Introduction

In the previous chapter, we had emphasised the enormous interest in studying fine-scale genetic variation even among closely related bacteria. Given the cost and time involved in pursuing Sanger sequencing for a large number of genomes, microarrays came into vogue for determining variation in gene content among related organisms. However, microarrays suffered from the following limitations. All said and done, hybridisation signals from microarrays remain a poor proxy for direct sequence determination. The exact nature and the extent of sequence divergence can not be determined. Second, the information that could be obtained depended on whether there were reference sequences for designing probes. Sequences that were unique to the genome sequence under investigation could not be determined. Therefore, there was a definite need for nucleic acid sequencing methods that could bypass the time and cost constraints of automated Sanger sequencing. In response to this need, there has been an explosion of next-generation or deep sequencing technologies in the last eight years or so. These technologies produce large volumes of sequence data in a short period of time and at a small fraction of the cost of a ‘first-generation’ automated Sanger sequencing experiment. They enable not just a reading of a DNA sequence (discussed in this chapter), but also permit a range of semi-quantitative applications typically associated with DNA microarrays. Popular next-generation sequencing systems are Illumina (also called Solexa), SOLiD (Applied Biosystems), 454 (Roche), Heliscope (Helicos Biosciences) and SMRT (Pacific Biosciences). Several other technologies including nanopore sequencing are in various stages of development and commercialisation. Benchtop sequencers, which are becoming increasingly available, make these technologies afford able to individual laboratories as opposed to core facilities.

In this chapter, we will provide a very brief overview of some general concepts underlying various next-generation sequencing technologies. This will be followed by an overview of the concepts underlying various computational procedures applicable to the use of these technologies for genome sequencing and re-sequencing.

Introduction

The sequencing of even a few members of a bacterial species has underlined the remarkable genetic diversity that underpins these organisms. These have suggested that in many taxa, there could be innumerable genetically unique members and that their genetic characterisation is best initiated by obtaining information on those genomic attributes that are unique to each member. However, genome sequencing using the Sanger methodology–described in the previous chapter–is expensive and time-consuming, and therefore not a practical option when it comes to sequencing ‘every’ genetically unique isolate of some relevance. However, various alternatives have been remarkably successful, and these have led to the field of ‘phylogenomics’ or ‘genomic epidemiology’.

DNA microarrays: The concept

The first approach that permits ‘phylogenomics’ is the use of DNA microarrays to probe sequence variations between different isolates.

DNA microarrays are based on the concept of hybridisation of a nucleic acid to one of complementary sequence–a fundamental principle underlying many molecular biology methods for detecting and quantifying nucleic acids of a defined sequence. These microarrays are slides containing many nucleic acid probes, allowing thousands to millions of hybridisation experiments to be run in parallel. The technology can be used to detect the presence and absence of genes or more subtle polymorphisms in a genome (comparative genome hybridisation or CGH, the subject of this chapter), calculate relative expression levels of all genes encoded in a genome and compare these levels across several conditions and genetic backgrounds (gene expression microarray), and even measure, semi-quantitatively, the levels of binding of a DNA-binding protein to various parts of the genome (chromatin immuniprecipitation chip or ChIP-chip).

Irrespective of the application, there are certain general points to be noted while analysing and interpreting microarray data. A few of these–they are not exhaustive–are as follows:

1 The general idea of a microarray: In a DNA microarray experiment, fluorescently-labelled nucleic acid sample is hybridised against unlabelled, complementary probe sequences.

Introduction

The first genome to be completely sequenced, in 1976, was that of an RNA bacteriophage MS2, which encoded only three genes. Though the concept of comparative genomics had yet to emerge, the authors mentioned the possibility of understanding viral evolution by performing sequence searches against other viral genomes that were forthcoming. This was rapidly followed by the sequencing–by Sanger and colleagues–of the 5.4 kb-long genomic DNA of phage ФX174, and later in 1982, considerably larger sequence (>48 kb) of the genome of the molecular biology workhorse, bacteriophage lambda (λ). As pointed out by Koonin and Galperin, neither work presented the concept of sequence comparison and homology, which is now routine in any genome analysis pipeline. This may be surprising as the PIR protein sequence database was already available, and the first sequence substitution matrix constructed. However, sequence databases were not sufficiently rich in information at that time, and sophisticated and rapid methods for searching sequence databases were not common, with the Smith–Waterman algorithm a then-recent innovation, and the much faster BLAST not due to be reported for another eight years.

The first tentative comparative genomics work, as identified by Koonin and Galperin, was published by Toh, Hayashida and Miyata in 1983. This remarkable short paper identified homologues of retroviral reverse transcriptases encoded by the genome of two DNA viruses, whose life cycle was known to involve reverse transcription. In another study, Argos and co workers showed striking similarities in certain gene sequences between animal picornaviruses and plant cowpea mosaic virus, suggesting evolutionary relatedness either by independent abstraction of common host genes, or via vertical descent from a common ancestor. Finally, McGeoch and Davison worked on considerably larger genomes of the varicella zoster virus (an α-herpesvirus), and the Epstein–Barr virus (a distantly related γ-herpesvirus), and found several homologues between them.

Introduction

In the previous chapters, we had discussed methods for studying the genetic content of bacteria on a genomic scale. In the process, we highlighted characteristics of certain bacterial genomes, besides discussing properties common to many bacterial genomes. We also documented intra-specific variation in gene content between bacteria. Different strains of the same bacterial species might show considerable variation in gene content, as a result of mutations getting fixed in a particular context, or the dynamics of gene acquisition and loss. Many of these variations might arise from selection resulting from their niches and/or lifestyles. However, this does not reflect the fact that the same bacterium might be phenotypically distinct under different environmental or cellular contexts. Much of these differences might be attributed to gene expression changes, i.e., under a given condition, a bacterium expresses only a subset of its genes, with the remaining genes being silent. Or, the expression level of the same gene might be quantitatively different between two conditions, in contrast to the dramatic on–off distinction made by the previous statement. Thus, the genetic content of a bacterium can be interpreted by a ‘gene expression machinery’ in different ways at different times.

The expression of genes, and the manner in which it is regulated, have been studied in recent years using genome-scale techniques. Many of these approaches use DNA microarrays or next-generation sequencing, the basics of which have been covered in the previous two chapters. In the present, core chapter of this book, we will discuss the application of these technologies to the study of gene expression and its control in bacteria. The discussion of techniques and data analysis will focus on gene expression measurements and investigation of genomic regions that bind to a protein of interest. We will also present particular exemplary research as case studies in the process.

Bacteria are the most dominant form of free-living life on Earth, and represent a major part of its genetic diversity. Some bacteria are closely adapted to a single environment in which they reside, whereas many others are capable of thriving across multiple environments. They range from the most benign inhabitants of the Earth to being deadly, multi-drug-resistant human pathogens. The success of bacterial life is exemplified by the wide variation in their genetic content, not just across phyla, but even among members of the same species. In addition is the plethora of gene regulatory mechanisms that—to state a most cliched phrase—ensure that appropriate genes are ex pressed when required. These two features of bacterial biology form the crux of this book.

It will not be wrong to state that the advent of genomics has considerably advanced our knowledge of both evolutionary and gene regulatory mechanisms in bacteria. The ambitious—and now historical—genome sequencing projects, driven by whole genome shotgun sequencing using automated Sanger sequencers, taught us many things about bacterial genetic diversity and the mechanisms underlying its generation. For example, by sequencing the genomes of many members of a species, such as Escherichia coli, we learnt that some bacteria have what is called an open pan genome: every new genome of a member of that species will invariably identify many genes hitherto un known in that species. Genome sequencing projects also stoked a controversy on the importance of horizontal gene transfer to genome growth and bacterial evolution itself: in the face of rampant horizontal transfer, does the concept of a bacterial species have any meaning? At the other end of the scale, genome reduction emerged as an important phenomenon under lying the evolution of obligate parasites, including those of major human pathogens such as the Rickettsia and Mycobacterium leprae.

Introduction

In the previous chapters, we had discussed genome-scale studies of bacterial adaptation by genome content and gene expression control. In these mechanisms, there is either a change in the genotype of the bacterium; or there is a change in the protein occupancy of the genome, which is not necessarily heritable. A third mechanism of establishing identity–best studied in complex eukaryotes–is by epigenetics. The word essentially means ‘in addition to genes’. This, in the broadest sense used by biologists today, refers to–for example–changes in gene expression which are heritable, but do not include a change in genotype. These are in contrast to the phenomena discussed in the previous chapters: Comparative genomic approaches reveal changes in genetic content, whereas mechanisms of regulation of gene expression as discussed earlier are not necessarily heritable.

A classical epigenetic mechanism involves methylation of DNA–namely adenine and cytosine bases. Methylation of cytosine at what are known as CpG islands is a reasonably well-studied gene regulatory mechanism in higher eukaryotes. Phenomena such as the specificity of certain DNA methylating enzymes to hemi-methylated DNA, as well as the competition between DNA methylating enzymes and other DNA-binding proteins, ensure that certain–if not all–DNA methylation patterns are inherited. In bacteria, the interplay of DNA methylation and DNA-binding transcription factors can cause phase variation, with distinct sub-populations exhibiting heritably different gene expression states; this is distinct from the genetic mechanism of phase variation at contingency loci in the genomes of bacteria such as Helicobacter pylori, discussed earlier in this book. The current short chapter does not deal extensively with the intricate biology of bacterial DNA methylation and epigenetics; for this the readers are referred to authoritative recent reviews by Casadesus and colleagues. Instead, we briefly discuss the comparative genomics of enzymatic systems involved in DNA methylation and novel genome-scale approaches for detecting DNA methylation at a single-base resolution.