1. Introduction

Phenotype Profiling of Single Gene Deletion Mutants of E. coli Using Biolog .....
any pair of compounds in the adjacency matrix using Dijkstra's algorithm.

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PHENOTYPE PROFILING of SINGLE GENE DELETION MUTANTS OF E. COLI USING BIOLOG
TECHONOLOGY
YUKAKO TOHSATO1 ? ? HIROTADA MORI2,3
yukako@sk.ritsumei.ac.jp ?? hmori@gtc.naist.jp 1 Department of Bioscience and Bioinformatics, Ritsumeikan University, 1-
1-1 Nojihigashi, Kusatsu, Shiga, 525-8577, Japan
2 Graduate School of Biological Sciences, Nara Institute of Science and
Technology, 8916-5 Takayama, Ikoma, Nara 630-0101, Japan
3 Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata
997-0017, Japan Phenotype MicroArray (PM) technology is high-throughput phenotyping
system [1] and is directly applicable to assay the effects of genetic
changes in cells. In this study, we performed comprehensive PM analysis
using single gene deletion mutants of central metabolic pathway and
related genes. To elucidate the structure of central metabolic networks
in Escherichia coli K-12, we focused 288 different PM conditions of
carbon and nitrogen sources and performed bioinformatic analysis. For
data processing, we employed noise reduction procedures. The distance
between each of the mutants was defined by Manhattan distance and
agglomerative Ward's hierarchical method was applied for clustering
analysis. As a result, five clusters were revealed which represented to
activate or repress cellular respiratory activities. Furthermore, the
results might suggest that Glyceraldehyde-3P plays a key role as a
molecular switch of central metabolic network.
Keywords: Phenotype MicroArray; phenotype; clustering; metabolic
pathway
Introduction The definition and testing of phenotypes has had a key role in genetics and
this is also true in present systems biology. For a long way to complete
understanding metabolic network in a cell, even though numerous
accumulation of knowledge of enzymes genetically and biochemically, still
it is too short to understand the whole system of this network. Since
genome sequencing project, especially in 1990s, new comprehensive
technology, such as DNA microarray for transcription and yeast two hybrid
or pull-down assay for protein-protein interaction by Mass spectrometry,
have been developed. And combinatorial analysis has had big contribution
not only basic scientific knowledge but seeking potential pharmacological
targets etc. The central metabolic pathway is one of the well-studied
cellular enzymatic networks, however, the whole regulatory mechanism of
this pathway including transcription, translation and enzymatic activity is
still remain to be analyzed. "Robustness" is one of the most important
features of cellular organisms and this is also the case in the central
metabolic pathway of Escherichia coli. E. coli cell, even such small
bacterial cell, accepts single gene deletion of most of the steps of
central metabolic pathway easily. Ishii and his colleagues proposed
compensatory mechanism of such gene deletion by alteration of
transcription, enzyme copy number and their activities to maintain cellular
homeostasis [2]. This is clear "Robustness" phenotype plausibly by
activation of alternative enzymes or bypass pathways, etc. In this study,
analysis using Phenotype MicroArray (PM) data [1] was performed to discover
new alternative pathways and identify functions of genes for which the
functions have yet to be determined.
PM technology was originally developed by Bochner to open up opportunity
for finding the unique traits of individual organisms and for recognizing
traits common to group of organisms, such as species [3] and expanding as a
high-throughput tool for global analysis of cellular phenotypes in post-
genomic era [1]. This system allows monitoring of cellular respiration
during cell growth on 96-well microtiter plates under a maximum of 1920
different medium conditions by colorimetrically detection of generation of
purple colored Formazane from Tetrazolium dye corresponding to the
intracellular reducing state by NADH simultaneously.
Several studies using PM have been reported [4, 5, 6], but most of those
used the absolute values generated by PM. However, experimental data,
especially by such comprehensive high-throughput analyses system, generally
includes a great deal of noises. In this study, to reduce noises and make
analysis more reliable, relative ratio and vector data from reference wild
type and mutant cells were used. We report here the results obtained by
applying the proposed method to PM data from wild-type cell and 45 single
gene deletion strains. Materials and Methods
IV Phenotype MicroArray Data and E.coli Strains Selected 45 single gene deletion mutants of glycolysis, TCA cycle and
pentose phosphate pathway from Keio collection [7] were used and listed in
Table 1. The wild-type host strain of Keio collection (BW25113 [8]) was
used as a reference strain.
Fig. 1 shows examples of ten times repeats Biolog test of wild type
BW25113 with time (hrs., X-axis) and NADH production level (Y-axis). Figs
1a and 1b show the results with ?-D-Glucose and Glycerol medium conditions
respectively.
96 time points at every 15 min for 24 hours under 288 different
conditions (Biolog Assay Plate No. 1 to 3) of carbon and nitrogen sources
were collected. These 288 screening conditions were listed in Appendix.
Experiments were repeated twice for each mutant strains, and ten times for
the wild-type strain under the same conditions. Table 1. List of 45 single-gene-knockout mutants used in this analysis. The
genes deleted were assigned to metabolic maps according to the KEGG
database [9]. Glycolysis (G), TCA cycle (T) and Pentose phosphate pathway
(P) in Map column. All the assigned pathways are listed.
|Gene |Function |Map |
|detecte| | |
|d | | |
|aceF |pyruvate dehydrogenase, |G |
| |dihydrolipoyltransacetylase component E2 | |
|acs |acetyl-CoA synthetase |G |
|adhC |alcohol dehydrogenase class III |G |
|adhE |CoA-linked acetaldehyde dehydrogenase, |G |
| |iron-dependent alcohol dehydrogenase | |
|adhP |alcohol dehydrogenase |G |
|agp |glucose-1-phosphatase |G |
|ascF |PTS family enzyme IIBC |G |
| |component,cellobiose/salicin/arbutin-specif| |
| |ic | |
|crr |PTS family enzyme IIA component |G |
|eda |???????? |P |
| |2-keto-4-hydroxyglutarate aldolase, | |
| |oxaloacetate decarboxylase | |
|edd |6-phosphogluconate dehydratase |P |
|fbaA |fructose-bisphosphate aldolase, class II |G, P|
|fbaB |fructose-bisphosphate aldolase class I |G, P|
|fbp |fructose-1,6-bisphosphatase |G, P|
|frdA |fumarate reductase, anaerobic, catalytic |T |
| |and NAD/flavoprotein subunit | |
|frdB |fumarate reductase, anaerobic, Fe-S subunit|T |
|frdC |fumarate reductase, anaerobic, membrane |T |
| |anchor polypeptide | |
|frdD |fumarate reductase, anaerobic, membrane |T |
| |anchor polypeptide | |
|fruA |PTS family enzyme IIB'BC, fructose-specific|G |
|galM |galactose-1-epimerase (mutarotase) |G |
|glk |glucokinase |G |
|glpX |fructose 1,6-bisphosphatase II, in glycerol|G |
| |metabolism | |
|gltA |citrate synthase |T |
|gndC |gluconate-6-phosphate dehydrogenase, |P |
| |decarboxylating | |
|icdA |e14 prophage; isocitrate dehydrogenase, |T |
| |specific for NADP+ | |
|malX |PTS family enzyme IIBC component, |G |
| |maltose/glucose-specific | |
|pck |phosphoenolpyruvate carboxykinase |T |
|pfkA |6-phosphofructokinase I |G, P|
|pfkB |6-phosphofructokinase II |G, P|
|pgi |glucosephosphate isomerase |G, P|
|pgm |phosphoglucomutase |G |
|ptsG |PTS family enzyme IIBC component, |G |
| |glucose-specific | |
|pykA |pyruvate kinase II |G |
|pykF |pyruvate kinase I |G |
|rpe |D-ribulose-5-phosphate 3-epimerase |P |
|rpi |ribosephosphate isomerase, constitutive |P |
|rpiB |ribose 5-phosphate isomerase B |P |
|sucC |succinyl-CoA synthetase, beta subunit |T |
|tktA |transketolase 1, thiamin-binding |P |
|tktB |transketolase 2, thiamin-binding |P |
|tpiA |triosephosphate isomerase |G |
|ybhE |putative isomerase |P |
|ybiC |putative dehydrogenase |T |
|yccX |predicted acylphosphatase |G |
|yibO |phosphoglycerate mutase III, |G |
| |cofactor-independent | |
|zwf |glucose-6-phosphate dehydrogenase |P |
V Vectorization of Data First, "zero-substitution" procedure was performed as follows; the original
raw data from each strain under 288 medium conditions less than a certain
threshold were sub