Design and engineering of E. coli metabolic sensor strains with a wide sensitivity range for glycerate

Journal Pre-proof Design and engineering of E. coli metabolic sensor strains with a wide sensitivity range for glycerate Selçuk Aslan, Elad Noor, Sara Benito Vaquerizo, Steffen N. Lindner, Arren Bar-Even PII:

S1096-7176(19)30198-3

DOI:

https://doi.org/10.1016/j.ymben.2019.09.002

Reference:

YMBEN 1599

To appear in:

Metabolic Engineering

Received Date: 4 May 2019 Revised Date:

5 August 2019

Accepted Date: 2 September 2019

Please cite this article as: Aslan, Selç., Noor, E., Vaquerizo, S.B., Lindner, S.N., Bar-Even, A., Design and engineering of E. coli metabolic sensor strains with a wide sensitivity range for glycerate, Metabolic Engineering (2019), doi: https://doi.org/10.1016/j.ymben.2019.09.002. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc. on behalf of International Metabolic Engineering Society.

1

Design and engineering of E. coli metabolic sensor strains with a wide

2

sensitivity range for glycerate

3

Selçuk Aslan1, Elad Noor2, Sara Benito Vaquerizo1, Steffen N. Lindner1, Arren Bar-Even1*

4

1

5

2

Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany Institute of Molecular Systems Biology, ETH Zürich, Otto-Stern-Weg 3, 8093 Zürich, Switzerland

6 7

*

8

Arren Bar-Even: phone: +49 331 567-8910; Email: [email protected]

Corresponding author

9 10

Running title: Metabolic sensor strains

11 12

Keywords: Auxotrophy / Constraint-based metabolic model / Growth selection / Synthetic biology

1

13

Abstract

14

Microbial biosensors are used to detect the presence of compounds provided externally or produced

15

internally. The latter case is commonly constrained by the need to screen a large library of enzyme or

16

pathway variants to identify those that can efficiently generate the desired compound. To address this

17

limitation, we suggest the use of metabolic sensor strains which can grow only if the relevant compound is

18

present and thus replace screening with direct selection. We used a computational platform to design

19

metabolic sensor strains with varying dependencies on a specific compound. Our method systematically

20

explores combinations of gene deletions and identifies how the growth requirement for a compound

21

changes with the media composition. We demonstrate this approach by constructing a set of E. coli

22

glycerate sensor strains. In each of these strains a different set of enzymes is disrupted such that central

23

metabolism is effectively dissected into multiple segments, each requiring a dedicated carbon source. We

24

find an almost perfect match between the predicted and experimental dependence on glycerate and show

25

that the strains can be used to accurately detect glycerate concentrations across two orders of magnitude.

26

Apart from demonstrating the potential application of metabolic sensor strains, our work reveals key

27

phenomena in central metabolism, including spontaneous degradation of central metabolites and the

28

importance of metabolic sinks for balancing small metabolic networks.

2

29

Introduction

30

Microbial biosensors are gaining prominence as valuable tools for detecting specific environmental

31

components such as toxic pollutants (Paitan et al., 2004; Trang et al., 2005), explosives (de las Heras et al.,

32

2008), and pathogens (Saeidi et al., 2011). Rather than relying on external cues, biosensors can be used to

33

detect internally produced compounds, thus assisting in optimizing the activity of biosynthetic routes. By

34

engineering molecular biosensors for pathway intermediates or products – e.g., transcription factors or

35

riboswitches – it is possible to screen thousands of different strains in short time (e.g., by flow cytometry),

36

and identify variants that support high pathway flux (Jaffrey, 2018; Liu et al., 2015; Williams et al., 2016;

37

Zhang et al., 2015). However, such high throughput screening can still be challenging, especially if the

38

tested library is very large.

39

In some cases, it is possible to use dedicated gene-deletion strains, auxotrophic for a compound, in order to

40

couple growth to the presence of this compound (He et al., 2018; Mainguet et al., 2013; Meyer et al., 2018;

41

Yishai et al., 2018; Yu and Liao, 2018). Such strains can replace screening techniques by direct selection to

42

identify the few biosynthetic variants whose activity is high enough to produce the relevant compound at

43

sufficiently high amounts, thus enabling growth. However, in most cases, it is difficult to fine-tune the level of

44

the selection strength. Hence, selection-based testing is usually a binary process, with growth observed

45

above a threshold level of the relevant compound.

46

One solution to this sensitivity challenge is to construct a series of gene-deletion strains, each displaying a

47

different dependency on the relevant compound to support growth. The growth phenotype of the different

48

strains can be used to accurately estimate the availability of the compound in question. Such ‘metabolic

49

sensor strains’ can be used to detect an externally available chemical or to provide quantitative information

50

on the activity of the biosynthetic pathway producing this compound.

51

Here, we use a computational procedure based on flux coupling (Antonovsky et al., 2016; Jensen et al.,

52

2019) for the design of metabolic sensor strains with varying dependencies on a given compound, which

53

can be further modulated by controlling the composition of the growth medium. As a first application of this

54

approach, we chose to design biosensors for glycerate – the expected metabolic product of recently

55

suggested synthetic photorespiration bypass routes which do not release CO2 while assimilating Rubisco’s

56

oxygenation product, 2-phosphoglycolate, into the Calvin Cycle (Trudeau et al., 2018). These pathways are

57

expected to enhance the carbon fixation rate of C3 plants under all relevant physiological conditions

58

(Trudeau et al., 2018). The glycerate sensor strains will thus provide a platform to test and characterize the

59

activity of these synthetic pathways.

60

Following the results of our model, we generated six gene-deletion strains, in which central metabolism is

61

divided into several segments. In each of these, glycerate serves as a carbon source for a different fraction

62

of cellular biomass. We show that these strains are sensitive to both exogenous supply and endogenous 3

63

production of glycerate. We systematically characterize the dependence of each strain on the concentration

64

of glycerate and demonstrate that the different strains span two orders of magnitude sensitivity towards this

65

compound. Importantly, we demonstrate an almost perfect correlation between the measured growth

66

dependencies on glycerate to those predicted by the computational platform. Our study therefore

67

demonstrates the applicability of metabolic sensor strains to provide an easy readout – growth yield – under

68

a wide range of compound concentrations.

69

Results

70

Computational model for the design of metabolic bio-sensor strains

71

Glycerate is assimilated in E. coli via the activity of glycerate 2-kinase (Zelcbuch et al., 2015), generating the

72

glycolytic metabolite 2-phosphoglycerate. To generate glycerate sensor strains, we aimed at strategic gene

73

deletions to isolate this metabolite from other segments of central metabolism. By doing so, we would be

74

able to control which essential cellular building blocks is derived from glycerate and which will be

75

synthesized from other carbon sources, thus determining cellular demand for glycerate.

76

To systematically identify potential glycerate sensor strains we used a dedicated computational tool. We

77

started with the core metabolic model of E. coli (Orth et al., 2009) and, as described in the Methods, we

78

modified it for our needs. In particular, we introduced the biosynthetic pathways for serine, glycine, and one-

79

carbon moieties, as these are derived from 3-phoshoglycerate, which is directly adjacent to the entry point

80

of glycerate to central metabolism. We aimed to systematically explore how different combinations of gene

81

deletions and supplemented carbon sources change the dependency of E. coli growth on the availability of

82

glycerate. We were especially interested in gene deletion sets which lead to a wide range of growth

83

dependencies on glycerate as a function of the supporting carbon sources (which are to be given in large

84

excess). The sensitivity of a strain carrying such deletions towards glycerate should be easily tuned by

85

controlling the composition of the medium.

86

We considered combinations of glycerate with three supporting carbon sources, each entering a different

87

segment of central metabolism – glycerol for upper metabolism, succinate for lower metabolism, and glycine

88

for middle metabolism. We further considered all possible combinations of 1 to 4 meaningful gene deletions

89

in central metabolism (Methods), as marked in yellow in Figure 1. For each combination of carbon sources

90

and gene deletions, we applied Flux Balance Analysis (FBA) to calculate the maximal biomass yield,

91

assuming that glycerate is the only limiting carbon source; that is, the concentration of all other carbon

92

sources is substantially higher than that of glycerate (Methods). We discarded non-viable combinations (i.e.

93

that could not grow even with supplemented carbon sources) and combinations in which glycerate is not

94

required to sustain growth. For the rest, we calculated the Glycerate:Biomass Ratio (GBR): the number of

95

glycerate units required to produce one unit of biomass (as defined by the biomass function). Throughout

96

this study, we report the GBR in units of millimoles glycerate per gram of cell dry weight. The GBR enabled 4

97

us to compare the glycerate requirement associated with different combinations of carbon sources and gene

98

deletions.

99

Out of the ~28,000 knockout combinations tested by our algorithm, 562 had a positive GBR values in at

100

least one condition (Supplementary Table 1). We ranked these strains by their smallest GBR across the

101

conditions that is, the condition where they are most sensitive to glycerate. The top 50 gene deletion

102

combinations are shown in Figure 2. Some of these suggested strains, e.g., ∆pgk ∆eno ∆glyA, were

103

sensitive to glycerate only under a single combination of carbon sources. Other strains, e.g., ∆pgk ∆pps

104

∆ppck, display different dependencies on glycerate for different combination of carbon sources, as indicated

105

by different GBR values. The strain selection flexibility, as calculated for each strain and shown at the right-

106

hand side of Figure 2, corresponds to the ratio between the highest and lowest GBR values across the

107

different combinations of carbon sources. As mentioned above, those strains with higher flexibility might be

108

preferable as their sensitivity towards glycerate can be easily controlled by modulating the composition of

109

the medium.

110

Overview of the chosen 6 metabolic sensor strains

111

Out of the many gene deletion possibilities, we chose to implement six, which together span a high

112

variability of GBR values across different combinations of carbon sources. These are marked by green

113

coloring in Figure 2. The ‘E’ strains (Fig. 3A), were deleted in enolase (∆eno), effectively separating and

114

completely isolating upper metabolism (upstream part of glycolysis, the pentose phosphate pathway, and

115

metabolic routes derived from them) from lower metabolism (downstream part of glycolysis, the TCA cycle,

116

and metabolic routes derived from them) (Zelcbuch et al., 2015). In these strains, succinate must be added

117

as a carbon source for energy production and for the biosynthesis of cellular building blocks that are derived

118

from lower metabolism – e.g., glutamate, aspartate, alanine, fatty acids – which together contribute ~72% of

119

the carbons within the cell (Neidhardt et al., 1990).

120

Strains E1, E2, and E3 differ from each other by the gene deletions that are added on top of enolase. The

121

E1 strain harbors only the enolase deletion, such that glycerate is responsible for the biosynthesis of all

122

cellular building blocks derived from upper metabolism – e.g., serine, glycine, adenine, histidine – together

123

providing ~28% of cellular carbons (Neidhardt et al., 1990). The E2 stain is further deleted in 3-

124

phosphoglycerate kinase (∆pgk) (Wellner et al., 2013), such that central metabolism is effectively separated

125

into three segments: (i) upper metabolism, mainly phosphosugar metabolism; (ii) ‘middle metabolism’,

126

representing phosphoglycerate and its downstream metabolites serine and glycine (from which other

127

essential cellular components are derived, e.g., one-carbons, purines, cysteine); and (iii) lower metabolism,

128

consisting mainly of pyruvate metabolism and the TCA cycle. This strain requires the addition of succinate

129

and glycerol as carbon sources for lower and upper metabolism, respectively. In this case, glycerate serves

130

as a carbon source only for ‘middle metabolism’, providing ~10% of the cellular carbons (Neidhardt et al.,

131

1990). The E3 strain is further deleted in serine hydroxymethyltransferase (∆glyA) (Yishai et al., 2017). This 5

132

strain requires that addition of succinate, glycerol and glycine, where glycerate is required only for the

133

biosynthesis of serine and its direct derivatives – e.g., cysteine – together contributing only ~3% of cellular

134

carbons (Neidhardt et al., 1990). As expected, we found that GBRE1 (4.1 mmol/gCDW) was higher than

135

GBRE2 (1.5 mmol/gCDW), which was higher than GBRE3 (0.46 mmol/gCDW), reflecting a decreased

136

dependency on glycerate for growth.

137

The ‘P’ strains are similar to the ‘E’ strains, but instead of enolase deletion they are deleted in

138

phosphoenolpyruvate (PEP) synthetase (∆ppsA) and PEP carboxykinase (∆pckA) (Fig. 1B). In these

139

strains, flux from lower metabolism to upper metabolism is blocked – i.e., PEP cannot be regenerated from

140

any lower metabolism intermediate – but flux from upper metabolism to lower metabolism is possible – i.e.,

141

PEP can be metabolized to pyruvate (via pyruvate kinase) or oxaloacetate (via PEP carboxylase). Hence,

142

the addition of succinate to these strains is not mandatory as glycerate can feed lower metabolism; yet, if

143

succinate is added, it would not be able to feed middle and upper metabolism. Another difference between

144

the ‘E’ strains and the ‘P’ strains if that, in the latter, PEP biosynthesis is strictly dependent on glycerate.

145

The P1, P2, and P3 strains corresponds to the same set of additional deletions as within the E1, E2, and E3

146

strains, respectively (Fig. 1).

147

For the ‘P’ strains, when succinate was available, we found that GBRP1 (4.7 mmol/gCDW) > GBRP2 (2

148

mmol/gCDW) > GBRP3 (1 mmol/gCDW). The higher GBR of the ‘P’ strains, as compared to the ‘E’ strains, is

149

attributed to the fact that, in the former strains, glycerate is also needed for the biosynthesis of PEP, thus

150

increasing biomass dependence on glycerate. When succinate was not provided, such that glycerate was

151

needed also for lower metabolism, we got, as predicted, very high GBR – GBRP1 (27 mmol/gCDW) > GBRP2

152

(13 mmol/gCDW) > GBRP3 (12 mmol/gCDW) – reflecting the very high dependence of these strains on

153

glycerate. As expected, unlike the ‘E’ strains, the ‘P’ strains are associated with high selection flexibility, as

154

the addition or omission of succinate dramatically changes their GBR.

155

Overall, the high variability of the calculated GBR values – spanning almost two orders of magnitudes –

156

indicates that our designed strains should indeed be suitable to serve as metabolic biosensors with different

157

sensitivities towards the desired compound.

158

The metabolic sensor strains detecting exogenous glycerate

159

After completing the construction of the different sensor strains (Methods) we tested whether they indeed

160

grow only on the expected combination of carbon sources. Figure 4A-F shows the sets of carbon sources

161

that supported the growth of each strain. Within a time frame of ~100 hours the observed growth profiles

162

matched those expected. Importantly, all strains, except E2 supplemented with glycine, required glycerate

163

for growth in all conditions (Fig. 4E). The glycerate-independent growth of the E2 strain – when glycine was

164

supplemented – is to be expected from the strain design as glycine can be converted to serine (Fig. 3A).

165

The E2 strain is thus intended to be used without the addition of glycine, such that both serine and glycine 6

166

biosynthesis are dependent on glycerate. Indeed, when glycine is omitted, growth of the E2 strain becomes

167

dependent on glycerate (Fig. 4E). While the addition of glycine is not necessary to obtain growth of the E1,

168

P1, and P2 strains, it was found to boost their growth (Fig. 4A, B, D), presumably as it reduces the

169

metabolic burden of glycerate metabolism which no longer needs to supply glycine and serine. The minute

170

growth of the E2 and E3 strains when glycerol is omitted (brown curves) can probably be attributed to the

171

existence of a glycogen reservoir than can feed upper metabolism but only to a limited extent (Wilson et al.,

172

2010).

173

However, longer cultivation resulted in an unexpected outcome, where the E3 strain (but not the other

174

strains) was able to grow without the addition of glycerate, albeit after a long delay and at a very low growth

175

rate (Fig. 4G). We emphasize that the growth experiments were performed in triplicates, which showed

176

identical growth curves (and hence are presented as a single line). Moreover, upon a repeated cultivation of

177

the strain an essentially identical growth profile emerged (two red lines in Fig. 4G, each corresponds to a

178

triplicate experiment performed independently). Hence, the growth of the E3 strain without glycerate

179

represents adaptation rather than mutation as in the latter case we would expect the different replicates to

180

have diverging growth curves. As the E3 strain is expected to show the smallest dependence on glycerate –

181

only serine biosynthesis should depend on glycerate (Fig. 3A) – we reasoned that a metabolic leakage of

182

low flux, which does not affect the other strains, short circuit the selection for glycerate in this strain.

183

To identify the source of the metabolic leakage, we cultivated the strain in the presence of either 13C-glycine

184

(and non-labeled glycerol and succinate) or 13C3-glycerol (and non-labeled glycine and succinate). We found

185

serine to be labeled thrice only when

186

metabolism (Fig. 5). We wondered whether this metabolic leakage depends on the canonical serine

187

biosynthesis route (starting from 3-phosphoglycerate) or rather corresponds to a completely new route.

188

Further deletion of 3-phosphoglycerate dehydrogenase (∆serA) abolished growth without the addition of

189

serine, confirming that the biosynthesis of serine still depends on the canonical route (purple curve in Fig.

190

4G). We hypothesized that the source of the metabolic leakage might be the spontaneous

191

dephosphorylation of 1,3-bisphosphoglycerate, which is produced by the reversible activity of

192

glyceraldehyde 3-phosphate dehydrogenase. Indeed, deletion of this enzyme (∆gapA) abolished the

193

glycerate-independent growth (yellow curve in Fig. 4G). For further experiments, we therefore used an

194

updated version of strain E3 in which gapA is deleted and is denoted strain E3*.

195

The spontaneous dephosphorylation of 1,3-bisphosphoglycerate to 3-phosphoglycerate can be attributed to

196

the general reactivity and instability of phosphoanhydrides. Yet, the very low cellular concentrations of 1,3-

197

bisphosphoglycerate (which is practically undetectable (Teusink et al., 2000)) results in very low production

198

rate of 3-phosphoglycerate, barely sufficient to support serine biosynthesis in strain E3 and too low to affect

199

the growth of the other selection strains which have a GBR ≥ 1 mmol/gCDW.

200

The metabolic sensor strains detecting in vivo production of glycerate

13

C3-glycerol was added, indicating a metabolic leakage from upper

7

201

After confirming the response of the sensor strains towards exogenously added glycerate, we wanted to

202

assess their response to glycerate produced endogenously. Towards this aim, we fed the strains with

203

saccharic acid, which is known to be endogenously metabolized to pyruvate and tartronate semialdehyde,

204

where the latter is reduced to glycerate (Monterrubio et al., 2000). As displayed in Figure 6A-C, all the ‘P’

205

strains were able to grow with saccharic acid as a sole source of glycerate. However, of the ‘E’ strains (Fig.

206

6D-F), only strain E1 grew with saccharic acid, while strains E2 and E3* – that require much less glycerate

207

for growth – were unable to grow. Our interpretation of these results is that consumption of saccharic acid

208

generates very high levels of glycerate and phosphoglycerate. The ‘P’ strains can dispose of excess

209

phosphoglycerate via lower metabolism. Yet, in the ‘E’ strains, lower metabolism is completely isolated from

210

upper metabolism, and hence excess phosphoglycerate cannot be easily disposed of. This problem is less

211

acute in strain E1 which requires a considerable amount of glycerate for growth and which can further

212

dispose of phosphoglycerate via upper metabolism, and especially via the oxidative pentose phosphate

213

cycle. However, strains E2 and E3* require low amounts of glycerate. Moreover, glycerate metabolism in

214

these strains is completely isolated from both upper metabolism and lower metabolism, such that excess

215

phosphoglycerate can easily accumulate and inhibit growth. Supporting this reasoning, we find that, when

216

glycerate is added to the medium, the intracellular concentration of phosphoglycerate in strains E2 and E3*

217

is 3.9±0.9 fold higher than in a WT strain (p-value < 10-7). This illustrates that high sensitivity of a sensor

218

strain – that is, growth on a very low concentration of a sensed compound – can come with a price, where

219

high production can severely inhibit growth. Providing a metabolic sink for the accumulated intermediates,

220

as exists in the ‘P’ strains, could therefore provide an important safety valve, making the sensor strain more

221

robust.

222

As further validation of the capability of the metabolic sensor strains to detect internally produced glycerate,

223

we have overexpressed the enzyme glyoxylate carboligase in all strains that were further deleted in the

224

corresponding native gcl gene. The expression of this enzyme should thus enable the self-condensation of

225

glyoxylate to generate tartronate semialdehyde, which E. coli can natively reduce to glycerate (Ornston and

226

Ornston, 1969). As shown in Figure 7, the sensor strains were indeed not able to grow with glyoxylate as a

227

glycerate source, due to the deletion of the native gcl gene; but upon overexpression of the enzyme, growth

228

with glyoxylate was restored. In this case, the rate of glyoxylate conversion to glycerate is low enough such

229

that strains E2 and E3* can consume the latter compound while avoiding the deleterious accumulation of its

230

downstream metabolites.

231

Quantitative dependence of the metabolic sensor strains on glycerate

232

Next, we aimed to quantitatively characterize the growth of each strain on varying concentrations of added

233

glycerate. We tested each strain with a gradient of 11 glycerate concentrations, each concentration being

234

1.5-fold higher than the previous. For the ‘P’ strains, we checked growth both with and without further

235

addition of succinate. For strains that were expected to be highly dependent on glycerate (i.e., with a high

236

GBR) – for example, strain P1 in which succinate is omitted – we tested a gradient of relatively high 8

237

glycerate concentrations (e.g., 520 µM to 30 mM). Conversely, for strains in which glycerate provides only a

238

small fraction of biomass building blocks (i.e., with a low GBR) – such as strain E3* – we tested a gradient

239

of relatively low glycerate concentrations (e.g., 9 µM to 520 µM).

240

Figure 8 shows that for all strains (with the exception of strain E2, as discussed below) the gradient of

241

glycerate concentrations resulted in a wide range of maximal OD600. Similar to the ~1.5 orders of magnitude

242

spanned by the 11 glycerate concentrations tested, so did the resulting OD600 span ~1.5 orders of

243

magnitude. The diauxic growth observed with some of the strains and carbon source combinations (e.g.,

244

Fig. 8B) is difficult to explain, but might be related to the presence of cellular pools (e.g., glycogen feeding

245

upper metabolism) that are initially used to support high growth rate and once depleted (at relatively low

246

OD) are replaced with exogenously provided carbon sources.

247

Strain E2 displayed a different growth behavior, where the final OD was very similar regardless of the

248

concentration of glycerate (Figure 8H). Moreover, the growth rate of this strain was very low, with ~250

249

hours required for the strain supplemented with 69 µM glycerate to reach maximal OD. We interpret these

250

results to indicate a slow metabolic leakage of threonine degradation towards glycine (Fraser and Newman,

251

1975), which can then be further metabolized to serine. This low flux seems to need the initial growth

252

priming provided by the low levels of glycerate and hence was not observed when we tested the strain in the

253

absence of glycerate (Figure 4E). Importantly, degradation of threonine does not enable growth of the other

254

strains as they either cannot metabolize glycine to serine or are dependent on the metabolism of glycerate

255

to cellular building blocks other than glycine and serine. Following this finding, we excluded strain E2 from

256

our list of suitable glycerate biosensors.

257

Correlation between predicted and measured strain sensitivity to glycerate

258

Figure 9 depicts the maximum OD600 of each strain as function of the initial glycerate concentration provided

259

in the growth media (both axes are in logarithmic scale). As expected, the measured values for each strain

260

are approximately arranged along a line with a slope of 1, indicating a linear dependency of growth on the

261

concentration of glycerate (strain E2 deviates from this relationship, as discussed above). The samples

262

associated with strains in which glycerate provides a high fraction of cellular carbons lie to the right-hand

263

side of the figure. Conversely, the samples on the left-hand side of the figure correspond to strains in which

264

glycerate provides only a few cellular building blocks. We define the Glycerate to OD Ratio (GODR) of each

265

strain as the ratio between the glycerate concentration and the maximum OD600 within a range of

266

concentrations where this ratio is approximately constant (see Methods section). Strains P1, P2, and P3 in

267

which succinate is not added use glycerate to produce most of the cellular building blocks and thus have a

268

high GODR > 20 mM/OD. With the addition of succinate, we observe that GODRP1 (6.3 mM/OD) > GODRP2

269

(2.3 mM/OD) > GODRP3 (0.76 mM/OD), as expected by the decreasing fraction of cellular carbons that are

270

derived from glycerate. Similarity, we observe that GODRE1 (3.2 mM/OD) > GODRE3 (0.45 mM/OD). The

271

GODR cannot be defined for strain E2 as it cannot serve as a true glycerate biosensor as described above. 9

272

Overall, our set of sensor strains display a high sensitivity range towards glycerate, with GODR spanning

273

almost two orders of magnitude – from GODRE3 = 0.45 mM/OD to GODRP1(no succinate) = 26.23 mM/OD.

274

Next, we checked whether the computationally calculated GBR indeed predicts the experimentally derived

275

GODR. We found that the two parameters correlate almost perfectly (R2 > 0.97, Fig. 10A). This supports the

276

validity of the glycerate sensitivity predictions of our computational model and further indicates that the cells

277

utilize their different carbon sources in a nearly optimal manner, without minimal loss of limiting resources.

278

Further analysis indicated that the predicted GBR values for strains P2 and P3 with succinate disrupt the

279

correlation between GBR and GODR, as they seem to be too low. Hence, we took a deeper look into the

280

predicted metabolic fluxes associated with these strains and found that the model achieves higher yields

281

(and therefore lower GBR values) by replacing the TCA cycle with an oxidative, cyclic flux via the oxidative

282

pentose phosphate pathway. In this predicted mode of growth, all cellular reducing power and energy are

283

derived from glycerol oxidation via this oxidative pentose phosphate cycle (OPPC), thus preventing

284

glycerate oxidation by the TCA cycle and maximizing the utilization of this feedstock for biomass formation.

285

However, replacing the TCA cycle with the OPPC is not supported by any experimental evidence and hence

286

is unrealistic. Therefore, to make the model more realistic, we removed glucose 6-phosphate

287

dehydrogenase, effectively abolishing the OPPC. This resulted in higher GBR values for strains P2 and P3

288

with succinate – GBRP2 = 23.7 mmol/gCDW and GODRP3 = 22.3 mmol/gCDW – as expected by the

289

additional use of glycerate for generation of reducing power and energy. Notably, the two newly derived

290

values improved the correlation between GBR and GODR substantially, and especially reduced the RMSE

291

by >50%, from 0.39 to 0.19 (Fig. 10B). This confirms that the OPPC is unlikely to take place and that

292

glycerate oxidation is used to energize the cell.

293

Discussion

294

The construction of the metabolic sensor strains required us to disrupt multiple enzymes in central

295

metabolism. While disruption of key glycolytic enzymes was demonstrated before, e.g., (Irani and Maitra,

296

1977; Wellner et al., 2013; Zelcbuch et al., 2015), our strains are unique as they harbor multiple such

297

deletions, effectively dissecting central metabolism into multiple disconnected segments. Each segment

298

represents a rather independent metabolic network which requires a dedicated carbon source as a

299

feedstock. Multiple feedstocks can be fed to each metabolic segment, e.g., acetate, pyruvate, and succinate

300

can feed lower metabolism, while glycerol, ribose and xylose can feed upper metabolism. We used glycerol

301

and succinate as carbon sources for upper and lower metabolism, respectively, as these supported the

302

highest growth rate. Notably, as previously reported (Irani and Maitra, 1977; Wellner et al., 2013)), glucose

303

cannot be used as an upper metabolism feedstock as it completely inhibits the growth of strains in which

304

middle glycolysis is disrupted, probably due to catabolite repression.

10

305

In strain E3, the metabolic segment that depends on glycerate as a carbon source is especially small,

306

consisting mainly of serine biosynthesis and downstream utilization. This low-demand segment enabled us

307

to observe a metabolic flux which is too small to have a physiological effect within a larger metabolic

308

network. Specifically, we identified that 1,3-bisphosphoglycerate, the product of glyceraldehyde 3-phosphate

309

dehydrogenase,

310

phosphoglycerate even if 3-phosphoglycerate kinase is deleted. The rate of the spontaneous

311

dephosphorylation is rather low – as evident by the fact that it supported the glycerate-independent growth

312

of only strain E3 – presumably since the concentration of this compound within the cell is very low, <1 µM

313

(Teusink et al., 2000). Our findings help in explaining why 1,3-bisphosphoglycerate is maintained at such a

314

low level, i.e., in order to minimize its wasteful, non-catalytic degradation. More generally, the approach we

315

used in this study, dissecting central metabolism into small segments, can be used to identify low fluxes –

316

spontaneous and enzymatic alike – which cannot be easily identified within a WT strain, but which could

317

shed light on central cellular phenomena.

318

We designed and implemented a method based on Flux Balance Analysis to calculate the maximal biomass

319

yield on limiting amounts of glycerate (and saturating amounts of the other carbon sources). This enables us

320

to assess the sensitivity of different strains towards glycerate, as expressed by the GBR. We found that our

321

computational estimations correspond almost perfectly to the experimentally derived GODR.

322

The nearly perfect match between the computational estimation and experimental results points to several

323

important aspects. First, it suggests that the cell is able to optimize the utilization of the limiting carbon

324

source even after undergoing severe disruption of its endogenous metabolism. This is especially true for the

325

‘P’ strains growing with succinate, which seem not to waste glycerate on lower metabolism, even though

326

pyruvate kinase and PEP carboxylase are not deleted and could potentially drain the glycerate pool for its

327

use for serine (and glycine) biosynthesis. Second, the observed correlation indicates that using constraint-

328

based metabolic models to quantify the growth yield on multiple carbon sources, only one of which is

329

limiting, is a valid approach to assess the sensitivity of different strains towards a compound. This paves the

330

way for future use of this approach to design metabolic biosensors for different metabolites.

331

Still, the model did fail in one notable case, where the oxidative activity of the TCA was predicted to be

332

replaced by the OPPC, in order to avoid glycerate ‘loss’ towards energy production. Upon removal of the

333

OPPC, the correlation between GBR and GODR became even better and the RMSE decreased

334

substantially. This serves as a clear reminder that the fluxes predicted by Flux Balance Analysis should

335

always be carefully checked according to available biochemical knowledge and that the underlining

336

metabolic model must be modified to avoid unrealistic fluxes.

337

Our metabolic sensor strains span two orders of magnitude in their sensitivity towards glycerate. This makes

338

them suitable to detect the exogenous supply or internal production of glycerate at various scales, hence

339

providing a useful tool to test and optimize photorespiration bypass routes that generate this compound

undergoes

(most

likely)

spontaneous

11

dephosphorylation,

thus

generating

3-

340

(Trudeau et al., 2018). Notably, the most sensitive strain can be used to detect concentrations as low as few

341

tens of µM. Yet, high sensitivity comes with a price, as demonstrated in Figure 6. That is, the growth of

342

strains which require only a small amount of a compound might be strongly inhibited when it is present at a

343

high concentration. In the case of strain E1 and E2, this toxic effect probably stems from the accumulation of

344

phosphoglycerate which cannot be effectively metabolized. This serves to emphasize two points. First, low

345

sensitivity strains, such as strain E1 (as compared to strains E2 and E3), play an important role, as they can

346

be used to detect the desired compound under conditions at which the high sensitivity strains fail to grow.

347

Second, providing a metabolic sink for small networks – as is the case in the ‘P’ strains but not ‘E’ strains –

348

is important to enable balanced growth also at unexpected conditions (e.g., high concentration of a

349

compound that is expected to be present at a low concentration).

350

The ‘P’ strains hold another advantage over the ‘E’ strains. Unlike the latter, whose feedstock requirement is

351

rather strict, the carbon sources of the former can be modulated, i.e., supplementation with succinate is not

352

mandatory but, when added, it dramatically contributes to growth. This flexibility would become useful when

353

slowly evolving higher activity of a glycerate biosynthesis route. First, when production rate is still slow,

354

succinate will be supplemented, thus maintaining a low selection pressure for glycerate. Once the activity of

355

the biosynthesis route increases, succinate would be omitted, thus dramatically increasing the selection for

356

faster glycerate production. The capability to tune the selection pressure by controlling the medium

357

composition can thus facilitate continuous evolution of a pathway across several orders of magnitudes of

358

activity.

359

Just as with genetic biosensors, which might not be fully specific to a single compound, metabolic sensor

360

strains might grow in the presence of compounds other than the ones they are intended to detect. For

361

example, the E3 strain can grow when serine replaces glycerate in the medium, which is to be expected as

362

within this strain glycerate is used solely for the biosynthesis of serine and compounds that are derived from

363

it. Similarly, the E2 and E3 strains can grow when glycerate is replaced with either glycine or serine. While

364

completely eliminating such false detection is impossible, it can be minimized by using sensor strains that

365

depend on the desired compound for the biosynthesis of multiple cellular building blocks which cannot be

366

easily interconverted. For example, the P2 and P3 strains, which depend on glycerate also for the

367

biosynthesis of PEP, are more specific to glycerate than the corresponding E2 and E3 strains, as the

368

presence of glycine and serine cannot support their growth. Using strains with higher specificity and lower

369

probability of false detection would be especially important when using a medium that contains numerous

370

different contaminants.

371

Overall, our study demonstrates the applicability of metabolic sensor strains to detect a compound over a

372

wide range of concentrations. Such strains could take a prime place alongside genetic biosensors in

373

detecting exogenous as well as endogenous compounds. In the latter case, metabolic sensor strains have

374

an inherent advantage as each single cell is equipped with its own selection mechanism for the

375

enzyme/pathway activity rather than relying on outside screening. This way, the only limitation on the scale 12

376

of the library is the number of cells that can be cultivate simultaneously, rather than the throughput of the

377

measuring instrument (such as a flow cytometer or plate reader), increasing the capacity by many orders of

378

magnitude.

13

379

Methods

380

Strains and gene deletions

381

Strains used in this study are listed in Table 1. Genomic gene deletions were performed by using two types

382

of E. coli strains. E. coli strain MG1655 (F– λ– ilvG– rfb-50 rph-1) was used as the base strain for the `P´

383

auxotroph strains and E. coli strain SIJ488 strain (Jensen et al., 2015) was used as the base strain for the

384

`E´ auxotroph strains. SIJ488 strain differs from MG1655 by carrying inducible genes that encode for a

385

recombinase (pRed/ET) and a flippase that allow fast turnover for multiple deletions. Most of genomic gene

386

deletions were performed using the Red/ET method (Zhang et al., 2000), recombining the selectable

387

kanamycin resistance at the desired genetic locus (Quick & Easy E. coli Gene Deletion Kit, Gene Bridge,

388

Heidelberg, Germany). The genes glyA, ppsA, and pckA were deleted by P1 phage transduction (Thomason

389

et al., 2007) using the glyA, ppsA, and pckA knock out strains from the Keio collection (Table 1) as a donor

390

(Baba et al., 2006). (We minimize the use of the P1 phage transduction method where possible as it

391

transfers ~100 kb DNA from the donor strain to the host strain and thus may introduce undesired mutations.)

392

For the recombinant gene deletion approach, kanamycin resistance cassettes were generated via PCR –

393

‘KO’ primers with 50 bp homologous arms are listed in Supplementary Table 1 – using the FRT-PGK-gb2-

394

neo-FRT (Km) cassette. Cells were inoculated in MY medium (see below); upon reaching OD 0.4-0.5, the

395

pRed/ET recombinase gene was induced by addition of 15 mM L-arabinose. After 45-60 min incubation at

396

37°C, cells were harvested and washed three times with ice cold 10 % glycerol (11,300 g, 30 sec, 2°C).

397

~300 ng of Km cassette PCR-product was transformed via electroporation (1 mm cuvette, 1.8 kV, 25 µF,

398

200 Ω). After selection on kanamycin, gene deletions were confirmed via PCR using ‘KO-Ver’ primers

399

(Supplementary Table 1). In order to perform a sequential gene deletion, Km cassette was removed by

400

transformation with a plasmid that encodes for flippase in ‘P’ strains (no needed for ‘E’ strains as the gene

401

encoding for filippase plasmid was integrated in genome). 50 mM L-rhamnose was added to induce flippase

402

gene expression, in exponentially growing 4 ml MY culture at OD 0.5; induction time was ≥ 3 h at 30°C. The

403

successful removal of antibiotic resistance cassette was screened for kanamycin sensitivity and confirmed

404

by PCR (using ‘KO-Ver’ primers).

405

For the overexpression of glyoxylate carboligase (gcl), the corresponding gene was amplified from E. coli

406

genomic DNA by PCR (PrimeSTAR® Max DNA Polymerase, TaKaRa) using primer pairs of gcl_F and

407

gcl_R (Supplementary Table 1). The amplicon was first cloned into pJET1.2/blunt (CloneJET PCR Cloning

408

Kit, ThermoFisher Scientific), and the correct insert was cleaved by using restriction enzymes Mph1103I and

409

XbaI to clone in pNivC vector. The correct insertion was confirmed by using primer pairs of pNiv_F and

410

pNiv_R (Supplementary Table 1). This construct was cleaved with EcoRI and PstI to clone it in expression

411

vector pZ-ASS, resulting in pZ-ASS-gcl. The plasmid harboring the pZ-ASS-gcl was transformed in each

412

glycerate sensor strains for further growth experiments.

14

413

Media and growth conditions

414

For generation of the gene deletion strains we used the MY medium: M9 medium with trace elements (50

415

mM Na2HPO4, 20 mM KH2PO4, 1 mM NaCl, 20 mM NH4Cl, 2 mM MgSO4 and 100 µM CaCl2, 134 µM EDTA,

416

13 µM FeCl3·6H2O, 6.2 µM ZnCl2, 0.76 µM CuCl2·2H2O, 0.42 µM CoCl2·2H2O, 1.62 µM H3BO3, 0.081 µM

417

MnCl2·4H2O), further supplemented with 4 mM glycerol, 40 mM succinate, and 5 g/L casamino acids, 20

418

mM of glycerate and 4 mM of glycine.

419

Growth experiments were performed in M9 medium with trace elements, supplemented with the appropriate

420

carbon sources (succinate, glycerol, and glycerate were added at 20 mM while glycine was added at 4

421

mM.). Overnight cultures for growth experiments were incubated in 4 mL in M9 medium with trace elements,

422

supplemented with all relevant carbon sources to ensure growth. Pre-cultures of the strains overexpressing

423

gcl were supplemented with glycerate to ensure the growth, yet glycerate was replaced with glyoxylate

424

when performing plate experiments. Cultures were harvested by centrifugation (11000 rpm, 30 sec, 4ºC)

425

and washed three times in M9 minimal medium to eliminate any residual carbon sources. Growth

426

experiments were inoculated to a starting OD600 of 0.005 and carried out in 96-well microtiter plates

427

(Nunclon Delta Surface, Thermo Scientific) at 37°C. Each well contained 150 µL of culture and 50 µL

428

mineral oil (Sigma-Aldrich, Germany) to avoid evaporation. A plate reader (Infinite M200 pro, Tecan) was

429

used for incubation, shaking and OD600 measurements (controlled by Tecan I-control v1.11.1.0). The

430

cultivation program was run as follows; three cycles of 4 shaking phases, 1 min of each: linear shaking,

431

orbital shaking at amplitude of 3 mm, linear shaking, and orbital shaking at amplitude of 2 mm. After each

432

round of shaking (~12.5 min), absorbance (OD 600 nm) was measured in each well. Raw data from the

433

plate reader were calibrated to cuvette values according to ODcuvette=ODplate/0.23. Growth curves were

434

plotted in MATLAB and represent averages of triplicate measurements; in all cases, variability between

435

triplicate measurements was less than 5%.

436

Isotopic-labeling experiments

437

In order to

438

relevant carbon sources. Glycerol-13C3, glycine-1-13C, glycine-2-13C (bought from Sigma-Aldrich, Germany)

439

were used as indicated in the main text. After reaching stationary phase, a volume equivalent to 1 mL of

440

OD600 = 0.5 was harvested and washed in H2O by centrifugation. Hydrolysis of proteins was carried out with

441

6 M HCl, at 95°C for 24 h (You et al., 2012). HCl was removed over night by incubation at 95°C under an air

442

stream. Samples were then resuspended in 1 ml H2O, centrifuged (5 min, 16,000g) to remove any insoluble

443

compounds, and supernatants used for further analysis. Proteinogenic amino acids were analyzed by

444

UPLC–ESI–MS described previously (Giavalisco et al., 2011) with a Waters Acquity UPLC system (Waters)

445

using a HSS T3 C18 reversed phase column (100 mm × 2.1 mm, 1.8 µm; Waters). The mobile phase was

446

0.1% formic acid in H2O (A) and 0.1% formic acid in acetonitrile (B). The flow rate was 0.4 mL/min with a

447

gradient of 0 to 1 min – 99% A; 1 to 5 min – linear gradient from 99% A to 82%; 5 to 6 min – linear gradient

13

C isotope tracing of proteinogenic amino acids cell were grown in M9 containing with the

15

448

from 82% A to 1% A; 6 to 8 min – kept at 1% A; 8-8.5 min – linear gradient to 99% A; 8.5-11 min – re-

449

equilibrate. Mass spectra were acquired using an Exactive mass spectrometer (Thermo Scientific) in

450

positive ionization mode, with a scan range of 50.0 to 300.0 m/z. The spectra were recorded during the first

451

5 min of the LC gradients. Data analysis was performed using Xcalibur (Thermo Scientific). Amino acid

452

standards (Sigma-Aldrich, Germany) were analyzed for determination of the retention times under the same

453

conditions.

454

Determination of metabolite concentrations

455

To assess the intracellular concentration of phosphoglycerate, we cultivated the strains in 500 ml shake

456

flasks until reaching mid-exponential phase (OD600 of 1.0). Culture densities were measured shortly before

457

sampling and the values were later used for normalization. Each culture was sampled three times by filtering

458

cells from 1 mL using MF-Millipore Membrane Filters (round, 0.45 µm, HLPV) and washing (on the filter)

459

with 1 mL of media. The filters with the cells were then immediately placed in cold extraction solution – 2:2:1

460

ratio of acetonitrile:methanol:water at -20°C. A fully

461

added to each sample. After ≥24 hours of incubation at -20°C, the supernatants were collected while cell

462

debris were separated by centrifugation. The supernatants were dried at 120 microbars (SpeedVac) at room

463

temperature, and stored at -20°C.

464

To assess the extracellular concentration of metabolites, we cultivated the strains in culture tubes (Greiner,

465

14mL) for ~60 hours, until all have reached stationary phase. Samples of 1 mL were taken from each culture

466

and centrifuged for 10 minutes at 4000g. The supernatants were stored at -20°C for further analysis.

467

The dried cell extracts were re-suspended in 100 µl of MilliQ water, while the extracellular samples were

468

diluted 1:50. All samples, including standards, controls, and calibration samples, were distributed in a sealed

469

96-well microtiter plate, and injected into an Agilent QTRAP LC-MS/MS with electrospray ionization (Yuan et

470

al., 2012). The results were integrated and analyzed using in-house software described before (Buescher et

471

al., 2010).

472

Computational platform for designing metabolic sensor strains

473

In order to design sensor strains, we used a standard constrained-based framework generally known as

474

Flux Balance Analysis. More specifically, we implement an algorithm that quantifies the strength of coupling

475

between a certain reaction to the biomass rate (Antonovsky et al., 2016), which we term GBR. Testing many

476

combinations of knockouts and carbon sources, ranked their potential to perform as glycerate biosensors

477

based on their GBR values. A related method (Tepper and Shlomi, 2011) and a more recent version called

478

OptAux (Lloyd et al., 2019) similarly identify combinations of gene knockouts that couple a target compound

479

to the growth of the cell (i.e. make it auxotrophic to the target compound) at a predefined growth rate. These

480

previous methods are based on bi-level MILP (Mixed Integer Linear Programming), and are more scalable

481

to large networks such as the genome-scale metabolic network of E. coli. The first of this studies (Tepper

13

C-labeled internal standard prepared from E. coli was

16

482

and Shlomi, 2011) suggests using integer-cuts (as described in (Pharkya et al., 2004)) to generate

483

alternative optimal knockout combinations for the same auxotrophy target. Although our exhaustive

484

enumeration of all knockout combinations is less efficient than these MILP-based methods, the small scale

485

of the network considered in this work did not warrant using advanced optimization tools, and we could

486

complete the search in about an hour on a 50-core server. Future applications might consider replacing the

487

exhaustive method with bi-level MILP, especially when developing biosensors for secondary metabolites

488

that are not included in the core model of E. coli (see below), or for other organisms with much larger

489

metabolic networks, as suggested by (Tepper and Shlomi, 2011).

490

We extended the core model of E. coli (Orth et al., 2010) to include the reactions involved in glycine and

491

serine metabolism. We adjusted the biomass function accordingly, i.e., replacing 3-phosphoglycerate with

492

glycine, serine and methylene-THF according the appropriate stoichiometry as derived from the biomass

493

composition reported in (Neidhardt et al., 1990). Overall, we added, corrected, and removed reactions as

494

listed herein:

495

1.

We added exchange and transport reactions for glycerate, glycerol, and glycine.

496

2.

We added a glycerate assimilation reaction: atp_c + glyc_c -> 2pg_c + adp_c.

497

3.

We added a glycerol assimilation reaction: atp_c + q8_c + glycerol_c -> adp_c + dhap_c + h_c +

498 499

q8h2_c. 4.

500

We added serine biosynthesis reaction: 3pg_c + glu_L_c + h2o_c + nad_c -> akg_c + h_c + nadh_c + pi_c + ser_c.

501

5.

We added a serine hydroxymethyltransferase reaction: h2o_c + mlthf_c + gly_c <=> thf_c + ser_c.

502

6.

We added a glycine cleavage reaction: nad_c + thf_c + gly_c -> co2_c + nadh_c + nh4_c + mlthf_c.

503

7.

We replaced the biomass precursor 3-phosphoglycerate (3pg_c) with the downstream precursors

504

serine, glycine and methylene-THF. The affected co-factors are also written in the following equation:

505

1.496 3pg_c + 1.496 glu_L_c + 1.033 thf_c + 1.033 nad_c -> 0.462 ser_c + 1.033 glycine_c + 1.033

506

mlthf_c + 1.033 nadh_c + 1.496 akg_c + 1.496 pi_c + 1.496 h_c.

507

8.

508 509 510 511 512

We replaced g3p_c with dhap_c in the biomass reaction, as it captures cellular metabolism more accurately.

9.

We changed the membrane transhydrogenase reaction to h_e + nadh_c + nadp_c -> h_c + nad_c + nadph_c (one proton translocated instead of two).

10. We changed succinate transport reaction to 2 h_e + succ_c -> 2 h_c + succ_e (two protons instead of one).

513

11. We removed the PFL reaction that works only under anaerobic conditions.

514

12. We removed the reaction of ED glycolysis as the relevant enzymes are expressed only when the cells

515 516 517

are fed with gluconate. 13. We removed the ATP maintenance reaction, as we are not interested in growth rate but rather in maximal biomass yield. 17

518

In order to calculate the Glycerate:Biomass ratio (GBR) of a specific knockout strain in a specific condition,

519

we followed the following procedure (the scripts and extended model are based on the COBRApy toolbox

520

(Ebrahim et al., 2013). and can be found at https://gitlab.com/elad.noor/sloppy under the "biosensors"

521

directory):

522

1.

Load the wild-type model from "core_model_extended.xml".

523

2.

Knock out the reactions in the list of knockouts.

524

3.

Change the lower bounds of the exchange reactions of all the carbon sources in the list to -1000 (i.e.

525

maximal uptake of 1000 mmol/gCDW) and both bounds of the target reaction (glycerate transport) to 0.

526

Calculate the maximal biomass rate. If the maximum is greater than 0, this combination does not

527

depend on glycerate at all, and we mark it as having a GBR of 0.

528

4.

Change the bounds of the target reaction to 1.

529

5.

Calculate the maximal biomass rate again. The GBR is then set to be the inverse of that maximum (i.e.

530

the rate of the target reaction – which was set to 1 – divided by the maximal biomass rate). If maximum

531

is 0, then growth is not possible and GBR is set to -1.

532

We scanned all combinations of 1-4 knockouts within the central metabolism of E. coli, from this list: G6PDH

533

(G6PDH2r), TAL (TALA), RPE, RPI, PGI, PFK, FBP, FBA, TPI, PGK, PGM, ENO, PYK, PPS, PDH, PPC,

534

PPCK, ME, CS, ICDH (ICDHyr), ICL, KGDH (AKGDH), SDH (SUCDi), FRD (FRD7), FUM, MDH, SER_ABC,

535

SHMT (glyA), GCV. Some reactions were not included since they are coupled with reactions that were

536

already in the list. For instance, in our model, a knockout of GAPD (glyceraldehyde 3-phosphate

537

dehydrogenase) is identical to a knockout of PGK (phosphoglycerate kinase) since they are the only two

538

reactions that generate and consume bisphosphoglycerate, which is not a biomass precursor. Hence,

539

removing GAPD is equivalent to removing PGK and does not represent a true alternative.

540

The following combinations of carbon sources were explored (on top of glycerate, which was in all

541

conditions): no other carbon source, glycerol, glycerol + glycine, succinate, glycerol + succinate, glycerol +

542

succinate + glycine.

543

We created a summary table by filtering out all mutants that never have a positive GBR in any condition, or

544

any knockout combination that gives identical results to a smaller gene deletion subset. We ranked the

545

strains based on the minimal GBR achievable across the tested growth conditions, as provided in

546

Supplementary Table 1.

547

Calculating GODR

548

The Glycerate:OD Ratio (GODR) of each strain is the ratio between the glycerate concentration and the

549

maximum OD600. This ratio is constant only within a certain range, since at very high glycerate

550

concentrations the cells reach densities that can inhibit growth from other reasons. Therefore, we had to find

551

a range where the measurements fall approximately on a line (with slope = 1 in logarithmic scale). In order 18

552

to find this range for each individual strain, we took all possible windows of 5 consecutive glycerate

553

concentrations, and chose the one with the best linear fit (in terms of Root Mean Squared Error). Since this

554

was done on a log-log plot, the offset of this line was used to determine the GODR, using this line formula:

555

log(OD) = a + log([gly]). Therefore, GODR = exp(-a) = [gly] / OD.

19

556

Acknowledgements

557

The authors thank Charlie Cotton for helpful discussions and Avi Flamholz for critical reading of the

558

manuscript; we further thank Patricia Walte and Antje Westendorf for technical assistance. This work was

559

supported by the Max Planck Society and by the European Union’s Horizon 2020 FET Programme under

560

the grant agreement No 686330 (FutureAgriculture).

561 562

Authors Contributions

563

A.B.-E. conceived and supervised the research. E.N. and S.B.V. performed the computational work. S.A.

564

and S.N.L. performed and experimental work. All authors analyzed the data and wrote the paper.

20

565

References

566 567 568

Antonovsky, N., Gleizer, S., Noor, E., Zohar, Y., Herz, E., Barenholz, U., Zelcbuch, L., Amram, S., Wides, A., Tepper, N., Davidi, D., Bar-On, Y., Bareia, T., Wernick, D. G., Shani, I., Malitsky, S., Jona, G., Bar-Even, A., Milo, R., 2016. Sugar Synthesis from CO2 in Escherichia coli. Cell. 166, 115-25.

569 570 571

Baba, T., Ara, T., Hasegawa, M., Takai, Y., Okumura, Y., Baba, M., Datsenko, K. A., Tomita, M., Wanner, B. L., Mori, H., 2006. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol Syst Biol. 2, 2006-2008.

572 573 574

Buescher, J. M., Moco, S., Sauer, U., Zamboni, N., 2010. Ultrahigh performance liquid chromatographytandem mass spectrometry method for fast and robust quantification of anionic and aromatic metabolites. Anal Chem. 82, 4403-12.

575 576

de las Heras, A., Carreno, C. A., de Lorenzo, V., 2008. Stable implantation of orthogonal sensor circuits in Gram-negative bacteria for environmental release. Environ Microbiol. 10, 3305-16.

577 578

Ebrahim, A., Lerman, J. A., Palsson, B. O., Hyduke, D. R., 2013. COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Biol. 7, 74.

579 580

Fraser, J., Newman, E. B., 1975. Derivation of glycine from threonine in Escherichia coli K-12 mutants. J Bacteriol. 122, 810-7.

581 582 583 584

Giavalisco, P., Li, Y., Matthes, A., Eckhardt, A., Hubberten, H. M., Hesse, H., Segu, S., Hummel, J., Köhl, K., Willmitzer, L., 2011. Elemental formula annotation of polar and lipophilic metabolites using 13C, 15N and 34S isotope labelling, in combination with high‐resolution mass spectrometry. Plant J. 68, 364-376.

585 586

He, H., Edlich-Muth, C., Lindner, S. N., Bar-Even, A., 2018. Ribulose Monophosphate Shunt Provides Nearly All Biomass and Energy Required for Growth of E. coli. ACS synthetic biology. 7, 1601-1611.

587 588

Irani, M. H., Maitra, P. K., 1977. Properties of Escherichia coli mutants deficient in enzymes of glycolysis. J Bacteriol. 132, 398-410.

589 590

Jaffrey, S. R., 2018. RNA-Based Fluorescent Biosensors for Detecting Metabolites in vitro and in Living Cells. Adv Pharmacol. 82, 187-203.

591 592 593

Jensen, K., Broeken, V., Hansen, A. S. L., Sonnenschein, N., Herrgard, M. J., 2019. OptCouple: Joint simulation of gene knockouts, insertions and medium modifications for prediction of growth-coupled strain designs. Metabolic engineering communications. 8, e00087.

594 595 596

Jensen, S. I., Lennen, R. M., Herrgard, M. J., Nielsen, A. T., 2015. Seven gene deletions in seven days: Fast generation of Escherichia coli strains tolerant to acetate and osmotic stress. Scientific reports. 5, 17874.

597 598

Liu, D., Evans, T., Zhang, F., 2015. Applications and advances of metabolite biosensors for metabolic engineering. Metab Eng. 31, 35-43.

599 600 601 602

Lloyd, C. J., King, Z. A., Sandberg, T. E., Hefner, Y., Olson, C. A., Phaneuf, P. V., O'Brien, E. J., Sanders, J. G., Salido, R. A., Sanders, K., Brennan, C., Humphrey, G., Knight, R., Feist, A. M., 2019. The genetic basis for adaptation of model-designed syntrophic co-cultures. PLoS Comput Biol. 15, e1006213.

603 604

Mainguet, S. E., Gronenberg, L. S., Wong, S. S., Liao, J. C., 2013. A reverse glyoxylate shunt to build a non-native route from C4 to C2 in Escherichia coli. Metab Eng. 19, 116-27. 21

605 606

Meyer, F., Keller, P., Hartl, J., Groninger, O. G., Kiefer, P., Vorholt, J. A., 2018. Methanol-essential growth of Escherichia coli. Nature communications. 9, 1508.

607 608 609

Monterrubio, R., Baldoma, L., Obradors, N., Aguilar, J., Badia, J., 2000. A common regulator for the operons encoding the enzymes involved in D-galactarate, D-glucarate, and D-glycerate utilization in Escherichia coli. J Bacteriol. 182, 2672-4.

610 611

Neidhardt, F. C., Ingraham, J. L., Schaechter, M., 1990. Building blocks needed to produce 1g of E. coli protoplasm. Physiology of the Bacterial Cell: A Molecular Approach. pp. 134-143.

612 613

Ornston, L. N., Ornston, M. K., 1969. Regulation of glyoxylate metabolism in Escherichia coli K-12. J Bacteriol. 98, 1098-108.

614 615

Orth, J. D., Fleming, R. M., Palsson, B. O., 2010. Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide. EcoSal Plus. 4.

616 617

Orth,

618 619

Paitan, Y., Biran, I., Shechter, N., Biran, D., Rishpon, J., Ron, E. Z., 2004. Monitoring aromatic hydrocarbons by whole cell electrochemical biosensors. Anal Biochem. 335, 175-83.

620 621

Pharkya, P., Burgard, A. P., Maranas, C. D., 2004. OptStrain: a computational framework for redesign of microbial production systems. Genome Res. 14, 2367-76.

622 623 624

Saeidi, N., Wong, C. K., Lo, T. M., Nguyen, H. X., Ling, H., Leong, S. S., Poh, C. L., Chang, M. W., 2011. Engineering microbes to sense and eradicate Pseudomonas aeruginosa, a human pathogen. Mol Syst Biol. 7, 521.

625 626

Tepper, N., Shlomi, T., 2011. Computational design of auxotrophy-dependent microbial biosensors for combinatorial metabolic engineering experiments. PLoS One. 6, e16274.

627 628 629 630

Teusink, B., Passarge, J., Reijenga, C. A., Esgalhado, E., van der Weijden, C. C., Schepper, M., Walsh, M. C., Bakker, B. M., van Dam, K., Westerhoff, H. V., Snoep, J. L., 2000. Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. Eur J Biochem. 267, 5313-29.

631 632

Thomason, L. C., Costantino, N., Court, D. L., 2007. E. coli genome manipulation by P1 transduction. Curr Protoc Mol Biol. Chapter 1, Unit 1 17.

633 634 635

Trang, P. T., Berg, M., Viet, P. H., Van Mui, N., Van Der Meer, J. R., 2005. Bacterial bioassay for rapid and accurate analysis of arsenic in highly variable groundwater samples. Environmental science & technology. 39, 7625-30.

636 637 638

Trudeau, D. L., Edlich-Muth, C., Zarzycki, J., Scheffen, M., Goldsmith, M., Khersonsky, O., Avizemer, Z., Fleishman, S. J., Cotton, C. A. R., Erb, T. J., Tawfik, D. S., Bar-Even, A., 2018. Design and in vitro realization of carbon-conserving photorespiration. Proc Natl Acad Sci U S A. 115, E11455-E11464.

639 640

Wellner, A., Raitses Gurevich, M., Tawfik, D. S., 2013. Mechanisms of protein sequence divergence and incompatibility. PLoS Genet. 9, e1003665.

641 642

Williams, T. C., Pretorius, I. S., Paulsen, I. T., 2016. Synthetic Evolution of Metabolic Productivity Using Biosensors. Trends Biotechnol. 34, 371-381.

J. D., Fleming, R. M. T., Palsson, http://gcrg.ucsd.edu/Downloads/EcoliCore, 2009.

22

B.

O.,

The

Core

E.

coli

Model.

643 644 645

Wilson, W. A., Roach, P. J., Montero, M., Baroja-Fernandez, E., Munoz, F. J., Eydallin, G., Viale, A. M., Pozueta-Romero, J., 2010. Regulation of glycogen metabolism in yeast and bacteria. FEMS Microbiol Rev. 34, 952-85.

646 647

Yishai, O., Bouzon, M., Doring, V., Bar-Even, A., 2018. In Vivo Assimilation of One-Carbon via a Synthetic Reductive Glycine Pathway in Escherichia coli. ACS synthetic biology.

648 649

Yishai, O., Goldbach, L., Tenenboim, H., Lindner, S. N., Bar-Even, A., 2017. Engineered Assimilation of Exogenous and Endogenous Formate in Escherichia coli. ACS synthetic biology.

650 651 652

You, L., Page, L., Feng, X., Berla, B., Pakrasi, H. B., Tang, Y. J., 2012. Metabolic pathway confirmation and discovery through 13C-labeling of proteinogenic amino acids. Journal of visualized experiments : JoVE. 59.

653 654

Yu, H., Liao, J. C., 2018. A modified serine cycle in Escherichia coli coverts methanol and CO2 to twocarbon compounds. Nature communications. 9, 3992.

655 656 657

Yuan, M., Breitkopf, S. B., Yang, X., Asara, J. M., 2012. A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nature protocols. 7, 872-81.

658 659 660 661

Zelcbuch, L., Razo-Mejia, M., Herz, E., Yahav, S., Antonovsky, N., Kroytoro, H., Milo, R., Bar-Even, A., 2015. An In Vivo Metabolic Approach for Deciphering the Product Specificity of Glycerate Kinase Proves that Both E. coli's Glycerate Kinases Generate 2-Phosphoglycerate. PLoS One. 10, e0122957.

662 663

Zhang, J., Jensen, M. K., Keasling, J. D., 2015. Development of biosensors and their application in metabolic engineering. Curr Opin Chem Biol. 28, 1-8.

664 665

Zhang, Y., Muyrers, J. P., Testa, G., Stewart, A. F., 2000. DNA cloning by homologous recombination in Escherichia coli. Nat Biotechnol. 18, 1314-7.

666

23

667

Tables

668

Table 1

669

Strains and the essential carbon sources used in this study. Strain name

Genotype

Parental strain

Reference

Essential carbon sources

MG1655 SIJ488 JW1692 JW3366 JW2535 E1 E2 E3 E3∆serA E3* P1 P2 P3

WT WT ppsA:: kan knockout Keio collection pckA:: kan knockout Keio collection glyA:: kan knockout Keio collection ∆eno ∆gcl ∆eno ∆gcl ∆pgk ∆eno ∆pgk ∆gcl ∆glyA ∆eno ∆pgk ∆gcl ∆glyA ∆serA ∆eno ∆pgk ∆gcl ∆glyA ∆gapA ∆gcl ∆pps ∆gcl ∆pps ∆pck ∆gcl ∆pps ∆pck ∆pgk ∆gcl ∆pps ∆pck ∆pgk ∆glyA

MG1655 SIJ488 BW25113 BW25113 BW25113 SIJ488 ∆eno E1 E2 E3 E3 MG1655 ∆gcl MG1655 ∆gcl ∆pps P1 P2

(Jensen et al., 2015) (Baba et al., 2006) (Baba et al., 2006) (Baba et al., 2006) This study This study This study This study This study This study This study This study This study

NA NA NA NA NA Succinate + Glycerate Succinate + Glycerol + Glycerate Succinate + Glycerol + Glycine + Glycerate Succinate + Glycerol + Glycine + Serine Succinate + Glycerol + Glycine + Glycerate NA Glycerate Glycerol + Glycerate Glycerol + Glycine + Glycerate

670

24

671

Figure legends

672

Figure 1

673

An overview of the structure of central metabolism of E. coli. We divided central metabolism into different

674

generalized pathways as indicated by the colors of the arrows. Reactions that were considered for deletion

675

by our software are marked in yellow frame. Potential carbon sources – i.e., glycerate, succinate, glycerol,

676

and glycine – are shown in bold; their entry point to central metabolism is marked by bold arrows.

677

Metabolites which are used to produce biomass building blocks – that is, that are consumed in the biomass

678

reaction – are circled in green.

679

Figure 2

680

Glycerate:Biomass Ratio (GBR) of different combinations of reaction deletions and carbon sources. Shown

681

are the 50 strains with the highest sensitivity towards glycerate – that is, lowest GBR – for one of the

682

relevant combinations of carbon sources. Empty cells represent combinations of reaction deletions and

683

carbon sources which cannot grow or for which growth is not dependent on glycerate; in both cases, GBR

684

cannot be defined. The strain selection flexibility, as calculated for each strain, corresponds to the ratio

685

between the highest and lowest GBR avlues across the different combinations of carbon sources. Strains

686

and combinations of carbon sources that were chosen for in vivo implementation are shown in green and

687

with a green ‘V’ sign, respectively.

688

Figure 3

689

The metabolic sensor strains chosen for implementation. In each strain, glycerate serves as the precursor

690

for a different set of cellular building block and thus contribute a different fraction of the cellular carbons. (A)

691

The ‘E’ strains in which upper and lower metabolism are completely isolated from each other by the deletion

692

of enolase (∆eno). Succinate serves as the carbon source of lower metabolism. Depending on further gene

693

deletions – of 3-phosphoglycerate kinase (∆pgk) and serine hydroxymethyltransferase (∆glyA) – addition of

694

glycerol and glycine are required to support growth. (B) The ‘P’ strains, deleted in PEP synthetase (∆ppsA)

695

and PEP carboxykinase (∆pckA), such that flux from lower metabolism to upper metabolism is blocked but

696

not vice versa. Addition of succinate is not mandatory as glycerate can feed lower metabolism. As before,

697

depending on further gene deletions – of 3-phosphoglycerate kinase and serine hydroxymethyltransferase –

698

addition of glycerol and glycine are required to support growth.

699

Figure 4

700

Systematic testing of the metabolic sensor strains. Each sensor strain was tested with multiple combinations

701

of carbon sources to confirm that growth was possible when an essential ingredient is missing. The growth

702

patterns observed in (A)-(F) match the expectation form Figure 1, confirming tight selection for the presence

703

of glycerate. Minute growth without glycerol in (C) and (F) can be attributed to the existence of a limited 25

704

internal glycogen reservoir. (G) Unexpectedly, after a long period, strain E3 was able to grow without

705

glycerate, indicating a metabolic leakage that short-circuited the selection for this compound (red curves).

706

Upon further deletion of serA or gapA this growth was abolished as shown in the purple and yellow lines. All

707

experiments were performed in triplicates that showed identical growth profile (±5%) and hence are

708

represented by a single curve. Where relevant, succinate, glycerol, and glycerate were added at 20 mM,

709

while glycine was added at 4 mM.

710

Figure 5

711

Serine biosynthesis in the E3 strain results from metabolic leakage from upper metabolism. Unlike within a

712

WT strain, feeding with completely labeled glycerol-13C3 (and unlabeled succinate and glycine) results in

713

almost fully labeled serine; serine is completely unlabeled when feeding with either glycine-1-13C or glycine-

714

2-13C (and unlabeled glycerol and succinate). Where relevant, succinate, glycerol, and glycerate were

715

added at 20 mM, while glycine was added at 4 mM.

716

Figure 6

717

Endogenous metabolism of saccharic acid provides glycerate for the growth of the sensor strains. (A-D)

718

Saccharic acid can replace glycerate when added to the growth medium of strains P1, P2, P3, and E1 (the

719

strains were further supplemented with the required additional carbon source, as indicated in Figure 2). (E-

720

F) Saccharic acid did not support the growth of strains E2 and E3*, probably due to the lack of metabolic

721

sink for phosphoglycerate. See main text for discussion. All experiments were performed in triplicates that

722

showed identical growth profile (±5%) and hence are represented by a single curve.

723

Figure 7

724

Metabolism of glyoxylate to glycerate via an overexpressed enzyme enables growth of the sensor strains.

725

Gcl (glyoxylate carboligase) was expressed in each sensor strain, further deleted in the native gcl gene.

726

Upon overexpression, glyoxylate could replace glycerate and support growth on the strains (the strains were

727

further supplemented with the required additional carbon source, as indicated in Figure 2). All experiments

728

were performed in triplicates that showed identical growth profile (±5%) and hence are represented by a

729

single curve. Where relevant, succinate, glycerol, and glyoxylate were added at 20 mM, while glycine was

730

added at 4 mM.

731

Figure 8

732

Maximal OD600 of the glycerate sensor strains is determined by the concentration of added glycerate. For

733

each strain, a gradient of 11 concentrations of glycerate was used, each concentration being 1.5-fold higher

734

than the previous. In all cases, with the exception of strain E2 (H), the gradient of glycerate concentrations

735

spanned a broad range of maximal OD600 values. Strain E2 (H) grew very slowly and reached a very similar

736

final OD regardless of the glycerate concentration, indicating the existence of a slow but persistent 26

737

metabolic leakage that short-circuited the selection for glycerate.

738

Figure 9

739

Maximal OD600 as function of glycerate concentrations. Strain E2 is shown only for completeness, as it

740

cannot serve as a true glycerate sensor strain (see main text). We define the Glycerate:OD Ratio (GODR)

741

as the scaling factor between glycerate concentration and the resulting maximal OD. GODR was calculated

742

as described in the methods, and the dashed lines correspond to their values. Our sensor strains display a

743

high sensitivity range towards glycerate concentration, as the GODR values span almost two orders of

744

magnitudes, from 0.45 (strain E3*) to 29 (strain P1 with no succinate).

745

Figure 10

746

The GBR values calculated by our model predict the experimentally derived GODR almost perfectly. (A) R2

747

= 0.96 and RMSE = 0.39. (B) Upon disruption of the unrealistic oxidative pentose phosphate cycle (OPPC),

748

which resulted in increased GBR values for strains P2 and P3 with succinate, the prediction improved even

749

further, such that R2 = 0.99 and RMSE = 0.19.

27

Figure 1 NADP+ NADPH

glucose-6P

gluconolactone-6P

G6PDH

PGI

H2O

gluconate-6P NADP+ NADPH

fructose-6P ATP PFK ADP

CO2

Pi

FBP fructose-1,6BP

glycerol

erythrose-4P

FBA TPI

dihydroxyacetone phosphate

TAL

glyceraldehyde-3P

sedoheptulose-7P

Pi, NAD+ NADH

glycerate-1,3BP SER_ABC

serine

GCV

THF Pi

SHMT glycine

NADH, NAD+, NH3, THF glycine CO2 NAD+ NADH

NAD+

PGM

H2O

ENO

glycerate-2P

phosphoenolpyruvate

ADP PYK ATP

pyruvate

CoA, NAD+ NADH

lactate

NADH NAD+ CoA

ribose-5P

PGK

RPI

glycerate-3P

2K G G NA lu DH

C1-THF

ADP ATP

CO2 acetyl-CoA Pi CoA

acetaldehyde acetyl-P NADH NAD+

ethanol

ADP ATP

acetate

RPE

ribulose-5P

glycerate H2O

ADP ATP

PPCK

PPC

AMP, Pi ATP PPS

PDH

xylulose-5P

H P) D( NA

D(

NA

Pi

+ P)

malate FUM MDH

MAE

CO2

fumarate

CoA

NAD+ NADH

FRD

MQred MQox

UQred UQox

SDH succinate

ATP, CoA ADP, Pi

oxaloacetate CS CoA

citrate

succinyl-CoA

glyoxylate ICL

H2O

aconitate

succinate

H2O

KGDH

CO2 NADH NH3 NADPH NADP+ NAD+

2-ketoglutarate ICDH

isocitrate

NADPH NADP+ CO2

glutamate

NADP+ NADPH

NH3 ATP

glutamine

ADP, Pi

Figure 10

GODR (Glycerate:OD Ratio) (mM/OD, measured)

(A) Original, R2 = 0.96, RMSE = 0.39

(A) OPPC deleted, R2 = 0.99, RMSE = 0.19

Strain E1

10

Strain E3* Strain P1 Strain P1, w/ succinate Strain P2 Strain P2, w/ succinate Strain P3

1

Strain P3, w/ succinate

1 10 GBR (Glycerate:Biomass Ratio) (mmol/gCDW, predicted)

1 10 GBR (Glycerate:Biomass Ratio) (mmol/gCDW, predicted)

glycerate succinate glycerol glycine

PGK | ENO | SHMT PGK | PPS | PPCK PGM | PPS | PPCK G6PDH | PGK | PPS | PPCK G6PDH | PGM | PPS | PPCK PGK | PGM | PPS | PPCK PGK | PPS | PPC | PPCK PGK | PPS | PPCK | SER_ABC PGM | PPS | PPC | PPCK PGM | PPS | PPCK | SER_ABC PGM | PPS | PPCK | SHMT RPE | PGK | PPS | PPCK RPE | PGM | PPS | PPCK TAL | PGK | PPS | PPCK TAL | PGM | PPS | PPCK PGK | PPS | PPCK | SHMT PGK | ENO PGK | ENO | GCV TPI | ENO TPI | ENO | SER_ABC TPI | ENO | SHMT PGK | PPS | PPCK | GCV TPI | PPS | PPCK G6PDH | TPI | PPS | PPCK RPE | TPI | PPS | PPCK TAL | TPI | PPS | PPCK TPI | PPS | PPC | PPCK TPI | PPS | PPCK | SER_ABC TPI | PPS | PPCK | SHMT TPI | ENO | GCV PGK | PPS | MDH PGM | PPS | MDH G6PDH | PGK | PPS | MDH G6PDH | PGM | PPS | MDH PGK | PGM | PPS | MDH PGK | PPS | MDH | SER_ABC PGK | PYK | PPS | MDH M | PPS | MDH | SER_ABC PGM | PPS | MDH | SHMT PGM | PYK | PPS | MDH RPE | PGK | PPS | MDH RPE | PGM | PPS | MDH TAL | PGK | PPS | MDH TAL | PGM | PPS | MDH TPI | PPS | PPCK | GCV PGK | PPS | MDH | SHMT ENO TAL | ENO RPE | ENO PPS | PPCK

+ -

+ + -

+ + +

+ + -

V

+ + + V

V

V

+ + + +

strain selection flexibility 1 10 20 30 40

V

V

Legend

10 GBR, log scale

carbon sources

Figure 2

V V

V

1

Figure 3 (A) Complete insulation scheme glycerol

succinate

glycerate

UPPER METABOLISM 18% of carbons

∆eno

∆pgk glycerate-2P ∆glyA

7% of carbons glycine

LOWER METABOLISM 72% of carbons + cellular energy

Strain E1: ∆eno Strain E2: ∆eno ∆pgk Strain E3: ∆eno ∆pgk ∆glyA legend XXX supporting carbon source

serine 3% of carbons

∆xxx

(B) One-side insulation scheme glycerol UPPER METABOLISM 18% of carbons

glycerate

4% of carbons

glycerate-2P

PEP

∆pgk

∆glyA 7% of carbons glycine

serine 3% of carbons

succinate

LOWER METABOLISM 68% of carbons ∆ppsA + cellular energy ∆pckA

∆xxx

key gene deletion optional gene deletion

Strain P1: ∆pps ∆pck Strain P2: ∆pps ∆pck ∆pgk Strain P3: ∆pps ∆pck ∆pgk ∆glyA

Figure 4 cc

e cin gly l ro ce gly te ina

su

ra

+ + + + + +

+ + + + + +

30

40

te

te

ra

+ + + + + + +

+ + + + + +

+ + + + + +

ce

ra

e cin gly te ina cc e t

ce

+ + + + + +

(C) Strain P3

gly

cc

ce

su

gly

+ + + +

e cin gly l ro ce gly te ina

su

(B) Strain P2

gly

(A) Strain P1

+ + + +

+ + + + + +

+ + + +

OD600

1

0.1

0.01 10

20

30

40

Time (hours)

50

20

30

50

Time (hours)

ce

cc

e cin gly l ro ce gly te ina te ra

(F) Strain E3

50

su

+ + + + + +

+ + + + + +

20

gly

e cin gly l ro ce gly te ina te ra

ce

cc

e cin gly te ina cc su rate ce

gly

+ + + + + +

+ + + + + + +

10

su

(E) Strain E2

+ + + +

40

Time (hours) gly

(D) Strain E1

10

+ + + + + +

+ + + + + +

+ + + +

+ + + + + +

+ + + +

OD600

1

0.1

0.01 20

40

60

Time (hours)

80

20

40

60

20

Time (hours)

40

60

Time (hours)

80

(G)

OD600

1

succinate + glycerol + glycine Strain E3 Strain E3 ∆serA Strain E3 ∆gapA: Strain E3*

0.1

0.01 50

100

150

Time (hours)

200

250

300

Figure 5

WT strain

E3 strain

Fraction of serine molecules

100% 80% 60% 40% 20% 0%

GLC GLY SUC

# labeled carbons 0

1

2

3

GLC GLY SUC

GLC GLY SUC

GLC GLY SUC

GLC GLY SUC

GLC GLY SUC

GLC: glycerol GLY: glycine GLC: glycerol-13C3 GLY: glycine-1-13C SUC: succinate GLY: glycine-2-13C

Figure 6 (A) Strain P1: [20 mM succinate] + 30 mM saccharic acid

(B) Strain P2: [20 mM succinate] + 20 mM glycerol + 30 mM saccharic acid

(C) Strain P3: [20 mM succinate] + 20 mM glycerol + 4 mM glycine + 30 mM saccharic acid

w/ succinate 1

OD600

w/o succinate 0.1

0.01

4

8 12 16 Time (hours)

20

(D) Strain E1: 20 mM succinate + 30 mM saccharic acid

5

10 15 20 Time (hours)

25

(E) Strain E2: 20 mM succinate + 20 mM glycerol + 30 mM saccharic acid

10

20 30 40 Time (hours)

50

(F) Strain E3*: 20 mM succinate + 20 mM glycerol + 4 mM glycine + 30 mM saccharic acid

OD600

1

0.1

0.01

5

10 15 20 Time (hours)

25

30

10

20 30 40 Time (hours)

50

10

20 30 40 Time (hours)

50

Figure 7

OD600

1

Strain E1 Strain E2 Strain E3* Strain P1 Strain P2 Strain P3

0.1

GCL overexpression no GCL

0.01 20

40

60

Time (hours)

80

Figure 8 (A) Strain P1: X glycerate

(B) Strain P1: 20 mM succinate + X glycerate

1

(C) Strain P2: 20 mM glycerol + X glycerate

1

30 mM

1

30 mM

0.1

OD600

OD600

OD600

1.5-fold steps

1.5-fold steps

0.1

1.5-fold steps

8.9 mM

0.1

520 µM

520 µM 150 µM

0.01 10

20

30

Time (hours)

40

10

(D) Strain P2: 20 mM succinate + 20 mM glycerol + X glycerate

10

OD600 0.1

30

(F) Strain P3: 20 mM succinate + 20 mM glycerol + 4 mM glycine + X glycerate 30 mM

1

20

Time (hours)

1

1.2 mM

1.5-fold steps

OD600

Time (hours)

0.01

40

1.5-fold steps

1.5-fold steps

0.1

30

(E) Strain P3: 20 mM glycerol + 4 mM glycine + X glycerate 4 mM

1

20

OD600

0.01

0.1

520 µM 20 µM 69 µM

0.01 10

20

30

40

0.01

50

Time (hours)

0.01 5

(G) Strain E1: 20 mM succinate + X glycerate

10

15

20

Time (hours)

25

10

(H) Strain E2: 20 mM succinate + 20 mM glycerol + X glycerate

20

30

Time (hours)

(I) Strain E3*: 20 mM succinate + 20 mM glycerol + 4 mM glycine + X glycerate

8.9 mM

1

1

69 µM

OD600

OD600

OD600

520 µM

0.1

0.1

0.01

0.01

1.5-fold steps

1.5-fold steps

0.1

1 4 mM

150 µM 9 µM

0.01 5

10

15

20

Time (hours)

25

50

100

150

200

Time (hours)

250

10

20

30

Time (hours)

40

Figure 9

Strain E1: succinate

Maximum OD600

1

0.1

0.01

0.1

1

Concentration of glycerate (mM)

10

GODR (glycerate:OD ratio, mM/OD) 3.2

Strain E2: succinate+glycerol

NA

Strain E3*: succinate+glycerol+glycine

0.45

Strain P1: (no other carbon source)

29

Strain P1: succinate

6.3

Strain P2: glycerol

27

Strain P2: succinate+glycerol

2.3

Strain P3: glycerol+glycine

26

Strain P3: succinate+glycerol+glycine

0.76

Design and engineering of E. coli metabolic sensor strains with a wide sensitivity range Highlights • • • • •

Metabolic sensor strains can grow only if the relevant compound is present We developed a computational platform to design metabolic sensor strains We constructed glycerate sensor strains, dissecting central metabolism to segments We find a perfect match between predicted and experimental dependence on glycerate Metabolic sensor strains reveal key phenomena in central metabolism