Phenotype Profiling of Single Gene Deletion
Mutants of E.coli Using Biolog Technology
Yukako Tohsato and Hirotada Mori

1) Department of Bioscience and Bioinformatics, Ritsumeikan University2) Graduate School of Biological Sciences, Nara Institute of Science and Technology

Phenotype MicroArray Phenotype MicroArray (PM) technology is designed to test alarge number of cellular phenotypes simultaneously (Bochneret al., 2001)
 The system allows monitoring of cellular respiration during
cell growth on 96-well microtiter plates under a maximum of1920 different medium conditions by colorimetricallydetection of generation of purple colored Formazane from
Tetrazolium dye corresponding to the intracellular reducing
state by NADH simultaneously.
one medium condition

Wild TypeTime

No.2

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Wile Type V.S. Mutant
 Compare Single Gene Deletion Mutant to Wild Typeto determine gene function

Wile Type

Comparison

MutantNo.3

What is clarified by PM?
 Genotype → Phenotype → Metabolic Pathway
 Knock out a gene → Which phenotypes change ?
 In this study, analysis using PM data was performed

to discover new alternative pathways and identifyfunctions of genes for which the functions have yetto be determined.

No.42

Our Approach and MethodMaterials
 Escherichia coli K-12
 1 host strain (BW25113)
 204 mutants (Keio collection library (Baba et al., 2006))
 Growth conditions : 1920 （ex. Suger, Nitrogen , Drug

NADH production leveletc）
 Measuring time： 15-minute intervals over 24-hour period.
 Host strain ：10 times，Mutants ： 2 times(a) α-D-Glucose(b) Glycerol(c) D-Glucose-6P

400

400

400

300

300

300

200

200

200

100

100

1000000 2 4 6 8 10 12 14 16 18 20 2202468 10 12 14 16 18 20 2202468 10 12 14 16 18 20 22TimeNo.6

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Web application for PM
 PM data consist ofenormous amount observation

points.
 It made both Excel and me overflow frequently!

 So, we are constructing web applications for PM

data to search and analysis.
 Acknowledgement
 Yusaku Mazaki
 Tomohiro Fujita

No.7

Our Approach We report the results obtained by applying theproposed method to PM data from wild-type
and 45 single gene deletion strains.
 The strains related to central metabolism.
 Selected 288 medium condition of carbon andnitrogen sources.

 Our methods
 Vectorization of raw data
 Hierarchical Clustering

No.84

Vectorization (1) The original raw data less than a threshold were substituted

NADH production levelwith zero (zero-substitution).
 The data were smoothed by taking an average ofconsecutive nine observation points (two hour).
1 k +8aijk = ∑ xijk '9 k '= k
 Each well is expressed with its maximum slope bij.
 PM data for each strain can be considered as 288-dimension
vector data.

bijaijkTimeNo.9

Why did we select maximum slopes ?
 Growth time shift.

12080400024681012

 Maximum values ?
 Areas ?

≠ Maximum slopes !

No.105

Vectorization (2) Calculated the ratio between vector data of mutant and wild.

Wile Type

Comparison

bwbw
bm

Mutantbm

 The ratio data are converted to a data of 0s, -1s and 1s bysetting thresholds for the vector ratios 1.2 and 0.8.
vk = (vk 1 , vk 2 ,K , vk 288 ) +1 indicates that the gene deletion activate the respiratory activity.
 -1 indicates that the gene deletion repress the respiratory activity.

No.11

Clustering Methodsn Manhattan distance d (v1 , v2 ) = ∑ v1k − v2 kk =1

 Distance between Knockout strain and other one
 The degree of similarity using the distancetends to become larger for pairs of vector datathat are less similar.

 Ward’s hierarchical method
 Less susceptible to noise and outliers ascompared to other hierarchical clustering
methods.
No.126

Assignment of condition and P-values to clusters
 Calculated a P-value for each experimental condition（Tavazoie et al., 1999).
C G − C k −1  i   n − i 
P±1 = 1 − ∑   G i =0n  G is the number of all strain data.
 C is the number of the selected group of strains.
 n is the number of strains with a value of +1 (or -1).
 k is the number of strains with a value of +1 (or -1)

within the selected strain group.

No.13

Results !

7

Selection of threshold for zero-substitution
 The value of 100 was set as the threshold for zero-Observed data frequency

substation.
 Low observation strength may lead to unstable
experimental measurement.
2500
2000
1500
1000
500
00100

200

300

400

Observation strength

No.15

Repeatability of the PM data Calculated Pearson correlation coefficients between

PM data for 10 trials in the wild-type.
 0.67 ～ 0.96 (Ave. 0.81)

Abundance ratio

0.4
0.3

High repeatability

0.2
0.1
00.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Correlation

1No.168

Clustering Result
 45 single-gene-knockout mutants in central

metabolism under 288 condition.
 Three major clusters C1 to C3 were obtained.

No.17

Characteristic growth condition
C1

C 21 C 22 C 31 C 32

C1

C 21 C 22 C 31 C 32

C101-A 03

01-F01

03-B 01

01-A 04

01-F0503-B 02

01-A 05
01-A 06

01-F0601-F0703-B 06
03-B 07

01-A 07

01-F0803-B 08

01-A 08

01-F0903-B 09

01-A 09

01-F1003-B 1001-A 1001-F1203-B 1101-A 1101-G 01

03-B 1201-A 1201-B 01

01-G 0301-G 04

03-C 03
03-C 04

01-B 02

01-G 05

03-C 08

01-B 0301-G 06

03-C 1201-B 04

01-G 07

03-D 1101-B 06

01-G 08

03-E0601-B 07

01-G 09

03-E0801-B 08
01-B 09

01-G 1001-G 1103-E1103-F01

01-B 1001-G 1203-F02

01-B 1101-H 01

03-F0301-C 01

01-H 05

01-C 02

01-H 07

03-F0501-C 03

01-H 08

03-G 01

01-C 04
01-C 07

01-H 0901-H 1003-G 02
03-G 03

01-C 08

02-A 06

03-G 04

01-C 09

02-A 1203-G 07

01-C 1002-B 02

03-G 08

01-C 1102-B 03

03-G 1101-C 1202-E05

03-H 01

01-D 01

02-E1203-H 02

01-D 06
01-D 08

02-F01
02-F0303-H 03
03-H 04

01-D 1202-G 02

03-H 05

01-E0102-H 0903-H 06

01-E0203-A 02

03-H 07

01-E0503-A 0703-H 0901-E0703-A 0803-H 1001-E0801-E1003-A 09
03-A 1003-H 1103-H 1201-E1103-A 1101-E1203-A 1203-F04

C 21 C 22 C 31 C 32

Phenotype profiles ofthese five clusters.
■ P+1 <= 0.05
■ P+1 <= 0.1■ P-1 <= 0.05
■ P-1 <= 0.1

C2 group activatedcellular respiratory
activity.
C3 group repressed
cellular respiratory
activity.

No.189Distribution of gene-knockout affects
Cluster C1: green
Cluster C21: red
Cluster C22: pink
Cluster C31: blue
Cluster C32: light blue

Glycolysis

Pentosephosphate
pathway

TCA cycle

 The mutants of cluster

C1 are located at theearly stage of theglycolysis.
 Four mutants in cluster
C31 are closely related
to the TCA cycle.
No.19

Analysis result for Phoshotransferase system PtsG and Crr form enzyme II complex as PTS(phosphotransferase) system .
 However, deletion of ptsG and crr genes affect opposite
direction in phenotype profiles.
D-Glucose

ptsG
crr

α-D-Glucose-6P
 Crr might function as switching for further steps after

transportation of Glucose.

No.20

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Phenotype Similarity and Pathway Distance (1) What kind of relationship exist between them ?
 Minimal pathway distance : The number of the compound on

shortest path between given genes.
D-Glucose
ptsG

Between ptsG and pgi
Distance: 1

α-D-Glucose
pgi
α-D-Glucose-6P

 Major metabolic path data is represented one adjacency matrix of adirected graph.
 The step between the two compounds in the same metabolic map
can be extracted using shortest paths algorithms.
No.21

Phenotype Similarity and Pathway Distance (2) We calculated the minimal pathway distance for all strain

Manhattan distancepairs whose knockout genes are involved in central
metabolism.
 For established pairs, phenotypic similarity were determined.
250
200
150
100
50
00

5

1015

Pathway distance The results showed no correlation between them!
No.22

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Conclusion
 We performed to analyze further insight into central

metabolic pathway network to PM data.
 Medium conditions that activate or repress cellularrespiratory activities for the different strain groups wereidentifies.
 These results suggested the possibility of metabolism
steps with unknown bypass route.
 However, our proposal methods have insufficient sensitivity
to continue to identify functions of genes of uncertain
function of to analysis for further large-scale data.
 Robustness
 Alternative passes
 Bypass route Unknown passes

No.23

Future works
 Computational method for prediction about boundstrength among known reactions.
 Double gene knockout experiments.
 Combination PM and another high-throughput data.

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Thank you very much for your attention.

No.25

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