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Towards Better Understanding of EBPR Systems via Linking Raman-Based Phenotypic Profiling with Phylogenetic Diversity Yueyun Li, Helen A. Cope, Sheikh Mokhlesur Rahman, Guangyu Li, Per Halkjær Nielsen, Alistair Philip David Elfick, and April Z Gu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01388 • Publication Date (Web): 26 Jun 2018 Downloaded from http://pubs.acs.org on June 28, 2018

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Environmental Science & Technology

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Towards Better Understanding of EBPR Systems via Linking

2

Raman-Based Phenotypic Profiling with Phylogenetic Diversity

3

Yueyun Li1, Helen A. Cope2, Sheikh M. Rahman1, Guangyu Li1, Per Halkjær Nielsen3,

4

Alistair Elfick2, and April Z. Gu 1,4*

5

1

Civil and Environmental Engineering Department, Northeastern University, Boston, MA 02115, USA

6 7

2

School of Engineering, Institute for Bioengineering, The University of Edinburgh, Edinburgh, UK

8 3

9

Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark

10 11

4

School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA

12 *

Corresponding author, E-mail: [email protected]

13 14

_____________________________________________________________________

15

Abstract

16

This study reports a proof-of concept study to demonstrate the novel approach of

17

phenotyping microbial communities in enhanced biological phosphorus removal

18

(EBPR) systems using single cell Raman microspectroscopy and link it with

19

phylogentic structures. We use hierarchical clustering analysis (HCA) of single-cell

20

Raman spectral fingerprints and intracellular polymers signatures to separate and

21

classify the functionally relevant populations in EBPR systems, namely

22

polyphosphate accumulating organisms (PAOs) and glycogen accumulating

23

organisms (GAOs), as well as other microbial populations. We then investigated the

ACS Paragon Plus Environment


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link between Raman-based community phenotyping and 16S rRNA gene-based

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phylogenetic characterization of four lab-scale EBPR systems with varying SRTs to

26

gain insights into possible genotype-function relationships. Combined and

27

simultaneous phylogenetic and phenotypic evaluation of EBPR ecosystems revealed

28

SRT-dependent phylogenetic and phenotypic characteristics of the PAOs and GAOs,

29

and their association with EBPR performance. The phenotypic diversity and plasticity

30

of PAO populations, which otherwise could not be obtained with phylogenetic

31

analysis alone, showed complex but potentially crucial association with EBPR

32

process stability.

33

TOC:

34 EBPR-Wastewater Treatment Anaerobic

Aerobic

System Performance Stability

Microbial Community Structure Functional Groups

Matrix correlation analysis

Hierarchical clustering

Function---Single-cell Raman Fingerprints Raman Intensity

Phylogeny--- Amplicon Sequences

35 36 37 38


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39 40

Introduction The enhanced biological phosphorus removal (EBPR) process has been

41

1, 2

42

widely applied to remove and recover phosphorus from waste water.

43

underlying ecological physiology that governs EBPR performance and stability is still

44

not well-understood, which hampers wide and effective applications of EBPR

45

systems.

46

have been attempted with functional omics approaches (meta-proteomics, meta-

47

metabolomics, etc.), stable isotope probing techniques such as secondary ion mass

48

spectrometry

49

microautoradiography (MAR) and combined Raman-FISH technique.

50

the potential of meta-omics to deduce phylogeny-function relationships is still rather

51

limited, particularly for high-diversity ecosystems.

52

limitations because they target only phylogenetically identified microbial groups.

53

Recent studies on EBPR biochemical pathways suggested that metabolic and

54

functional diversities within PAOs and GAOs are likely to be greater than is currently

55

known.

56

methods that can elucidate the phenotypic and metabolic diversity among EBPR

57

microbial populations.

3

The

Efforts to link EBPR activities with the microbial population community

12-14,15, 16

(SIMS),

fluorescence in

situ hybridization

10

(FISH)4-9

However,

FISH-based methods also have 11

These challenges and unknowns mandate development of new

58

The rapid development and increasing throughput of next generation

59

sequencing (NGS) platforms have facilitated major advances in our understanding of

60

taxonomic composition and phylogenetic diversity of microbial communities of

61

natural and engineered ecological systems.

62

of new phylotypes that are classified based on their molecular signatures alone,

17, 18 19, 20


There are an increasing number


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63

however, many of these have no cultured representatives available yet for further

64

physiological characterization. 21 To elucidate the functions of uncultured phylotypes,

65

various culture-independent technologies have been demonstrated, including

66

functional

67

metaproteomics) and stable isotope probing.

68

methods, such as nuclear magnetic resonance (NMR), Fourier Transform Infrared

69

(FTIR) and Raman microspectroscopy, have also shown great potential in assisting

70

the exploration of cellular functional characteristics in complex environmental

71

ecosystems. 24, 25

72

gene-based

microarrays,

meta-omics 21-23

(e.g.

metametabolomics,

Alternatively, spectroscopic

Raman-based phenotypic analysis has been successfully applied as a powerful 26, 27

73

tool for single-cell microbial identification with minimum sample preparation.

74

Early works with Raman spectroscopy to discriminate specific microbial populations

75

were based on small numbers of selected and identified peaks to analyze targeted

76

cellular functional groups such as polymers (glycogen, PolyP, etc.), and proteins. 28, 29

77

Recent studies have focused on the use of multivariate statistics (e.g. hierarchical

78

clustering analysis (HCA)) to evaluate the full Raman spectral signature across all

79

collected wavenumbers (cm-1) since this offers high discrimination power.

80

spectra yield fingerprint-like information about all chemical components within one

81

cell and, in combination with multivariate methods, offer the potential to reveal

82

physiological diversity of microbes in complex environmental ecosystems. In

83

addition, the ability to differentiate and classify organisms down to species or even

84

strain level has been demonstrated. 31, 32


30

Raman

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85

Our group has previously developed methods for identification and

86

quantification of PAOs and GAOs via simultaneous detection of the PolyP,

87

polyhydroxyalkanoate (PHA) and glycogen peaks in single-cell Raman spectra.

88

34

89

based phenotypic characterization. In this study, we explored the capacity of single-

90

cell Raman fingerprinting for phenotypic characterization of an EBPR community.

91

We then investigated the link between Raman-based community phenotyping and

92

16S rRNA gene-based phylogenetic characterization of four lab-scale EBPR systems

93

to gain insights into possible genotype-function relationships. Phenotypic diversity

94

among EBPR microorganisms was successfully revealed from the multivariate

95

analysis of single-cell Raman signatures. The congruence of the phenotypic and

96

phylogenetic diversity was, for the first time, probed to demonstrate how

97

phylogenetic and phenotypic diversity together could provide more comprehensive

98

insights than each alone and, therefore contributes to the better understanding of

99

important engineered microbial ecosystems such as EBPR.

28, 33,

This makes EBPR an ideal system for further exploration of the single-cell Raman

100

Materials and Methods

101

Lab-scale EBPR systems

102

Four lab-scale sequencing batch reactor (SBR) EBPR systems, originally inoculated

103

at the same time with activated sludge from a facility in US, were operated in parallel

104

with solid retention times (SRTs) of 5, 10, 20 and 30 days, respectively. The details

105

of SBRs operation was as described in previous publications.

106

controlled at room temperature of 20-22ºC and operated with HRT of 12 hours, with

107

four 6-hour cycles per day and each cycle consisted of: 7 minutes fill followed by

108

110 minutes of anaerobic phase, 180 minutes of aerobic phase, 58 minutes of settling


28, 33-35

The SBRs were


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109

and then 5 minutes of withdrawing. Phosphorus was added as sodium phosphate

110

monobasic (NaH2PO4 • 2H2O) to 8 mg-P/L. A total chemical oxygen demand (COD)

111

of 200 mg/L was added to each cycle, containing 312 mg/L sodium acetate

112

(CH3COONa • 3H2O) with supplement of 30 mg/L of casamino acids and 8 mg/L

113

yeast extract.28, 33-35 The influent feed COD (chemical oxygen demand) / bioavailable

114

phosphorus P mass ratio was kept at 25 mg COD/mg P. Inorganic nitrogen (8 mg/L)

115

was added as ammonium chloride (NH4Cl). Allylthiourea was added at 4 mg/L of

116

feeding to inhibit nitrification.36 Each SBR was operated for at least three times the

117

SRT length to achieve steady-state prior to performance and population analysis.

118

EBPR performance monitoring and methods for chemical analysis of soluble

119

orthophosphate (ortho-P) as P (PO43--P), acetate, nitrate (NO3-), nitrite (NO2-) and

120

ammonium as N (NH4+-N) concentrations were described previously

121

summarized in supporting information. Total suspended solids (TSS) and volatile

122

suspended solids (VSS) were determined weekly according to Standard Methods

123

(methods 2540B and 2540E, respectively).37

28

and

124

Long-term stability of EBPR performance was measured by the median P

125

removal efficiency and the cumulative frequency of effluent ortho-P being below 1

126

mg P/L (Table S1), since most wastewater treatment plants in the U.S. need to meet

127

an effluent P limit of 1 mg P/L.38

128

Raman Spectra Acquisition and Data Pre-processing

129

From each SBR, mixed sludge samples were collected from the end of aerobic phase

130

at one time point during stable operation, diluted and homogenized by a 26 gauge

131

syringe needle and then dried on optically polished CaF2 windows (Crystran Ltd,


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39

132

Dorset, UK) as described previously.

133

Raman microscope (Renishaw plc, UK) equipped with a 100x/0.9 NA objective

134

(11566202, Leica Microsystems, Germany). Excitation (50 mW at the sample) was

135

provided by a 785 nm diode laser (Toptica, Germany). Spectra were collected from

136

400 to 1800 cm-1in extended scan mode, with a dwell time of 200 seconds and a 1200

137

line/mm diffraction grating. A total of 168 Raman spectra with 40-50 single cells

138

(from ~10 microscopic fields of well-dispersed cells) from each of the four reactors

139

were analyzed. The statistical sufficiency of the sampling size was evaluated with

140

radial based function kernel and Eigen-decomposition method

141

sample size is sufficient for assessing the Raman-based phenotypic diversity in our

142

lab-scale EBPR reactors

143

recommended by He et al.

144

variability arising from cosmic rays using the Renishaw software (WiRE™ 2.0),

145

smooth background substrate signal using a Savitzky-Golay filter and subsequently

146

vector correction according to Maquelin et al. 41 and remove the baseline via Small-

147

Window Moving Average (SWiMA) method described by Schulze et al. 42

148

Multivariate Analysis of Pre-processed Raman Spectra

149

The whole wavenumber region, 400 to 1800 cm-1, of each pre-processed Raman

150

spectrum as described above was used for the multivariate analysis.

151

multivariate analysis procedure consisted of two steps: spectral transformation and

152

classification. To ensure comparability between the spectra, and to correct for

153

potential minor variation in spectral resolution and quality, each pre-processed

154

spectrum was transformed by binning the Raman intensity over regions of size 5 cm-1,

28

40

Raman spectra were acquired with an InVia

40

(Figure S1-a). The

(Figure S1-b), and is consistent with what has been Each spectrum was pre-processed to remove unwanted


43

The


30

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155

as suggested by Webb-Robertson et al.

156

analysis, HCA, using correlation coefficient as the distance measuring criteria, was

157

performed on all of the single-cell Raman spectra collected from the four EBPR

158

systems to classify these Raman spectra, based on intra-spectral similarities. A

159

dendrogram was derived from the HCA that depicts different groups of Raman

160

spectra with similar features. Here we defined the major Raman-classified groups as

161

the operational phenotypic units (OPUs) (Figure 1a), which could be further broken

162

down into sub-OPUs. The OPU is defined based on cluster analysis of Raman

163

spectra, using the same rationale as that of the widely accepted OTU, where OTU is

164

defined based on similarities between nucleotide sequences and OPU is defined based

165

on similarities of single-cell Raman spectra. The cut-off distance for sub-OPUs was

166

set at 0.7.

167

MATLAB 2014a (Statistics and Machine Learning Toolbox).

168

Raman-based PAOs and GAOs identification

169

As described in our previous study28, candidate PAOs and GAOs were identified by

170

the expected intracellular polymers Raman spectra, based on the detection of PolyP,

171

glycogen and PHA signature peaks (Table 1). Briefly, cells with the two PolyP

172

signature peaks, 690-700 cm-1 for P-O-P vibrations and 1168-1177 cm-1 for PO2-

173

stretching band, were assigned as candidate PAOs, and cells with intracellular

174

glycogen (480 cm-1, ~852 cm-1 and ~937cm-1 34) but no PolyP peaks, were assigned as

175

candidate GAOs. 28 The presence of PHA in PAO or GAO cells was identified by the

176

three signature peaks at ~433 cm-1, 830 cm-1, and 1730 cm-1.

177

glycogen and PHA peaks, Raman peaks for other biochemical cellular constituents

44

An unsupervised multivariate statistics

Clustering analysis and dendrogram plotting were performed in


34

Besides PolyP,

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178

(i.e. fatty acids, saccharides, DNA/RNA, proteins and primary/ other metabolites)

179

were also identified according to literature. 43

180

Table 1. Raman wavenumber assignments of PolyP， glycogen, PHA signature

181

peaks based on literature. 43 PolyP

Glycogen

PHA

Signature Peaks

690-700 cm-1,

480 cm-1, ~852

~433 cm-1, 830 cm-1,

Position, cm-1

1168-1177 cm-1

cm-1 and ~937cm-1

and 1730 cm-1

182 183

Phylogenetic Analysis with 16S rRNA Gene Targeted Amplicon Sequencing

184

Genomic DNA was extracted using the Fast-DNA Spin kit for Soil (Bio101, Vista,

185

CA, USA). Amplicon sequencing on the variable region 1 to 3 (V1-3) of the bacterial

186

16S rRNA gene was conducted with the V1-3 primer pair (27F: 5’-

187

AGAGTTTGATCCTGGCTCAG-3’,

188

3’) using the following procedures:

189

in duplicate with Platinum High Fidelity Taq Polymerase (Invitrogen), with an initial

190

denaturation step for 2 minutes at 95°C followed by 30 cycles of denaturing (95°C for

191

20 seconds), annealing (56°C for 30 seconds), and elongation (72°C for 60 seconds)

192

steps, ending with 5 minutes of final extension at 72°C. DNA concentrations were

193

measured using the Quant-iT HS DNA Assay Kit (Molecular Probes); then the

194

barcoded amplicons were pooled in equimolar amounts and paired-end sequenced on

195

an Illumina MiSeq (Illumina Inc.) using a MiSeq Reagent Kit v3 (2 × 300 paired

196

end).

45

534R:

5’-ATTACCGCGGCTGCTGG-

First, the PCR amplifications were carried out



197

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The raw 16S rRNA gene sequence data (NCBI accession number of 45

198

SRP077653) was processed using QIIME v.1.4.0

199

check quality, remove singletons and cluster merged reads into unique operational

200

taxonomic units (OTUs) (97% similarity) level. Default parameters were used in all

201

cases except that to assign taxonomy, for which the Ribosomal Database Project

202

(RDP) classifier 46 was used with the SILVA database (http://www.arb-silva.de/).

203

PolyP-DAPI Staining for PAO Quantification

204

The differential staining of PAOs and non-PAOs was performed with 4',6-Diamidino-

205

2-phenylindole (DAPI) at 50 µg/mL for 1 min.

206

distributions was carried out using the software DAIME version 2.0. 48 Around 20-25

207

separate randomly chosen images were evaluated to reflect the cumulative

208

biovolumetric fractions of total PAOs among all cells.

209

FISH Analysis

210

To visualize and quantify the abundance of the identified candidate PAO/GAO

211

groups, sludge samples were fixed and hybridized with FISH probes as described

212

previously (Table S2).38, 49 After hybridization, the slides were counterstained with 1

213

µg/mL of DAPI solution for 3 min to quantify total cells. DAPI counterstain instead

214

of EUB mix probe was employed to quantify the total cells, to be consistent with the

215

total PAO quantification with PolyP-DAPI staining. 20-25 separate randomly chosen

216

images were observed with an epifluorescent microscope (Zeiss Axioplan 2, Zeiss,

217

Oberkochen, Germany). Quantification of population distributions were carried out

218

using the software DAIME version 2.0. 48


47

to demultiplex the sequences,

Quantification of population

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219

Correlation Analysis between Phenotypic and Phylogenetic Profiles of EBPR

220

Communities

221

Principal component analysis (PCA) with MATLAB 2014a (Statistics and Machine

222

Learning Toolbox) was performed on the relative abundances of the 12 Raman-based

223

sub-OPUs in the four EBPR ecosystems to reduce dimensionality and to reveal the

224

phenotypic profiling pattern among these four EBPR ecosystems. A parallel PCA

225

analysis was also performed on the relative abundances of each 16S rRNA gene

226

based OTUs retrieved from the four EBPR ecosystems to reveal the phylogenetic

227

separation patterns of the four EBPR communities. To statistically quantify the

228

relationship between the phenotypic and phylogenetic separations among the four

229

EBPR ecosystems, we applied a statistical analysis described by Fierer et al.

230

correlating two distribution matrixes (i.e. two PCA plots here). Pearson correlation

231

was assessed between scores on the first principal components that explained the

232

greatest variance for each distribution matrix.

233

Results and Discussion

234

Raman-based Phenotypic Classification of Microorganisms in EBPR

235

Single cell Raman spectra captured features from key functionally-relevant

236

intracellular polymers to EBPR (PolyP, glycogen, PHA) and from other biochemical

237

cellular constituents (i.e. fatty acids, saccharides, DNA/RNA, proteins and primary/

238

other metabolites) allowed for cellular phenotypic profiling and clustering. HCA

239

analysis of all single cell Raman spectra retrieved from the four EBPR reactors

240

yielded 4 major groups, namely OPU1 to OPU4, and 12 sub-OPUs (Figure 1a, 1b).

241

The Raman-based relative abundance quantification of PAOs (58-71% by cell count)


50

for


242

in the four EBPR systems showed similar trends but with higher abundance than

243

those determined from DAPI-PolyP staining (35-57% by biovolumetric fractions,

244

Figure S2). The discrepancy could also be explained by possible ignorance of hardly

245

distinguishable cells for Raman analysis, sensitivity and specificity limits of DAPI

246

staining method.51 The Raman-based quantification of candidate GAOs (2-17% by

247

cell count) could not be verified independently because there is currently no other

248

phenotype-based method available for total GAO quantification. 28

249

Most (91%) Raman-identified PAOs clustered into four groups, namely

250

OPU1, OPU2g, OPU3 and OPU4 (Figure 1b). Most candidate GAOs clustered in

251

OPU2d. The sub-OPUs 2a, 2b and 2c were composed mostly of non-PAO and non-

252

GAO cells, since no PolyP or glycogen characteristic peaks were detected in their

253

Raman spectra. OPU2e and OPU2f included mixtures of PAOs, GAOs, non-PAOs

254

and non-GAOs (Figure 1a, b). The phenotypic diversities within PAOs/GAOs were

255

revealed by comparing the mean spectra calculated from single-cell spectra within

256

each sub-OPU (Figure 2a, 2b).

257


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a)12000

Glycogen

Glycogen PolyP

PolyP

OPU4a

6000 0 12000

OPU4b

6000 0 12000

OPU3a

6000 0 12000

OPU3b 6000 0 12000

OPU2a

6000

CCDCounts

0 12000

OPU2b

6000 0 12000

OPU2c

6000 0 12000

OPU2d

6000 0 12000

OPU2e

6000 0 12000

OPU2f 6000 0 12000

OPU2g 6000 0 12000

OPU1

6000 0

400

258 259 260 261 262

600

800

1000

1200

1400

1600

1800

Wavenumbers (cm-1)

Figure 1a) Pre-processed single-cell Raman spectra collected from the four lab scale EBPR systems, clustered by HCA into each subOPUs, with the signature peaks for glycogen and PolyP labelled. b) Dendrogram from HCA of single-cell Raman spectra, with major and sub-OPUs labeled. The spectra and dendrogram are color-coded based on Raman analysis as follows: pink – candidate PAOs; blue – candidate GAOs; black – other cells).



263

Phenotypic diversity among PAOs sub-OPUs: We further compared and

264

tentatively characterized the different sub-OPUs of PAOs, based on the predominant

265

and functionally relevant intracellular polymers, namely PolyP, glycogen, and PHA

266

peaks (Table 1). The results implied great phenotypic diversity among PAO sub-

267

groups.

268

The Raman intensities of the two PolyP signature peaks in each individual

269

PAO cells could be used as an indication of the cellular PolyP storage content, which

270

was found to vary greatly among PAO sub-OPUs. The highest mean PolyP peak

271

intensities were observed in OPU4-PAOs, followed by OPU1-PAOs, OPU3-PAOs,

272

and the lowest intensities in PAOs within OPU2. Further investigation would be

273

needed to determine whether our observation of the PolyP level heterogeneity

274

resulted from external (e.g. different life histories, flow hydraulics etc.) or internal

275

(e.g. phylogeny, metabolism etc.) mechanisms. 52


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276 277 278 279 280 281 282 283 284 285 286

Figure 2 Mean spectra calculated from pre-processed single-cell spectra of a) PAOs and b) GAOs in each sub-OPU. Spectra are vertically shifted for clarity. The tentative assignments of vibrational modes (stretches: υ; deformations: δ/def) for the EBPRfunctionally relevant peaks (PolyP, glycogen), as well as the general characteristic regions for structural/metabolic characteristic peaks (i.e. fatty acids, saccharides, DNA/RNA, proteins and primary or other metabolites) are labelled 43. The following peaks were used for PolyP, glycogen and PHA identification: PolyP – 690-700 cm-1 and 1168-1177 cm-1, 33 glycogen – 480 cm-1, ~852 cm-1 and ~937cm-13;4 PHA- ~433 cm-1, 830 cm-1, and 1730 cm-134. The sub-OPUs (2a, 2b and 2c) that do not contain any candidate PAOs or GAOs are not shown.

287 288 289 290



291 292

Table 2 Summarized possible phenotypic variations among different sub-OPUs of PAOs, indicated by PolyP peaks intensities, PolyP peak position shift and glycogen peak intensities.

PAO sub-OPU

4a

4b

293 294

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Phenotypic characteristics1 High PolyP content; Short PolyP length; No glycogen

High PolyP content; Long PolyP length; No glycogen

3a

Medium PolyP content; Medium PolyP length; Medium glycogen

3b

Medium PolyP content; Medium PolyP length; High-Medium glycogen

2d

Low PolyP content; Long PolyP length; High glycogen

2e

Low PolyP content; Long PolyP length; Low glycogen

2g

Median PolyP content; Long PolyP length; Low glycogen

1

Median PolyP content; Median PolyP length; No glycogen

PolyP content 2

High (4359, 5436)

High (4253, 5841)

Medium (1681, 2142)

Medium (2002, 2488)

Medium (1496, 2108)

Low (883, 1189)

Medium (1714, 2502) Medium (2205, 2888)

1

PolyP length3 Short (458)

Long (477)

Glycogen content4

BD5

BD5

Medium (466)

Medium (449)

Medium (463)

Medium (636)

Long (474)

Long (476)

Long (479)

Medium (468)

High (1031)

Low (187)

Low (206)

BD5

Summarized phenotypic characteristics indicated from PolyP peak intensities, PolyP peak position shift and glycogen peak intensities.


Page 17 of 36


295 296 297 298 299 300 301 302 303 304

2

Mean PolyP intensities (CCD counts) at ~690 cm-1 for P-O-P vibrations and ~1170 cm-1 for PO2- stretching bands are shown in parenthesis: (Intensity for P-O-P vibrations, Intensity for PO2- stretching bands). See detailed PolyP intensity ranging data in Table S3. 3 Larger PolyP peak position distance potentially indicates longer PolyP chain length. Mean distance between PolyP peak position (cm-1) for P-O-P vibrations and for PO2- stretching bands are shown in parenthesis. See detailed PolyP position ranging data in Table S3. 4 Increased ranking number for higher glycogen intensity. Mean glycogen intensity (CCD counts) at 480 cm-1 are shown in parenthesis. Please see detailed glycogen intensity ranging data in Table S3. 5 BD: below detection limit.

305

In addition to the intensity of PolyP signature peaks, the distance between the

306

two PolyP peaks has been suggested to be associated with PolyP structural changes

307

such as variation in the length of PolyP chains

308

with other molecules, such as proteins.

309

reported to be associated with EBPR performance and stability,

310

linked with different mechanisms and enzymes involved in PolyP metabolism.

311

Single-cell level Raman-based phenotyping revealed possibly varying PolyP chain

312

lengths among PAO sub-OPUs (Figure 2a)

313

concentrations of cellular PolyP content, such as those in OPU-4a and 4b, exhibited

314

varied PolyP chain length and/or structure, therefore implying possibly distinct

315

effects on EBPR performance.

53

33

and complexation of PolyP chains

The PolyP chain length in PAOs has been 54

and might be 53

The PAOs that contained similar

316

In addition to PolyP intensity, the varying amount of intracellular glycogen

317

among PAOs belonging to different sub-OPU clusters was also revealed (Figure 2a).

318

The intracellular glycogen content (measured at the end of aerobic phase) has been

319

suggested to be an indicator for the extent of involvement of the glycolysis pathway

320

during the EBPR anaerobic phase for energy and reducing power supply with

321

implications for EBPR performance.

322

OPU2d was found with a detectable level of PHA) as expected and noticed in our

28,55

PHA level was low (only one cell from



34

323

previous study

324

internal PHA storage.

Page 18 of 36

because PAOs at the end of aerobic phase would have utilized the

325

In addition to the EBPR functionally-relevant polymers discussed above, the

326

information-rich Raman fingerprints over the whole spectral region allows for high-

327

resolution phenotypic separation and classification in consideration of cellular

328

metabolic features, which may be directly or indirectly associated with EBPR

329

function. In addition to EBPR function-relevant intracellular polymers peaks, the

330

cellular fatty acid profile revealed from Raman spectra, could potentially be used as a

331

biomarker for detecting rapid microbial populations changes.56 The previously

332

identified Raman signatures for DNA/RNA (600-800 cm-1), saccharides (1000-1200

333

cm-1), proteins (1200-1800 cm-1) and primary (intermediates of TCA cycle etc.) /other

334

metabolites (700-800 cm-1) 43, although overlapping with the two intense PolyP peaks

335

(690, 1170 cm-1) of PAO cells, could still represent other cellular phenotypic features.

336

For example, OPU4a-PAOs were distinct from all the other PAOs, partly by their

337

higher intensities at 970cm-1 and 1043 cm-1 (Figure 2a), which might be possibly

338

corresponding to the asymmetric υ(POC) stretch of phosphate functional group,

339

and pseudosymmetric υ(C=C=O) stretch, 34 respectively.

340

Phenotypic diversity among GAO sub-OPUs: Compared to PAOs, the analyzed

341

GAO cells from our EBPR systems showed more conserved phenotypic

342

characteristics based on our Raman signature analysis. This was indicated by the

343

considerably less variations among GAO spectra (Figure 2b) compared with PAO

344

spectra (Figure 2b). PAO phenotypes clustered in 8 sub-OPUs whereas there were

345

only two sub-OPUs containing GAOs: OPU2d and OPU2f. OPU2d-GAOs were


43

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346

found to have higher glycogen content than OPU2f-GAO (Figure 2b). In addition,

347

variation in other spectral regions were observed between the two sub-OPUs of

348

GAOs.

349

Phylogenetic Characterization of EBPR Microorganisms

350

Phylogenetic characterization of the four EBPR systems were performed with both

351

FISH and 16S rRNA gene based amplicon sequencing (Figure 3). In our EBPR

352

systems, the most dominant phylum was Proteobacteria (54-91%), followed by

353

Bacteroidetes (5-20%), and within Proteobacteria, Betaproteobacteria was the

354

dominant class (Figure 3a). Note that these are relative abundances based on the 16S

355

rRNA gene counts, which could be affected by extraction bias, PCR bias as well as

356

the bacterial rRNA operon copy number

357

equivalent to microbial cell abundance.

57

, therefore they are not necessarily



a) 100 80 60 40 20 0

Relative Abundance (%)

b)100

5

10

20

30

5 days 10 days 20 days 30 days

80 60 40 20 0

c) 20

c__Alphaproteobacteria c__Betaproteobacteria c__Epsilonproteobacteria c__Deltaproteobacteria c__Gammaproteobacteria unclassified proteobacteria p__Candidate division TM7 p__Bacteroidetes p__Actinobacteria sum of minor phyla(

Towards Better Understanding of EBPR Systems ... - ACS Publications

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