, 122

bacterial production, and Chl a). Salinity was negatively correlated with nitrate and 123

silicate in both day and night samples, and with ammonium at night (Fig. 3). It was also 124

negatively correlated with cell counts and bacterial production during the day, but no 125

correlation between salinity and biological parameters was observed at night (Table S2). 126

Light intensity (PAR, photosynthetically active radiation between 400 and 700 nm) was 127

negatively correlated with cell counts during the day, but no statistically significant 128

correlations between light intensity and other parameters were identified. Bacterial 129

production was positively correlated with Chl a, nitrate, and silicate during the day, but 130

not with any environmental or biological parameters at night (Fig. 3). Both Chl a and 131

silicate are associated with primary producers, since algae, diatoms, and cyanobacteria 132

use Chl a to capture light energy and diatoms synthesize Si-rich frustules. In total, these 133

correlations suggest that heterotrophic activity (as indicated by bacterial production) is 134

highest in the places with the most primary producers (as indicated by Chl a and silicate). 135

136

Rhodopsin gene abundance in the Chesapeake Bay 137

To determine the genetic potential of the microbial communities in the 138

Chesapeake Bay to produce rhodopsins, the abundance of rhodopsin-encoding genes was 139


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quantified using qPCR. Primers capable of amplifying SAR11-type proteorhodopsins 140

(SAR-PR), and LG1-type actinorhodopsins (LG1; Table 1) were used in qPCR to 141

estimate gene abundances along the Bay. Using the assumption that, on average, 142

microbial genomes encode 1.9 copies of the 16S rRNA gene (35), we estimate that the 143

percentage of genomes in the Chesapeake Bay encoding SAR-PR increases from 0.7% at 144

0.1 ppt salinity to 116% at 35 ppt (Fig. S1). This change indicates that salinity strongly 145

affects microbial community structure. 146

Trends in the qPCR data were also analyzed with rcorr. The abundance of SAR-147

PR genes is strongly correlated with salinity during the day (Pearson’s r ~ 0.70; Fig. 3 148

and Table S2). Although SAR-PR gene abundance is clearly correlated with salinity, it is 149

also strongly negatively correlated with total cell counts, bacterial production, nitrate, and 150

silicate during the day, and negatively correlated with nitrate, ammonium, and silicate in 151

the night samples (Fig. 3 and Table S2). 152

In contrast, actinorhodopsin genes of the LG1 group (15, 41) are present at low 153

levels along the entire length of the bay, decrease as salinity increases, and are 154

consistently more abundant at night than during the day (Fig. S1). 155

156

Functional rhodopsin abundance in the Chesapeake Bay 157

Abundance of functional rhodopsins was quantified using direct cell counts with 158

TIRF microscopy. In the TIRF microscopy system, fluorophores are excited by the 159

evanescent wave generated when the laser light reflects off of the sample-coverglass 160

interface; since this wave propagates less than 200 nm into the sample, background 161

fluorescence in this system is minimal, and molecules such as rhodopsins with low 162


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fluorescent yields can be detected (33, 42). To differentiate microbial cells from 163

autofluorescent organic matter, samples were stained with DAPI prior to analysis. All 164

fields of view were sequentially excited with a 405-nm laser, to illuminate DAPI-stained 165

cells, a 561-nm laser, to identify rhodopsin- and phycobiliprotein-containing cells, and a 166

641-nm laser to identify Chl a-containing cells (Fig. S2). Cells were identified as 167

containing rhodopsins if they fluoresced when excited with the 405- and 561-nm lasers, 168

but not the 641-nm laser. This method measures autofluorescence of the rhodopsin-retinal 169

complex: neither retinal nor the aporhodopsin is autofluorescent alone under these 170

conditions (33). Cells that fluoresced when excited with both the 561- and 641-nm lasers 171

were interpreted as cyanobacteria with phycobiliproteins, not rhodopsins, since this 172

method cannot distinguish between rhodopsins and phycobiliproteins (33). 173

The number of cells with rhodopsin fluorescence is strongly correlated with 174

salinity during both day and night (Figs. 3 and 4; Pearson’s r coefficients of 0.76 and 175

0.94, respectively). At night, the percentage of cells producing rhodopsins ranges from 176

~4.6% in the freshwater sample to ~30% in the most marine sample, increasing linearly 177

with salinity. During the day, the lowest number of cells with rhodopsins was observed in 178

the mid-salinity (7-15 ppt) range, but this number generally increased with salinity as 179

well. 180

The ratio of cells with functional rhodopsins to those with rhodopsin genes should 181

indicate how many organisms with the potential to synthesize rhodopsin actually do so. 182

Correlation of changes in this ratio with environmental parameters may thus identify 183

environmental controls on light utilization via rhodopsin-type photosystems. At night, 184

this ratio is not significantly correlated with any measured environmental parameter (Fig. 185


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3). Although no consistent trend with salinity is observed, the functional rhodopsin to 186

rhodopsin-encoding gene ratio is highest when the salinity is in the ranges of 5-10 ppt 187

and 20-25 ppt (Fig. 5A). In the surface daytime samples, this ratio is positively correlated 188

with bacterial production, Chl a, nitrate, and silicate concentrations, and is negatively 189

correlated with the percentage of genomes encoding SAR11-type proteorhodopsins (Fig. 190

3, Table S2). Further, this ratio, the Chl a concentration, and bacterial production all 191

decrease as light intensity increases (Table S2), while the TIRF: qPCR ratio and bacterial 192

production both increase with Chl a concentration (Fig. 5B). This combination of 193

indicators of primary producers and heterotrophic activity strongly implies local 194

bioavailable organic carbon, and suggests in turn that rhodopsin production is associated 195

with heterotrophic activity. 196

197

Metagenomic and metatranscriptomic sequence data. 198

DNA and RNA from two cell size fractions (0.2 to 0.8 µm and > 0.8 µm) was 199

sequenced from 3 sites along the Chesapeake Bay, representing low-, mid-, and high-200

salinity environments. The estimated number of copies of rhodopsin genes per genome 201

varied along the Chesapeake Bay salinity gradient and with size fraction (Figure 6 and 202

Table 2). The number of genomes was estimated from the number of reads mapping to 203

rplB, which encodes the 50S ribosomal protein L2 and is a single-copy gene in microbial 204

genomes (43). In the mid-salinity, smaller size fraction (15 ppt, < 0.8 μm), the ratio of 205

rhodopsin to rplB was ~1, suggesting that most genomes in this sample encode at least 206

one rhodopsin (Table 3). The freshwater sample, where only the larger size fraction was 207

analyzed, had the fewest rhodopsin genes: ~30% of the genomes are estimated to encode 208


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a rhodopsin. In both cases where paired size fraction data was available (sites 17 and 33, 209

15 ppt and 31 ppt salinity respectively), the < 0.8 μm size fraction sample had more 210

rhodopsin genes per genome than the > 0.8 μm size fraction sample (Fig. 6A and Table 3). 211

Transcription of rhodopsin genes also appeared to vary along the salinity gradient 212

(Fig. 6B). Gene expression ratios were clearly correlated to copies of rhodopsin per 213

genome (R=0.95, p <0.05). Total rhodopsin gene expression, when normalized to rplB 214

gene expression, was highest in the mid-salinity, < 0.8 μm size fraction samples. In the 215

size fraction greater than 0.8 µm, patterns of rhodopsin expression were different from 216

patterns of rhodopsin gene distribution (Fig. 7). The transcription of Actinobacterial 217

rhodopsin genes in both size fractions decreases in relative abundance along the salinity 218

gradient. The relative abundances of rhodopsin transcripts and genes from SAR11 and 219

other Alphaproteobacteria are similar at all three sites. However, transcripts of 220

rhodopsins from the Bacteroidetes/Chlorobi group (primarily transcripts and genes from 221

Flavobacteria; Fig. 8) are highly abundant in the larger size fraction at the mid-salinity 222

site. Rhodopsin genes from eukaryotes are not detectable in the metagenomic data set, 223

but eukaryotic rhodopsin transcripts are present in the mid-salinity and marine sites. 224

At the mid-salinity site (15 ppt), although only 15% of the small (< 0.8 µm) 225

heterotrophic microbes produce functional rhodopsins, the qPCR results indicate that 226

close to 40% of genomes in this size fraction encode either SAR-PR or LG1-type 227

rhodopsins, and the metagenome analysis suggests that nearly all genomes encode a 228

rhodopsin (Table 3). In the higher-salinity samples, more cells produce functional 229

rhodopsins, the qPCR results suggest that rhodopsin genes are highly abundant, and the 230

metagenomic data set suggests that ~74% of genomes encode rhodopsins. The 231


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discrepancies between the qPCR and metagenomics data are likely due to variability of 232

qPCR efficiency and to the fact that neither qPCR reaction targeted rhodopsins from 233

Bacteroidetes, which were abundant in the mid-salinity metagenomic data set (Fig. 7). 234

The phylogenetic diversity of rhodopsins varied along the salinity gradient (Fig. 235

7), but was consistent between the metagenomes and metatranscriptomes. The freshwater 236

samples were dominated by rhodopsins associated with Bacteroidetes and Actinobacteria. 237

The < 0.8 μm size fraction samples from 15 and 31 ppt salinity were dominated by 238

rhodopsins in the SAR11 clade within the Alphaproteobacteria class. In one of the mid-239

salinity > 0.8 μm size fraction RNA samples, ~88% of the rhodopsin transcripts appeared 240

to originate from one taxon (OTU23) similar to Phaeodactylibacter xiamenensis (Fig. 8). 241

This Bacteroidetes species was originally isolated from the phycosphere of a marine 242

microalga (44, 45), providing additional support for the proposed association between 243

rhodopsin-encoding heterotrophs and primary producers in the Chesapeake Bay. 244

The dominant OTUs associated with the larger size fraction were mostly within 245

the Bacteroidetes phylum, with some also associated with Actinobacteria, 246

Gammaproteobacteria, and SAR11 groups (Figs. 7 and 8). In the small size fractions, the 247

Actinobacteria and SAR11 groups together comprised 55% of the rhodopsin genes in 248

high salinity metagenome and 81% of the rhodopsin genes in the mid-salinity 249

metagenome. At least in the mid-salinity samples, targeting only SAR-PR and LG1 250

captured most of the rhodopsin-encoding genes present. 251

Rhodopsins may either pump ions or initiate a signal cascade, and TIRF 252

microscopy does not distinguish between the two types of rhodopsins. However, none of 253

the rhodopsin genes identified in either the metagenomic and metatranscriptomic 254


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analyses were related to known sensory rhodopsins: all fell into clades with known light-255

activated ion pumps, and in fact sensory rhodopsins were used as the outgroup in our 256

phylogenetic analysis (Fig. 8). 257

258

Discussion 259

The physiological role(s) of rhodopsins vary among microbial taxa and may change as 260

environmental conditions change, but the environmental factors contributing to 261

distribution and expression of rhodopsins are not well understood. For this reason, we 262

measured both rhodopsin gene abundance and functional rhodopsins along environmental 263

gradients. If genetic potential is the primary factor controlling rhodopsin production, the 264

ratio of organisms that produce functional rhodopsins to genomes encoding rhodopsin 265

genes will not change as environmental conditions change. However, if environmental 266

parameters affect rhodopsin production, changes in the functional protein to gene ratio 267

will be correlated with changes in those parameters. 268

269

Rhodopsin production is correlated with heterotrophy in the Chesapeake Bay 270

We tested the hypothesis that rhodopsin protein production is associated with light and 271

salinity. Neither of these factors was significantly correlated with the ratio of cells 272

producing functional rhodopsins to genomes encoding rhodopsins. Instead, during the 273

day, this ratio is positively correlated with heterotrophic bacterial production, Chl a (a 274

general indicator of photosynthesis), and dissolved silicate, which is associated with 275

diatom abundance (46). Based on the correlations between primary production, 276

heterotrophy, and functional rhodopsin production, we propose that in the Chesapeake 277


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Bay, microbial rhodopsin activity is primarily associated with heterotrophic activity 278

fueled by algal or cyanobacterial exudates. In the light, the number of primary producers 279

increases, resulting in greater availability of organic carbon. This organic carbon in turn 280

supports more bacterial production (47, 48). The heterotrophs that consume 281

photosynthetically-produced organic carbon in illuminated waters are exposed to light, so 282

light energy is a potential resource for them as well. Cells with rhodopsins could use light 283

to power processes that utilize ion (H+ or Na

+) gradients, including motility, transport of 284

organic or inorganic carbon, or cofactor import. Indeed, abundance of proteorhodopsins 285

of the SAR92 group has been correlated with Chl a in the Arctic Ocean, potentially 286

indicating that proteorhodopsin-encoding SAR92-related organisms respond to algal 287

blooms (49). Further, upregulation of ion-dependent transport functions in the light has 288

been observed in (proteo)rhodopsin-encoding cells (4, 26, 28). Thus, light may provide 289

energy that allows heterotrophs in carbon-replete conditions to better coordinate organic 290

carbon import and processing with organic carbon release by primary producers. 291

Although our data shows a clear association between the presence of light and the 292

production of rhodopsins, no effect of light intensity on rhodopsin production was 293

observed. 294

295

Some cyanobacteria and algae encode rhodopsin genes (50), so we considered the 296

possibility that the correlation between Chl a and rhodopsin abundance was due to 297

primary producers that also synthesize rhodopsins. However, Chl a-producing organisms 298

observed were excluded from the TIRF analysis (see Methods), and no sequences related 299

to cyanobacterial rhodopsins were observed in the metagenomic or metatranscriptomic 300


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data sets. For these reasons, the observed correlation is not due to organisms such as 301

Anabaena sp. PCC7120 or Gloeobacter violaceus that have both chlorophyll- and 302

rhodopsin-type photosystems (51, 52). 303

304

Although the ratio of cells producing functional rhodopsins to genomes encoding 305

rhodopsins was not significantly correlated with salinity, this ratio is highest in salinity 306

ranges of 5-10 ppt, where freshwater organisms may be exposed to higher salinity than 307

they prefer, and 25-30 ppt, where marine organisms are exposed to lower salinity than 308

their optimum. Previous work in the Chesapeake Bay has suggested that salinity has the 309

largest effect on microbial community structure in low (<5 ppt) or high (>30 ppt) salinity 310

regions (68). On the borders of freshwater or marine sytems, rhodopsins may be 311

expressed either by freshwater or marine organisms experiencing osmotic stress. In these 312

cases, ion-pumping rhodopsins might be involved in maintaining osmotic balance or in 313

conserving energy in a specific stress condition, as has been shown for the marine 314

bacterium Psychroflexus torquis (30). 315

316

Rhodopsin gene abundance is correlated with salinity in the Chesapeake Bay 317

Because environmental microbial community composition is strongly dependent on 318

salinity (53–57) and rhodopsins are common in marine microbes in surface waters (13, 14, 319

32, 58–60), we had hypothesized that the abundance of rhodopsin-encoding genes typical 320

of marine microbes, such as SAR11-type proteorhodopsins, would be positively 321

correlated with salinity, while actinorhodopsins, which are typical of freshwater 322

Actinobacteria , would be negatively correlated with salinity (14, 15, 31, 41, 49, 57, 61). 323


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As predicted, the percentage of cells encoding SAR11-type proteorhodopsins increased 324

with salinity in the qPCR, metagenomic, and metatranscriptomic analyses, indicating that 325

the influence of marine waters strongly affects the composition of the microbial 326

communities in the Bay. The percentage of cells encoding actinorhodopsin decreased as 327

salinity increased, but only in the samples collected at night, when actinorhodopsin-328

encoding genomes have a much greater relative abundance than during the day. These 329

opposing trends have also been observed for Alphaproteobacteria and Actinobacteria in 330

the nearby Delaware Bay (62) and for proteorhodopsins and actinorhodopsins in the 331

Baltic Sea (31, 63). The discrepancy between day and night samples may be due to 332

diurnal movement of primary producers. Algae and cyanobacteria tend to sink through 333

the water column during the late afternoon and evening (64–66), which may make the 334

Actinobacteria larger fractions of the surface water microbial communities at night. 335

336

The high abundance of SAR11-type proteorhodopsin genes detected by qPCR in the 337

marine sample – over 100% of microbial genomes are predicted to encode a rhodopsin – 338

may indicate that some genomes encode multiple proteorhodopsins. This estimate is 339

based on the assumption that the average copy number of 16S rRNA genes in the 340

Chesapeake Bay is ~1.9 (67). If this assumption is incorrect, the percentage of genomes 341

encoding rhodopsins may be nearly 2-fold overestimated. However, the observed trends 342

with salinity would hold true, regardless of the precise number of rhodopsin or 16S rRNA 343

gene copies per genome. Alternatively, this high estimate may be a result of the different 344

efficiencies of the 16S rRNA gene and SAR-PR gene qPCR reactions. 345

346


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The qPCR analysis described here did not amplify rhodopsin genes associated with 347

Bacteroidetes, Alphaproteobacteria other than SAR11, or Gammaproteobacteria. 348

However, in the metagenomic data from the mid-salinity small size fraction sample, 349

rhodopsins from Actinobacteria and SAR11-type Alphaproteobacteria accounted for 81% 350

of the observed rhodopsins, suggesting that the qPCR likely detected the majority of the 351

rhodopsin genes present. In the highest salinity, small size fraction sample, rhodopsins 352

from Bacteroidetes and Alphaproteobacteria other than SAR11 were larger fractions of 353

the rhodopsin pool in the metagenomic and metatranscriptomic data sets. The 354

Bacteroidetes primers that we tested were developed for amplification of rhodopsins from 355

marine Bacteroidetes (35). After we obtained the metagenomic data sets, these primers 356

were tested against the assembled rhodopsin genes in silico, and would not have 357

successfully amplified the rhodopsin genes associated with Bacteroidetes in the 358

Chesapeake Bay samples. Thus, in the mid-salinity samples, the ratio of cells with 359

rhodopsin fluorescence to cells with rhodopsin genes may be lower than we calculated 360

here. If this is indeed the case, the correlation of this ratio with bacterial production 361

would also be greater, strengthening the argument that production of functional 362

rhodopsins is associated with heterotrophic activity rather than nutrient limitation in the 363

Chesapeake Bay. Although we lack metagenomic data for the small size fraction in the 364

freshwater sample, we would predict based on the other metagenomic data that a greater 365

fraction of detectable rhodopsins would be actinorhodopsins and fewer would be SAR11-366

type proteorhodopsins. The fraction of rhodopsins associated with Bacteroidetes might be 367

larger than in the mid-salinity sample, but because Bacteroidetes cells are not typically as 368

small as the SAR11-type Alphaproteobacteria and Actinobacteria, they might not have 369


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passed through the 0.8-µm filter. In a 2007 study of Chesapeake Bay microbial 370

communities, SAR11-type Alphaproteobacteria and Actinobacteria together comprised 371

70% of the microbial community near the headwaters of the Bay, while Bacteroidetes 372

were ~11% of the community (68). For these reasons, we conclude that the qPCR data 373

presented here, while not a comprehensive analysis of all possible rhodopsin genes, 374

reflects the main groups of rhodopsins in the small size fraction of the Chesapeake Bay 375

microbial community. 376

377

Trends in the rhodopsin-encoding genes in the metagenomic data are generally consistent 378

with trends in the qPCR and TIRF data, though only two of the metagenomic and 379

metatranscriptomic samples are from the same size fraction as the TIRF microscopy and 380

qPCR samples. Between the mid-and high-salinity samples, the relative abundance of 381

SAR-PR gene decreases, likely reflecting the relative increase in abundance of 382

rhodopsins associated with Alphaproteobacteria of the Rhodobacterales group. The 383

metagenomic data shows that the qPCR analysis missed rhodopsins from 384

Alphaproteobacteria other than SAR11 and from the Bacteroidetes, which encode a 385

variety of rhodopsins (69, 70). Additionally, the metagenomic data suggest that in the 386

mid- and high-salinity samples (15 and 31 ppt, respectively), rhodopsins from the 387

Bacteroidetes group are at least as abundant as actinorhodopsins in the small size fraction 388

(< 0.8 µm) samples. The Bacteroidetes-type rhodopsins are much more abundant in the 389

larger size fraction, suggesting that these cells may be common in multicellular and/or 390

particle-associated aggregates. Although rhodopsins from marine Bacteroidetes have 391

been well-characterized recently (4, 26, 27, 30, 35, 70–72), rhodopsins in freshwater 392


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Bacteroidetes have not, though rhodopsin genes associated with Bacteroidetes 393

(specifically Flavobacteria) have been observed (73). Given the abundance of genes and 394

transcripts from Bacteroidetes in the freshwater and mid-salinity samples, this group of 395

rhodopsins clearly merits more study. 396

397

Rhodopsin abundance patterns in the Chesapeake Bay compared to other environments 398

In the Chesapeake Bay, a mesotrophic estuary with generally high concentrations of 399

nutrients and salinity that decreases with distance from the ocean, rhodopsin gene 400

abundance seems to be primarily controlled by salinity. Overall, the results described 401

here are similar to studies of rhodopsin gene abundance and expression in the Baltic Sea, 402

an estuary with a salinity gradient similar to that of the Chesapeake Bay. In the Baltic Sea, 403

salinity affected the abundance of rhodopsin genes, but not rhodopsin expression (31, 63). 404

Instead, quality and bioavailability of organic carbon also contributes to bacterial growth 405

efficiency (74, 75), and availability of dissolved organic carbon may control rhodopsin 406

expression there (31), suggesting that light may be linked to regulation of heterotrophy. 407

408

In contrast to estuaries, with their steep salinity gradients, the eastern Mediterranean Sea 409

has fairly constant salinity and steep nutrient gradients. Recent work by Gomez-410

Consarnau et al. using retinal as a proxy for functional rhodopsin concentrations in the 411

Mediterranean Sea and Eastern Atlantic Ocean found that retinal concentration was 412

inversely proportional to Chl a concentration, and that the highest concentration of retinal 413

was found in the most oligotrophic areas of the Mediterranean (19). All of their samples 414

were marine, removing salinity as a major driver of rhodopsin gene abundance; the major 415


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gradient in their samples was nutrient concentrations, and they conclude that rhodopsins 416

are abundant enough in the oligotrophic regions to meet cellular maintenance energy 417

requirements (19). Similarly, previous analysis had shown that the number of rhodopsin-418

encoding genes in metagenomic data sets in the Mediterranean increase as nutrient 419

concentrations decrease (60). 420

421

Perhaps in oligotrophic environments such as the eastern Mediterranean or open ocean, 422

energy supplementation is the most important physiological role of these rhodopsins, 423

while in environments with higher levels of nutrients, rhodopsins power active transport 424

(26) or other processes, enhancing the ability of heterotrophic bacteria to take advantage 425

of organic carbon or small molecules produced by phytoplankton. Since the supplemental 426

energy provided by rhodopsins may be used for physiological activities other than 427

maintenance energy in environments where C, N, and P are not limiting, rhodopsin 428

production in the Chesapeake Bay may be controlled by different factors than in typical 429

marine environments. 430

431

Summary 432

Light is a ubiquitous resource in surface environments. This work demonstrates that light 433

is actively captured via functional rhodopsins in the Chesapeake Bay, where up to 40% of 434

the microbes in the surface water produce active rhodopsins. Salinity controls the 435

distribution of microbial rhodopsin genes in the Chesapeake Bay, while time of day and 436

bacterial production appear to control the percentage of cells that synthesize rhodopsins. 437

The association of functional rhodopsin abundance with Chl a and bacterial production, 438


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proxies for locally available photosynthate, suggests that in the Chesapeake Bay, 439

rhodopsins are utilized by active heterotrophic microbes that do not suffer from nutrient 440

or energy limitation. We hypothesize that the light-dependent proton motive force 441

supplied by rhodopsins contributes to heterotrophy not solely by enabling additional ATP 442

synthesis, but also by powering proton-motive-force dependent transport of organic 443

carbon and/or cofactors released by primary producers. It is clear that combinations of 444

genetic and environmental factors work uniquely in different microbes to control 445

rhodopsin gene expression, possibly separately from synthesis of the retinal cofactor (30). 446

Future work in this field would likely benefit from high-throughput cultivation methods 447

(71) or functional metagenomic screens (76) that link rhodopsins with physiological traits 448

or specific genetic pathways. 449

450

Materials and Methods 451

Sample collection and storage. Surface water samples were obtained with a 12 Niskin 452

bottle rosette sampler with a CTD on the R/V Sharpe sampled along the length of the 453

Chesapeake, from its source at the Susquehanna River to the Atlantic Ocean, from April 454

11, 2015 to April 16, 2015. Water quality data, including temperature, salinity, dissolved 455

oxygen, turbidity, and fluorescence were measured for each cast using a Sea-Bird data 456

sonde. Samples were also collected for later determination of nutrient concentrations 457

(NO32-

, NH4+, PO4

3-, SiO4

2-) and bacterial production as described previously (62, 77, 78). 458

Rhodopsins were quantified by TIRF microscopy and qPCR in the samples 459

collected at 11:00 AM and 11:00 PM EST each day. Samples (100 mL) for TIRF 460

microscopy were pre-filtered through 1-µm filters, fixed in 4% paraformaldehyde, and 461


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stored at 4C until analysis. Samples (100 mL) for qPCR analysis were pre-filtered 462

through 1 µm filters, collected on 0.2-µm filters provided with the MoBIO PowerWater 463

kits (Catalog #14900, MoBIO, Carlsbad, CA), and stored at -20C until analysis. An 464

equal volume of RNA later (Invitrogen) was added to a liter of water immediately after 465

the rosette sampler was placed on the ship for metagenomic and metatranscriptomic 466

analysis. Water was filtered through 0.8 and 0.22 µm filters and filters were frozen at -467

80°C within one hour of collection. 468

469

DNA extraction and qPCR. DNA extractions were performed using a MoBIO 470

PowerWater kit following the manufacturer’s instructions. Preliminary PCR was 471

performed on positive controls using LG1 (41), SAR-PR (35), and 16S (79) primer sets to 472

optimize conditions for amplification of actinorhodopsins, SAR11-type proteorhodopsins, 473

and 16S rRNA genes, respectively (Table 1). Additional primer sets were tested but not 474

used because they did not amplify anything from the Chesapeake Bay samples. These 475

included primers designed to amplify proteorhodopsins from Bacteroidetes, specifically 476

Sphingomonads and Flavobacteria (35). The positive control template for SAR-PR was a 477

SAR11-type proteorhodopsin amplified from water collected from the Delaware River 478

and cloned into pCR4 (33); the positive control for LG1 was Rhodoluna (R.) lacicola 479

genomic DNA, since R. lacicola has a proton-pumping actinorhodopsin (41, 80, 81). 480

Quantitative PCR (qPCR) was performed in triplicate with 5 µL of DNA (2.5 to 481

5.4 ng µl-1

) in a final volume of 20 µL using the Quanta Biosciences PerfeCTa SYBR 482

Green FastMix for iQ (Quanta Biosciences, catalog no. 95071, Gaithersburg, MD). All 483

primer concentrations were 0.25 µM. Standard curves were made using genomic DNA 484


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from R. lacicola (81) as the template for the actinorhodopsin and 16S primers, and pCR4-485

SAR11 as the template for the SAR11-type proteorhodopsin. Average amplification 486

efficiencies were as follows: 16S rRNA = 54%, LG1 = 39%, and SAR11 PR = 73%. 487

Rhodopsins were amplified using the following program: 95°C 2.5 min; followed by 40 488

cycles of amplification at 95°C for 15 s, the indicated annealing temperature (Table 1) for 489

30 s, and 72°C for 30 s. 490

The number of copies of each gene was calculated using the Ct values and the 491

standard curves for each reaction. To estimate the percentage of genomes encoding 492

rhodopsins, we assumed no more than one rhodopsin and 1.9 copies of the 16S rRNA 493

gene per genome (14). 494

495

Nucleic acid extraction for metagenomic and metatranscriptomic analysis. Samples for 496

metagenomic and metatranscriptomic sequencing were collected from sites 1, 17, and 33. 497

Samples were filtered through 0.8 and 0.22 µm filters to separate cells into large and 498

small cell-size fractions, respectively. DNA and RNA were extracted from the filters 499

simultaneously using the Qiagen AllPrep Kit following the manufacturer’s instructions. 500

DNA was removed from the resulting RNA preps with the Ambion® Turbo DNA-free™ 501

DNase kit (Invitrogen) and the RNA was checked for DNA contamination using standard 502

PCR with universal bacterial primers 1369F and 1492R targeting the 16S rRNA gene 503

(79). 504

Nucleic acids (RNA and DNA) for large and small size fractions were obtained 505

from sites 17 and 33, and for the large size fraction alone from site 1. All samples were 506

sent to the Joint Genome Institute (JGI) for library preparation and sequencing on the 507


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Illumina HiSeq 2000 following their standard protocols and as outlined separately (SI 508

appendix). Sequences averaged 150 bp in length and 46 million to 275 million reads 509

were obtained from each sample (Table 2). All sequences are found on the JGI Genome 510

Portal with the following Project IDs: 1110833, 1110835, 1110838, 1110849, 1110851, 511

1110854, 1110841, 1110843, 1110846, 1110865, 1110867, 1110870, 1110857, 1110859, 512

1110862, 1110881, 1110883, 1110886, 1110873, 1110875, 1110878, 1110897, 1110899, 513

1110902, 1110889, 1110891, 1110894, 1110905, 1110907, 1110910, 1110921, 1110923, 514

1110926, 1110913, 1110915, and 1110918. 515

516

Metagenomic and metatranscriptomic sequence analysis. Two genes (rhodopsin and 517

rplB) were assembled using the Xander program, which utilizes hidden Markov models 518

(HMM) of the gene of interest in directing gene assemblies (43). The HMM used for the 519

rplB gene was downloaded from the functional gene repository 520

(http://fungene.cme.msu.edu/). The HMM for the rhodopsin gene was generated from a 521

seed database of 17 phylogenetically diverse rhodopsins and alignments and taxonomic 522

identities were generated compared to a reference database of 1000 rhodopsins. Default 523

assembly parameters were used and no chimeras were identified from the assembled gene 524

contigs. Xander coverage files were used to estimate the total number of sequences per 525

gene and mean coverage as described in the original program (43). The number of 526

rhodopsins per genome equivalent was determined as the ratio of the rhodopsin to rplB 527

abundance, since rplB is a single copy gene encoding the 50S ribosomal protein L2, 528

which is found in most, if not all, bacteria, and has been utilized previously in this 529

manner (43). Translated contigs were clustered at 0.2 distance to generate OTUs and 530


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abundance data for diversity and phylogenetic analyses. The phylogenetic positions of 531

representative rhodopsin OTUs were determined via Blastp analysis and assignment in 532

MEGAN, as previously described (82). Phylogenetic relationships of the rhodopsin 533

OTUs were further resolved using MEGA version 6 (83) to generate a ClustalW 534

alignment followed by construction of a Maximum Likelihood tree with the default 535

parameters (JTT model, 500 bootstraps, partial deletion). 536

537

TIRF microscopy. Fixed water samples were concentrated by filtration onto a 0.2 µm 538

filter, stained with 4',6-diamidino-2-phenylindole (DAPI; NucBlue Fixed Cell 539

ReadyProbes Reagent, Life Technologies, catalog no. R37606), and adhered to gelatin-540

coated coverslips as described previously (33, 84). Samples were sequentially excited 541

with 405 nm-, 561 nm-, and 641 nm-lasers and viewed using a lab-built TIRF imaging 542

system to visualize all cells, rhodopsin- and phycobiliprotein-containing cells, and Chl a-543

containing cells, respectively (33). Positive controls for TIRF microscopy were analyzed 544

during each microscopy session and included an algal isolate for Chl a fluorescence and 545

E. coli expressing actinorhodopsin and grown with retinal for rhodopsin fluorescence (33, 546

84); both were stained with DAPI prior to microscopy. Objects that fluoresced when 547

excited with the 405 and 561 lasers only were counted as rhodopsin-containing cells. 548

Three independent slides were prepared from each sample, and 15-20 fields of view were 549

imaged for each slide. 550

551

Statistical analysis 552


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Statistical analyses comparing samples collected at all sites were performed using 553

PRIMER 7 (85, 86). Environmental and biological (TIRF, LG1-qPCR, and SAR11-554

qPCR) data were log-transformed and normalized, then a Bray-Curtis similarity matrix 555

was calculated. The non-metric multidimensional scaling routine (nMDS) in PRIMER 7 556

was used to identify overall trends in the data. Since the day and night samples were 557

clearly differentiated in the nMDS plot (Fig. S3), they were analyzed separately in 558

subsequent analyses. 559

Correlation coefficients (Pearson and Spearman) between individual variables 560

were calculated on both untransformed and log-transformed data using the R package 561

rcorr. Most iterations of this analysis identified the same variables as correlated. 562

Therefore, for simplicity, Pearson correlation coefficients (r) calculated based on 563

untransformed data are presented here. The sample at site 1 had an anomalously high 564

TIRF: qPCR ratio (~24, implying that far more cells produced rhodopsins than encoded 565

detectable rhodopsin genes) and was not included in the statistical analyses. 566

567

568

Acknowledgements 569

Research reported in this publication was supported in part by an Institutional 570

Development Award (IDeA) from the National Institute of General Medical Sciences of 571

the National Institutes of Health under grant number 5 P30 GM103519. We thank the 572

crew of the R/V ‘Hugh R. Sharp’ for sample collection and David Kirchman, Matt 573

Cottrell and Liying Yu for technical and sampling support; this research cruise was 574

supported by National Science Foundation grants to B.J.C. (OCE-0825468) and 575


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metagenome and metatranscriptome sequencing was supported by a DOE/JGI grant to 576

B.J.C. (CSP-1621). Microscopy access was supported by the INBRE program with a 577

grant from the NIH-NIGMS (P20 GM103446) and the State of Delaware. 578

579

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76. Pushkarev A, Beja O. 2016. Functional metagenomic screen reveals new and 810

diverse microbial rhodopsins. ISME J. International Society for Microbial Ecology. 811

77. Cottrell MT, Mannino A, Kirchman DL. 2006. Aerobic anoxygenic phototrophic 812

bacteria in the Mid-Atlantic Bight and the North Pacific Gyre. Appl Environ 813

Microbiol 72:557–64. 814

78. Preen K, Kirchman DL. 2004. Microbial respiration and production in the 815

Delaware Estuary. Aquat Microb Ecol 37:109–119. 816

79. Suzuki MT, Taylor LT, DeLong EF. 2000. Quantitative analysis of small-subunit 817

rRNA genes in mixed microbial populations via 5’-nuclease assays. Appl Environ 818

Microbiol 66:4605–14. 819

80. Keffer JL, Hahn MW, Maresca JA. 2015. Characterization of an unconventional 820

rhodopsin from the freshwater Actinobacterium Rhodoluna lacicola. J Bacteriol 821

197:2704 –2712. 822

81. Hahn M, Schmidt J, Taipale SJ, Doolittle WF, Koll U. 2014. Rhodoluna lacicola 823

gen. nov., sp. nov., a planktonic freshwater bacterium with stream-lined genome. 824

Int J Syst Evol Microbiol 64:3254–3263. 825

82. Mitra S, Klar B, Huson DH. 2009. Visual and statistical comparison of 826

metagenomes. Bioinformatics 25:1849–1855. 827

83. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. 2013. MEGA6: Molecular 828


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Evolutionary Genetics Analysis Version 6.0. Mol Biol Evol 30:2725–2729. 829

84. Maresca JA, Keffer JL, Miller KJ. 2016. Biochemical analysis of microbial 830

rhodopsins. Curr Protoc Microbiol 41:1F.4.1-1F.4.18. 831

85. Clarke K, Gorley R. 2015. PRIMER-E v. 7. PRIMER-E, Plymouth, UK. 832

86. Clarke RR, Gorley RN, Somerfield PJ, Warwick RM. 2014. Change in marine 833

communities : an approach to statistical analysis and interpretation, 3rd edition, 834

3rded. PRIMER-E, Plymouth, UK. 835

836

837

838


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Table Legends 839

Table 1. Primers used for qPCR analysis. Annealing temperatures for rhodopsin 840

primers were optimized using genomic DNA from R. lacicola (for actinorhodopsin; (75)), 841

and a cloned amplicon from the Chesapeake Bay (this work), respectively. 16S primers 842

were tested using R. lacicola. Standard curves for these genes used the same templates. 843

Table 2. Reads obtained in metagenomic and metatranscriptomic sequence data sets. 844

All samples were sequenced at the Joint Genome Institute on an Illumina HiSeq2000. 845

Reads mapping to either rplB or to a known rhodopsin were assembled using Xander. 846

Table 3. Comparison of cells synthesizing functional rhodopsins or genomes 847

encoding rhodopsin genes, quantified by different methods. Since the TIRF microscopy 848

and qPCR analyses were only performed on filtered samples (cells < 0.8 µm), only the 849

small size fraction metagenomic data is included here. Note that “actinorhodopsins” in 850

the last column includes all rhodopsins identified in Actinobacteria (15). 851

852

Figure Legends 853

Figure 1. Map of cruise track. Samples were collected April 11-16, 2015. Sampling 854

sites are numbered chronologically. Samples for rhodopsin analyses were collected daily 855

at 11:00 AM (white circles) and 11:00 PM (dark circles). The Susquehanna River drains 856

into the Chesapeake Bay just north of Site 2. Site 36, since it is a coastal ocean site rather 857

than estuarine, was excluded from most analyses. 858

859

Figure 2. Environmental data. All data are plotted as functions of latitude. Each data 860

point is the average of 3 measurements, and error bars indicate 1 standard deviation. (A) 861


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Salinity decreases linearly with distance from the ocean (ocean is at the lower latitudes). 862

(B) Nitrate is higher in samples further from the ocean. (C) Ammonium (gray squares) 863

does not clearly vary with latitude. Phosphate (black diamonds) was below the detection 864

limit in most samples. (D) Cell counts are similar on average along the length of the 865

Chesapeake. (E) Bacterial production (gray squares) and Chl a (black circles) are highest 866

in the fresh water closer to the Susquehanna River. 867

868

Figure 3. Correlations (Pearson’s r) between environmental and biological 869

parameters. Any correlation with a p-value < 0.05 is plotted. Daytime samples are in the 870

lower left half of the grid, and night samples are in the upper right half. Red hues indicate 871

negative correlations, and blue hues indicate positive correlations. Salinity was measured 872

in units of parts per thousand and is strongly correlated with most abiotic and biological 873

parameters. PAR indicates photosynthetically active radiation and was not measured for 874

night samples (indicated by “o”). 875

876

Figure 4. Abundance of functional rhodopsins (TIRF microscopy). Cells producing 877

functional rhodopsins were quantified by TIRF microscopy. Cells with active rhodopsins 878

are consistently more abundant during the day (gray symbols, solid line) than at night 879

(black symbols, dashed line), and increase as salinity increases. In the daytime samples, 880

the abundance of functional rhodopsins is correlated with both salinity and with SARPR 881

gene abundance (r = 0.76 and 0.7). At night, the correlation with salinity is stronger (R = 882

0.94), but the correlation with rhodopsin gene abundance is not significant. 883

884


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41

Figure 5. Correlation of rhodopsin production to environmental parameters. The 885

ratio of cells producing functional rhodopsins (as quantified by TIRF microscopy) to 886

those encoding rhodopsin genes (as quantified by qPCR) should indicate the percentage 887

of cells capable of producing rhodopsin that actually do so. (A) This ratio varies as 888

salinity increases, for both daytime (gray squares) and nighttime samples (black squares), 889

but not with any consistent pattern. (B) This ratio (black triangles) decreases with light 890

intensity and is negatively correlated with both bacterial production (white squares) and 891

Chl a (gray circles) in daytime samples (Pearson’s r = -0.81 and -0.84, respectively). No 892

correlation of this ratio with any parameters was observed in night samples (data not 893

shown). 894

895

Figure 6. Rhodopsin gene and transcript abundance. (A) Ratio of rhodopsin genes to 896

rplB genes in metagenomic data sets. This ratio is higher in the smaller size fraction and 897

suggests that nearly all of the small planktonic microbes in the mid-salinity zone of the 898

Chesapeake Bay encode rhodopsins. (B) Ratio of rhodopsin transcripts to rplB transcripts. 899

Rhodopsin is much more highly expressed than rplB, especially in the mid-salinity zone. 900

901

Figure 7. Taxonomic affiliations of rhodopsin genes and transcripts in the 902

Chesapeake Bay. Relative abundance of rhodopsin genes and transcripts affiliated with 903

specific phyla is plotted against salinity. DNA and RNA were sequenced from the large 904

(> 0.8 µm) size fractions at three salinities (0.1, 15, and 31 ppt), and from the small (< 0.8 905

µm) size fraction at 15 and 31 ppt. SAR11-type proteorhodopsins are the most abundant 906


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42

rhodopsin type in the small size fractions, while rhodopsin transcripts from Bacteroidetes 907

are highly abundant in the larger size fractions. 908

909

Figure 8. Phylogeny of rhodopsins found in the Chesapeake Bay. SAR11-associated 910

proteorhodopsins in the Chesapeake Bay includes at least 7 subtypes of rhodopsins, and 911

Bacteroidetes-associated rhodopsins are similarly diverse. All identified rhodopsins in 912

these datasets belong to families of proton-pumping rhodopsins, so sensory rhodopsins 913

were used as the outgroup. 914


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*

< 0.8 μm > 0.8 μm

0.1 ppt

15 ppt

31 ppt

Actinobacteria

Other

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Bacteroidetes


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Primer name Sequence (5’ to 3’) Target and reference

Product size (bp)

Annealing temp.

LG1_F1 TAYMGNTAYGTNGAYTGG Actino-

rhodopsin (15)

300 46.6 oC LG1_F2 MGNTAYATHGAYTGGYT

LG1_R ATNGGRTANACNCCCCA

SARPR_125F THGGWGGATAYTTAGGWGAAGC SAR11 Proteo-

rhodopsin (8)

200 54 oC

SARPR_203R ACCTACTGTAACRATCATTCTYA

BACT1369F CGGTGAATACGTTCYCGG 16S rRNA (79)

300 - 350 58 oC

PROK1541R AAGGAGGTGATCCRGCCGCA


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Total number of reads rplB reads Rhodopsin reads

Sample DNA RNA1 RNA2 DNA RNA1 RNA2 DNA RNA1 RNA2

0.1 ppt

salinity,

large size

fraction

275,115,509 244,957,573 221,016,604 3,024 849 253 861 699 330

15 ppt

salinity,

large size

fraction

46,892,062 202,188,144 188,686,852 273 207 71 222 1,030 736

15 ppt

salinity,

small size

fraction

112,382,916 224,879,392 215,379,319 1,527 5,295 4,673 1,630 77,284 81,400

31 ppt

salinity,

large size

fraction

163,930,010 206,846,939 211,972,827 637 299 184 379 1,080 635

31 ppt

salinity,

small size

fraction

73,255,605 227,496,320 176,122,914 1,031 10,416 9,389 761 71,570 66,651


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Sample

Cells with rhodopsins

(TIRF)a

Genomes with

SARPR (qPCR)

b

Genomes with LG1 (qPCR)

b

Genomes with rhodopsins

(metagenomes)c

Genomes with SARPR

(metagenomes)

Genomes with actinorhodopsin (metagenomes)

15 ppt salinity,

<0.8 µm size

fraction

15% 39.8% 0.75% 107% 63% 23%

31 ppt salinity,

<0.8 µm size

fraction

40.3% 122% 0.77% 74% 36% 4%

aPercent of cells with functional rhodopsins, calculated as

100 ✕ �� ℎ 1 � ��− �

bPercent of genomes encoding SARPR or LG1-type rhodopsins, as estimated by qPCR analysis.

Calculated as 100 ✕ � ℎ �� 16�1.9

cPercent of genomes encoding any rhodopsin gene, estimated from metagenomics data. Calculated as

100 ✕ ℎ �


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