Background: Random community genomes (metagenomes) are now commonly used to study microbes in different environments. Over the past few years, the major challenge associated with metagenomics shifted from generating to analyzing sequences. High-throughput, low-cost nextgeneration sequencing has provided access to metagenomics to a wide range of researchers.

Previously, we presented evidence that it is possible to predict functional coupling between genes based on conservation of gene clusters between genomes. With the rapid increase in the availability of prokaryotic sequence data, it has become possible to verify and apply the technique. In this paper, we extend our characterization of the parameters that determine the utility of the approach, and we generalize the approach in a way that supports detection of common classes of functionally coupled genes (e.g., transport and signal transduction clusters). Now that the analysis includes over 30 complete or nearly complete genomes, it has become clear that this approach will play a significant role in supporting efforts to assign functionality to the remaining uncharacterized genes in sequenced genomes.Gene clusters are known to be prominent features of bacterial chromosomes. Demerec and Hartman (1) postulated in 1959 that ''regardless of how the gene clusters originated, natural selection must act to prevent their separation'' and the ''mere existence of such arrangements shows that they must be beneficial, conferring an evolutionary advantage on individuals and populations which exhibit them.'' One of the most striking features of prokaryotic gene clusters is that typically they are composed of functionally related genes. For the past 40 years, there has been vigorous, ongoing discussion on the functional significance of gene arrangement on the chromosome, as well as the origin and mechanisms of maintenance of gene clusters (see, for example, refs. 2-5).Here, we present a method that uses conserved gene clusters from a large number of genomes to predict functional coupling between genes in those genomes. This article further develops the approach that we previously reported (6) and uses this method to reconstruct several major metabolic and functional subsystems. MethodologyThe data presented below are computed via the WIT system (http:͞͞wit.mcs.anl.gov͞WIT2͞), developed by Overbeek et al. (7) at Argonne National Laboratory. WIT was designed and implemented to support genetic sequence analysis, metabolic reconstructions, and comparative analysis of sequenced genomes; it currently contains data from over 30 genomes, albeit a few of them are incomplete.Our approach to detection of conserved clusters of genes is based on the following definitions: a set of genes occurring on a prokaryotic chromosome will be called a ''run'' if and only if they all occur on the same strand and the gaps between adjacent genes are 300 bp or less. Any pair of genes occurring within a single run is called ''close.'' Given two genes X a and X b from two genomes G a and G b , X a and X b are called a ''bidirectional best hit (BBH)'' if and only if recognizable similarity exists between them (in our case, we required FASTA3 scores lower than 1.0 ϫ 10 Ϫ5 ), there is no gene Z b in G b that is more similar than X b is to X a , and there is no gene Z a in G a that is more similar than X a is to Computation of PCBBHs for 31 complete or ne...

Defining the gene products that play an essential role in an organism's functional repertoire is vital to understanding the system level organization of living cells. We used a genetic footprinting technique for a genome-wide assessment of genes required for robust aerobic growth of Escherichia coli in rich media. We identified 620 genes as essential and 3,126 genes as dispensable for growth under these conditions. Functional context analysis of these data allows individual functional assignments to be refined. Evolutionary context analysis demonstrates a significant tendency of essential E. coli genes to be preserved throughout the bacterial kingdom. Projection of these data over metabolic subsystems reveals topologic modules with essential and evolutionarily preserved enzymes with reduced capacity for error tolerance.Sequencing and comparative analysis of multiple diverse genomes is revolutionizing contemporary biology by providing a framework for interpreting and predicting the physiologic properties of an organism. A variety of emerging postgenomic techniques such as genome-wide expression profiling and monitoring of macromolecular complex formation can reveal the detailed molecular compositions of cells. New computational approaches to exploring the inherent organization of cellular networks, the mode and dynamics of interactions among cellular constituents, are in early stages of development (14,22,23). These techniques allow us to begin unraveling a major paradigm of cellular biology: how biological properties arise from the large number of components making up an individual cell.

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Brucella melitensis is a facultative intracellular bacterial pathogen that causes abortion in goats and sheep and Malta fever in humans. The genome of B. melitensis strain 16M was sequenced and found to contain 3,294,935 bp distributed over two circular chromosomes of 2,117,144 bp and 1,177,787 bp encoding 3,197 ORFs. By using the bioinformatics suite ERGO, 2,487 (78%) ORFs were assigned functions. The origins of replication of the two chromosomes are similar to those of other ␣-proteobacteria. Housekeeping genes, including those involved in DNA replication, transcription, translation, core metabolism, and cell wall biosynthesis, are distributed on both chromosomes. Type I, II, and III secretion systems are absent, but genes encoding sec-dependent, sec-independent, and flagellaspecific type III, type IV, and type V secretion systems as well as adhesins, invasins, and hemolysins were identified. Several features of the B. melitensis genome are similar to those of the symbiotic Sinorhizobium meliloti.

We present a complete DNA sequence and metabolic analysis of the dominant oral bacterium Fusobacterium nucleatum. Although not considered a major dental pathogen on its own, this anaerobe facilitates the aggregation and establishment of several other species including the dental pathogens Porphyromonas gingivalis and Bacteroides forsythus. The F. nucleatum strain ATCC 25586 genome was assembled from shotgun sequences and analyzed using the ERGO bioinformatics suite (http://www.integratedgenomics.com). The genome contains 2.17 Mb encoding 2,067 open reading frames, organized on a single circular chromosome with 27% GC content. Despite its taxonomic position among the gram-negative bacteria, several features of its core metabolism are similar to that of gram-positive Clostridium spp., Enterococcus spp., and Lactococcus spp. The genome analysis has revealed several key aspects of the pathways of organic acid, amino acid, carbohydrate, and lipid metabolism. Nine very-high-molecular-weight outer membrane proteins are predicted from the sequence, none of which has been reported in the literature. More than 137 transporters for the uptake of a variety of substrates such as peptides, sugars, metal ions, and cofactors have been identified. Biosynthetic pathways exist for only three amino acids: glutamate, aspartate, and asparagine. The remaining amino acids are imported as such or as di-or oligopeptides that are subsequently degraded in the cytoplasm. A principal source of energy appears to be the fermentation of glutamate to butyrate. Additionally, desulfuration of cysteine and methionine yields ammonia, H 2 S, methyl mercaptan, and butyrate, which are capable of arresting fibroblast growth, thus preventing wound healing and aiding penetration of the gingival epithelium. The metabolic capabilities of F. nucleatum revealed by its genome are therefore consistent with its specialized niche in the mouth.

Novel drug targets are required in order to design new defenses against antibiotic-resistant pathogens. Comparative genomics provides new opportunities for finding optimal targets among previously unexplored cellular functions, based on an understanding of related biological processes in bacterial pathogens and their hosts. We describe an integrated approach to identification and prioritization of broad-spectrum drug targets. Our strategy is based on genetic footprinting in Escherichia coli followed by metabolic context analysis of essential gene orthologs in various species. Genes required for viability of E. coli in rich medium were identified on a whole-genome scale using the genetic footprinting technique. Potential target pathways were deduced from these data and compared with a panel of representative bacterial pathogens by using metabolic reconstructions from genomic data. Conserved and indispensable functions revealed by this analysis potentially represent broad-spectrum antibacterial targets. Further target prioritization involves comparison of the corresponding pathways and individual functions between pathogens and the human host. The most promising targets are validated by direct knockouts in model pathogens. The efficacy of this approach is illustrated using examples from metabolism of adenylate cofactors NAD(P), coenzyme A, and flavin adenine dinucleotide. Several drug targets within these pathways, including three distantly related adenylyltransferases (orthologs of the E. coli genes nadD, coaD, and ribF), are discussed in detail.

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