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as well as a broad range of gram-negative and gram-positive eubacterialspecies and it is estimated that .100 additional microbial sequencingprojects are under way (see TIGR homepage; Box 1). Some of these areavailable for analysis as partial genome sequences [see unfinishedgenomes homepage at the National Center for BiotechnologyInformation (NCBI, Bethesda, MD, USA); Box 1].

Computational genome analysisFrom the point of view of an industrial scientist, the goal of computationalanalysis of microbial genomes is to extract as much information aspossible in silico, in order to simplify target selection for antimicrobialdrug discovery. Given the vast amount of data, this is a difficult task.Parameters that are critical for bioinformatics-based target selectioninclude the degree of conservation of potential targets in variousbacterial species and the success with which functional hypothesescan be generated. Developments in bioinformatics in recent yearsprovide a set of tools that can be used in an integrated way to selectgenes of particular interest4 (Fig. 1). Thus, specific lists of targetcandidates are generated that can then be validated rapidly usingbacterial genetics5.

Homology-based analysesThe wealth of genomic information from a broad variety of evolutionarilydistant bacterial species makes the automated comparison of bacterialgenomes a powerful tool with which to categorize genes and theproteins they encode5,6. In addition to the primary methodology ofusing sequence comparison programs, such as BLAST or PSI-BLAST, the concept of orthologous families in establishing the clustersof orthologous groups (COGs) has set the basis for homology-basedgenome comparisons7. The gene categories that are generated by theseapproaches allow for the preselection of target candidates on a whole-genome scale, that is, scientists are able to define targets according tothe tics required from a given antibacterial treatment.

For example, genes that have orthologs in a wide variety of evo-lutionary distant organisms are target candidates for broad-spectrumapplications. Conversely, genes that are present only in a small subsetof bacterial species represent possible targets for narrow-spectrumantibacterial compounds. Using this approach, target candidates can

THE use of antimicrobial drugs to control infectious diseases is one ofthe greatest achievements of medicine in this century and the worldmarket for these drugs is now an estimated US$23 billion (Ref. 1).Most of the existing classes of antibiotics were discovered by systematicscreening of natural-product libraries within two decades of the intro-duction of penicillin. The difficulty of introducing novel chemicalclasses for antibacterial treatment is demonstrated by the fact thatlinezolid (Zyvox) is the first new chemical class of antibiotic toreceive FDA approval in over 30 years. Antibiotic therapy also facessevere challenges in other ways, namely resistance to the well-establishedantibiotics, the increased occurrence of opportunistic infections andthe emergence of novel pathogens associated with chronic diseases(e.g. Helicobacter pylori). Short-term measures, such as chemicalmodification of existing antibiotics and development of inhibitors ofresistance genes, will have a significant impact on antibacterial therapyin the immediate future. However, it is evident that the field of anti-biotic therapy requires additional targets, innovative assay-developmentstrategies and new chemical entities2.

Bacterial genome sequencing: current statusThe Institute for Genomic Research (TIGR, Rockville, MD, USA)uses the strategy of shotgun sequencing combined with computer-based assembly of DNA sequence fragments to a closed genome andhas set the speed for sequencing microbial genomes with the pub-lication of the first, complete bacterial whole-genome sequence(Haemophilus influenzae) in 1995 (Ref. 3). To date, ,30 bacterialwhole-genome sequences are available publicly, covering the Archaea

Hannes Loferer PhDSenior Director Microbiology

GPC Biotech AG, Fraunhoferstrasse 20, D-82152Martinsried/Munich, Germany.

Tel: 1149 89 85652650Fax: 1149 89 85652610

e-mail: Hannes.Loferer@gpc-biotech.com

Mining bacterial genomes forantimicrobial targets

Hannes Loferer

The elucidation of whole-genome sequences is expected to have a revolutionary impact on the discoveryof novel medicines. With the availability of complete genome sequences of more than 30 different species,the field of antimicrobial drug discovery has the opportunity to access a remarkable diversity of genomicinformation. In this review, I summarize how microbial genomics has changed strategies of drug discoveryby applying bioinformatics, novel genetic approaches and genomics-based technologies, includinganalysis of gene expression using DNA microarrays.

be selected that are specific to the spectrum of bacterial species foundin a given disease (e.g. H. pylori in the case of duodenal ulcers8). Thiscategory of targets is particularly important in chronic infections. Here,narrow-spectrum drugs would be highly advantageous with regard tothe spread of resistance and the side effects caused by destruction of thecommensal bacterial flora, both of which are major disadvantages oflong-term treatment with broad-spectrum antibiotics.

Comparative genomics of closely related organisms will provideinformation about a given species9,10 or even from strain variants8. Forexample, the comparative analysis of the genomes of Chlamydiatrachomatis and Chlamydia pneumoniae has generated testablehypotheses of genes that might be responsible for the differences intropism and pathologies between these two organisms9.

The concept of homology-based comparisons is enhanced throughthe analysis of phylogenetic profiles11,12. The phylogenetic profiles foreach family of orthologs are defined by the set of genomes in which thefamily is represented. Genes that function within the same pathwayfrequently have the same phylogenetic profile, given that a respectivepathway rarely is present in every organism. Searching the genome forproteins that have phylogenetic profiles that match the one from knowncomponents of that pathway, for example, can identify additionalmembers of a relevant pathway12.

Structural genomicsProteins with low amino acid sequence identity frequently have similarfolds. This is consistent with the observation that, of all the three-dimensional (3D) protein structures determined to date, only ,700 proteinfolds are observed13. Thus, a powerful approach for assigning previouslyunknown molecular function to a protein is to determine the 3D structureof the protein and compare this, rather than the amino acid sequence, withthose of the protein structure database. If there are significant structuralhomologs, the protein under investigation is predicted to have similarmolecular properties. These predictions can than be tested experimentally14.

The success rate for obtaining high-quality protein crystals isunpredictable, however, and this step represents a crucial time lag insuch projects. However, the power of the structural genomics approachwill increase when a larger number of protein structures become available.Furthermore, some computational modelingstrategies indicate that this approach providesimproved functional predictions, compared toalignments of amino acid sequences15.

Motif analysisThe rapid growth of publicly available DNAand protein sequence databases, combinedwith large amounts of experimental data, hasidentified signature sequence motifs indicativeof certain biochemical activities. Several data-bases exist that search for such motifs in a newsequence (e.g. the PROSITE database; Box 1).

Arigoni et al. identified a number ofpotential antibacterial targets among geneswith unknown functions5. Motif analysisproposed that three of these targets encodenucleotide-binding proteins, one encodes ametalloprotease and no motifs were detectedin the remaining proteins5. A decision oninvesting resources for assay developmentwould favor the putative metalloprotease, as

inhibitors of this class of proteins have already been developed.This shows that the identification of biochemical signature motifs isvaluable in order to prioritize target candidates.

Metabolic modelingIn addition to categorizing genes purely by sequence-based comparisons,the coding sequences elucidated in new sequencing projects can be

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Figure 1. Mining genomic information for target and drug discovery. Data from computational and ex-perimental genome analyses can be used in an integrated manner to select targets and prioritize drug candidates (see text for details).

Molecular Medicine Today

Computational genome analysis• Comparative genomics• Phylogenetic patterns• Motif analysis• Molecular/metabolic• Gene fusion

Experimental genome analysis• Microarrays• Proteomics• Genetics• Interaction technologies

Genomic information base

Target identification/drug development priorities

Box 1. Some useful web pages

Basic Linear Alignment Search Tool (BLAST) and PSI-BLAST (iterative BLAST) at NCBI:http://www.ncbi.nlm.nih.gov/BLAST/

Clusters of Orthologous Groups (COGs) of proteins at NCBI:http://www.ncbi.nlm.nih.gov/COG/

TIGR web site. Contains a comprehensive list of links to genome sequencing projects and databases:http://www.tigr.org

Unfinished genomes web site at NCBI. Allows homology searchingin genome sequences that are not complete:http://www.ncbi.nlm.nih.gov/Microb_blast/unfinishedgenome.html

PROSITE database of protein families and domains:http://www.expasy.ch/prosite/

WIT web site. A resource from Argon Ntl Labs for functional assignment of genes:http://wit.mcs.anl.gov/WIT2

KEGG web site: resource for searching pathway and other genomedatabases:http://www.genome.ad.jp/kegg/kegg.html

mapped to metabolic pathways. Although metabolic modeling basedon whole-genome sequence information is still a new field, somestrategies have emerged. The bioinformatics company, DoubleTwist(Oakland, CA, USA), uses the approach of comparing annotatedgenome sequences to reference databases of cellular pathwaysamassed mainly using Escherichia coli and Bacillus subtilis16. Theprocedure assigns a score to each pathway that indicates the strength ofthe evidence of the particular pathway in the organism in question.This strategy allows the assessment of the metabolic capabilities of anewly sequenced organism, identification of putatively essentialpathways and postulation of missing components of pathways. Karpet al.16 outline the results of such an analysis for H. pylori, postulating74 pathways for this organism, compared with 138 for E. coli.

Other approaches are represented by the Kyoto Encyclopedia ofGenes and Genomes (KEGG) and What is There? (WIT) web sites(see Box 1). These approaches do not take an annotated genome astheir starting point rather they use raw protein sequences. Furthermore, theKEGG database disregards organism-specific composition of pathwaysand neither integrate experimental information from the literature.

Putative physical interactionsRecently, a computational method to infer protein interactions fromgenome sequences has been proposed, based on the observation thatsome pairs of proteins that interact have homologs in other organismsthat are fused into a single protein chain17. Several interactions knownfrom the literature have been confirmed in this study. For example, inE. coli the GyrA and GyrB subunits of DNA gyrase form a complexand both components are fused within a single protein in yeasttopoisomerase II (Ref. 17). Thus, this strategy might provide functionalinformation for putative target proteins.

Experimental functional genomicsLarge-scale geneticsAs an alternative to the strategy of placing bioinformatic analyses at theforefront of target selection, efficient genome-wide genetic technologiesthat allow the experimental scanning of the bacterial genome forantibacterial targets in a reasonable timescale have been developed.

The basis for these approaches is the knowledge of the whole-genomesequence of a given organism. Bioinformatic tools, as outlined in theprevious section, will then be used to select the best targets for drug-discovery programs.

Traditionally, the search for novel genes required for bacterialsurvival or virulence has been based on a variety of methods to inducerandom mutagenesis of a bacterial genome followed by scoring for therelevant phenotype18. Until recently, however, state-of-the-art methods(e.g. transposon or chemically induced mutagenesis) were unlikely toinvolve all genes encoded by a bacterial genome.

A breakthrough in using transposon mutagenesis to map genesrequired for viability has been achieved by combining transpositiontechniques in vitro with natural transformation of some bacterialspecies19. In this method (called genomic analysis and mapping by in vitro transposition, or GAMBIT) the genome of the respectivebacterium is covered by an overlapping set of long-range PCR products,each of approximately 10 000 bp. Each PCR product is subjected to in vitro transposition, a procedure that leads to high frequency, almostcompletely random insertions into the target DNA (Ref. 20).Subsequently, the bacterial strain is transformed with the mutagenizedPCR product and the locations of transposon insertions are mappedby PCR. With this procedure, several insertions are obtained withineach open reading frame (ORF). Hence, genes essential for survivalunder standard laboratory conditions can be recognized by their lackof transposon insertions. Thus, within a relatively short period of time,the majority of essential genes of a small-genome bacterium, such asH. influenzae, can be mapped. Furthermore, the pool of transposonmutants obtained using GAMBIT can be used to identify genes thatare required under non-standard conditions, including virulence,similar to the signature-tagged mutagenesis approach21.

Microarray analysisSeveral technologies, such as gene expression profiling using DNA microarrays, large-scale protein interaction mapping22 and proteomics23,have gained paramount importance with the knowledge of whole-genome sequences24 and will be instrumental in functional analysis ofgenes and identification of target candidates.

In the microarray approach, DNA probes specific for the entireset of genes of an organism are immobilized on a solid support.Such an array can be hybridized with a labelled RNA sample andthe hybridization pattern reflects the expression status of thegenome under a given condition (see Ref. 24 for an overview of thistechnology).

Using microarray technology, functional information is gained byinvestigating the temporal patterns of gene expression of a biologicalprocess of interest. Because it can be assumed that genes with relatedfunctions tend to be expressed in similar patterns, possible roles forgenes of unknown function can be suggested, based on their temporalassociation with genes of known function25. In genetically accessibleorganisms, these hypothetical gene functions can then be testedby mutation of the genes in question and the analysis of theireffects.

A landmark study of this kind has analyzed the transcriptionalprogram of sporulation in yeast25. Of the 6200 protein-encodinggenes in the yeast genome, .1000 showed significant changes inmRNA levels during sporulation. The investigation of the levelof expression of every yeast gene in the course of sporulationprovides clues to potential functions of hundreds of previouslyuncharacterized genes.

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GlossaryBLAST – Basic Linear Alignment Search Tool developed at NCBI(see Box 1).

Clusters of orthologous groups (COGs) – Orthologous gene families are grouped into clusters (see Box 1).

Orthologs – Conserved genes that perform the same function indifferent species. Orthologs evolve by speciation.

PSI-BLAST – A modification of BLAST that performs iterative homology searches (see Box 1).

Phylogenetic profile – The pattern of occurrence of orthologs indifferent species.

Surrogate ligands – Non-natural ligands (e.g. peptides).

Tropism – Some bacteria can only infect specific tissues in a hostbecause of specific mechanisms for interaction with the host (e.g.adhesion). This phenomenon is called tropism.

This analysis of the already well-characterized process of sporulationbenefits from the fact that the biochemical functions of many proteinsrequired for this process are well established, which leads to highlyaccurate predictions about protein function.

However, this approach is also relevant to antibacterial researchas it can generate functional hypotheses even in less-studied systems.For example, 34 ORFs of unknown function were shown to be up- ordownregulated following heat-shock of E. coli (Ref. 26). Similar tothe experiments described above, bacteria can be challenged withconditions that are relevant for antibacterial therapy. For example,genes can be identified that are induced in animal models or in in vitro models of infection21. Zhang and Normark investigated thechanges in the transcriptional program of uropathogenic E. coli followingP-pili-mediated adhesion to its host cell receptor27. At the time of thisstudy, the whole-genome sequence of E. coli was not available andthe authors used differential RNA display to assess differences ingene expression. One set of genes induced following adhesion encoded a,hitherto uncharacterized, two-component regulatory system. A mutant,defective in responding to iron starvation, was detected. In contrast tothe wild-type bacteria, this mutant is unable to grow in urine, indicatingthat these genes are required under conditions prevailing in theurinary tract.

Genes of unknown functionA significant proportion of all bacterial genes sequenced to date areof unknown function28. Even in well-studied model organisms such asE. coli, approximately 38% of all genes fall into this category28,29.Two pilot studies show that novel antibacterial drug targets can bediscovered within this category of genes. First, 26 genes of unknownfunction that are broadly conserved among diverse bacterial species(including Mycoplasma genitalium, which is the smallest bacterialgenome sequenced so far) were investigated using bioinformaticsand genetics, and six novel genes essential for the growth of E. coliand B. subtilis identified5. The second study, a global transposonmutagenesis experiment using M. genitalium, revealed that ,100genes of unknown function are essential for growth of this organismunder laboratory conditions30.

Concluding remarksFrom the point of view of drug discovery, microbial genomics faceschallenging questions, such as, how quickly can whole-genomesequence information be generated, how efficiently can relevant targetsbe identified and, how can genomics-based technologies be integratedinto the drug discovery process? The question of whether genomicsfundamentally changes the discovery of antimicrobial drugs must alsobe asked. In this regard, microbial genomics can be viewed as a fore-runner for the field of human genomics, which will eventually face thesame questions.

Given the large number of complete bacterial genomes alreadysequenced, there is no doubt that microbial whole-genome informationcan be generated rapidly. Furthermore, the technologies describedin this review will identify most of the targets required for bacterialsurvival. One might argue that there are many potential antibacterialtargets described in the literature that have not been explored inantibiotic research. However, given the fact that resistance develop-ment will always be a part of the game, all possible targets ought tobe explored. This strategy is supported by the fact that technologiessuch as surrogate-ligand-based screening are now available, thatenable screening for inhibitors in a uniform assay format without

detailed functional knowledge31. This will allow the exploration ofa large number of targets for lead candidates in a manageabletimescale.

Furthermore, genomics-based technologies (e.g. DNA chips) willalso revolutionize diagnostics. Novel and rapid diagnostic techniqueswill allow us to identify targets for narrow-spectrum antibiotics foruse in acute infections, which up to now are only tackled by broad-spectrum drugs. The main limiting factor for putting this vision intopractice is technology development: at the moment genomicstechnologies are expensive, and issues such as the reproducible production of DNA arrays on an industrial scale or handling of largedata sets are still a challenge. Eventually, these issues will be resolved, and we will use genomics technologies as an integrated tool.Thereby, the results of computational analyses, genetics and otherfunctional experiments (including gene-expression profiling, protein-interaction studies, etc.) will be integrated into databases. Such databaseswill also include data obtained from the analysis (e.g. by gene-expression profiling) of the biological effects of drug candidates viathe drug discovery process24.

Acknowledgement. I thank Bernd Hutter for comments on the manuscript.

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