Chapter 2 : Studying Evolution Using Genome Sequence Data

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The accumulation of well-annotated, complete genome sequences now offers an unprecedented view into the biology of microorganisms, including powerful and practical applications such as the inference of metabolic pathways and lifestyle choices without any experimental evidence. The chapter discusses how genome sequences can be used to examine microbial evolution. It reviews the many ways genomes can change over time, how these processes are inferred by analysis of extant sequences, and how these data sets can be employed to address broad-scale evolutionary questions. Genes certainly change by point mutation, and it is primarily this sort of variation among genomes that has been under scrutiny since the dawn of microbial population biology in the 1970s. As bacteria reproduce by binary fission, homologous recombination between genes found among closely related strains was once thought to be rare, with periodic selection of rare advantageous mutations being a popular and fairly well-supported view. In addition to the alteration of existing DNA, genomes may change by the gain of novel genetic information from outside sources. The bacterial origin of genes responsible for fungal invasion of ruminant intestinal tracts was also evident without genome sequences. Deletions can be a major driving force during genome reduction, whereby large portions--perhaps even the majority--of the genetic material of an organism are lost over time, with little gain of new genes. The chapter focuses on inference of phylogeny and organismal evolution, and new biological approaches enabled by complete genome sequences.

Citation: Lawrence J. 2006. Studying Evolution Using Genome Sequence Data, p 11-33. In Seifert H, DiRita V (ed), Evolution of Microbial Pathogens. ASM Press, Washington, DC. doi: 10.1128/9781555815622.ch2
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Image of FIGURE 1

Replication slippage events. (A) Slippage at polynucleotide repeats can affect promoter regions, thereby altering expression at downstream loci. Here, replication slippage in the polyguanosine region can alter the separation and relative orientation of sites required for σ-factor recognition of the gene’s promoter. (B) Slippage within open reading frames can stochastically move downstream regions in and out of frame. Here, addition of a pentameric repeat to a region found in-frame will cause the downstream region of the gene to be translated out-of-frame, resulting in a useless protein or premature translation termination.

Citation: Lawrence J. 2006. Studying Evolution Using Genome Sequence Data, p 11-33. In Seifert H, DiRita V (ed), Evolution of Microbial Pathogens. ASM Press, Washington, DC. doi: 10.1128/9781555815622.ch2
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Image of FIGURE 2

Occurrences of a skewed, asymmetrically distributed sequence in the genome of the α-proteobacterium . The lower panel depicts each sequence on either the Watson (top) or Crick (bottom) strand as a hash mark. The abundance of this sequence on each strand is tabulated in the graph above; the origin and terminus of replication can be inferred from analyses of G+C skew ( ) as well as the distribution of the octomer depicted here. Data are from H. Hendrickson and J. G. Lawrence ( ). The origin was distinguished from the terminus by (i) the position of the gene (typically origin-proximal), and (ii) the orientation of operons encoding rRNAs (typically transcribed away from the origin of replication).

Citation: Lawrence J. 2006. Studying Evolution Using Genome Sequence Data, p 11-33. In Seifert H, DiRita V (ed), Evolution of Microbial Pathogens. ASM Press, Washington, DC. doi: 10.1128/9781555815622.ch2
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Image of FIGURE 3

A portion of the Z2491 genome ( ) containing the operon (encoding an NADH dehydrogenase). The genes depicted in gray have been inserted into this genome, likely by horizontal gene transfer, dispersing the genes. The plot above shows the overall G+C content over a 200-bp sliding window; non- genes show atypical G+C content, as well as other unusual properties. The average G+C content for each gene is shown as a horizontal bar on the plot.

Citation: Lawrence J. 2006. Studying Evolution Using Genome Sequence Data, p 11-33. In Seifert H, DiRita V (ed), Evolution of Microbial Pathogens. ASM Press, Washington, DC. doi: 10.1128/9781555815622.ch2
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