by Patricia Del Portillo, Alejandro Reyes, Leiria Salazar, María del Carmen Menéndez and María Jesús García

4.1. Impact of new technologies on Mycobacterium tuberculosis genomics

A new wave in the analysis of the physiological secrets of microorganisms started more than a decade ago with the reading of the first complete genome sequence, corresponding to the bacterium Haemophilus influenzae (Fleishman 1995). Nowadays, the accessibility to hundreds of bacterial genome sequences has changed our way of studying the bacterial world, including bacterial pathogens such as M. tuberculosis.

The overwhelming information displayed by genome sequences started the era of "omics" technologies. These technologies are in accordance to the currently fast times. A quick search in PubMed, limiting results to the last 10 years, showed more than 27,000 papers devoted to "omics" issues: more than three thousand concerning bacteria, and almost three hundred concerning Mycobacterium tuberculosis. Up to five different "omics" methodologies have been described so far, all concerning the global study of the target organism, analyzing all its genes, transcriptional products, proteins, etc.

• Genomics involves the study of all genes that are present in the genomes
• Transcriptomics concerns the analysis of the cellular functions at the messenger ribonucleic acid (mRNA) level
• Proteomics refers to the detection and identification of all proteins in a cell
• Metabolomics comprises the complete set of all metabolites formed by the cell and its association with its metabolism
• Fluxomics compares the cellular networks (Fiehn 2003, Nielsen 2005)

In the tuberculosis (TB) field, only papers concerning genomics, transcriptomics, and proteomics have been published. Integration of data derived from the several "omics" by bioinformatics will probably allow a rational insight into M. tuberculosis biology and its interactions with the host, leading to true control of the disease.

Undoubtedly, the biggest step in our knowledge on TB during the last decade was the description of the complete genome sequence of the laboratory reference M. tuberculosis strain H37Rv (Cole 1998a). For example, the identification of genes involved in the bacterial cell wall biosynthesis, the routes for lipid metabolism, the location of insertion sequences and the variability in the PE_PPE genes allowed scientists to merge the fragments of knowledge derived from the pre-genomic era in a more comprehensive way. The sequence of the genome, and its comparison to sequences of other microorganisms reported in several databases, allowed the assignation of precise functions to 40 % of the predicted proteins and the identification of 44 % of orthologues (genes with very similar functions in a different species), leaving 16 % as unique unknown proteins.

The elucidation of complete genome sequences and the development of microarray-based comparative genomics have been powerful tools in the progress of new areas by the application of robotics to basic molecular biology. Comparative genomics and genomic tools have also been used to identify factors associated with the pathogenicity of M. tuberculosis, such as virulence factors and genes involved in persistence of the pathogen in host cells. Moreover, these tools allowed a description of the evolutionary scenario of the genus (see Chapter 2).

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Structural genomics was the starting point. As more accurate technologies became available, the interest was focused into functional genomics. Thus, information on specific mRNA actively synthesized by bacteria inside macrophages or during in vitro starvation, opened ways to the analysis of gene expression. Microarray technology was applied to the detection of global gene activity in M. tuberculosis under several environmental conditions. However, bacterial function cannot be understood by looking at the mRNA level alone. A major barrier for genomic studies has been the great number of genes with unknown function that have been identified. Up to 60 % of the open reading frames (ORFs) had unknown functions after the initial annotation of genes (identification of the protein unrevealed by the corresponding ORF's amino acid sequence) (Cole 1998a). The elucidation of protein function was possible with the global analysis of bacterial proteins, giving insights into the functional role of several so far unknown proteins. Thanks to the joint contributions of biochemical techniques and mass spectrometry, up to 1,044 non-redundant proteins were reported in different cellular fractions (Mawuenyega 2005). The upcoming task will be to assign them all a functional role. As more results are obtained from the proteomic analysis, it is expected that the function of more ORFs will be unveiled with the aid of new data on transcriptomics and proteomics. Genomics and other molecular tools allowed studies on gene expression and regulation, which were unthinkable years ago. M. tuberculosis is a restricted human pathogen; therefore it must have developed mechanisms enabling its quick and efficient adaptation to a variety of "intra-human" environments, which are, in fact, its natural habitat. Understanding how the bacillus regulates its different genes according to environmental changes will probably lead to the comprehension of many interesting aspects of M. tuberculosis, including latency and host-adaptation. This chapter will address the general basics, as well as the state-of-the-art genomics, transcriptomics and proteomics in relation to M. tuberculosis. Finally, a general overview will be made on lipids, the most peculiar metabolites of this bacterium. 4.2. M. tuberculosis genome 4.2.1. Genomic organization and genes TB research made huge progress with the availability of the genome sequence of the type strain M. tuberculosis H37Rv (Cole 1998a). Expectations were generated on the elucidation of some unique characteristics of the biology of the tubercle bacillus, such as its characteristic slow growth, the nature of its complex cell wall, certain genes related to its virulence and persistence, and the apparent stability of its genome. This first available genome sequence of a pathogenic M. tuberculosis strain helped to answer some of these questions and, what is even more stimulating, to open many more. We describe herein the main characteristics of the M. tuberculosis genome sequences completed thus far and highlight some of the most interesting questions answered and opened with this advance in TB research. M. tuberculosis H37Rv (Cole 1998a) was revealed to possess a sequence of 4,411,529 bp, the second largest microbial genome sequenced at that time. The characteristically high guanine plus cytosine (G+C content; 65.5 %) was found to be uniform along most of the genome, confirming the hypothesis that horizontal gene transfer events are virtually absent in modern M. tuberculosis (Sreevatsan 1997). Only a few regions showed a skew in this G+C content. A conspicuous group of genes with a very high G+C content (> 80 %) appear to be unique in mycobacteria and belong to the family of PE or PPE proteins. In turn, the few genes with particularly low (< 50 %) G+C content are those coding for transmembrane proteins or polyketide synthases. This deviation to low G+C content is believed to be a consequence of the required hydrophobic amino acids, essential in any transmembrane domain, that are coded by low G+C content codons. Fifty genes were found to code for functional RNAs. As previously described (Kempsell 1992), there was only one ribosomal RNA operon (rrn). This operon was found to be located at 1.5 Mbp from the origin of replication (oriC locus). Most eubacteria have more than one rrn operon located much closer to the oriC locus to exploit the gene-dosage effect during replication (Cole 1994). The possession of a single rrn operon in a position relatively distant from oriC has been postulated to be a factor contributing to the slow growth phenotype of the tubercle bacillus (Brosch 2000a). One of the most thoroughly studied characteristic of M. tuberculosis is the presence and distribution of insertion sequences (IS). Of particular interest is IS6110, a sequence of the IS3 family that has been widely used for strain typing and molecular epidemiology due to its variation in insertion site and copy number (van Embden 1993, see Chapter 9). Sixteen copies of IS6110 were identified in the genome of M. tuberculosis H37Rv; some IS6110 insertion sites were clustered in sites named insertional hot-spots. The same strain was found to harbor six copies of the more stable IS1081, an insertion sequence that yields almost identical profiles in most strains when analyzed by Restriction Fragment Length Polymorphism (RFLP) (Sola 2001, Kanduma 2003). Another 32 different insertion sequences were found, of which seven belonged to the 13E12 family of repetitive sequences; the other insertion sequences had not been described in other organisms (Cole 1998b). Virtually all the ISs found in M. tuberculosis so far belong to previously described IS families (Chandler 2002). The only exception is IS1556, which does not fit into any known IS family (Cole 1999). Two prophages were detected in the genome sequence; both are similar in length and also similarly organized. One is the prophage PhiRv1, which in the M. tuberculosis H37Rv genome interrupts a repetitive sequence of the family 13E12. This prophage is deleted or rearranged in other M. tuberculosis strains (Fleischmann 2002). The genome of M. tuberculosis possesses seven potential att sites for PhiRv1 insertion, which explains the variability of its position between strains (Cole 1999). The second prophage, PhiRv2 has proven to be much more stable, with less variability among strains (Cole 1999). Regarding protein coding genes, it was determined that M. tuberculosis H37Rv codes for 3,924 ORFs accounting for 91 % of the coding capacity of the genome (Cole 1998a). The alternative initiation codon GTG is used in 35 % of cases compared to 14 % or 9 % in Bacillus subtilis or Escherichia coli respectively. This contributes to the high G+C bias in the codon usage of mycobacteria. A bias in the overall orientation of genes with respect to the direction of replication was also found. On average, bacteria such as B. subtilis have 75 % of their genes in the same orientation as that of the replication fork, while M. tuberculosis only has 59 %. This finding has led to the hypothesis that such a bias could also be part of the slow growing phenotype of the tubercle bacillus (Cole 1999). This conjecture, however, does not take into account the fact that E. coli, a bacterium that grows much faster than M. tuberculosis, has only 55 % of its genes in the same direction as the replication origin (Li 2005). From the predicted ORFs, all proteins have been classified in 11 broad functional groups (Table 4-1), more precisely classified into COG functional categories (http://www.ncbi.nlm.nih.gov/sutils/coxik.cgi?gi=135) according to the National Center for Biotechnology Information (NCBI) of the United States (US). The analysis of the codon usage showed a preference for G+C-rich codons. It was also found that the number of genes that arose by duplication is similar to the number seen in E. coli or B. subtilis, but the degree of conservation of duplicated genes is higher in M. tuberculosis. The lack of divergence of duplicated genes is consistent with the hypothesis of a recent evolutionary descent or a recent bottleneck in mycobacterial evolution (Brosch 2002, Sreevatsan 1997, see chapter 2). From the genome sequence it is clear that M. tuberculosis has the potential to switch from one metabolic route to another including aerobic (e.g. oxidative phosphorylation) and anaerobic respiration (e.g. nitrate reduction). This flexibility is useful for survival in the changing environments within the human host that range from high oxygen tension in the lung alveolus to microaerophilic/anaerobic conditions within the tuberculous granuloma. Another characteristic of the M. tuberculosis genome is the presence of genes for synthesis and degradation of almost all kinds of lipids from simple fatty acids to complex molecules such as mycolic acids. In total, there are genes encoding for 250 distinct enzymes involved in fatty acid metabolism, compared to only 50 in the genome of E. coli (Cole 1999). Concerning transcriptional regulation, M. tuberculosis codifies for 13 putative sigma factors and more than 100 regulatory proteins (see section 4.3 of this chapter). Among the most interesting protein gene families found in M. tuberculosis are the PE and PPE multigene families, which account for almost 10 % of the genome capacity. The names PE and PPE derive from the motifs of Pro-Glu (PE) and Pro-Pro-Glu (PPE) found near the protein N-terminus in most cases. These proteins are believed to play an important role in survival and multiplication of mycobacteria in different environments (Marri 2006). There are about 100 members of the PE family, which is further divided into three sub-families, the most important of which is the polymorphic GC-rich sequences (PGRS) class that contains 61 members. Proteins in this class contain multiple tandem repetitions of the motif Gly-Gly-Ala, hence, their glycine concentration is superior to 50 %. The PE_PGRS proteins have been found to be exclusive to the M. tuberculosis complex (Marri 2006) and resemble the Epstein-Barr virus nuclear antigens (EBNA), which are known to inhibit antigen presentation through the histocompatibility complex (MHC) class I (Cole 1999). Interestingly, the analysis of the desoxyribonucleic acid (DNA) metabolic system of M. tuberculosis indicates a very efficient DNA repair system, in other words, replication machinery of exceptionally high fidelity. The genome of M. tuberculosis lacks the MutS-based mismatch repair system. However, this absence is overcome by the presence of nearly 45 genes related to DNA repair mechanisms (Mizrahi 1998), including three copies of the mutT gene. This gene encodes the enzyme in charge of removing oxidized guanines whose incorporation during replication causes base-pair mismatching (Mizrahi 1998, Cole 1999). With the aim of making the information publicly available and the search and analysis of information easier, the Pasteur Institute (http://www.pasteur.fr/recherche/unites/Lgmb/) has created a database system incorporating not only all genes and annotation but other search tools such as Blast or FastA, that allow the user to search for homologue sequences of a query sequence inside the M. tuberculosis genome. This database is freely available for use on the Internet and is known as the Tuberculist Web Server http://genolist.pasteur.fr/TubercuList/). As more information was generated, databases grew bigger, more experimental information became available, and better and more accurate algorithms for gene identification and prediction were released. The initial genome annotation in M. tuberculosis H37Rv strain soon became out of date. For this reason, a re-annotation of that genome sequence was published in 2002. This re-annotation incorporated 82 additional genes. The gene nomenclature was not altered; the new genes have the name of the preceding gene followed by A, B or D, for example, two new ORFs were described between Rv3724 and Rv3725, hence, they were named Rv3724A and Rv3724B. The letter C was not included since it usually stands for "complementary", which means that the gene is located in the complementary strand. As expected, the classes that exhibited the greatest numbers of changes were the unknown category and the conserved hypothetical category (Table 4-1). The re-annotation of the genome sequence allowed the identification of four sequencing errors making the current sequence size change from 4,411,529 to 4,411,532 bp (Camus 2002). As shown in Table 4-1, the information obtained from a single sequenced genome is enormous. The advances made on the analysis of such information have accelerated TB research. Table 4-1: Functional classification of M. tuberculosis H37Rv and re-annotation* Class Function Number of genes (1998) Number of genes (2002) 0 Virulence, detoxification, adaptation 91 99 1 Lipid metabolism 225 233 2 Information pathways 207 229 3 Cell-wall and cell processes 516 708 4 Stable RNAs 50 50 5 Insertion sequences and phages 137 149 6 PE and PPE proteins 167 170 7 Intermediary metabolism and respiration 877 894 8 Proteins of unknown function 606 272 9 Regulatory proteins 188 189 10 Conserved hypothetical proteins 910 1,051 * Data taken from Fleischman 2002 4.2.2. Comparative genomics In recent times, new technologies have been developed at an overwhelming pace, in particular those related to sequencing and tools for genome sequence data management, storage and analysis. As of April 2007, 484 microbial genomes have been finished and projects are underway aimed at the sequencing of other 1,155 microorganisms (http://www.genomesonline.org/gold.cgi). Mycobacteria are not an exception in this titanic genome-sequencing race; since 1998, when the first mycobacterial genome sequence was published (Cole 1998a); many genome projects have been initiated. Until April 2007, 34 projects on the genome sequencing of different mycobacterial species are finished or in-process. Of these, 15 are directed towards M. tuberculosis strains, and 5 towards other members of the M. tuberculosis complex. This information will be invaluable to improve the knowledge about M. tuberculosis in the next few years. Currently, there are only two M. tuberculosis (H37Rv and CDC1551) and two M. bovis (AF2122/97 and BCG Pasteur) genome sequences annotated and published. For this reason, these are the strains that have been used as reference strains for comparative genomics both in vitro and in silico. The pioneer of in vitro assays of comparative mycobacterial genomics involved comparison of restriction profiles using low frequency restriction enzymes and pulsed-field gel electrophoresis (PFGE). These studies allowed a rough analysis of differences among M. bovis bacille Calmette-Guérin (BCG) isolates (Zhang 1995) and most importantly, contributed to the construction of the first physical maps, which were essential for the generation of the first genome sequence (Philipp 1996). The next step in comparative genomics was the use of genomic subtractive hybridization or bacteria artificial chromosome hybridization for the identification of regions of difference among the strains under analysis (Mahairas 1996, Gordon 1999). Mahairas et al. (Mahairas 1996) used subtractive hybridization to identify regions of difference that account for the avirulent phenotype of the vaccine strain M. bovis BCG. As a result of their studies, they identified three regions of difference (RD1-RD3) in the genome of M. tuberculosis H37Rv that appeared to be absent from M. bovis BCG. Further studies of these regions showed that RD3 corresponded to the prophage PhiRv1, a sequence that has been shown to vary among M. tuberculosis clinical isolates and laboratory strains (see section 4.2.1). RD2 was only deleted in isolates of M. bovis BCG that were re-cultured after 1925. Finally, RD1 turned out to be the only sequence deleted from all M. bovis BCG strains and present in pathogenic strains. However, complementation assays did not reconstitute the full virulent phenotype in M. bovis BCG (Mahairas 1996). The RD1 region contains eight ORFs, including members of the Early Secretory Antigenic Target 6 (ESAT-6) gene cluster (Brosch 2000a). The ESAT-6 proteins have been shown to act as potent stimulators of the immune system (Brodin 2002).The genome of H37Rv contains 23 copies of ESAT-6 family proteins distributed in 11 different regions. Except for esxQ, all are clustered in pairs belonging to the ESAT-6 and CFP-10 protein families (Stanley 2003, Gey Van Pittius 2001). Gordon et al. (Gordon 1999) used ordered bacteria artificial chromosome arrays to determine genomic differences between M. tuberculosis H37Rv and M. bovis BCG. As a result, they identified 10 regions of difference, including the three previously described (Mahairas 1996). Interestingly, two of the newly described regions (RD5 and RD8) also contained members of the ESAT-6 family of proteins. In addition, RD5 contained three genes coding for phospholipase C, a gene with a putative role in mycobacterial pathogenesis (Johansen 1996). Several members of the PE and PPE family proteins were also found in the regions of difference. One copy of IS1532 was identified in RD6 and one copy of IS6110 in RD5. Furthermore, the study searched for regions present in M. bovis BCG but absent from M. tuberculosis H37Rv. Two regions with this characteristic were found and were named RvD1 and RvD2 standing for H37Rv Deleted. Almost all ORFs from these regions code for unknown proteins, so the role of these deletions has not been elucidated. Until 2002, most studies concerning comparative genomics were based on differences among the strain type M. tuberculosis H37Rv and other tuberculous bacilli (Behr 1999, Brosch 1999, Brosch 2002). Different approaches using DNA hybridization techniques, such as microarrays, allowed identification of regions of difference with more accuracy and sensitivity than previous methodologies. In total, 16 regions of difference have been found in M. tuberculosis H37Rv that were deleted from M. bovis BCG. The basic idea behind the identification of regions of difference between the avirulent strain M. bovis BCG and the virulent laboratory strain M. tuberculosis H37Rv was the identification of specific deletions in all BCG strains that could be responsible for their lack of virulence. However, nine of the regions of difference were also absent in pathogenic isolates of M. bovis. Other studies have been done comparing M. tuberculosis H37Rv to its avirulent counterpart M. tuberculosis H37Ra (Brosch 1999), in which other Rv-deleted regions were identified. These regions, named RvD3 to RvD5, were found to be products of homologous recombination of adjacent IS6110, as with RvD2. Finally, only RD1 was found to be absent in all M. bovis BCG strains and present in other members of the complex. The regions of difference were used as markers of the molecular evolution of M. tuberculosis (Brosch 2002) and are represented in Figure 4-1. The use of deletions as molecular markers has been described in Chapter 2. Besides the above mentioned deletions, two duplications were identified in the M. bovis BCG genome (Brosch 2000b). These duplications, named DU1 and DU2, apparently arose from independent events. DU1 seems to be restricted to the BCG Pasteur strain and comprises the OriC locus, indicating that BCG Pasteur is diploid for OriC and some other neighboring genes. The DU2 region has been found in all BCG substrains tested and includes the sigma factor sigH, which has been related to the heat-shock response (Brosch 2001). Some excellent reviews are available on comparative genomics, made before the publication of the second M. tuberculosis genome (Cole 1998a, Brosch 2000a, Brosch 2000c, Brosch 2001, Domenech 2001, Cole 2002a, Cole 2002b). In 2002, the second M. tuberculosis genome sequence was completed, namely the clinical strain CDC1551, which had been previously involved in a TB outbreak. This strain was considered to be highly transmissible and virulent for human beings (Fleischmann 2002). With the sequence of this second strain, a first approach to the bioinformatic analysis of intraspecies variability became possible. In the initial comparison by sequence alignment, H37Rv presented a total of 37 insertions (greater than 10bp) relative to strain CDC1551; from these, 26 affected ORFs while the remaining 11 were intergenic. On the other hand, CDC1551 presented 49 insertions relative to M. tuberculosis H37Rv; 35 affecting ORFs and 14 intergenic. A total of 80 ORFs were inserted in either genome, 25 (31.2 %) of them were hypothetical or conserved hypothetical ORFs, while 36 (45 %) corresponded to the family of PE/PPE proteins, showing the potential role of this family of proteins in antigenic variability and thus in pathogenicity. Deletion M. tuberculosis H37Rv M. africanum M. microti M. bovis M. bovis BCG RD2 RD14 RD1 RD4 RD12 RD13 RD7 RD8 RD10 RD9 RvD1 TbD1 Figure 4-1: Distribution of deleted regions in M. tuberculosis complex members. Dark gray filled cells indicate the presence in all strains tested, light gray indicate the presence in some strains, white is absence from all strains tested. Data taken from (Gordon 1999, Brosch 2002, Brosch 2000b, Marmiesse 2004) Only one major rearrangement was found, consisting of the PhiRv1 (RD3),which was found in the genome of M. tuberculosis H37Rv on coordinates 1,779,312 associated with a protein of the REP13E12 family. On the genome of CDC1551, it was found to be located on the complementary strand at coordinates 3,870,803, also associated with a REP13E12 protein. M. tuberculosis CDC1551 was found to have four copies of IS6110 while M. tuberculosis H37Rv had 16. Interestingly, four of the 16 IS6110 copies found in M. tuberculosis H37Rv lacked the characteristic 3 to 4 base pair direct repeat and were adjacent to regions deleted in M. tuberculosis H37Rv relative to M. tuberculosis CDC1551, which suggests homologous recombination. Since 2002, a large number of studies has been based on Large Sequence Polymorphisms (LSPs) and Single Nucleotide Polymorphisms (SNPs), identified by the comparison of the first two M. tuberculosis genome sequences (Hughes 2002, Gutacker 2002). These studies have been complemented with data obtained from the genome sequence of a third organism of the M. tuberculosis complex. The complete genome of Mycobacterium bovis AF2122/97, a fully virulent strain isolated from a diseased cow in 1997 in Great Britain, was published in 2003 (Garnier 2003). This genome was composed of 4,345,492 bp with a G+C content of 65.63 %, 3,952 putative coding genes, one prophage (PhiRv2), and four IS elements. As expected, similarity of more than 99.95 % was found with a complete colinearity, without evidence of extensive rearrangements. With regard to LSP, most of them have been described above as regions of difference. Sequencing confirmed the absence of 11 regions of difference, and the presence of only one insertion in comparison to the sequenced M. tuberculosis genomes: the region named M. tuberculosis specific deletion 1 (TbD1), is a reflection that deletion events relative to M. tuberculosis have shaped the M. bovis genome. The comparison of the three genomes reflects the high degree of conservation among the members of the M. tuberculosis complex, as well as the divergence of M. bovis related to M. tuberculosis strains. For specific proteins or genes that vary between M. bovis and M. tuberculosis, a detailed list can be found in Garnier et al. (Garnier 2003). However, it is important to mention that the greatest degree of variation among these bacilli is found in genes encoding cell wall components and secreted proteins. Extensive variations have been found in genes of the PE/PPE family of proteins as well as in genes from the ESAT-6 family, where six of the more than 20 members are absent or altered in M. bovis. Some other changes are registered in genes coding for lipid synthesis and secretion as the mmpL and mmpS family of genes. Deletions responsible for the M. bovis requirement of pyruvate as a carbon source were also identified (Garnier 2003). The analysis of the genome sequence of members of the M. tuberculosis complex has led to great advances in the knowledge of the biology and pathogenesis of these bacteria. The sequencing of whole genomes of Mycobacterium leprae (Cole 2001), Mycobacterium avium subspecies paratuberculosis (Li 2005) and of other members of the genus, such as Mycobacterium smegmatis and M. bovis, has also made huge contributions to the understanding of the lifestyle of mycobacteria. Recently, a report compared the metabolic pathways shared among five of the mycobacterial genomes that have been sequenced (the genome sequence of M. smegmatis was not included on this report) (Marri 2006). The characteristics of the sequenced genomes of organisms in the genus Mycobacterium are presented in Table 4-2. The main differences were found in ISs, the PE/PPE gene family, genes involved in lipid metabolism and those encoding hypothetical proteins. The members of the M. tuberculosis complex had the highest number of IS elements, which might suggest higher intra-species variability in M. tuberculosis compared to other species of mycobacteria. Table 4-2: Features of sequenced genomes of species belonging to the Mycobacterium genus* Feature M. tuberculosis H37Rv M. tuberculosis CDC1551 M. bovis AF2122/97C M. leprae M. avium subsp. paratuberculosis M. smegmatis Genome size (bp) 4,411,529 4,403,836 4,345,492 3,268,203 4,829,781 6,988,209 Protein coding genes 3,927 4,186 3,920 1,604 4,350 6,897 G+C (%) 65.6 65.6 65.6 57.79 69.3 67.40 Protein coding (%) 91.3 ~ 91 90.8 49.5 91.5 92.42 Gene density (bp/gene) 1,114 1,052 1,099 2,037 1,112 1,013 Average gene length 1,012 952 995 1,011 1,015 936 tRNAs 45 45 45 45 45 47 rRNA operon 1 1 1 1 1 2 *Data taken from Li 2005, Marri 2006 The comparison of the proteins encoded within the five sequenced genomes revealed a core, or a number of shared proteins, of 1,326 proteins, compared to the 219 core genes described by macroarray and bioinformatic analyses (Marmiesse 2004). Unique genes ranged between 966 (M. avium subsp paratuberculosis) and 26 (M. tuberculosis H37Rv) depending on the genome, and most of these proteins are hypothetical. Regarding the PE/PPE family proteins, it is worth mentioning that M. tuberculosis and M. bovis contained the highest number of these proteins, while neither M. leprae nor M. avium subsp paratuberculosis have PE_PGRS proteins. Also, a wide variation has been noted in the mmpL gene family, known to participate in lipid transport and secretion. It has been proposed that these variations could be involved in host specificity (Marsh 2005). 4.2.3. Comparing genomes of clinical strains of M. tuberculosis Genome comparison has shown that gene content can vary between strains of M. tuberculosis. The analysis of complete genome sequences identified SNPs, LSPs, and regions of difference (RDs) when clinical isolates of M. tuberculosis were compared (Fleischmann 2002, Gutacker 2002, Tsolaki 2004, Filliol 2006). The microarray approach allows the comparison of a large number of genomes, providing information on the diversity, frequency, and phenotypic effects of polymorphisms in the population (Tsolaki 2004). This kind of genomic analysis is also useful for the investigation of outbreaks. Particularly when applied to genomics, DNA microarrays allow the identification of sequences present in the M. tuberculosis reference strain, but absent from different clinical isolates. Unfortunately, the microarray technique cannot detect genes present in a clinical isolate that are absent in the reference strain. These changes can originate from small deletions, deletions in homologous repetitive elements, point mutations, genome rearrangements, frame-shift mutations, and multi-copy genes (Ochman 2001, Schoolnik 2002). Fleischeman et al. suggested that genetic variation among M. tuberculosis strains might denote selective pressure, and therefore might play an important role in bacterial pathogenesis and immunity (Fleischmann 2002). Although associations between host and pathogen populations seems to be highly stable, the evolutionary, epidemiological, and clinical relevance of genomic deletions and genetic variation regions remain ill-defined, as do the molecular bases of virulence and transmissibility (Hirsh 2004). Up to six M. tuberculosis lineages adapted to specific human populations have been described by Gagneux et al. using comparative genomics and molecular genotyping tools: the Indo-Oceanic lineage, East-Asian lineage, East-African-Indian lineage, Euro-American lineage, and two West-African lineages (Gagneux 2006, see chapter 2). Specific deletions associated with the hypervirulent Beijing/W strains of M. tuberculosis were identified (Tsolaki 2005). Evidently, these differences cannot include sequences present in clinical isolates that are absent from M. tuberculosis H37Rv, so they necessarily represent a small part of the total potential genetic variability. Up to 13 complete genome sequences of representative M. tuberculosis clinical isolates are currently under progress (http://www.genomesonline.org/gold.cgi). That number accounts for near half of all the mycobacterial strains that are currently undergoing complete genome sequencing. All major functional categories are represented among deleted genes in clinical isolates of M. tuberculosis. Mobile genetic elements (insertion sequences or prophages) are frequently deleted. DNA loss frequently results from the activity of the insertion sequence IS6110 (Brosch 1999, Gordon 1999). The rate of deletion in genes involved in intermediary metabolism and respiration, and in cell wall synthesis is surprisingly high. Some of the genes encoding for potential antigens (plcA, plcD, lpqH, lppA, esx, or PE/PPE genes) might be deleted under the influence of host selective pressures, which would confer an adaptational advantage during infection or help transmission (Tsolaki 2005). Some of these missing genes (e.g. esx genes) encode proteins from the ESAT-6 family (Marmiesse 2004). The use of microarray-based comparative genomics for the study of the genetic variability of pathogens provides interesting information. Not only the identification of the deleted or absent genes is important, but also the differential hybridization signal between samples is of interest. These differential signals can indicate sequence divergence or a difference in the copy number, which may provide an insight into strain evolution and pathogenesis (Taboada 2005). 4.2.4. Functional genomics Functional genomics is the analysis of the biological function of the genes and their products within a cell or organism. Unlike genomics and proteomics, functional genomics focus on gene transcription, translation, and protein-protein interactions. Genes operate as long as they are expressed and their expression is regulated at the transcriptional or post-transcriptional level. Functional genomics uses mRNA expression profiling to provide a picture of the transcriptome in a specific condition or time, in order to identify co-regulated genes that perform common metabolic and biosynthetic functions. A set of co-regulated genes is known as a regulon. Microarrays have additional applications in functional genomics apart from gene expression studies, and other uses have also been reported. Using this new powerful technique, Sassetti et al. developed a method to map transposon insertion sites in order to identify essential genes in mycobacteria. The probes were synthesized from a transposon library and then used for hybridization in the array. A number of mutants carrying an insert in each gene were obtained, which were later isolated and identified (Sassetti 2001). The results of studies on comparative mycobacterial genomics have been validated by functional analysis, involving transcriptomics and proteomics. In fact, gene knock-out followed by transcript analysis and proteome definition seems to be the way to identify essential genes. For example, M. tuberculosis genes that encode functions essential for growth are prime choices for further investigation as targets for the development of new drugs or diagnostic methods (Cole 2002b). Subsequently, research derived from comparative genomic studies was directed towards the study of particular genes. That is the case of the deletion designated as RD750 (corresponding to the Rv1519 gene) in the genome of the M. tuberculosis strain named CH, from a large outbreak that occurred in a community of Indian immigrants in the United Kingdom (Rajakumar 2004) and belonging to the East African-Indian lineage. Complementation and combination of in vitro and in vivo assay systems indicated the participation of the gene Rv1519 in the persistence and outbreak potential of this M. tuberculosis lineage in human populations (Gagneux 2006, Newton 2006). Construction and transcription analysis of the appropriate mutant have revealed the functional role of the Rv3676 gene, a member of the cyclic adenosine monophosphate (cAMP) receptor protein family of transcription factors. This factor is required for virulence of M. tuberculosis in the mouse model. The functional map obtained from the transcriptome revealed information about regulatory pathways. The global transcription profiling experiments, comparing the wild type M. tuberculosis H37Rv strain and Rv3676 mutant grown in vitro, identified some of the genes that are co-regulated, directly or indirectly, by Rv3676 in M. tuberculosis (Rickman 2005). 4.3. Gene expression in M. tuberculosis 4.3.1. Control of gene expression The ability of M. tuberculosis to survive within host cells requires a complex and tightly controlled gene regulation. The genes that are used under different conditions could be readily inferred from the corresponding mRNAs. Thanks to the development of highly specific and sensitive technologies, such as microarrays and quantitative real-time Polymerase Chain Reaction (qRT-PCR), it is now possible to analyze the global expression from both the bacillus and the infected host. Taken together, all this could help us to understand the adaptative machinery of M. tuberculosis. The deciphering of the complete M. tuberculosis genome sequence has unveiled its well-equipped machinery, which accounts for its high degree of adaptability. Thirteen putative sigma (s) factors and 192 regulatory proteins seem to be involved in the control of M. tuberculosis gene expression (Cole 1998a). Interchangeable s factors regulate the function of RNA polymerase, initiating transcription and conferring promoter specificity to the holoenzyme (Kazmierczak 2005, Mooney 2005). To date, consensus promoter sequences have been proposed for six s factors, besides the housekeeping s factor, sA (for a review, see Rodriguez 2006). Gene expression levels could be further modified by the action of transcriptional activators and repressors: regulatory proteins (Barnard 2004). These regulatory proteins include 11 two-component systems, five unpaired response regulators, seven wbl genes, and more than 130 other putative transcriptional regulators (Cole 1998a). The differential expression of these regulatory gene products throughout different stages of the lifespan of M. tuberculosis must be determinant for the pathogen's successful infection and/or persistence within the human host. In recent years, a number of reports have correlated the response of several of these transcriptional regulators to a variety of environmental stresses (for a summary, see Table 4-3 at http://www.tuberculosistextbook.com/pdf/Table 4-3.pdf), such as cold shock, heat shock, hypoxia, iron or zinc starvation, nitric oxide, surface stress and oxidative stress (Manganelli 1999, Raman 2001, Sherman 2001, Shires 2001, Manganelli 2002, Stewart 2002, Park 2003, Rodriguez 2003, Voskuil 2003, Canneva 2005, Geiman 2006). However, the biological signals that stimulate the expression of the majority of them are still poorly recognized. Likewise, the connections between the different regulatory circuits of the complex network that controls gene expression in M. tuberculosis are incompletely established. An example of the intricacy of this network is the genetic regulation of sigB, which is induced by sE in response to surface stress (Manganelli 2001) or by sH under heat shock and oxidative stress (Manganelli 2002). The regulation of sigB expression seems to be more complex than the above cited, given that sF- and sL-dependent promoters were identified in the regulatory promoter region of sigB; and sL-dependent transcription was originated upstream to sigB (Dainese 2006). It has been shown that sH is also responsible of the induction of sigE after heat shock and exposure to diamine (Raman 2001). DNA microarray experiments with M. tuberculosis mutants revealed that some s factors control the expression of their own structural gene (Manganelli 2002, Geiman 2004, Sun 2004, Raman 2004, Dainese 2006). Autoregulation has also been demonstrated for the six two-component systems studied so far in M. tuberculosis, which are senX3-regX3 (Himpens 2000), trcRS (Haydel 2002), prrAB (Ewann 2004), dosRS (Bagchi 2005), mprAB (He 2005), and phoPR (Gupta 2006). Two-component signal transduction systems are composed of a histidine kinase sensor and a cytoplasmic response regulator that is activated by the cognate histidine kinase (West 2001). One of these systems, dosRS, is induced by hypoxia, exposure to ethanol or the nitric oxide donor S-nitrosoglutathione (Sherman 2001, Voskuil 2003, Kendall 2004). This regulon is responsible for the transcriptional changes during oxygen limitation, which is considered an important stimulus for the entry of M. tuberculosis into a dormant state (Wayne 2001). For this reason, the genes included under control of dosRS are considered members of the dormancy regulon. Recently, the induction of sigB and sigE has been shown to depend on the two-component system MprA/MprB when the bacilli are subjected to surface stress (He 2006). The transcriptional regulator WhiB3 seems to positively regulate the expression of the housekeeping s factor named sigA, by interacting with the subregion 4.2 of s factor (Steyn 2002). WhiB3 is encoded by one of the seven whiB-like genes described in the M. tuberculosis genome (Cole 1998a) and belongs to the wbl family of genes, which encodes putative transcription factors, which are unique to actinomycetes (Molle 2000, Soliveri 2000). A recent analysis has demonstrated that the expression of M. tuberculosis whiB-like genes is modified in response to anti-mycobacterial agents and environmental stress conditions (Geiman 2006). Additionally, whiB1 transcription is regulated by cAMP levels via direct binding of the activated form of the product of Rv3676 (CRP protein-cAMP) to a consensus site adjacent to the whiB1 promoter (Agarwal 2006). A post-translational regulation has also been reported for several s factors. Antagonist proteins, known as anti-s factors, can negatively regulate some s factors by sequestering them and preventing their association with RNA polymerase. Many of these anti-s factors are located downstream of their cognate s factor-encoding gene and both genes are usually co-transcribed (Bashyam 2004). The functions of five specific anti-s factors of M. tuberculosis have so far been examined: RseA (Rodrigue 2006); RshA (Song 2003); RslA (Hahn 2005, Dainese 2006); RsbW or UsfX (Beaucher 2002); and RskA (Saïd-Salim 2006). Interestingly, RsbW, the sF-specific antagonist, is post-translationally regulated by two anti-anti-s factors: RsfA and RskB (Beaucher 2002, Parida 2005). Although the function of many of these mycobacterial transcriptional regulators and signal transduction systems remains poorly defined, recent studies have begun to provide evidence of the biological role of these regulatory circuits throughout each stage of the lifecycle of M. tuberculosis inside the human host. The expression of sigA, sigE and sigG (Manganelli 2001, Capelli 2006, Volpe 2006), that of some two-component systems (Ewann 2002, Haydel 2004, Walters 2006), as well as that of the transcriptional regulator whiB3 are induced during macrophage infection. The role of these transcriptional regulators in pathogenesis and virulence became even more evident in animal model experiments, where disruption or deletion of these genes was shown to affect M. tuberculosis virulence in mice (Parish 2003a, Parish 2003b, Sun 2004, Manganelli 2004, Raman 2004, Hahn 2005, Walters 2006). Studies on mutagenesis and the expression profile of several regulators during the growth of M. tuberculosis in the macrophages and in organs of experimental animal models are currently underway. These regulators can modify bacterial physiology and are able to modulate host-pathogen interactions in response to environmental signals. 4.3.2. In vitro gene expression M. tuberculosis is an obligate mammalian pathogen that is able to infect many different cells, including macrophages, dendritic cells, alveolar-epithelial cells, and neutrophils. It is also able to reside extracellularly in the lung, inside granulomas. As mentioned previously, the tubercle bacillus adapts its transcriptome to the environment in which it replicates. The adaptation of a bacterium to harsh environments involves the transcriptional activation of genes whose final products help the bacterium to reprogram its physiology, thus ensuring survival. Among the genetic determinants that the bacterium must modulate are those involved in intermediary and secondary metabolism, cell wall processes, stress responses and signal transduction pathways. By utilizing the microarray technology, quantitative RT-PCR and laboratory generated mutants, studies on M. tuberculosis global gene expression have been undertaken using broth cultures, cell cultures, and animal models. None of these models reproduce several key features of TB in the human infection; and, unfortunately, no data is available from human tissues. Table 4-4 summarizes the most important genes whose expression is modulated by the transcriptional regulators mentioned previously (see section 4.3.1). Table 4-4: Regulation of cell process genes Transcriptional regulation Condition Cell process genes Up regulated Down regulated sC Growth curve Two-component systems: senX3, mtrA, hspX (a-crystallin), fbpC (antigen 85C) sD Exponential growth rpfC (resuscitation factor), Rv1815, Rv3413c PE_PGRS family genes sH Diamine exposure, heat stressb Heat shock proteins: hsp, clp, trxB2C operon, transcriptional regulators: sigE, sigB sM Log-phase growth Esx family genes, PPE1, PPE19 PPE60, kasA-kasB, fas, pks2, pks3 sE Exponential growth, SDS exposure Icl1 (isocitrate lyase), heat-shock proteins, transcriptional regulators: sigB, mprAB sL sigL-rslA, pks10-pks7, mpt53-Rv2877c, Rv1139c-Rv1138c sF Stationary-phase growth HP and CHP family of proteins, transcriptional regulators: sigC, sigF, and MarA, GntR TetR family, cell envelope: murB DosS-DosR Standing cultures hspX (a-crystallin), Rv3130c (CHP), Rv1738 (CHP), Rv0572c (CHP) PhoPR Exponential growth Cell envelope components MprA Mid-exponential phase ~95 genes of M. tuberculosis. PE/PPE gene family, HP, CHP CHP = conserved hypothetical protein For example, analysis of a mutant of M. tuberculosis sigC showed that this s factor induces the expression of some virulence-associated genes. On the contrary, the genes hspX (encoding the a-crystalline homologue), senX3 (sensor kinase), mtrA (response regulator), and fbpC (mycolyl transferase and fibronectine binding protein or antigen 85C) were down-regulated in that mutant strain at different times of the growth curve (Sun 2004). Genes induced by sD include the resuscitation promoting factor rfpC, several chaperone genes and genes involved in lipid metabolism and cell wall processes (Raman 2004). Thirty-nine genes were shown to be under the control of sH. These include genes coding for some heat shock proteins (hsp and clp), the trxB2C operon and some transcriptional regulators (Manganelli 2002). Quantification of mRNA by primer extension under different stresses demonstrated that the transcription of trxB2, dnaK, clp and sigE could be induced from sH-dependent promoters located upstream of these genes (Raman 2001). sE seems to regulate the expression of proteins involved in fatty acid degradation, such as the isocitrate lyase (coded by icl1), two proteins related to fatty acid degradation (fadE23 and fadE24), heat shock proteins, and the transcriptional regulators sigB and mprAB (Manganelli 2001). Recently, it was reported that sM induces the expression of two pairs of secreted proteins of the Esx family and two PPE genes, while it negatively regulates PPE60 expression as well as the expression of several genes involved in surface lipid biosynthesis and transport (Raman 2006). At least four small operons appear to be directly regulated by sL: sigL-rslA, pks10-pks7, mpt53-Rv2877c, and Rv1139c-Rv1138c, which clearly have a sL-consensus promoter sequence in their regulatory region (Hahn 2005, Dainese 2006). The pks genes are involved in the biosynthesis of phthiocerol dimycocerosate, a component of the cell envelope associated with virulence (Sirakova 2003); and the mpt operon contains genes involved in fatty acid transport (Sonden 2005). DNA microarrays of a mutant of M. tuberculosis lacking a functional sigF, have revealed that this s factor is able to induce gene expression almost exclusively during the stationary phase of growth, supporting the hypothesis of a major role of sF in the adaptation to the stationary phase. Among the sF-targeted genes, 50 % coded for hypothetical proteins or proteins of unknown function, some were transcriptional repressor/activators (MarR, GntR and TetR family of DNA binding regulators) and others were found to be involved in the biosynthesis and structure of the cell envelope (Geiman 2004). A complete genomic microarray analysis has also been performed on M. tuberculosis strains mutated in two-component regulatory systems. The dormancy-related two-component system dosRS was inactivated in M. tuberculosis using different methodologies (Parish 2003b, Park 2003). It was shown that the expression of this two-component system is highly induced under hypoxia (Sherman 2001b, Park 2003). A consensus dosR-specific binding motif was reported to be located upstream of hypoxic response genes (Park 2003, Kendall 2004). The microarray expression profiles of mutants in each of the components (dosR and dosS) showed that DosR is required for the expression of genes usually induced under oxygen limitation, such as hspX gene. Several putative operons with unknown function were also strongly regulated by DosRS. Up to 30 genes were found to be up-regulated, and another 68 genes down-regulated in a mutant of M. tuberculosis senX3-regX3. However, it has not been clearly determined if the changes found in gene expression were directly or indirectly related to the lack of this two-component regulatory system (Parish 2003a). Recently, the global transcriptional profile of the two-component systems PhoP and MprA has been reported. One of these studies provided evidence that the PhoP/PhoR system is a positive transcriptional regulator of genes involved in the synthesis of the cell envelope of M. tuberculosis (Walters 2006). On the other hand, MprA regulates sigB and sigE and many other genes previously reported to be associated to various stress conditions (He 2006). In order to analyze the mechanisms involved in bacilli intracellular survival, mycobacterial gene expression was determined in M. tuberculosis infected macrophages from different sources. Macrophages play a crucial role in TB infection because they represent both the effector cells for bacterial killing and the primary habitat in which the persisting bacilli reside. Macrophages have been investigated at different time points post-infection for the differential expression of various two-component system regulators (regX3, phoP, prrA, mprA kdpE, tcr, devR and tcrX) (Haydel 2004). More recently, the gene expression profile of M. tuberculosis grown in human macrophages compared to that of bacteria growing in synthetic culture medium was published (Capelli 2006). In this work, the authors reported that approximately one-third (32 %) of the genes upregulated by M. tuberculosis in macrophages correspond to conserved hypothetical proteins, with unknown function; this finding highlights the considerable gap that still remains in the knowledge of how this bacterium survives intracellularly. Genes involved in cell wall processes (19.5 %), regulation and information pathways (16 %), and PE family proteins (3.6 %) were also upregulated. Interestingly, the authors observed high induction of the sigma factor sigG and 13 other putative transcriptional regulators. Upregulation of sigA, sigE, and sigG was also reported in a similar study (Volpe 2006). The whiB3 gene was also induced in M. tuberculosis during infection of naïve bone marrow-derived macrophages in comparison to bacteria in broth culture mid-log growth (Banaiee 2006). 4.3.3. In vivo gene expression The use of microarrays for profiling transcriptomes of bacteria inside the host cell has been limited by the paucity of bacterial mRNA in samples containing a preponderance of mammalian RNA. Therefore, while significant work has been performed on the gene expression profile of the host, information on M. tuberculosis expression inside infected hosts is still limited. So far, there is only one publication concerning global mycobacterial transcription expression in the animal model, using microarray as the analytical method (Talaat 2004). Differential expression levels of M. tuberculosis during infection in Balb/c or Severe Combined Immunodeficiency (SCID) mice were evaluated and compared to the levels found in mycobacteria grown in broth culture. These authors identified up to 40 genes whose expression significantly changed during Balb/c and SCID mouse infection. These genes include rubB, dinF, and fdxA. The same genes were also found to be induced 24 hours post-infection in murine bone marrow macrophages (Schnappinger 2003). Additionally, several genes were regulated up or down only in Balb/c mice, such as proZ (transport system permease protein), aceAa (probable isocitrate lyase involved in lipid metabolism), and genes encoding for regulatory proteins, such as sigK, sigE and kdpE. The authors concluded that the expression profile of M. tuberculosis in SCID mice resembles the profile found in bacilli grown in vitro, while the expression profile in Balb/c mice resembles that reported in multiplication within the macrophages (Schnappinger 2003). Exceptionally, some genes were found to be expressed only in Balb/c mice. A small number of studies applied quantitative RT-PCR to investigate the expression of mycobacterial genes in the animal model. These studies focused on the analysis of a few particular genes. Examination of lungs of infected C57BL/6 mice showed that the transcriptional regulator genes whiB3, fdxA (electron transfer), hspX (a-crystalline), acg (unknown function), Rv1738 (unknown function), and Rv2626c (unknown function) were markedly induced during the course of infection (Banaiee 2006). A gene required for extrapulmonary dissemination (hbhA) was also upregulated in the lung but not in the spleen during the early stages of infection (Delogu 2006). While the expression of PE_PGRS16 was up-regulated in the spleens and lungs of infected mice, the expression of PE_PGRS26 was down-regulated (Dheenadhayalan 2006). A study on human lung biopsies revealed a high variability in expression profiles of specific M. tuberculosis genes among the specimens analyzed (Timm 2003). The biopsies were obtained from four HIV-negative patients with chronic active TB that was unresponsive to therapy. Although some differences were observed when comparing human and murine lung, the authors admitted that it was difficult to ascertain whether the infection stage in the analyzed human lung specimens could be correlated with the persistent infection in mice. 4.4. M. tuberculosis proteome With the availability of the genomes of M. tuberculosis H37Rv, M. tuberculosis CDC1551, M. bovis, and the ongoing sequencing projects, attention in the coming years must be focused on the interpretation of the sequences determining the structure and function of the proteins. Proteomics, the global study of proteins that are translated in a given physiological state is one of the most important and ambitious goals in M. tuberculosis research. The proteome of an organism implies not only an inventory of its gene products but also the transduction rate and the post-transcriptional events that occur in the organism (Betts 2002). Classical studies of proteomics involve two dimensional electrophoresis (2-DE), in which proteins are first separated by the isoelectric point and then by the molecular weight (O'Farrel 1975). Every spot of protein is then isolated, hydrolyzed and subjected to techniques of mass spectrometry (MS), tandem MS (MS/MS); matrix-assisted laser desorption/ionization mass spectrometry (MALDI/MS) and, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF/MS). For a good review on the different techniques used in protein mapping, readers are referred to Patterson et al (2000). Techniques different from two dimensional electrophoresis have also been implemented. For instance, the use of one dimension electrophoresis has been shown to be very useful for the separation of hydrophobic proteins (Simpson 2000). Other approaches that do not involve the use of gels, such as two-dimensional liquid chromatography (LC) and the subsequent analysis by MS (2 DLC/MS), have been shown to be very efficient in the identification of hydrophobic and membrane proteins (Isobe 1991). In 1999, the isotope-coded affinity tag (ICAT) technology was reported (Gygi 1999). In this, mixtures of proteins from bacteria in two different conditions are covalently labeled with isotopically labeled heavy or light forms of the reagents. The samples are combined and subjected to proteolysis. After purification of the labeled peptides through affinity tag, which is part of the reagents, they are analyzed by LC-MS/MS. This new technology has proven to be very useful in the quantitation of complex mixtures of proteins. Before the disclosure of the M. tuberculosis genome, antigens and proteins were identified by one- and two-dimension polyacrilamyde gel electrophoresis, and the use of cumbersome immunological methods (Nagai 1991, Garbe 1996). With the advance in high-resolution 2-DE and analytical chemistry, M. tuberculosis proteome is at present a reality. Pioneering studies in the proteomic field included the mapping of 32 N-terminal sequences by MS and the identification of culture filtrate proteins by 2-DE and immunodetection (Sonnenberg 1997). Very soon after the publication of its genome, bioinformatic tools were applied to predict the proteomic profile of M. tuberculosis (Tekaia 1999). The in silico analysis showed characteristics of the tubercle bacillus as the duplication of numerous genes, especially those involved in gene regulation and in lipid metabolism, and those coding for the PE/PPE protein family. The study also showed a reduced repertoire of proteins devoted to transport, which might reflect the intracellular lifestyle. The bioinformatics-predicted proteome was compared to 2-DE protein maps obtained from M. tuberculosis whole cell lysates, which were separated in a broad pH range between 2.3 and 11.0. This work demonstrated that proteins with a molecular mass below 10 kDa were not predicted from the genome sequence and also that experimentally basic and high molecular mass proteins could not be resolved by 2-DE (Urquhart 1998). Since 1999, the huge amount of data in proteomics has led to the creation of 2-DE databases, where images generated in different laboratories can be stored and analyzed. These databases are accessible on the internet at: http://web.mpiib-berlin.mpg.de/cgi-bin/pdbs/2d-page/extern/overview.cgi?gel=16 (Mollenkopf 1999) and http://www.ssi.dk/sw14644.asp (Rosenkrands 2000b). 4.4.1. Structural proteomics of M. tuberculosis Thanks to recent technological advances, the subcellular protein profile of M. tuberculosis can now be drawn. The global analysis of compartmentalized proteins will shed light on host-pathogen interactions, metabolic pathways and cell communication, just to mention some of the mechanisms related to pathogenesis. In addition, many pathogenic bacteria secrete proteins that are involved in virulence (Finlay 1997) and thus culture filtrates of M. tuberculosis could be a source for identification of virulence factors. Cell wall proteins play a fundamental role in cell architecture, resistance of the pathogen to chemical injury and dehydratation, and many other key functions of this microorganism. Thus, the identification of proteins localized in this subcellular fraction may lead, in the near future, to the development of new diagnostic tests and drugs. Membrane proteins demand special attention, because they are involved in host-pathogen interactions, nutrient transport, quorum sensing mechanisms, etc. Knowledge of these proteins could be the clue to the development of novel vaccines. Finally, the identification of cytosol proteins and the intricate network of their interaction will reveal metabolic pathways that can be targets for the design of rational drugs against TB. Even though we are still far from identifying the almost 4,000 genes predicted by genomics, the number of identified proteins increases each year and shows how genomic and proteomic technologies complement each other. The biochemical methods developed for the separation of the cell wall, membrane and cytosol fractions have facilitated proteomic studies in M. tuberculosis (Hirschfield 1990, Lee 1992). Jungblut et al. identified 53 proteins from cell lysates and 54 from culture filtrates using 2-DE and MALDI/MS (Jungblut 1999). These authors performed comparative proteomics in M. tuberculosis, which will be discussed later in this chapter. Reference maps of cellular fractions and culture filtrate proteins were constructed using 2-DE, N-terminal sequencing and antibodies against previously identified antigens (Rosenkrands 2000b). As many as 1,184 spot proteins were visualized after silver staining. Only 10 % of them were identified. In order to map less abundant proteins, different methods were applied for their separation, which allowed the identification of 12 novel proteins, five of them with a known function (Rosenkrands 2000b). The study also showed the identification of a protein that was not predicted by genomics and revealed the presence of alternative start codons. The implementation of immobilized pH gradient for 2-DE and MALDI/MS allowed the identification of 288 proteins (Rosenkrands 2000a). Six proteins were identified, all of them with molecular masses between 13,200 and 7,200 kDa and with isoelectric point (pI) ranges between 4.5 and 5.9. Five of these proteins were correctly identified in the genome of the clinical strain CDC1551 (Jungblut 2001). In spite of the enormous usefulness of 2-DE in proteomic studies, there are certain disadvantages inherent to its technique, such as the low resolution of proteins with very high or very low molecular masses, or proteins that are very acidic, very basic or hydrophobic. But in particular, the technique is biased towards the preferential identification of the most abundant proteins. Therefore, less abundant proteins, such as transcriptional regulators, are rarely detected when whole cell lysates are analyzed (Gygi 1999). To overcome these inconveniences, alternative techniques have been applied in proteomic studies. For example ICAT reagent method and LC-MS/MS were used as a complement of 2-DE-MS/MS. Using these approaches, 388 M. tuberculosis proteins were quantified and identified. Each one of these techniques has been shown to be adequate for the identification of certain classes of proteins. For example, the ICAT method performed better for the identification of cell membrane and high molecular mass proteins, while 2-DE showed better results in the identification of low molecular mass and cysteine-free proteins (Schmidt 2004). Interestingly, none of these techniques allowed the identification of proteins in the following subclasses: cell division, IS elements, repeated sequences, phages, PE/PPE families, cytochrome P450 enzymes, cyclases, and chelatases. By 2004, only about 400 proteins had been identified in the proteome of M. tuberculosis, probably due to the limitations of 2-DE-based separation methods. The use of automated two-dimensional, capillary high-performance liquid chromatography (HPLC) coupled with MS gave a wider and more accurate proteomic profile of M. tuberculosis (Mawuenyega 2005). Proteins of the cell wall, membrane and cytosol subcellular fractions could be identified. The number of identified proteins increased to 1,044 non-redundant proteins, 67 % more than those obtained by conventional 2-DE. This study identified proteins in extreme pI ranges, among them the most acidic proteins ( PE_PGRS, Rv3512) with a pI of 3.89 and the most basic proteins (rps2, a 30S ribosomal protein) with a pI of 12.18. Proteins of high molecular mass, such as the 230,621 Da polyketide synthase ppsC, were also identified. A total of 705 proteins were identified in the membrane, 306 were localized in the cell wall, and 356 in the cytosol fraction. Forty-seven were present in all analyzed fractions. The study also included a computational analysis of protein networks, one of the most exciting fields in the coming years. Readers are invited to consult the supplementary table of this work (Mawuenyega 2005). M. tuberculosis is an intracellular pathogen, the bacillus is engulfed by alveolar macrophages where it can survive and grow by altering the intracellular compartments to preclude the normal maturation to phagolysosomes or to prevent fusion of phagosomes to lysosomes (Clark-Curtiss 2003). The interaction between host and pathogen is thought to be mediated by membrane proteins. Therefore, the characterization of membrane proteins is a topic of intensive research. As mentioned before, most of the studies regarding M. tuberculosis proteomics have been carried out by 2-DE. However, the number of membrane and membrane-associated proteins has been underestimated by the 2-DE technology due to the hydrophobic nature of this class of proteins and their low solubility. In order to overcome these problems, fractions of cellular membranes were prepared by differential centrifugation and separated by one-dimensional electrophoresis. The separated bands were then excised and hydrolyzed prior to LC and MS/MS (Gu 2003). This approach allowed the identification of up to 739 membrane and membrane-associated proteins. Very hydrophobic proteins, including those with 15 transmembrane helices, were detected in this study. The use of alternative solubilizing agents, such as Triton X-114, has proven to be a good choice for membrane fractionation. The detergent was shown to be useful in the identification of nine novel proteins that have been already incorporated in the M. tuberculosis proteome (Sinha 2005). Interestingly, when analyzing the interferon-gamma (IFN-g) response of BCG-vaccinated healthy individuals from an endemic area to these newly identified proteins, the strongest response was found to be that against ribosomal proteins. Other membrane associated proteins, such as ESAT-6, did not contribute significantly to the T-cell response in these individuals. 4.4.2. Comparative proteomics The comparative proteomic analysis using 2-DE and MALDI/MS was applied to compare proteins present in two virulent laboratory M. tuberculosis strains (H37Rv and Erdman strains) with those present in two M. bovis BCG strains (Chicago and Copenhagen BCG strains) (Jungblut 1999). The results showed that, as expected, the two M. tuberculosis strains differed from each other in only a few proteins. Of the 18 variant proteins, 16 were identified. L-alanine dehydrogenase (Rv2780) was not detected in the Erdman strain, and the protease IV was absent in this strain. On the other hand, the Soj protein and the hypothetical protein Rv2641 were absent from the M. tuberculosis H37Rv proteome. Some of the 18 proteins were over-expressed in one or the other strain, and some shifted their mobility probably due to the presence of amino acid substitutions. The comparison of M. tuberculosis H37Rv with M. bovis BCG revealed the presence of 13 protein spots exclusive to the tubercle bacilli, six of which were identified. The differential proteins comprised L-alanine dehydrogenase (40 kDa protein), isopropyl malate synthase nicotinate-nucleotide pyrophosphatase (Rv1596), MPT64 (Rv1980c), and two hypothetical conserved proteins (Rv2449c and Rv0036c). On the other hand, M. tuberculosis H37Rv lacked eight spots compared to M. bovis BCG. In another study using 2-DE and MS, a comparison of the proteins present in M. tuberculosis and M. bovis BCG revealed the presence of 56 unique protein spots in M. tuberculosis and 40 in the attenuated strain BCG (Mattow, 2001). Of these, 32 were identified as exclusive proteins of M. tuberculosis, of which 12 had been previously reported to be deleted in M. bovis BCG. The remaining 20 spots were newly identified as absent from M. bovis BCG. A third comparative proteomic study of M. tuberculosis and M. bovis BCG was performed using 2-DE and ICAT technology (Schmidt 2004). This work demonstrated the presence of only three exclusive proteins in M. tuberculosis H37Rv. One is Rv0223c, a protein belonging to the aldehyde dehydrogenase family. The second is Rv0570, a ribonucleotide reductase class II. The third is a hypothetical protein named Rv1513. The studies on comparative proteomics allowed the identification of isopropyl malate synthase exclusively in the M. tuberculosis proteome. Recently, this enzyme was included in a new class of virulence factors known as 'anchorless adhesins' (Kinhicard 2006) that were absent from the avirulent BCG, thus proving the usefulness of this methodology. Of special interest in the coming years will be the proteomic comparison between circulating M. tuberculosis strains differing in virulence, transmissibility, tissue tropism, and/or ability to acquire drug resistance. As a matter of fact, the proteomic profile of M. tuberculosis H37Rv has already been compared with that of the clinical strain CDC1551 at different time points during in vitro growth (Betts 2000). Subscribing the low structural DNA polymorphism observed in M. tuberculosis (Sreevatsan 1997), the resulting patterns of the protein-spot were found to be both highly reproducible and highly similar between the two strains during growth. One unique protein was identified in M. tuberculosis CDC1551, namely Rv0927c, a probable alcohol dehydrogenase. Similarly, a spot corresponding to the HisA protein, which is involved in the histidine biosynthetic pathway, was detected in the M. tuberculosis H37Rv proteome but was absent from the M. tuberculosis CDC1551 protein profile. Oddly enough, both genes were found to be present in both M. tuberculosis strains. Thus, the described proteomic differences between H37Rv and CD1551 might be ascribed to post-translational events or to degradation during the manipulation of the specimens. Another interesting feature in the same study was the mobility variations of the transcriptional regulator MoxR, which the authors attributed either to amino acid changes or to post-translational modifications. A BlastP analysis of both genomes showed a single amino acid substitution of histidine in M. tuberculosis H37Rv to asparagine in M. tuberculosis CDC1551 that might explain the variation in mobility. Transcriptional regulation differences between strains might be the key to understanding how virulence factors are involved in a variety of roles, including host-cell invasion, survival within the host cell, and long-term persistence. Therefore, comparative proteomic studies are of special interest in the post genomic era, helping to understand the manifestation of disease produced by different strains involved in the current TB epidemic. 4.4.3. Environmental proteomics The information obtained by genomic studies is static, because DNA is not essentially affected by the environment. In contrast, the proteomic profile of an organism in a particular physiological situation complements and helps to decipher its interaction with the environment. The study of the M. tuberculosis proteome in different physiological states is one of the most fascinating fields of research. Being an intracellular pathogen, the bacillus is challenged by a variety of environmental changes. Inside the mammalian macrophage, the microorganism is subjected to a series of different weapons. Inside the granuloma, it faces low oxygen tension, starvation, low pH, reactive nitrogen, and reactive oxygen species, among other offenses (Schnappinger, 2006). The bacillus has developed adaptive mechanisms for survival and persistence in these hostile environments. The identification of proteins expressed under such conditions is a matter of demanding research. M. tuberculosis has another outstanding characteristic: it is able to persist for years in its host causing latent infection, in a state known as dormancy. The mechanisms governing this state are still not fully understood and the protein expression profile in models mimicking the dormant state is an issue of intense research. Different in vitro models have been developed, aimed at simulating the in vivo conditions inducing dormancy (Wayne 1996, Betts 2002, Voskuil 2003). In the Wayne's model, which has been applied in proteomic studies, the level of oxygen is gradually depleted due to bacterial growth, defining two non-replicating stages: a microaerophilic stage NRP-1 (non-replicating persistance-1), followed by an anaerobic stage NRP-2. Still, the evidence linking human M. tuberculosis latent infection with M. tuberculosis dormant stages attained in vitro remains merely circumstantial. Hypoxia is among the most conspicuous conditions encountered by the tubercle bacilli in the central part of the granuloma, where bacilli are considered to remain dormant. The hypoxic response of M. tuberculosis in the Wayne's model was investigated by performing a proteomic study of cell lysates and culture filtrates (Rosendkrands, 2002). The comparison of the protein content between aerobic and anaerobic cultures identified up to seven proteins that were more abundant in hypoxic conditions. The main proteins characterized were fructose biphosphate aldolase (in culture filtrate only), hypothetical protein Rv0569, and alpha-crystallin protein, also known as HspX. Other proteins identified included hypothetical proteins Rv2623 and Rv2626c, L-alanine dehydrogenase (only in culture filtrates), and BfrB, a bacterioferritin involved in iron uptake and storage. Using a modified Wayne's model, Stark et al. (Stark 2004) visualized 13 unique and 37 more abundant spots under hypoxia, revealed by 2-DE and MALDI-TOF/MS. Of these 50 spots, 16 proteins were identified, including some that had not been previously detected such as GroEL2, KasB, Ef-Tu, ScoB, TrxB2, and CmaA2. Among the hypothetical proteins found were Rv2005c, with similarity to universal stress proteins, Rv0560c, Rv2185c, and Rv3866. Applying the ICAT technology to the comparison of the physiological NRP-1/NRP-2 states versus active growth (logarithmic phase), the number of newly identified proteins increased up to 875 (Cho 2006). A total of 586 proteins were identified and quantified in the microaerobic stage of nonreplicating persistence stage (NRP-1), 628 others were detected in the anaerobic dormancy state (NRP-2), and 339 were common to both non-replicating persistence stages. Proteomic comparison between the NPR-1 and the logarithmic phase of growth showed that 6.5 % of the proteins were up-regulated in NRP-1, while 20.4 % of the proteins were up-regulated in NRP-2. The analysis of the proteomic profile showed that the NRP-1 state displayed a significant increase in proteins involved in small molecule degradation and the NRP-2 state a significant increase in energy metabolism, altogether suggesting an adaptive mechanism of M. tuberculosis to enter into the anaerobic environment. M. tuberculosis pathogenicity is directly associated with its ability to establish invasion and division inside the host's macrophages, despite the antimicrobial properties of these cells. The study of the M. tuberculosis proteome in this physiological environment is a crucial step towards understanding the mechanisms involved in its pathogenicity. In spite of the enormous advances in biochemical analytical techniques, the purification and identification of proteins is not always an easy task. In a recent paper, Mattow et al. (Mattow 2006) applied subcellular fractionation of infected murine bone marrow-derived macrophages in combination with high-resolution 2-DE and MS/MS to analyze the proteome of M. tuberculosis inside the phagosome. The proteome was compared with that derived from broth cultures. Using this approach, 121 unique spots were detected in intra-phagosomal mycobacteria. Only 11 of them were identified as M. tuberculosis proteins, the remaining 110 were identified as murine proteins. The 11 identified proteins were: Rv1240 (Malate dehydrogenase, MdH), Rv1077 (Cystathione (beta)-synthase, CysM2), Rv3396c (GMP synthase, GuaA), Rv0489 (Phosphoglycerate mutase I, Gpm), Rv2773c (Dihydrodipicolinate reductase, DapB), Rv0009 (Peptidyl-prolyl cis-trans isomerase, PpiA), Rv1627c (lipid carrier protein), Rv2961 (putative potassium uptake protein, TrkA), and hypothetical protein Rv1130, which was detected in two separates spots, Rv0428c, and Rv1191. Some of these proteins (CysM2, DapB, GuaA, MdH and PpiA) had previously been detected using 2-DE patterns of whole cell lysates and/or culture supernatants of M. tuberculosis H37Rv, indicating that they are not exclusive of the phagosomal millieu. Proteomic studies seem to be a successful way to discover new virulence factors, drug target molecules and proteins involved in pathogenic mechanisms. Thousands of proteins have now been identified and many more await identification. 4.5. An insight into M. tuberculosis metabolomics 4.5.1. Metabolomics state-of-the-art The term metabolomics was first coined in 1998 (Oliver 1998) to describe the "change in the relative concentrations of metabolites as the result of deletion or over-expression of a gene". At the same time, the term metabolome analysis referred to the analysis of metabolites in the phenotypic profile of E. coli (Tweeddale 1998). Later on, metabolomics was considered the detection and measurement, under defined conditions, of cellular metabolites such as low molecular weight molecules present in an organism or biological sample. The field also includes information on the level of metabolite activities in the cell. Metabolites are in general defined as those small molecules, usually intermediate and final products of metabolism, but the definition also applies to high molecular weight molecules such as lipids, peptides and carbohydrates (sometimes referred to as "lipidomics", etc). Metabolomic approaches are now feasible due to the rapid improvements that have taken place during the last decade in two areas: analytical chemistry and bioinformatics. Metabolomic methodologies include the combination of classical technologies, such as gas chromatography-mass spectrometry (GC-MS), or nuclear magnetic resonance (NMR), with new developments to achieve improved sensitivity and discriminative power. Sophisticated informatic analysis and data mining are an important part of the methodology. The complete analysis involves in silico models on metabolite-protein interactions. This analysis can be qualitative or quantitative. In the latter case, all the conditions required for an accurate quantification should be considered, such as the use of appropriate data standards, etc (Nielsen 2005). Metabolomics can also help to validate in silico pathways prepared on the basis of available genome sequences and established databases (Park 2005). Metabolomic analysis has mainly been used in studies on plants and human pathology; in this latter case, the attention was focused on searching for metabolites associated with disease, in other words, "metabolites as biomarkers of disease" (Weckwerth 2005). Microbial metabolomics has initially been devoted to explore bacterial or fungal strains carrying improved phenotypes with a certain biotechnology usefulness value (Wang 2006). Metabolomic approaches have also been directed to the development of new drugs addressed against novel microbial targets. A review on the basics and applications of microbial metabolomics can be read in van der Werf et al. (Werf 2005). 4.5.2. Has the metabolomic analysis of tuberculosis actually started? From the beginning, a unique property of mycobacterial cells called the attention of scientists: the remarkably high lipid content of the cell envelope, which accounts for the most conspicuous mycobacterial features, including physiology and pathogenicity (Asselineau 1998, Barry 2001) (see Chapter 3). Many studies have been published on identification, characterization, and even practical applications (e.g. diagnostics) of several mycobacterial lipids. Almost all books on TB or mycobacteria have at least one chapter dedicated to lipids. Older books refer in more depth to the structural and chemical characterization of the envelope, as well as to the biosynthesis of lipids (Ratledge 1982, Kubica 1984); more recent books lay stress on the genetics and genes related to lipid metabolism (Cole 2005). Thus, the analysis of the lipid metabolic profiling cannot be regarded as a new field in mycobacteria, at least when considering all lipids as metabolites (Ortalo-Magne 1996). The main objection to doing so is the size of mycobacterial lipids. Indeed, mycobacterial lipids are rather big and complex. Most of them have a fatty acid backbone covalently linked to other kind of molecules, most frequently several types of saccharides (Asselineau 1998). Many lipids also belong to the molecular structure of bacterial lipoproteins (Sutclife 2004). A detailed revision of mycobacterial lipids has been published relatively recently (Kremer 2005). The most representative lipids in mycobacteria are the mycolic acids. These molecules are larger in mycobacteria, compared to those of other related bacteria, such as Corynebacterium or Nocardia. Mycolic acids are the lipid component in the structure of complex glycolipids, including Mycolyl-ArabinoGalactan (mAG) and Trehalose-6-6'-Dimycolate (TDM). Another important group of lipids also contains trehalose as the glycosyl radical molecules and their fatty acids chains are multi-methylated. This group includes Di- Tri- and Pentaacyl Trehalose (DAT, TAT and PAT) and Sulfolipids (SL); the Phthiocerol Dimycocerosates (PDIMs) are also very important. These compounds and the closely related Phenolic Glycolipids (PGL) participate in the integrity of the cell envelope of M. tuberculosis. A last group of glycolipids contain D-mannan and D-arabinan in their molecules and have been considered of relevance in bacterial pathogenicity for a long time: Lipomannans (LM) and Lipoarabinomannans (LAM) (see Chapter 3). Many lipids are unique to mycobacteria and therefore their metabolic analysis cannot be addressed by comparative lipidomic studies with other bacteria. Such specific metabolic pathways are viewed, in turn, as excellent targets for the design of new specific drugs (Draper 2000). Renewed efforts have been applied to the detection of metabolic routes and genes that participate in the biosynthesis and degradation of complex lipids (Brennan 2003, Reed 2004, Portevin 2004, Veyron-Churlet 2004, Trivedi 2005, Kaur 2006), as well as genes involved in lipid transportation (Domenech 2004, Jain 2005). A review has recently been published on the biosynthesis, regulation and transport of long-chain multiple methyl-branched fatty acids, such as PDIM, PGL, and SL (Jackson 2006). This paper updates the knowledge on these complex topics, indicating that mycobacterial lipids share mechanisms in their metabolic routes, and that changes in a pathway could influence another pathway; in fact, some small molecules, namely metabolites, could be precursors of the more complex synthesis of lipids, and also be synthesized themselves during the lipids' metabolic pathway, thus being by-products or secondary products of the lipid's metabolism. Few studies deal with the small metabolites from mycobacterial. A recent study on Rv2221, coding for a highly efficient M. tuberculosis adenyl-cyclase, indicates that its catalytic activity is regulated by fatty acids. Thus, M. tuberculosis lipids seem to be involved in signal transduction through the main metabolite cAMP (Abdel-Motaal 2006). In fact, small metabolites are often involved in signaling transmission in many bacteria. The relevant PhoP/PhoR two-component system was demonstrated to be related to lipid metabolism in M. tuberculosis (Gonzalo Asensio 2006). Although the specific signal sensed by PhoR is still unknown (Jackson 2006), some small molecules (metabolites) might behave as its signaling effectors. In fact, the homologous two-component system (PhoP/PhoQ) is sensored by magnesium in other bacteria (Martin-Orozco 2006). The association of mycobacterial lipids to M. tuberculosis pathogenicity is a matter of renewed interest (Riley 2006). A role in the establishment and progress of the pathology caused by the tubercle bacilli has been classically assigned for years to many of those lipids (Bloom 1994). However, most of the studies were conducted using lipids as isolated molecules, overlooking the interactions with other molecules within the bacterial cell and the environment. In fact, the lipid contents of the bacillus change according to the environmental conditions. Garton et al. described an increase of lipophilic inclusions according to the lipid content in the in vitro culture medium where bacteria were grown (Garton 2002). Lipid availability is probably not low inside man, the natural host, and in vivo bacilli could be lipolitic rather than lipogenic (Wheeler 1994). Trafficking of mycobacterial lipids from bacterial vacuoles to the endosomes of macrophages was demonstrated in M. tuberculosis infected macrophages, and mycobacterial lipids were detected even in uninfected cells. These findings indicate that through its own lipids, M. tuberculosis exerts a wide influence on its environment that extends beyond truly infected cells (Beatty 2000). Altogether, these data underline the great importance of the metabolomic analysis for the interpretation of the biology of the tubercle bacillus and its relation with the host. 4.6. Concluding remarks The availability of the first M. tuberculosis genome sequences triggered an overwhelming amount of knowledge on the genetics and the biology of M. tuberculosis. The way was opened for comparative and functional genomics. Scientists and medical doctors started to appreciate the potential coding capacity of this extraordinary organism. A broad picture of the M. tuberculosis gene content and coding capacity has been revealed. Sequencing and comparison with other genomes have shown the close relations that exist among the members of the M. tuberculosis complex, and have allowed the identification of a core mycobacterial genome, a minimal set of genes conserved in the different mycobacterial species. These advances were generated in a little more than seven years by no more than five publicly available genomes. Now, with the advent of new technologies and 21 genome projects in process, the study of mycobacteria and comparative genomics seems not only promising but very exciting. The application of new technologies, such as DNA microarray for the comparison of M. tuberculosis wild isolates, is a promising approach towards understanding its natural biology and adaptative evolution in the human population. As research on the biology of TB expands, new and more accurate information is generated. In the coming years, knowledge about the real coding capacity of the tubercle bacillus will increase exponentially, and genome sequences will feed back from transcriptome and proteome analysis, filling old gaps and opening new ones in the understanding of M. tuberculosis biology. Functional genomics has become a key tool in the understanding of the biology of M. tuberculosis. By providing information about the pathogenesis of the disease, it is expected to promote the discovery of vaccine candidates and the investigation of novel drug targets. Investigations on complex biological systems can be now envisaged under a metabolomic perspective (Forst 2006). Metabolomics is a newborn methodology in microbiology and is even younger in mycobacteriology, therefore, almost everything remains to be learned in TB concerning that discipline. It is clear that a long way still remains to be walked to understand how the tubercle bacillus behaves inside the host, its unique known environment. 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