The Introduction Of High Throughput Microbial Genome Biology Essay

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For over 10 years, transcriptomics in eukaryotes have been very useful in characterizing essential regulatory mechanisms. However, transcriptomics in prokaryotes have not been carried out until recently due to the observation that microbial gene structures are simple, and technical challenges in mRNA enrichment that do not contain poly(A) tails. The huge increase in sequencing capacity through new sequencing technologies along with specialized mRNA enrichment and tiling array techniques has recently made it possible to investigate whole bacterial transcriptomes (Wang et al., 2009; Sorek and Cossart, 2010). Currently, there are two main techniques to probe the transcriptional state of the bacterial cell: deep sequencing and microarrays.

RNA- sequencing: Latest ultra-high-throughput sequencing technologies provide cost-effective direct sequencing of whole transcriptomes to a great extent; examples include the Roche 454 and Illumina Genome Analyzer. First of all, total RNA is isolated from the organism and converted into cDNA by reverse transcription (RT). Since bacterial mRNAs lack the poly(A) tail, alternative priming approaches are used, such as random hexamer priming and oligo(dT) priming from artificially polyadenylated mRNAs (Perkins et al., 2009; Yoder et al., 2009; Frias-Lopez et al., 2008). A crucial step before RT is the enrichment for mRNAs. RNA-seq output often consists of millions of short (25-200bp) sequence reads that represent RNA fragments. A transcriptome map is generated by computationally mapping these reads to the reference genome, and expressed regions are established on the basis of their continuous coverage by RNA-seq reads. Selecting a suitable cDNA library construction protocol should also be considered (Perkins et al., 2009; Yoder et al., 2009).

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Tiling arrays: The genomic tiling arrays are a subtype of microarray chips, and usually correspond to both genome strands at high densities. After cDNA synthesis, the library is hybridized to the array and expression is assessed using signal intensities. For this approach thousands of probes are required and is also restricted by background noise and cross hybridization, and so extensive normalization is essential. The data can then be used to deduce contiguous transcription, similarly to RNA-seq. mRNA enrichment is not necessary, and the experimental procedures are well established. Most tiling arrays, however, have a lower density, due to the cost associated with the large number of probes needed. Hence the transcriptome maps that result from tiling arrays are usually of a lower resolution than the maps produced by RNA-seq, which have single-base-pair resolution. Yet the fairly small size of bacterial genomes makes the tiling array technique appealing for future transcriptomics in other bacteria (McGrath et al., 2007; Toledo-Arana et al., 2009; Rasmussen et al., 2009; Selinger et al., 2000).

So far, many important studies have used various combinations of the techniques discussed above to study the transcriptomes of numerous bacteria. The results of these studies are now revolutionizing our understanding of the complexity, regulation and pathogenecity of bacterial transcriptomes. The untranslated regions (UTRs) of prokaryotic mRNAs have been reported to contain important regulatory elements, such as riboswitches and small regulatory RNA binding sites (Waters and Storz, 2009). Transcriptomics can globally map UTRs across the entire genome, where expression extending into the flanking intergenic region of a protein-coding gene suggests a 5′ or 3′ UTR. A riboswitch might be present if this contiguous expression is interrupted in one growth condition and not in another one. This technique was used in a study in B. anthracis where 37 5′ UTRs longer than 100 bp were detected. 5′ UTRs in bacteria are typically shorter than 30 bp, therefore, the presence of longer UTRs in B. anthracis suggests that they have functional roles (Passalacqua et al, 2009). Also, 25 genes were found to have long 5′ UTRs in Salmonella Typhi, two of which reside in a pathogenicity island (genomic region comprising virulence genes), which indicates a role for these UTRs in virulence regulation (Perkins et al., 2009).

sRNAs (small non-coding RNAs) are usually between 50 and 500 bp long in bacteria, and regulate important biological processes, such as virulence, stress response and quorum sensing (Toledo-Arana et al., 2007; Masse et al., 2007). Transcriptomic analysis provides the global interrogation of sRNA abundance in any species primarily by detecting expression from non-protein-coding regions. A tilling-array based study has shown that the number of sRNAs in L. monocytogenes has doubled to 50 sRNA. Two of the sRNAs in L. monocytogenes were involved in virulence, as their deletion mutants showed altered pathogenic capabilities (Toledo-Arana et al., 2009).

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Figure 1: Left- L. monocytogenes sRNA relative expression. Color code bar indicates expression fold-change compared to the reference condition. Right- Examples of transcriptional tiling maps of L. monocytogenes sRNAs in different conditions. The plots demonstrate normalized hybridization intensities (y axis) and genomic coordinates (x axis, in bp). Each dot represents the average of intensity signals from three independent biological repetitions for one probe. Annotated ORFs and sRNAs are shown as blue and orange arrows, respectively. (Taken from (Toledo-Arana et al., 2009)).

In a cis-antisense genomic locus two partially overlapping genes are transcribed from opposite DNA strands. RNA transcribed from the sense gene may associate with the antisense RNA, leading to regulation of transcription, translation or degradation (Lavorgna et al., 2004). Transcriptomic studies have detected numerous antisense transcripts in multiple genomes, and 3% to13% of all protein-coding genes were observed overlapped by cis-antisense transcripts in several bacteria. However, in L. monocytogenes and Synechocystis spp., additional assessment of specific transcripts indicated that they might be involved in the downregulation of sense transcripts (Toledo-Arana et al., 2009; Georg et al., 2009). Studies have shown that antisense transcripts are long, which span over one ORF and can function as non-coding RNAs, however some transcripts, such as in L. monocytogenes, have a overlapping portion comprising the 5′ UTR or the 3′ UTR of a flanking protein-coding gene (Toledo-Arana et al., 2009). Thus cis-antisense transcripts might be a frequent regulation process in bacterial genomes. Further transcriptomic studies of such transcripts in various genomes are required to establish their functional significance in pathogenesis. Therefore, transcriptomics can be used to study the involvement of elements, such as ncRNAs, riboswitches and cis-antisense regulators, in any bacterial pathogenesis.

Since there are many bacterial pathogenesis being investigated using transcriptomic techniques, here we focus on the role of transcriptomics in understanding the pathogenesis of two bacteria, Neisseria meningitidis and Yersinia pestis, in detail.

N. meningitidis is a gram-negative diplococcal bacterium and is responsible for meningitis and other forms of meningococcal disease worldwide. In the following studies transcriptomics have been used to monitor global changes in gene, in both the pathogen and the host, during the infectious process. Microarray transcription-profile comparisons have been used in several studies to examine the meningococcal NMB0595/NMB0594 two-component regulatory system at the transcriptomic levels, which can lead to determining the virulence system of this pathogen. A two-component regulatory system with homology to the PhoPQ system was identified in meningococci and have been involved in controlling virulence gene expression (Johnson et al., 2001; Hitchen et al., 2002; Teng et al., 2002). Detecting the genes regulated by PhoP demonstrated that the meningococcal PhoPQ is a magnesium-sensing two-component system that controls remodelling of the bacterial surface in the host environment. In the phoP knock-out mutant, magnesium-regulated changes in gene expression were mostly abolished. In comparison to the wild-type strain, many genes were expressed at different levels after growth of the mutant on blood agar. The results indicate that PhoPQ system may contribute significantly to host adaptation by meningococci (Newcombe et al., 2005).

NMB0595 are of considerable interest for vaccine development because studies demonstrate they might be involved in promoting phase and strain variation of lipo-oligosaccharide (LOS) structure (Rahman et al., 2001). Microarray observations revealed that the inactivation of this two-component system, encoded by NMB0595/NMB0594 and known as misR/misS, in a serogroup C meningococcal strain, modifies the expression of 281 genes in the mutant compared with the parental strain. These genes also include several virulence genes, such as nspA and genes that play a role in LOS synthesis. However, these findings were not confirmed by other biochemical or genetic methods, and the direct regulatory targets of MisR were not recognised (Newcombe et al., 2005).

Researchers often use combination of transcriptomics and bioinformatics with biochemical and genetic experiments to characterize a regulon. These studies identify genes and operons directly regulated by a transcriptional regulator and consistent model building is used. MisR regulation was assessed by transcriptional profile analyses of a wild-type MenB parent strain and the respective MenB misR mutant (Tzeng et al., 2008). A total of 78 were upregulated and 39 genes were downregulated in the MenB misR mutant. Real-time reverse transcription RT-PCR, reporter assays and an electrophoretic mobility shift assay were carried out to validate the MisR regulatory effects on a panel of 25 genes identified by microarray. The results revealed that MisR/S system directly or indirectly regulates genes implicated in a wide range of functional groups: metabolism, chaperoning, protein folding, type I protein transport, iron assimilation and sensitivity to oxidative stress and human serum, many of which functions are involved in meningococcal pathogenesis.

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DNA-array technology has been used in many studies to investigate the range of host cellular responses to an infection with pathogenic Neisseria spp. In one study a specialized cDNA microarray, 'IronChip,' was used to determine alterations in several host genes involved in iron homeostasis, demonstrating that N. meningitidis alters the iron regulatory network of epithelial cells (Bonnah et al., 2004). It was also established that Neisseria LOS does not play a significant role in alterations of iron homeostasis in epithelial cells, as IronChip analyses demonstrated that similar stress response is induced by the epithelial cells when infected with either N. meningitidis wild-type strain 8013.3 or the LOS-lacking mutant (Bonnah et al., 2005).

Transcriptional analysis of human brain microvascular endothelial cells (HBMEC) was performed, where these cells were affected in the pathogenesis of meningococcal disease, 4 h (saturation of bacterial adhesion) and 8 h (maximum of bacterial internalization) after interaction with meningococcus. This study highlighted the bacterium-mediated effects on the cytoskeleton organization, host cell function, monolayer integrity and abundance of cell receptor or secreted molecules other than cytokines (Schubert-Unkmeir et al., 2007). The initial study on the commensal relationship of meningococci with their hosts involved investigating the effects of pilus-mediated adhesion and the production of two RTX-proteins (especially FrpC and FrpC-like secreted proteins) on human cells (Linhartova et al., 2006).

Gene product functional classification

No. of genes with indicated MC58-infected HBMEC/uninfected HBMEC ratio at:

4 h p.i.

8 h p.i.

0.5 or less

0.5-<2.0

2.0 or more

0.5 or less

0.5-<2.0

2.0 or more

All

888

366

374

252

496

741

Cell surface antigens

11

3

4

1

7

11

Transcription

53

20

27

12

29

63

Cell cycle

5

6

6

4

7

6

Cell adhesion receptors/proteins

13

2

5

1

7

9

Immune system proteins

1

1

7

3

9

Extracellular transporter/carrier proteins

10

2

3

3

4

10

Oncogenes/tumor suppressors

9

6

2

2

7

10

Stress response proteins

7

4

3

3

8

5

Membrane channels and transporters

21

9

18

1

11

33

Extracellular matrix proteins

3

1

1

5

Trafficking/targeting proteins

24

14

10

8

21

28

Metabolism

56

63

63

17

70

82

Posttranslational/protein folding

25

16

12

7

19

21

Translation

13

46

40

23

45

34

Apoptosis-associated proteins

9

5

4

4

4

17

RNA processing/turnover/transport

21

12

9

6

15

21

DNA binding and chromatin proteins

10

5

10

4

5

12

Cell receptors

27

2

7

3

18

Cell signaling, extracellular communication proteins

23

5

5

3

7

24

Intracellular transducers/effectors/modulators

80

20

45

9

41

77

Protein turnover

20

15

14

6

17

28

Cell receptors (by activities)

6

3

2

1

1

9

Cytoskeleton/motility proteins

22

22

8

5

20

27

DNA synthesis/recombination/repair

11

4

5

3

4

13

Functionally unclassified

72

27

31

16

50

65

Not classified

336

54

32

109

94

104

Table 1: Gene expression profiles of HBMEC infected with N. meningitidis strain MC58 at 4 h and 8 h post-infection (Taken from (Schubert-Unkmeir et al., 2007)).

Iron plays a prominent role in a variety of metabolic pathways in bacterial pathogenesis. Iron is bound to proteins such as ferritin, lactoferrin, and transferrin. Over the years pathogenic bacteria have evolved iron-acquisition methods, majority of which are controlled by the ferric uptake regulator protein Fur in Neisseria (Perkins-Balding et al., 2004). Comparative transcriptomics studies have been performed to identify the Fur regulon of N. meningitidis which involved comparing the gene expression of bacterial cultures supplemented with ferric nitrate with the gene expression of iron-depleted bacterial cultures (Grifantini et al., 2003). A total of 233 genes were iron-regulated, of which 203 belonged to putative transcriptional units. It was established that only 50% of the iron-regulated genes comprised of Fur-binding consensus sequences in their promoter region. Gel-shift analysis revealed that a number of genes, that were known to be Fur-regulated, bind Fur. After addition of iron, 10 Fur-regulated genes were upregulated, indicating that Fur can also act as a transcriptional activator. Many virulence-associated genes were overexpressed in iron-depleted conditions, such as genes involved in toxin production, multidrug resistance and cell adhesion. Computational analysis of the genes NMB1436, NMB1437, and NMB1438 showed homologies to oxidoreductases carrying iron-sulphur clusters. Thus further investigation of the deletion mutants of this operon demonstrated that the operon (while not being regulated by oxidative stress) is needed for protecting meningococci from hydrogen peroxide-mediated killing (Grifantini et al., 2004). In another microarray approach, the meningococcal genes differentially expressed in the presence or absence of the Fur protein and in response to iron limitations were identified (Delany et al., 2006). The order of direct and indirect Fur-mediated control mechanisms was biochemically examined by means of footprinting analysis. Result revealed that transcription of 83 genes is regulated by Fur, either by binding directly to their promoters or through indirect mechanisms. The heat shock genes were expressed at higher levels in a fur mutant, suggesting that these genes are Fur-repressed but independent of iron limitations.

Yersinia pestis is a gram-negative bacterium and is the causative agent of plague. To gain insight into the environmental modulation of global gene expression in Y. pestis, the gene expression profiles of 25 different stress conditions was analysed using cDNA microarray. Results revealed that under several environmental alterations, nearly all known virulence genes of Y. pestis were differentially regulated. Using microarray data collections of operons was analysed and some were validated by RT-PCR. Examining clustered genes predicted several regulatory DNA motifs and electrophoretic mobility shift assay (EMSA) confirmed a Fur binding site in the resulting promoter regions. The authors believe that this comparative transcriptomics analysis can enhance our understanding of the regulatory mechanisms and molecular determinants of Y. pestis pathogenesis (Han et al., 2007).

In another study, a plague model in mice was used for investigating the disease progression by transcriptional profiling of Y. pestis and mice using qRT-PCR and microarray, respectively. The transcription of key Y. pestis virulence genes and mice genes involved in immune, inflammatory defense and stimuli responses were increased, indicating interaction between Y. pestis and mice during pneumonic plague development. Early and continuous up-regulation of the Y. pestis virulent factors, caf 1, psa A and lcr V in vivo, suggest their involvement in the resistance of host innate immune responses. fur, ybt A and hms H virulent factors were up-regulated in vivo indicating their ability of Y. pestis for obtaining iron. During plague development, transcription regulators (pho P, oxy R and omp R) were up-regulated, indicating their function in interaction between Y. pestis and mice. During infection, several genes encoding cytokines in the host were also up-regulated, indicating their role as mediators that stimulate host responses against pathogens (Liu et al., 2008).

The role of Hfq (post-transcriptional regulator that induces interactions between sRNAs and their mRNA targets) in Y. pestis virulence was also assessed using macrophage and mouse infection models, and the gene expression affected by Hfq was established using microarray-based transcriptomics and real time PCR. The macrophage infection assay demonstrated that there was no significant difference in the ability of Y. pestis hfq deletion strain to interact with J774A.1 macrophage cells. But hfq deletion significantly disrupted the ability of Y. pestis in resisting phagocytosis and macrophage survival during the initial stage of infection. The hfq deletion strain was also highly attenuated after injection in mice,. Thus transcriptomics analysis confirmed the attenuated strain of hfq mutant and demonstrated that hfq gene deletion can lead to considerable alterations in mRNA abundance of 243 genes in more than 13 functional groups, of which 23% are known or assumed to be involved in virulence and stress resistance. The overall results indicate that Hfq is a key regulator that affects Y. pestis stress resistance, intracellular survival and pathogenesis. Hfq might act by regulating expression of many virulence- and stress-associated genes, possibly in combination with sRNAs (Geng et al., 2009).

 From the results obtained from these studies it can be concluded that transcriptomics has already made invaluable contributions to the understanding of bacterial pathogenesis. So far, transcriptomics has been very effective in refining the annotation of well-studied bacterial genomes. As with other events characterized with high-throughput techniques, further thorough examination of these events will be needed to determine their functional importance for bacterial pathogenicity. However, one of the limitations of transcriptomic techniques is that they require millions of cells as a starting material (Rotem and Cossart, 2010). Advances in technology might overcome such difficulties. Progress was recently made in studying the transcriptomes of species with no available reference genome (Metatranscriptomics) (Tringe and Rubin, 2005). Now that the powerful tool of transcriptomics can be used to study the bacterial RNA, researchers are optimistic that a new set of unexpected RNA-based mechanisms involved in bacterial pathogenesis might be discovered.

Word count: 2188 (excluding figures/tables/references) 379 over