Cel Mutation Rates and Target Size Epigenetic in ONA methylation perates via change in DNA sequence. Point mutation 1o1o1o1010°101o101o°10 rates vary between organisms, and values range up to about per base pair per generation for certain RNA iruses. around 10-6 to 10-8 for most microbes, and 10 per base pair per cellular generation for human cells. Repeat vanation Rates of phenotypic change associated with different types of inheritance In general, mutation frequencies increase with increasing population sizes and decreasing information content of e genome, which results in a surprisingly stable mutation Figure 1. The Timescales of Inheritance rate of roughly 1/300 non-neutral mutations per genom oth inheritance and selection can act on a wide array of different time- per generation(Drake, 1999). However, matters are com- scales, ranging from fewer than one cellular (or organismal generation to more than one billion generations. A number of different mecha- plicated by the fact that mutation rates vary across the ge- isms exist that regulate the stability of biological phenotypes. Phend nome. Early studies on the Escherichia coli Lac repressor, types inherited epigenetically often exhibit rapid variation, whereas stabilized against random mutation. across the gene( Miller et al, 1977), while recent genomic Here, we show rough timescales, in units of cellular generation, for the studies on silent site mutations in humans revealed hot stability of phenotypes regulated by the indicated mechanisms spots and cold spots that cover hundreds of kilobases (Chuang and Li, 2004). The reason for variation in mutation frequencies in the complex human genome is poorly un- of biological mechanisms that are seemingly designed to derstood. In the much simpler genomes of bacteria regulate or at least influence the timing or location of phe- some mutational hot spots have been linked to special notypic variation Where possible, we will explore the cor- DNA sequences such as inverted or tandem repeats relation between the variability of a given phenotype and (see below). the variability of the selective pressure that is proposed Even if mutation rates were uniform across the genome to act upon it. More specifically, we will argue that organ- not every phenotype would vary at the same rate because isms appear to have developed mechanisms to tune the of differences in the so-called" target size"of the pheno- timescale of their own heritable variability to match the types. As an illustrative example, consider a phenotype timescale of the acting selective pressure. For example that depends on the function of several proteins, including pathogenic organisms often exhibit rapid variation in the a massive protein with many essential amino acids and a expression of cell-surface molecules that might be recog- required C-terminal domain. This phenotype will be lost if nized by the immune system and which switch between any of the essential amino acids are mutated in any of the different expression states as rapidly as every 50 genera- proteins or if mutation to a premature stop codon prevents tions. In this case, rapid switching is likely to provide the the required c terminus from being expressed. Con- pathogen with a way to escape immune responses, with versely, a phentoype that depends solely on one small the antigenic switching rates tuned to the timescale of protein with few vital domains presents a much smaller he host immune response (for a review, see van der target size. Target size cannot be calculated from se loude and Baumler, 2004). Such mechanisms contradict quence; it obviously depends very strongly on which pro- the total randomness of heritable variability, which is one teins are required for the phenotype in question, which of the foundations of today,'s generally accepted theory amino acids are essential for the proteins function, which of evolution codons are used by these amino acids, and many other This subject can be construed extremely broadly, and factors. Hence, it is difficult to estimate the precise impact we note some intentional limitations to our Review. First, of the target size on phenotypic variability. Perhaps ad we will focus our Review on unicellular organisms, as their vances in computational protein-structure prediction will rapid generation time and high population sizes have enable some intuition concerning target size for the mis- folding of arbitrary proteins We will, however, discuss selected examples of related ies may identify the number of proteins required in a given phenomena of interest in multicellular organisms. It is pathway also important to note that for many of the phenotypes dis While mutation rate and target size are somewhat diffi- cussed, detailed studies of selective pressure in ecologi- cult to measure, the product of the two can be directly ally relevant environments are sparse, so any discussion measured and is given (for traits that can be scored as egarding temporal variation in selective pressure is present or absent) as the rate of gain/loss of a phenotype largely speculative by necessity. per generation due to mutation. For example, haploid Before we elaborate on some of the examples where the yeast mutants lacking orotidine 5-phosphate decarboxy timing or location of variability is regulated by complex ge- lase(uracil biosynthesis) occur at w10 per generation netic or epigenetic mechanisms, it is useful to first con-(Boeke et aL., 1984). For continuously varying"quantitative sider random sequence mutation, which is arguably the raits, the experimental correlate of mutation rate times most common mechanism for phenotypic change arget size is the mutational variance Vm of a phenotype 656 Cell 128, 655-668, February 23, 2007 @2007 Elsevier Indof biological mechanisms that are seemingly designed to regulate or at least influence the timing or location of phe￾notypic variation. Where possible, we will explore the cor￾relation between the variability of a given phenotype and the variability of the selective pressure that is proposed to act upon it. More specifically, we will argue that organ￾isms appear to have developed mechanisms to tune the timescale of their own heritable variability to match the timescale of the acting selective pressure. For example, pathogenic organisms often exhibit rapid variation in the expression of cell-surface molecules that might be recog￾nized by the immune system and which switch between different expression states as rapidly as every 50 genera￾tions. In this case, rapid switching is likely to provide the pathogen with a way to escape immune responses, with the antigenic switching rates tuned to the timescale of the host immune response (for a review, see van der Woude and Baumler, 2004). Such mechanisms contradict the total randomness of heritable variability, which is one of the foundations of today’s generally accepted theory of evolution. This subject can be construed extremely broadly, and we note some intentional limitations to our Review. First, we will focus our Review on unicellular organisms, as their rapid generation time and high population sizes have en￾abled the experimental study of rare phenotypic changes. We will, however, discuss selected examples of related phenomena of interest in multicellular organisms. It is also important to note that for many of the phenotypes dis￾cussed, detailed studies of selective pressure in ecologi￾cally relevant environments are sparse, so any discussion regarding temporal variation in selective pressure is largely speculative by necessity. Before we elaborate on some of the examples where the timing or location of variability is regulated by complex ge￾netic or epigenetic mechanisms, it is useful to first con￾sider random sequence mutation, which is arguably the most common mechanism for phenotypic change. Mutation Rates and Target Size The best understood mechanism for phenotypic change operates via change in DNA sequence. Point mutation rates vary between organisms, and values range up to about 104 per base pair per generation for certain RNA viruses, around 106 to 108 for most microbes, and 109 per base pair per cellular generation for human cells. In general, mutation frequencies increase with increasing population sizes and decreasing information content of the genome, which results in a surprisingly stable mutation rate of roughly 1/300 non-neutral mutations per genome per generation (Drake, 1999). However, matters are com￾plicated by the fact that mutation rates vary across the ge￾nome. Early studies on the Escherichia coli Lac repressor, for example, revealed significant mutation-rate variation across the gene (Miller et al., 1977), while recent genomic studies on silent site mutations in humans revealed hot spots and cold spots that cover hundreds of kilobases (Chuang and Li, 2004). The reason for variation in mutation frequencies in the complex human genome is poorly un￾derstood. In the much simpler genomes of bacteria, some mutational hot spots have been linked to special DNA sequences such as inverted or tandem repeats (see below). Even if mutation rates were uniform across the genome, not every phenotype would vary at the same rate because of differences in the so-called ‘‘target size’’ of the pheno￾types. As an illustrative example, consider a phenotype that depends on the function of several proteins, including a massive protein with many essential amino acids and a required C-terminal domain. This phenotype will be lost if any of the essential amino acids are mutated in any of the proteins or if mutation to a premature stop codon prevents the required C terminus from being expressed. Con￾versely, a phentoype that depends solely on one small protein with few vital domains presents a much smaller target size. Target size cannot be calculated from se￾quence; it obviously depends very strongly on which pro￾teins are required for the phenotype in question, which amino acids are essential for the proteins’ function, which codons are used by these amino acids, and many other factors. Hence, it is difficult to estimate the precise impact of the target size on phenotypic variability. Perhaps ad￾vances in computational protein-structure prediction will enable some intuition concerning target size for the mis￾folding of arbitrary proteins, and functional genomic stud￾ies may identify the number of proteins required in a given pathway. While mutation rate and target size are somewhat diffi- cult to measure, the product of the two can be directly measured and is given (for traits that can be scored as present or absent) as the rate of gain/loss of a phenotype per generation due to mutation. For example, haploid yeast mutants lacking orotidine 50 -phosphate decarboxy￾lase (uracil biosynthesis) occur at 107 per generation (Boeke et al., 1984). For continuously varying ‘‘quantitative traits,’’ the experimental correlate of mutation rate times target size is the mutational variance Vm of a phenotype. Figure 1. The Timescales of Inheritance Both inheritance and selection can act on a wide array of different time￾scales, ranging from fewer than one cellular (or organismal) generation to more than one billion generations. A number of different mecha￾nisms exist that regulate the stability of biological phenotypes. Pheno￾types inherited epigenetically often exhibit rapid variation, whereas genetically robust phenotypes are stabilized against random mutation. Here, we show rough timescales, in units of cellular generation, for the stability of phenotypes regulated by the indicated mechanisms. 656 Cell 128, 655–668, February 23, 2007 ª2007 Elsevier Inc.