Improving Phenylalanine Production by Escherichia Coli

8/8/2019 Improving Phenylalanine Production by Escherichia Coli

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Improving phenylalanine

production by Escherichia coli

using comprehensive

metabolomics information

Marit J. van der Werf

TNO: Netherlands Organization for AppliedScientific Research Independent research organisation (NGO)

Founded in 1932

>5400 Employees

Turnover (2005) 562 MEuro

2/3 = market turnover

broad knowledge and technology base

International client base

offices in Detroit, Boston, Tokyo

TNO improves the competiveness of companies and assists

governments with policy matters


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TNO: Industrial Biotechnology

FeedstockEngineering

FungalBiotechnology

Metabolomics

Track record of >25 years Seven out of 10 leading companies in industrial biotechnology are our customers

Broad technology base From established technologies to the newest technologies

Worldwide forefront position on specific topics

Improving the economics ofmicrobial production processes

The costs of large-scale microbial production

processes are primarily determined by substrate

costs

Use cheaper substrates/feed stocks Yield improvement

Strain improvement

Medium (fermentation) optimization


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Microbial Strain Improvement

The genetic possibilities are almost infinite to the

metabolic engineer

Target selection: Which gene(s) to pick?

Target selection current state-of-the-art

Rational selection

Target the genes that you think to be important

A lot of educated guess and gut feeling

Metabolic (flux) models

Construct a (limited) metabolic model from substrate toproduct

Predict in silico which genes should be targeted

Reactions that are not known to exist, or that are not put

into the model, are not considered

Metabolomics


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Why metabolomics? Closest to the phenotype

Data analysis/biostatistics Translation of differences in metabolomes into phenotypic

differences

Target selection by metabolomics

Genome

Transcriptome

Proteome

Metabolome

Phenotype

Analyze all

metabolites

Phenotype

Identify and

understand key

biological processesIdentify correlationsIn multiple data sets

Biostatistics

Question driven metabolomics approach

Results in an un-biased, global view of the

biological processes involved

Starting point for generating hypothesis

Hypothesis-free approach

Fishing expedition

Key to successful

application is

EXPERIMENTAL DESIGN Optimization of the information

content of the metabolomics data

set with respect to the question

under study


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TNOs Metabolomics

platformRapid sampling and

collection of quenchedsample(s)

Extracellularmetabolites

Sample for determinationof biomass concentration

Extraction

Intracellular + interstitialmetabolites

Add IS biomass normalization

Concentratedcell suspension

Split sample for analysis by the

different analytical methods

Add ISs for every analytical method

Further work-up of the sub-samples

as required for the different methods

10 15 20 25 30 35

0

100,000

200,000

300,000

Add IS for volume

normalization

(Diluted)Extracelluar fluid

Add IS for normalization injection volume

Add ISs for derivatization efficiency

Add ISs for retention time normalization

Analyze the sub-samples

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505.83

807.87

425.84

524.63

521.82

338.80 338.80860.25864.24

238.86765.09741.72

447.81 816.82844.29 811.21

NL:2.55E6

BasePeak m/z=220.00-1200.00F:ITMS -cESIFull ms [120.00-1200.00]MSfullms_esiposneg120_1200

_nr16

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505.83

807.87

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521.82

338.80 338.80860.25864.24

238.86765.09741.72

447.81 816.82844.29 811.21

NL:2.55E6


_nr16

0 5 10 15 20 25 30 35 40

Time(min)

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10

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30

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505.83

807.87

425.84

524.63

521.82

338.80 338.80860.25864.24

238.86765.09741.72

447.81 816.82844.29 811.21

NL:2.55E6


_nr16

0 5 10 15 20 25 30 35 40

Time(min)

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10

20

30

40

50

60

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90

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505.83

807.87

425.84

524.63

521.82

338.80 338.80860.25864.24

238.86765.09741.72

447.81 816.82844.29 811.21

NL:2.55E6


_nr16

6 complementary analytical

methods Quantitative

Allows the detection of 95-97% of

the microbial metabolites

Has been extensively validated in

order to allow the analysis of

snapshot samples

Has been successfully applied

for the analysis of metabolomes

of many (micro-) organisms

Koek et al (2006) Anal. Chem. 78:1272

Coulier et al (2006) Anal. Chem. 78:6573

Are targets identified by the combined

metabolomics/MVDA approach really important for

strain improvement?

Phenylalanine production by Escherichia coli

Patented strain that had already been optimized

by > 8 steps of rational design

Demonstration project


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Sampling times:

16, 24, 40

and 48 hours

Carbon

Source:

glucose

Phosphateconcentration:

1x

Oxygentension:

set at 30%

pH:

6.5

Strain:

Phe-

overproducer

Exp2:

32 hoursExp6:

1/3 x

Exp5:

3x

Exp4:

300 rpm

Exp3:

2%

Exp7:

Succinate

Exp9:

wild-typeExp10:

pH=7

Reference

fermentation

Generation of samples under (growth) conditions that

result in large differences in phenylalanine production

Controlled batch fermentations

28 metabolome samples analyzed

Experimental design

Analysis of the metabolomes

8.00 12.00 16.00 20.00 24.00 28.00 32.00 36.00 40.00 44.00 48.00 52.00 56.00

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

4000000

4500000

Time-->

AbundanceTIC: DSM-X90.D


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Differential expression 100s of targets

Assumption: the larger the response, the higher the

biological relevance

But: is a constitutive gene that is 20% upregulated less

relevant than an inducible gene that is 1000-fold

induced?

Multivariate data analysis tools

Data analysis- Finding the needle in the haystack

Unbiased data interpretation

No a-prioriknowledge about the biological system

required

Strength of the correlation of the biomolecule with

the phenotype of interest forms the basis for

ranking the targets

Multivariate data analysis


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PLS - Ranking of the importantmetabolites

[P] = b1A + b2B + b3C + ...........

Top 15

Phenylalanine production by Escherichia coli

Unknown 14.5615

Unknown 17.0414

2,3-Dihydroxybenzoate13

N-acetyl-aspartate12

Unknown- 15.8511

N-acetylglutamate10

Dipeptide with a glycine ?9

Phenyllactate8

2-Amino-4-hydroxy-6-(erythro-1,2,3-trohydroxypropyl)-

dihydropteridine triphosphate (folate intermediate) (?)

7

Tyrosine6

3,5-Dihydroxypentanoate (?)5

Ethylmalate (?)4

Glycine3

Chorismate2

4-Hydroxybenzylalcohol1

IdentityRank


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Relation identified metabolite- Phenylalanine titer

4-hydroxybenzylalcohol

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0,00 0,50 1,00 1,50 2,00 2,50

Phe titer (g/l)

Peakareame

tabolite

mutant

WT

Chorismate

0,000

0,005

0,010

0,0150,020

0,025

0,030

0,035

0 0,5 1 1,5 2 2,5

Phe titer (g/l)

Metabolite

concentra

tion

muatant

WT

Glycine

0,E+00

2,E+04

4,E+04

6,E+04

8,E+04

1,E+05

1,E+05

1,E+05

0,000 0,500 1,000 1,500 2,000 2,500

Phe titer (g/l)

Peakareametabolite

4-Hydroxybenzylalcohol

N-Acetylglutamate

050000

100000

150000

200000

250000

300000

350000

0 0,5 1 1,5 2 2,5

Phe titer (g/l)

peakareametabolite

mutant

WT

Classification of relevant metabolites

Unknown 14.56

Unknown - 17.04

N-Acetyl-aspartate

Unknown -15.85

N-Acetyl-glutamate

Dipeptide with a glycine?

3,5-Dihydroxypentanoate

Ethylmalate

Glycine

Seemingly unrelated metabolites

2,3-Dihydroxybenzoate

Phenyllactate

2-Amino-4-hydroxy-6-(erythro-1,2,3-trohydroxypropyl)-dihydropteridine triphosphate (folate intermediate)

Tyrosine

4-Hydroxybenzylalcohol

Side routes of phenylalanine biosynthesis

Erythrose-4-phosphate

Chorismate

Phenylalanine biosynthesis


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Leads identified in

the phenylalanine

biosynthesis route

All positive

correlations

Intermediates:

Overexpress intermediate

converting enzyme (gene)

Intermediates of side routes:

Knock-out side-route


Prephenate

L-Tryptophan

L-TyrosineL-Phenylalanine

Chorismate


Thiamine

-

-

-

-

2,3-dihydroxy-

benzoate

Enterobactin

2-Amino-4-hydroxy-6-

(erythro-1,2,3-trihydroxypropyl)-

dihydropteridine triphosphate

Folate

Ubiquinone-8


Prephenate

L-Tryptophan


Chorismate


Thiamine

-

-

-

-

2,3-dihydroxy-

benzoate

Enterobactin




Folate

Ubiquinone-8


Prephenate

L-Tryptophan


Chorismate


Thiamine

-

-

-

-

2,3-dihydroxy-

benzoate

Enterobactin




Folate

Ubiquinone-8

0,000

0,005

0,010

0,015

0,020

0,025

0,030

0,035

0,0 1,0 2,0 3,0 4,0

mutant

WT

Phenylalanine yieldChorismateconc.

0

20

40

60

80

100

120

140

160

180

N+L

N+D

N+P

-E+L

-E+D

-E+P

-T

+L

-T

+D

-T

+PR

elative(specific)pheny

lalanineconcentration(%)

Relative Phenylalanine concentration

Relative Specific Phenylalanine concentration

Validation of leads

Metabolomicsre

sults:50%

improveme

nt

ofanindustrialst

rain

WT


11/12

40

60

80

100

120

Reference + CI0355 + CI0353 + CI0254 - CI0241 + CI0247 + CI0243 + CI0121

RelativeProductConcentration(%)

Medium improvement

- Validation of leads

Metabolom

icsresults:

12%impro

vement

ofanindu

strialprod

uctionpro

cess

Conclusions

Proven that leads identified by a question driven

metabolomics/MVDA approach are relevant for

strain and medium improvement 12-50% improvement

Multivariate data analysis tools are powerful tools to

extract relevant information from large data sets Unbiased identification and ranking of targets

Metabolomics/MVDA works like a navigator Helps you find the quickest way and make the largest

steps

Opens up the black box of cellular metabolism


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Acknowledgements

Microbiology Karin Overkamp, Nicole van Luijk, Roelie Bijl, Annette Maathuis,

Alwin Albers, Marloes van Beek, Machtelt Braaksma, Robert van

den Berg

Analytical Chemistry Thomas Hankemeier, Maud Koek, Bas Muilwijk, Leon Coulier,

Richard Bas, Leo van Stee

Biostatistics Sabina Bijlsma, Carina Rubingh, Bianca van der Vat, Jack Vogels,

Renger Jellema, Age Smilde, Albert Tas

DSM

Documents

Improving Phenylalanine Production by Escherichia Coli