wendish, 2003; genome-wideexpressionanalysis in corynebacteriumglutamicum

Genome-wide expression analysis in Corynebacteriumglutamicum using DNA microarrays

Volker F. Wendisch *

Institute of Biotechnology, 1 Research Center Julich, D-52428 Julich, Germany

Received 20 November 2002; received in revised form 20 January 2003; accepted 11 March 2003

Abstract

DNA microarray technology has become an important research tool for microbiology and biotechnology as it allows

for comprehensive DNA and RNA analyses to characterize genetic diversity and gene expression in a genome-wide

manner. DNA microarrays have been applied extensively to study the biology of many bacteria including

Mycobacterium tuberculosis , but only recently have they been used for the related high-GC Gram-positive

Corynebacterium glutamicum , which is widely used for biotechnological amino acid production. Besides the design

and generation of microarrays as well as their use in hybridization experiments and subsequent data analysis, recent

applications of DNA microarray technology in C. glutamicum including the characterization of ribose-specific gene

expression and the valine stress response will be described. Emerging perspectives of functional genomics to enlarge our

insight into fundamental biology of C. glutamicum and their impact on applied biotechnology will be discussed.

# 2003 Elsevier B.V. All rights reserved.

Keywords: Genome-wide gene expression analysis; DNA chips; DNA microarrays; Cluster analysis; Genome map image; Global

regulatory mechanisms; Corynebacterium ; Mycobacterium

1. Introduction

C. glutamicum was isolated as a natural L-

glutamate producer and high-producing strains

for the production of L-glutamate, but also of L-

lysine and other amino acids, have been generated

through classical mutation and selection (Sahm et

al., 1996). With the advent of molecular biology

methods for this organism, targeted metabolic

pathway engineering became possible. Besides the

molecular elucidation of amino acid biosynthesis

and central metabolic pathways on the genetic and

biochemical level, carbon flux analyses have al-

lowed to gain a quantitative understanding of the

corynebacterial central metabolism. Furthermore,

these studies have led to the rational improvement

of C. glutamicum strains by ‘metabolic design’ for

the production of D-pantothenate, L-isoleucine, L-

valine, L-threonine and L-lysine (Sahm et al.,

2000). After the determination of the C. glutami-

cum genome sequence (Tauch et al., 2002), genetic

methods can now be performed on the basis of the

whole genome information. Genome-wide gene

expression analyses with DNA microarrays allow* Tel.: �/49-2461-615169; fax: �/49-2461-612710.

E-mail address: [email protected] (V.F. Wendisch).

Journal of Biotechnology 104 (2003) 273�/285

www.elsevier.com/locate/jbiotec

0168-1656/03/$ - see front matter # 2003 Elsevier B.V. All rights reserved.

doi:10.1016/S0168-1656(03)00147-0

mailto:[email protected]

to unravel global regulatory mechanisms and holdthe promise for targeted biotechnological strain

improvement off the known pathways of central

metabolism and amino acid biosynthesis.

This review focuses on the applications of

whole-genome DNA microarrays for genome-

wide expression analysis (transcriptomics) and

comparative genomics of C. glutamicum and,

when useful, refers to similar analyses of anothermember of the Corynebacterianeae , M. tuberculo-

sis . On a gene by gene basis, gene expression

analyses in C. glutamicum have been performed

previously on the transcriptional and RNA level

using Northern blotting (e.g. Follettie et al., 1988;

Schwinde et al., 1993; Mateos et al., 1994), primer

extension (e.g. Schwinde et al., 1993; Mateos et al.,

1994; Patek et al., 1996) and plasmid-borne orchromosomal transcriptional reporter gene fusions

(e.g. Eikmanns et al., 1991; Vasicova et al., 1998).

The analysis of 33 promoters from C. glutamicum

revealed conserved sequences centred about 10 bp

(TA.aaT) and 35 bp (ttGcca) upstream of the

transcriptional start site with the consensus hex-

amer of the �/35 region being much less conserved

than in Escherichia coli , Bacillus subtilis , Lactoba-

cilli and Streptococci (Patek et al., 1996). The

availability of the genome-based methods for C.

glutamicum will allow to refine this analysis,

taking into account several identified RNA poly-

merase sigma factors. Similarly, a combination of

a bioinformatic approach to predict all putative

operons based on a detailed analysis of the genome

sequence with an experimental approach based ongene expression analysis using DNA microarrays

for their verification, as recently performed for E.

coli (Sabatti et al., 2002), will amend our under-

standing of the basic mechanisms of gene expres-

sion in C. glutamicum .

2. Construction and use of whole-genome C.glutamicum DNA microarrays

For the analysis of genome-wide expression

patterns, several techniques are currently in use:

DNA microarrays (Schena et al., 1995; Lockhart

et al., 1996), proteome analyses, which also allow

to detect post-translational modifications of pro-

teins and which are established for C. glutamicum

(Hermann et al., 2001; Schaffer et al., 2001),

differential display (Liang and Pardee, 1992),

SAGE (Velculescu et al., 1995) and MPSS (Bren-

ner et al., 2000). The latter two methods involve

cloning and sequencing of expressed RNAs

whereas DNA microarray experiments rely on

rapid nucleic acid hybridization. It should be

noted that different RNA levels indicate differen-tial gene expression, but it remains to be deter-

mined whether they are due to transcriptional

control or regulated RNA degradation. DNA

microarrays are based either on single-stranded

DNA oligonucleotides (Lockhart et al., 1996) or

on double-stranded DNA fragments (Schena et

al., 1995) generated by PCR amplification of each

gene of an organism (reviewed in Ye et al., 2001;van Hal et al., 2000; Rhodius et al., 2002) or in

rare cases of inserts from a random DNA library

(Hayward et al., 2000). Whereas for M. tubercu-

losis , both DNA microarray types are in use (e.g.

Wilson et al., 1999; Salamon et al., 2000), so far

genome-wide expression analysis in C. glutamicum

was performed only using DNA microarrays with

double-stranded DNA (Muffler et al., 2002; Langeet al., 2003; Gerstmeir et al., 2003; Wendisch,

unpublished results).

2.1. PCR-product-based whole-genome

microarrays of C. glutamicum

The generation of PCR-product-based whole-

genome microarrays and their use for gene expres-

sion analysis was pioneered for Saccharomyces

cerevisiae by the labs of Pat Brown and David

Botstein (Schena et al., 1995; DeRisi et al., 1997;

Ye et al., 2001). Subsequently, genome-wide ex-

pression analysis was established for the model

bacteria E. coli (e.g. Richmond et al., 1999;

Khodursky et al., 2000; Zimmer et al., 2000; Wei

et al., 2001) and B. subtilis (e.g. Fawcett et al.,

2000), for a member of the Corynebacterianeae ,M. tuberculosis (Wilson et al., 1999; Behr et al.,

1999) and several other bacteria (reviewed in Ye et

al., 2001). For many organisms including C.

glutamicum , preliminary tests of parallel transcript

profiling of few genes have been performed

(Schena et al., 1995; de Saizieu et al., 1998;

V.F. Wendisch / Journal of Biotechnology 104 (2003) 273�/285274

Hayashi et al., 2002; Loos et al., 2001). Now,whole-genome DNA microarrays allow to perform

genome-wide gene expression analysis as well as

comparative genomics experiments in C. glutami-

cum (Muffler et al., 2002; Lange et al., 2003; Ishige

et al., 2003; Gerstmeir et al., 2003). The C.

glutamicum whole-genome DNA microarrays

used in the author’s laboratory comprise 3673

PCR products covering 2860 of the 2994 genes(506 in duplicate) described for the genome

according to NCBI NC003450 and 284 further

putative coding sequences (23 in duplicate). Ad-

ditionally, 100 spots of C. glutamicum genomic

DNA are present as normalization controls and 16

spots of l-DNA, 16 spots of E. coli and 1 spot of

the E. coli aceK gene as negative controls.

2.2. RNA isolation and fluorescent labelling, whole-

genome DNA microarray hybridization and

fluorescence scanning

In general, only minor modifications of methods

that had been developed for DNA microarray

analysis of other microorganisms were needed to

transfer this technique to C. glutamicum . Typi-

cally, total RNA of sufficient quality and quantitycan be purified after mechanical disruption of C.

glutamicum cells (Lange et al., 2003). Quenching

to minimize RNA degradation, a problem much

more pronounced in bacteria than in eucarya, can

be achieved by rapid cooling or by chemical

treatment, e.g. with azide (Lange et al., 2003;

Ishige et al., 2003; Gerstmeir et al., 2003). A

protocol for the selective isolation and purificationof bacterial mRNA is also available (Wendisch et

al., 2001). RNA can be labelled green- or red-

fluorescently in a reverse transcription reaction

primed with random dNTP hexamers, either

directly using e.g. Cy3-dUTP or Cy5-dUTP or

indirectly using aminoallyl-dUTP followed by

reacting the modified cDNA with monoreactive

Cy3- or Cy5-dyes. After labelled cDNAs arepurified from dNTPs not incorporated, hybridiza-

tion to the whole-genome DNA microarrays is

performed in humid chambers. Hybridization

periods may vary between 5 and 18 h and stringent

washing is performed under low salt conditions.

Several commercial fluorescence scanners can be

used for determining fluorescence at 635 and 532nm with a resolution between 5 and 20 mm. The

recorded fluorograms can be exported to a number

of file formats containing the fluorescence infor-

mation of both fluorophores for each pixel.

2.3. Storage and analysis of raw data from C.

glutamicum whole-genome DNA microarray

hybridizations

Whole-genome DNA microarray experiments

generate many data and hence the need to store

primary data rather than derived values in a

searchable format. Several commercial (e.g. Axon

Acuity, Lion arraySCOUT, Silicon Genetics

GeneSpring) and academic solutions (e.g. Stanford

Microarray Database, EcoReg) are available. Fig.

1 summarizes some features of such a database (T.Polen, unpublished). All relevant experimental

information (e.g. strain, media, pre-culture condi-

tions, growth curve, number of generations for

which a balanced condition has been maintained

or time point of harvest after a particular stimulus,

etc.) for each experiment are stored in an accom-

panying data table. Raw primary data including

position on the array, number of pixels represent-ing the spot or the local background, etc., and

derived data such as fluorescence ratios are stored

in a relational database. The fluorogram of the

DNA microarray hybridization (Fig. 1A) is stored

and hybridization spots on all visual representa-

tions are linked to the raw data and further

available information about that gene in a web-

based manner (Fig. 1B). Genome map images(Zimmer et al., 2000) representing hybridization

signals of each gene arranged according to the

position on the genome (Fig. 1C) can also be

generated. To compare expression of a particular

gene throughout the experimental conditions re-

presented in the database, expression changes are

summarized, grouped (B/0.5; B/0.66 and �/0.5;

between 0.66 and 1.5; �/1.5 and B/2; �/2) andgraphically represented (Fig. 1D).

Relative RNA levels of a gene are calculated

from the net fluorescence intensities obtained in a

DNA microarray hybridization experiment. It is

important that relative RNA levels are calculated

only from fluorescence signals clearly exceeding

V.F. Wendisch / Journal of Biotechnology 104 (2003) 273�/285 275

background noise and when neither fluorescence

signal shows a signal-to-noise-ratio greater than a

defined threshold, e.g. threefold, the signals should

be considered too low to derive a relative RNA

level for that gene. In fluorescence images scanned

at high resolution (5 or 10 mm), hybridization spots

are represented typically by at least 100 pixels.

Calculations taking the variance of pixel informa-

Fig. 1. Example of a relational database for storage and analysis of whole-genome DNA microarray data based on the freely available

database management system mySQL. Fluorogram of a genome-wide expression analysis using a whole genome C. glutamicum DNA

microarray (A) linked to information on raw data as well as background information (B). Relative RNA levels of operons can be

visualized as a genome map image (C). Expression data of individual genes can be searched in all experiments, grouped (ratios B/0.5 or

between 0.5 and 0.66 or between 0.66 and 1.5 or between 1.5 and 2 or �/2) and summarized graphically (D).


tion for hybridization spots into account, such asthe ratio of medians, offer robust means to

calculate relative RNA levels from hybridization

images.

2.4. Bioinformatic tools

Genome-wide expression analyses pose the

challenge to carefully control all experimental

parameters starting from the pre-cultivation tothe final data analysis. The problem of noise in

such analyses is obvious and needs to be addressed

for interpretation of the DNA microarray results.

Several statistical approaches have been estab-

lished to identify statistically significant gene

expression changes (reviewed, e.g. in Pan, 2002).

Among those, the two-sample t-test has been used

to identify significant gene expression changes ingenome-wide expression analysis (e.g. Arfin et al.,

2000; Lehnen et al., 2002; Polen et al., 2003). It is

clearly a prerequisite to include more than two

independent replicates in a genome-wide expres-

sion analysis and these replicates have to be

obtained from independent cultivations to take

biological as well as experimental noise into

account.A versatile tool to identify and visualize gen-

ome-wide expression patterns of many different

experimental conditions was introduced by Eisen

et al. (1998). By hierarchical clustering of gene

expression data, genes as well as experimental

conditions are sorted according to similarities in

gene expression patterns. This and similar analyses

(reviewed in Tamames et al., 2002; Rhodius et al.,2002) are well suited to identify operons based on

experimentally determined expression (for E. coli ,

Sabatti et al., 2002) as can be demonstrated by

hierarchical clustering of a set of 220 C. glutami-

cum whole-genome DNA microarray experiments

from our lab (Fig. 2). Whereas none of the

experimental conditions aimed at the analysis of

the regulation of arginine biosynthesis (specifi-cally, neither experiments using arginine auxo-

trophic mutants nor experiments using arginine as

nitrogen or carbon source or other medium

component were included), the subtle gene expres-

sion changes of the putative argCJBDFRGH

operon revealed that expression of each gene

within this group is more similar to each otherthan to any other gene (Fig. 2A). It becomes

evident as well that some operons physically

distant on the genome are exhibiting very similar

expression patterns and form syn-expression

groups (Niehrs and Pollet, 1999). It remains to

be shown whether syn-expression groups, such as

that comprising the ctaE-qcrCAB (Niebisch and

Bott, 2001) and sdhCAB operons (Fig. 2B) areregulated by one common regulator and thus form

a regulon or whether they respond to one stimulus

and thus form a stimulon. With the help of pattern

recognition as realized by clustering global gene

expression data, one can efficiently deduce hy-

potheses on global gene regulation in bacteria such

as C. glutamicum and help to plan experiments

aimed at their verification (or falsification).

3. Applications and perspectives

3.1. Comparative genomics or genomotyping of C.

glutamicum strains

Comparative genomic studies aim at the identi-

fication of differences between genomes and canbe carried out by hybridization of labelled genomic

DNA to DNA microarrays. Oligonucleotide DNA

microarrays can be used to detect genomic differ-

ences down to the single nucleotide level, i.e. the

identification of SNPs (Hacia et al., 1996). One of

the first examples of DNA microarray analysis in

the Corynebacterianeae was the comparative geno-

mics study of Behr et al. (1999) in M. tuberculosis

which led to the identification of genome differ-

ences of the different Bacille Calmette-Guerin

(BCG) strains used for vaccination throughout

the world. This study demonstrated that DNA

microarrays based on PCR products are efficient

tools for the detection of gene deletions or

amplifications although they offer only a resolu-

tion of about 1 kb. Similarly, C. glutamicum

whole-genome DNA microarrays which are based

on PCR products are efficient for genomotyping.

As shown in Fig. 3, using C. glutamicum whole-

genome DNA microarrays, it is possible to detect

chromosomal rearrangements such as deletions at

the gene level. In a comparison of genomic DNA


from leuA deletion strains and their respective

isogenic parent strains (WT, WTDleuA , MH20-

22B and MH20-22BDleuA ), the absence or pre-

sence of the leuA gene was detected with two leuA

PCR products whereas no false-positive signal was

detected (Fig. 3). Due to unspecific hybridization,

the ratios of hybridization signals are smaller than

theoretically expected (Behr et al., 1999). In future

applications, it will be important to identify

genomic differences between amino acid-produ-

cing strains obtained by repeated mutageneses and

selections and the respective wild-type strain. The

deletions or amplifications identified in such a

manner will be tested individually regarding their

relevance for amino acid production. Introduction

of these mutations into the wild-type strain, alone

and in combination, should give rise to well-

defined, high-level amino acid-producing strains.

This approach, also named ‘genome breeding’ or

‘inverse metabolic engineering’, recently led to a

first success (Ohnishi et al., 2002) when the

combinatory introduction of a limited number of

previously known, beneficial mutations present

only in L-lysine production strains into the

Fig. 2. Hierarchical cluster analysis of 220 global gene expression experiments using whole-genome C. glutamicum DNA microarrays.

Two details show co-expression of the putative argCJBDFRGH operon or cluster (A) and of a syn-expression group comprising two

operons separated by about 1.4 Mbp: the succinate dehydrogenase operon sdhCAB with an adjacent ORF and the ctaE-qcrCAB

operon encoding subunit III of cytochrome aa3 and the three subunits of the cytochrome bc1 complex (B). The cluster analysis was

performed on 736 genes that were reliably detected in more than 180 of the 220 experiments and that in one or more of them showed an

at least twofold RNA level change. Genes referred to by gene name or Ncgl number of NC003450 are in lines and experiments in

columns. The scale depicts the color coding for relative RNA levels whereas grey color represents signals too low to deduce a relative

RNA level.


C. glutamicum wild-type strain endowed the re-

sulting defined strain with the capability to pro-

duce about 100 g l�1L-lysine.

Whole-genome DNA microarrays can also be

used to determine which genes confer beneficial or

detrimental traits, e.g. growth (dis)advantages or

improved/decreased product yields. This form of

parallel gene-trait mapping consists of a genome-

altering step to generate a pool of mutants, e.g. by

deletion mutagenesis or by transforming a strain

with a genomic library, followed by an enrichment

of a subpopulation containing the trait conferring

gene(s) by selection and using DNA microarrays

to identify the enriched or counterselected genes

(Cho et al., 1998). This method can readily be

applied, e.g. for the identification of conditionally

Fig. 3. Comparative genomics of C. glutamicum strains. The ratios of hybridization signals in DNA microarray experiments

comparing genomic DNA from C. glutamicum ATCC 13032 (WT) to WTDleuA (A) and from MH20-22B to MH20-22BDleuA (B) are

shown according to the respective gene numbers in NCBI NC003450. The dotted lines indicate ratios of hybridization signals of 2 and

1/2, respectively. The absence or presence of the leuA gene was detected for both isogenic strain pairs with both the two leuA PCR

products that cover different parts of the leuA gene.


essential genes (Sassetti et al., 2001) or genesconferring antibiotic resistance (Gill et al., 2002).

However, whereas the diagnostic readout of such

experiments by DNA microarray analysis is

straightforward, the challenge for the biotechnol-

ogist will be to work out schemes that allow to

select subpopulations with improved product

yields.

3.2. Global regulation and the carbon, energy,

nitrogen or phosphorous sources

In the model bacteria E. coli and B. subtilis , the

global regulatory mechanisms that control the

utilization of carbon, energy, nitrogen and phos-

phorous sources are well known. With the excep-

tion of the regulation of the acetate metabolism(reviewed in Gerstmeir et al., 2003) and nitrogen

control (reviewed by Burkovski, 2003), little is

known about global regulatory mechanisms in C.

glutamicum . Genome-wide expression analyses

with DNA microarrays efficiently allow to identify

regulons or stimulons, i.e. the groups of genes

controlled by one regulator or responding to one

stimulus, respectively. The specificity of this typeof analysis may be demonstrated by the analysis of

ribose-specific gene expression in C. glutamicum .

In a comparison of gene expression from C.

glutamicum ATCC 13032 cultures grown exponen-

tially by repeated dilution for at least 10 genera-

tions on either glucose or ribose as sole carbon and

energy source, only eight genes in two clusters/

putative operons exhibited significantly changedRNA levels. The putative rbsACBD operon en-

codes the subunits of an ABC transport system

and its binding protein shows sequence similarities

to ribose-specific binding proteins from other

bacteria. The second gene cluster comprises rbsK

encoding ribokinase which phosphorylates ribose

to ribose-5-phosphate, an intermediate of the

pentose phosphate shunt, and two adjacent genes.Expression of the pentose phosphate pathway

genes did not change. Thus, only those genes

encoding proteins required for the uptake of ribose

and its entry into the central carbon metabolism

exhibit higher RNA levels with ribose as a carbon

source.

As opposed to the constricted regulation ofribose-specific gene expression, a complex gene

expression response results when C. glutamicum is

starved for phosphate, its preferred source of

phosphorous (Ishige et al., 2003). The phosphate

starvation stimulon of C. glutamicum apparently

comprises more than 100 genes and time-resolved

analysis of the gene expression changes after

eliciting phosphate starvation demonstrated gra-dual and timed gene expression changes indicating

a multi-level regulatory control (Ishige et al.,

2003). A central cornerstone of this regulation

became evident with the demonstration that the

regulon of one two-component regulatory system

largely overlaps with the phosphate starvation

stimulon (Mickova, unpublished results). The

definition of the phosphate starvation stimulonalso allowed to deduce a common theme in

phosphorous metabolism of C. glutamicum . This

bacterium primarily relies on phosphate as a

source of phosphorous and, when starved for it,

increases expression of the genes encoding a high-

affinity phosphate uptake system. Only when by

this means phosphate starvation is not overcome,

C. glutamicum starts to scavenge other sources ofphosphorous and genes for uptake and utilization

of organophosphates exhibit increased RNA le-

vels.

3.3. Amino acid production, metabolic pathway

engineering and the impact of genomics

The interest in C. glutamicum arises primarily

from its application as a highly efficient aminoacid producer. Classical strain optimization by

mutation and selection was successful from an

industrial point of view, but not very rewarding for

the microbiologist attempting to understand ami-

no acid production. Then, based on the knowledge

of the most important pathways in the conversion

of the carbon source to the wanted product,

targeted metabolic engineering allowed to improveamino acid production on a rational basis. This

rational approach came with a price as it was

reductionistic and did not take into account any

influence of more than 2500 genes, gene products

or their activities. With the diagnostic power of C.

glutamicum whole-genome DNA microarrays, it


appears possible to widen the rational

metabolic engineering approach from its

traditionally constricted focus to the full genome

level.Metabolic pathway engineering of the wild-type

strain ATCC 13032 which does not overproduce

valine led to a valine production strain (Radma-

cher et al., 2002). Growth of this valine-producing

strain as well as of the L-lysine-producing strain

MH20-22B was inhibited by valine added to the

growth medium whereas growth of the wild type

was unaffected (Eggeling et al., 1997; Lange et al.,

2003). DNA microarray analysis of the valine

stress response of the valine-producing strain

identified gene expression changes indicating an

intracellular isoleucine limitation: increased ex-

pression of the branched-chain amino acid bio-

synthesis genes ilvBNC , which was confirmed by

proteome analysis and determination of enzyme

activities, and of an isoleucine tRNA synthetase

gene. Subsequently, it was shown that isoleucine

limitation as a consequence of valine addition was

linked to the ilvA deletion in the isoleucine

auxotrophic valine-producing strain and could be

overcome by addition of high isoleucine concen-

trations. Finally, the fact that supplementation of

the valine-producing strain with the dipeptide

isoleucyl-isoleucine relieved the inhibitory effect

of valine identified competition for uptake of

isoleucine by the carrier BrnQ, which transports

all branched-chain amino acids, as the cause of

valine inhibition (Lange et al., 2003). Based on the

surprising finding that valine increased ilvBN

expression, it was shown that addition of

external valine stimulated valine production

by the valine-producing strain indicating that

activity of the ilvBN encoded acetohydroxy

acid synthase may still be a limiting factor

for valine production in this valine-producing

strain.

Clearly, the impact of genome-wide expression

analyses on our understanding of amino acid

production by C. glutamicum from a whole-cell

point of view is just emerging, but pointing

to DNA microarray analysis as enabling technol-

ogy for the optimization of amino acid produc-

tion.

3.4. Common regulatory themes in M. tuberculosis

and C. glutamicum

Global gene expression patterns of the pathogen

M. tuberculosis , which as the non-pathogenic C.

glutamicum belongs to the Corynebacterianeae ,

have been studied with a particular focus on the

tuberculosis pathology. The comparative func-

tional genomic analysis of several members ofthe Corynebacterianeae will help to unravel glob-

ally genus-specific gene expression differences as

well as those relevant for pathogenicity. As M.

tuberculosis and C. glutamicum are close relatives,

global regulatory circuits controlling general func-

tions such as the heat- or acid-shock response are

likely to be conserved and progress made for either

of the different members of the Corynebacteria-

neae should be easily transferred to the other. In

line of this view, it was already shown that

molecular biology methods and vectors developed

for C. glutamicum function in C. diphtheriae and

that nitrogen control in these Corynebacterianeae

is similar (Nolden et al., 2002).

In C. glutamicum , DNA microarray analysis of

the heat-shock response revealed increased RNAlevels of various chaperone genes (dnaJ , dnaK ,

groEL ), Clp protease subunit genes, genes for

thioredoxin recycling and for the extracytoplasmic

function sigma factor E (sE) of RNA polymerase

(Muffler et al., 2002). In M. tuberculosis , the heat-

shock response comprises more genes (Stewart et

al., 2002) and its complex regulation begins to

emerge as the regulons of the sigma factors sE andsH and of the regulators HcaR and HspR were

determined by DNA microarray analysis (Manga-

nelli et al., 2001, 2002; Stewart et al., 2002;

Kaushal et al., 2002). Interestingly, the heat shock

response and the oxidative stress response in M.

tuberculosis seem to be interrelated as a sH mutant

not only shows a reduced immunopathology and

increased heat-shock sensitivity but also increasedsensitivity to oxidative stresses (Manganelli et al.,

2002; Kaushal et al., 2002). For C. glutamicum

biotechnology, these results may guide experi-

ments aimed at understanding induction of L-

glutamate production by a temperature-shift and

maintaining it over long production periods.

Similarly, L-lysine production at temperatures


higher than the optimal growth temperature of

30 8C for wild-type C. glutamicum is desirable to

minimize cooling during amino acid production.

The response to hypoxic conditions, which as

heat-shock and nutrient starvation (Betts et al.,

2002) pertains to M. tuberculosis pathogenicity or

latency, has been determined by DNA microarray

analysis (Sherman et al., 2001). In biotechnologi-

cal processes involving C. glutamicum , sufficient

oxygen supply is a minor problem for smaller

scales but still remains a problem for certain zones

of the large bioreactors used in industrial amino

acid production. Genes responsive to hypoxia

identified in M. tuberculosis might serve useful in

monitoring oxygen availability of C. glutamicum

cells during large-scale amino acid production on

the gene expression level.

Iron-complexing compounds as protocatechuic

acid are required for the cultivation of C. gluta-

micum (Liebl et al., 1989) while M. tuberculosis

faces iron-limiting conditions within the human

host (Rodriguez et al., 2002). The transcriptional

regulator of iron metabolism genes, IdeR, is a

homologue of DtxR of C. diphtheriae and is

essential in M. tuberculosis . DNA microarray

analyses have shown that the regulon of IdeR in

M. tuberculosis comprises many putative trans-

porters, proteins involved in siderophore synthesis

and iron storage, transcriptional regulators and

enzymes involved in lipid metabolism. The binding

sites of IdeR upstream of iron-responsive genes

have been identified (Rodriguez et al., 2002) and

will allow for an in silico analysis of C. glutamicum

promoters to predict putative iron-responsive

genes which in turn might serve as diagnostic

markers for iron limitation in amino acid produc-

tion processes.

The genome of C. glutamicum is more than 1

Mbp smaller and contains about 1000 genes less

than that of M. tuberculosis . It is conceivable that

many global regulatory mechanisms turn out to be

less complex in C. glutamicum than in M. tubercu-

losis . Therefore, as can already be seen with

respect to the central carbon, nitrogen and phos-

phate metabolism, amino acid and lipid biosynth-

esis as well as cell wall biogenesis (Eggeling and

Sahm, 2001) and the maintenance of homeostasis,

C. glutamicum might become a model organism ofthe Corynebacterianeae.

4. Conclusion

We can now use C. glutamicum whole-genome

DNA microarrays to characterize global gene

regulatory mechanisms on the RNA level and

subsequently we can determine whether changed

RNA levels are due to transcriptional regulation

and/or differential RNA stability. However, to

gain a complete view of global regulation we haveto follow a more comprehensive approach by

complementing transcriptomics with proteomics

and biochemistry to study regulation of transla-

tion, protein folding, degradation or modification

and with metabolic flux analysis and metabolo-

mics to study in vivo activities and allosteric

control of enzymes.

Although the application of whole-genomeDNA microarrays to study C. glutamicum is in

its infancy, DNA microarrays will be a corner-

stone in a comprehensive experimental approach

to increase our knowledge about fundamental and

applied aspects of corynebacterial physiology.

They hold the promise to enable strain and process

optimization for amino acid production with C.

glutamicum .

Acknowledgements

I would like to thank Hermann Sahm for

continuous support, Michael Bott for critically

reading the manuscript as well as stimulating

discussions and Degussa AG for making available

the C. glutamicum genome sequence. In particular,

I would like to thank Doris Rittmann, Tino Polen,

Georg Sindelar, Christian Lange, Takeru Ishige,

Andrea Veit, Malgorzata Krause, Andreas Krug,Sandra Knebel and Corinna Stansen for their

work in various projects as well as Tino Polen,

Georg Sindelar, Silke Rosnowsky and Thorsten

Kloesges for sharing results prior to publication.

The support of the EU (QLK3-2000-00497),

BMBF (Netzwerk Genomforschung an Mikroor-


ganismen) and Degussa AG is gratefully acknowl-edged.

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wendish, 2003; genome-wideexpressionanalysis in corynebacteriumglutamicum

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